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Geophysical inversion in an integrated exploration program : examples from the San Nicolas deposit Phillips, Nigel David 2002

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GEOPHYSICAL PROGRAM:  INVERSION IN A N I N T E G R A T E D EXAMPLES F R O M T H E SAN  EXPLORATION  NICOLAS  By Nigel David Phillips B . Sc. Colorado School of Mines,  A  THESIS THE  1996  SUBMITTED IN PARTIAL F U L F I L L M E N T REQUIREMENTS FOR T H E DEGREE M A S T E R  OF  OF  SCIENCE  in THE  FACULTY EARTH  OF GRADUATE  A N D OCEAN  STUDIES  SCIENCES  We accept this thesis as conforming to the required standard  THE  UNIVERSITY OF BRITISH  COLUMBIA  A p r i l 2002 © Nigel D a v i d Phillips, 2002  OF  DEPOSIT  In presenting this thesis i n partial fulfilment of the requirements for an advanced degree at the University of B r i t i s h Columbia, I agree that the L i b r a r y shall make it freely available for reference and study.  I further agree that permission for extensive copying of this  thesis for scholarly purposes may be granted by the head of my department or by his or her representatives.  It is understood that copying or publication of this thesis for  financial gain shall not be allowed without my written permission.  E a r t h and Ocean Sciences T h e University of B r i t i s h C o l u m b i a 129-2219 M a i n M a l l Vancouver, C a n a d a V 6 T 1Z4  Date:  Abstract  T h e recent ability to produce three-dimensional physical property models of the subsurface from surface geophysical data, coupled w i t h an increasing need to explore for minerals i n concealed terranes, results i n geophysical inversions providing more significant information to the exploration team. T h i s thesis examines the role that geophysical inversion can play i n an integrated mineral exploration program, and the impact it can have on the results. A s an example, geophysical data from the San Nicolas copper-zinc massive sulphide deposit i n Mexico are inverted. Such deposits are distinguished by high density, magnetic susceptibility, conductivity, and chargeability values. W i t h i n the framework of an integrated exploration program (which is communicated through the use of a flowchart) many different, and hard-to-interpret, geophysical data sets are given geologic context. T h i s is achieved by interpreting physical property models that have been generated by inversion modeling. T h e a i m of generating these models, and interpreting geological information from them, is to: 1) assist mineral exploration i n the area around the deposit, and roughly define areas of mineralization that can be used as a starting point for more detailed modeling ; 2) define the size and location of the San Nicolas deposit; 3) further improve the models and delineate ore more accurately by combining the detailed modeling w i t h core physical property measurements; and 4 ) determine how best to use the information that has been acquired through modeling to find additional sulphide ore-bodies, including those that may be deeper t h a n the existing deposit. Density and magnetic susceptibility distribution models, inverted from regional gravity and magnetic data respectively, define large-scale structures that reflect the tectonic  ii  setting of the region. Several distinct anomalies that exhibit high density and magnetic susceptibility values are identified. Since massive sulphides are often dense and magnetic, a correlation method is employed that determines volumes that have high density and magnetic susceptibility. The correlation procedure isolates five anomalies. T w o of these are easily dismissed as having poor exploration potential, and two of the remaining anomalies, one of which is the San Nicolas deposit, are associated w i t h mineralization. A t a more detailed scale, the deposit is well defined by gravity, magnetic, C S A M T , and I P methods individually. However, a drill hole that is targeted on the intersection of these favorable physical property distributions would have intersected the heart of the deposit. T h i s demonstrates the advantages of using these methods i n concert. Other sources of information, such as core physical property measurements and geologic constraints, are also used to improve modeling results.  T h e inclusion of data from a single drill-hole  is shown to significantly enhance detailed physical property distributions, and produces models that correlate better which known mineralization.  •.  Finally, forward modeling of the physical property models is used to demonstrate how deep ore can be detected by implementing different survey designs that increase signalto-noise of the data. T h i s newly acquired information can then be used i n the next step of the exploration program as the search for mineralization continues i n the surrounding area. In addition to the m a i n focus of the thesis, it was found that polarizable material is needed i n order to fit time-domain, airborne electromagnetic data collected over the deposit. T h i s demonstrates the potential for detecting chargeable bodies from the air, and has significant implications for future mineral exploration.  iii  Table of Contents  Abstract  ii  List of Tables  xi  List of Figures  xii  Acknowledgments  xxviii  Dedication 1  xxix  Introduction  1  1.1  E x p l o r a t i o n methodology  3  1.2  S a n Nicolas  5  1.2.1  6  Geology  1.3  Physical Properties  10  1.4  Geophysical E x p l o r a t i o n M o d e l  12  1.5  Data  12  1.6  Modeling  13  1.6.1  Forward modeling  13  1.6.2  Inverse modeling  13  1.7  Outline of the thesis  14  1.7.1  Inversion methodology  14  1.7.2  Regional-scale modeling  14  1.7.3  Deposit-scale modeling  14 iv  1.8  1.7.4  Detailed modeling using drill-hole information  16  1.7.5  Detection of deep ore: survey design  16  Summary  17  2 Geophysical Inversion  18  2.1  Introduction  18  2.2  Inversion Basics  18  2.2.1  Data  18  2.2.2  Discretization and forward modeling  19  2.2.3  Non-uniqueness and the M o d e l Objective Function  20  2.2.4  Signal-to-noise and data misfit  22  2.2.5  Optimization  23  2.2.6  Appraisal  24  2.3  2.4  A synthetic example  25  2.3.1  The data  26  2.3.2  Presentation and interpretation of inversion models  27  2.3.3  Recovered models  27  Conclusions  32  3 Regional Scale Modeling  34  3.1  Introduction  34  3.2  Regional geology  36  3.3  Introduction to the inversion results  40  3.4  Density-contrast models  40  3.4.1  Regional gravity inversion results  42  3.4.2  Summary of regional gravity results  49  3.5  Magnetic susceptibility models  49 v  3.6  3.5.1  Data  50  3.5.2  Composite susceptibility model  52  3.5.3  Regional susceptibility model analysis  52  3.5.4  Geologic interpretation  56  Regional conductivity models  57  3.6.1  Frequency-domain data  57  3.6.2  Time-domain data  60  3.6.3  Forward modeling the Questem data  67  3.6.4  Summary of regional conductivity methods  73  3.7  Chargeability models  74  3.8  Physical property correlation  78  3.9  Conclusions  81  4 Deposit Scale Modeling 4.1  4.2  84  Introduction  84  4.1.1  86  L o c a l geology  Density  89  4.2.1  Regional Removal  89  4.2.2  Inversion of Residual G r a v i t y D a t a  89  4.3  Magnetic Susceptibility  4.4  Conductivity Data  96  4.4.1  97  4.5  • • •  Testing the 3 D resistivity model  Chargeability  92  98  4.5.1  Chargeability data  101  4.5.2  T h e resistivity model  101  4.5.3  Removal of electrode effects  103  vi  4.5.4 4.6  5  103  Conclusions  105  Detailed Modeling: Incorporating a priori information  108  5.1  Introduction  108  5.1.1  109  5.2  5.3  6  The chargeability model  A priori information at San Nicolas  Inversion of gravity observations  Ill  5.2.1  Setting bounds on the density-contrast model  113  5.2.2  A priori density values from drill-hole S A L - 3 1  117  5.2.3  A priori density values from all drill-holes  119  Magnetic Susceptibility  123  5.3.1  Core Measurements  123  5.3.2  Bounding the magnetic susceptibility model  125  5.3.3  Magnetic inversion using a priori information from S A L - 6 9 . . . .  125  5.3.4  Magnetic inversion using a priori information from a l l drill-holes .  126  5.4  Discussion on the discontinuous nature of the models  129  5.5  Conclusion  129  Forward Modeling and Survey Design: Detecting the keel  132  6.1  Introduction  132  6.2  G r a v i t y Observations  135  6.2.1  Simulation of gravity field observations  136  6.2.2  Synthetic, downhole, gravity observations  138  6.2.3  Summary of gravity forward modeling  142  6.3  Magnetic Observations  142  6.3.1  Simulating the ground magnetic field data  143  6.3.2  Synthetic downhole magnetic observations  143  vii  6.3.3 6.4  6.5  6.6  6.7  Summary of magnetic forward modeling  147  C S A M T / C S E M observations  147  6.4.1  Simulated C S A M T observations  . . . .  . .  148  6.4.2  C S A M T / C S E M survey design  152  6.4.3  Summary of C S A M T / C S E M forward modeling  158  D C resistivity  159  6.5.1  Simulation of Realsection resistivity observations  159  6.5.2  Forward modeling of dipole-dipole resistivity observations  160  6.5.3  Summary of D C resistivity forward modeling  ;  . . . .  .  162  Chargeability  164  6.6.1  Simulating Realsection IP field observations  166  6.6.2  Synthetic dipole-dipole chargeability observations  167  6.6.3  Summary of chargeability forward modeling  167  Conclusions  169  7 Conclusions and Suggestions for Further Work  171  7.1  Summary of results  173  7.2  Further work  175  References  178  Appendices  184  A Coordinate System  184  B Physical Property Analysis  185  B.l  Introduction  185  B.2  Density  185  viii  B.3  Magnetic Susceptibility  185  B.3.1  Magnetic Susceptibility core measurements  186  B . 3.2  Composite models for bounding inversions  188  B.4  Resistivity  189  B.5  Chargeability  189  B. 6  Physical  property  rock-models  191  C Geophysical methods  193  C l  T h e gravity method  193  C. 2  T h e magnetic method  195  C.3  Inductive electromagnetic methods  197  C. 3.1  Frequency-domain electromagnetic airborne surveys.  C.3.2  Time-domain electromagnetic airborne surveys  .  197 199  C.4  C S A M T surveys  200  C.5  D C resistivity methods  203  C.6  I P methods  204  D Data  206  E Inversion  220  E.l  Introduction  220  E.2  GRAV3D  220  E.3  MAG3D  221  E.4  CSAMT1D  221  E.5  EM1DFM  222  E.6  TEM  222  E.6.1  ID Complex forward modeling  ix  222  F  E.7  DCIP3D  222  E.8  EHSD  223  E . 9 EH3DTD  223  Gradient I P data modeled using MAG3D  225  F. l  Four synthetic examples  226  F.2  Modeling the electric field  229  F.3  Summary  233  G I P electrode weighting  234  x  List of Tables  1.1  Estimated physical properties for the five major rock types encountered at San Nicolas. T h e massive sulphide has contrasting values i n a l l physical properties apart from resistivity. A low resistivity value of 20 O h m - m for sulphides, similar to the Tertiary breccia, makes detection of the deposit more difficult when using some geophysical methods  3.1  Questem time-decay ( ^ )  11  data for one station located over the deposit.  T h e center time of each time channel is given (time after turn-off of p r i m a r y field) along w i t h the z-component data i n micro-volts and i n p p m , as normalized by the peak z-component of the primary field  65  B.l  Range of densities for rock-types encountered at San Nicolas  B.2  Range of resistivities for rock-types encountered at San Nicolas.  B.3  Range of chargeabilities for rock-types encountered at San Nicolas  191  D. l  Geophysical survey specifications  219  E. l  Inversion parameters  224  xi  186 . . . . .  190  List of Figures  1.1  Geophysical E x p l o r a t i o n Methodology Flowchart: Defining the geophysical exploration model  1.2  2  M a p of Mexico (upper panel) and Zacatecas State (lower panel) showing the location of San Nicolas  1.3  7  D a t a coverage over the San Nicolas deposit and the surrounding area. Top: the regional-scale area w i t h data coverage and outcrop geology (legend shown). Center: Intermediate-scale data coverage shown on top of outcrop geology. B o t t o m : deposit-scale data coverage.  T h e size of the  rock-hammer symbol approximately corresponds to the extents of the surface projection of the San Nicolas deposit  15  2.1  T h e earth is discretized and represented by an orthogonal 3 D mesh. . . .  20  2.2  Perspective view of synthetic density-contrast model. T h e model has been volume rendered so that cells w i t h density contrasts less t h a n l g / c m  3  have  been removed 2.3  25  Surficial synthetic gravity observations w i t h 3% + 0.0005 m G a l random noise added  2.4  26  Perspective view of recovered density-contrast model. T h e model is viewed from the southeast (local coordinate system), and is volume-rendered w i t h a cut off at 0.3 g / c m  2.5  28  3  North-facing cross-section through recovered density-contrast model w i t h location of true model shown  28  xii  2.6  North-facing cross-sections through true model and seven recovered models for seven different inversions. A l l of the inversion models are shown on the same color-scale w i t h dark blue representing 0 g / c m and below, and 3  magenta representing 0.5 g / c m and above. The original model, an outline 3  of which is shown on all the recovered models, is shown at a different scale.  30  3.1  Geophysical E x p l o r a t i o n Methodology Flowchart: Regional Scale Modeling. 35  3.2  Plan-view of regional geologic outcrop at San Nicolas. Modified from Johnson et al., 2000. Most of the region is overlain by Quarternary a l l u v i u m . Inset shows a key to the stratigraphic units  3.3  37  P l a n view of the regional density contrast model w i t h cells below a value of 0.1 g / c m  3  invisible. Outcrop geology is overlaid, and the boundary of  the gravity survey is represented by a purple line. A l l coordinates are i n the local grid system 3.4  43  West-facing cross section of the regional density contrast model w i t h cells below a value of 0.1 g/cc invisible. T h i s cross-section is located at -1600m east and shows an interpreted horst  3.5  44  Surface projection of the regional density-contrast model w i t h interpreted faults overlaid.  Northwest-southeast  trending faults are shown i n red,  while north-northeast trending faults are shown i n black.  T h e mapped  fault is shown i n yellow. M a t e r i a l w i t h a density-contrast of less than 0.1 g / c m has been removed  45  3  3.6  Horizontal slice through the regional density-contrast model at an elevation of 1700m (400m below the surface at the location of San Nicolas). A n outline of geology outcrops is overlaid, and the extent of data coverage is shown by a purple line  47  xiii  3.7  N o r t h facing cross-section through a regional density contrast model at a northing of -400m (coincident w i t h the deposit)  3.8  48  Comparison of individual magnetic susceptibility inversion models w i t h composite model of mean values.  The models are shown i n plan-view  looking at a depth of 300m (within the depth of the deposit) 3.9  51  Plan-view of horizontal slices through the composite magnetic susceptibility model. The upper panel shows a slice at 300m depth, while the lower panel shows a slice through the model at 1200m depth  53  3.10 N o r t h - and west-facing cross-sections (upper and lower sections respectively) through the magnetic susceptibility model generated by combining inversion results from Dighem, Fugro, and B H P airborne  magnetic  data. T h e north-facing cross-section is positioned at -400m south, while the west-facing cross-section is positioned at -1600m east  55  3.11 Horizontal slice 60m below the surface of the 3D resistivity model generated by interpolating between lines of I D models inverted from D i g h e m frequency-domain airborne electromagnetic data  59  3.12 P l a n view of a slice through the 3-D resistivity model generated by i n terpolating between lines of I D models from the inversion of Questem time-domain airborne electromagnetic data. The slice (at a depth of 200m below the surface) shows, i n red, the thicker areas of overburden that continue to this depth.  The underlying, more resistive, volcanic rocks are  represented by cooler colors  62  xiv  3.13 Vertical component, time-channel data, flight height, total field magnetics, and resistivity inversion model for line 20110 of the Questem data. T h e data are presented as parts-per-million of the peak z-component of the waveform, w i t h channels 1 and 15 corresponding to the earliest and latest times respectively. The inversion model is produced by "stitching" together I D models. T h e location of the deposit corresponds to a coherent pattern of negative data values i n time-channels 12 through 15 (1), and a magnetic high. Other regions of negative data that could be above the noise level are also observed (2, 3 and 4)  64  3.14 Logarithmic plot of Questem vertical-component time-decay data given i n Table 3.1. Positive data are shown by squares, and negative data (the last four) are shown w i t h triangles. T h e data are from over the deposit and show a typical decay pattern for stations that contains negative data.  . .  66  3.15 Observed and predicted time-decay data, and corresponding I D conductivity models for Questem station located over the deposit.  Frequency-  independent conductivity layers do not reproduce the observed negative data  . . . . .  69  3.16 Observed and predicted data, and I D conductivity models for Questem station located over the deposit. A layer w i t h complex (frequency-dependent) conductivity (based on the Cole-Cole dispersion model), positioned at the approximate depth of the deposit, can produce data that has the same character as the observations.  T h e conductivity of the overburden was  also decreased i n an attempt to more closely fit the early-time data. . . .  xv  71  3.17 Plan-view of a slice at 300m below the surface through the chargeability model generated by inverting gradient-array I P data as magnetic ..data using MAG3D.  T h e deposit is the largest chargeable b o d y detected by  the survey. T h e extents of the survey are shown by the red outline and outcrop geology is shown i n black  75  3.18 N o r t h - and west-facing cross-sections (upper and lower sections respectively) through the chargeability model. T h e chargeable anomaly is well defined yet extends below the elevation of the deposit, as defined by drilling. 76 3.19 Plan-views of density contrast, magnetic susceptibility, and cut-off correlation models. T h e upper images show the projection-to-surface of cells, w i t h i n the first 1000m from the surface, that exhibit high density contrast and magnetic susceptibility values. T h e lower image shows regions that are high i n b o t h density and susceptibility. T h e image is a projection to surface of the volume intersection of the upper models.  A blue border  shows the extents of the correlation analysis  79  3.20 Normalized density-susceptibility regional correlation values. T h e values are the product of normalized regional density-contrast and magnetic susceptibility models  82  4.1  Geophysical Exploration Methodology Flowchart: Deposit scale modeling.  85  4.2  Geologic cross-sections through the San Nicolas deposit, modified from sections provided by Teck Corp. T h e m a i n geologic units are labeled.  4.3  . .  87  Simplified stratigraphic section i n the vicinity of the San Nicolas deposit, modified from [Danielson, 2000]  88  xvi  4.4  Perspective view, facing north-west, of regional density-contrast used for calculation of the regional  signal.  model  The volume that has been  removed contains the deposit and two smaller high density anomalies, and is the volume on which the more detailed inversion is focused 4.5  90  South-east perspective view of the detailed, density-contrast model shown as a volume-rendered isosurface plot using a cut-off of 0.18 g/cc.  Cells  w i t h density contrasts less than this value are invisible 4.6  Cross-sections through deposit-scale density-contrast  91 model at the San  Nicolas deposit  93  4.7  Perspective view of the detailed, magnetic susceptibility model  94  4.8  Cross-sections through deposit-scale magnetic susceptibility model at the San Nicolas deposit  4.9  95  Perspective view of resistivity model created by interpolating between lines of "stitched" I D models.  The inversion has successfully discriminated  between the overburden and the deposit  96  4.10 Cross section of resistivity model w i t h geology. The resistivity model was produced by "stitching" together 60 I D inversion models  97  4.11 Comparison of C S A M T field data w i t h forward modeled data from the  stitched  1-D inversion resistivity model(left plots) and a physical property  resistivity model (right plots).  The field observations are displayed i n  red while the modeled data is shown i n blue. A t 8Hz (upper plots), the physical property model fits the field data well while the 1-D inversion model fits the field data better at a frequency of 256Hz (lower plots).  xvii  . .  99  4.12 North-facing, cross-sectional diagram of the geology at San Nicolas showing the skin-depths associated w i t h propagating plane-waves at frequencies of 256Hz and 8Hz. T h e skin-depths were calculated for a 25 O h m - m whole space. T h e higher frequency signal samples geology that is much more "one-dimensional" than that sampled by the low frequency signal. .  100  4.13 Plan-views of the recovered chargeability models at the surface. T h e high chargeability values that correspond w i t h electrode positions have been reduced through the use of a weighting function  104  4.14 Perspective view of the volume-rendered chargeability model w i t h a cut-off value of 40 msec  105  4.15 Cross-sections through deposit-scale chargeability model at the San Nicolas deposit 5.1  106  Geophysical Exploration Methodology Flowchart: Using drill-hole information  5.2  110  Plan-view of drill-hole locations. Density measurements on core from.drillhole S A L - 3 1 are used to set bounds on the model values i n Section 5.2.2 (shown by a red square), while measurements made on core from a l l of the drill-holes shown (apart from S A L - 9 0 and S A L - 9 1 ) are used to provide a priori density contrast information i n Section 5.2.3. Susceptibility measurements made on drill-cores from S A L - 6 9 are used to set bounds i n Section 5.3.3 (shown by a green triangle), while bounds are assigned i n Section 5.3.4 based on magnetic susceptibility values made on cores from thirteen drill-holes that are denoted by a blue diamond  xviii  112  5.3  Drill-hole S A L - 3 1 . Displayed as a function of depth from left to right: 1) Geology, 2) density (g/cm ), 3) percent copper, and 4) percent zinc. T h e 3  average density of the sulphide is 4.2 g/cm  and the density of the rocks  3  below the deposit is approximately 2.8 g/cm  114  3  5.4  N o r t h - and west-facing cross-sections through recovered deposit-scale densitycontrast models. T h e upper cross-sections show the results from the original deposit-scale gravity inversion, described i n Section 4.2.2. T h e lower cross-sections show the results of inverting the gravity data w i t h the model bounded using the density measurements made on core from drill-hole S A L - 3 1 . Geologic contacts have been overlaid  5.5  118  North-facing cross-section of interpreted geology w i t h drill-hole locations. Density measurements made on core from the drill-holes were used to bound model cells during the inversion of gravity data.  O n l y the drill-  holes along line -400m S are displayed, although many more were used to bound the model 5.6  120  Cross-sections through recovered deposit-scale density-contrast models. The upper cross-sections show the results from the original deposit-scale gravity inversion, described i n Section 4.2.2. T h e lower cross-sections show the results of inverting the gravity data w i t h the model bounded by density measurements made on core from a l l drill-holes. Interpreted geologic contacts are shown on each cross-section. Note: the lower cross-sections are shown w i t h a range of density-contrast that is different from those previously presented  122  xix  5.7  Magnetic susceptibility core measurements and geology for drill-holes S A L 25, S A L - 3 4 , S A L - 4 7 , and S A L - 7 0 .  Note: the upper-most geologic unit  is the Tertiary breccia, although it may appear a similar colour to the sulphide ore, which is shown i n red. Reference the geologic section and recognize it as an interpretation 5.8  124  North- and west-facing cross-sections through recovered deposit-scale magnetic susceptibility models. T h e upper cross-sections show the results from the original deposit-scale magnetic susceptibility inversion, described i n Section 4.3.  T h e lower cross-sections show the results of inverting the  magnetic data w i t h the model bounded by magnetic susceptibility measurements made on core from drill-hole S A L - 6 9 .  A n outline of geology  interpreted from drill-holes is overlaid on each section 5.9  127  N o r t h and west-facing cross-sections through recovered deposit-scale magnetic susceptibility models. T h e upper cross-sections show the results from the original deposit-scale magnetic inversion, described i n Section 4.3. T h e lower cross-sections show the results of inverting the magnetic data w i t h the model bounded by magnetic susceptibility measurements made on core from twelve drill-holes. Geologic contacts as interpreted from drill-holes are overlaid on each section  128  6.1  Geophysical Exploration Methodology: Survey design  134  6.2  Size and position of the keel relative to the deposit  135  6.3  T h e two density rock-models that were used to generate synthetic gravity measurements  137  xx  6.4  Synthetic gravity data produced from a density rock-model of San Nicolas. D a t a are produced from models w i t h and without the keel. T h e difference is also plotted  6.5  139  West-facing cross-section of density rock-model.  T h e m a i n ore-zone is  represented by the higher density values i n the top half of the model while the keel is represented by the anomaly i n the lower half of the model. T h e locations of six drill-holes, w i t h i n which synthetic gravity measurements are made, are also shown. Drill-hole A is 25m north of the keel and each subsequent drill-hole is positioned 25m further north than the last one. 6.6  .  140  Down-hole plots of synthetic gravity data made i n six drill-holes. T h e data are the difference between the response of a density rock-model w i t h the keel, and a model w i t h the keel removed. Note the change i n horizontal scale for plots C , E , and F  6.7  141  Synthetic magnetic data produced from a susceptibility rock-model of San Nicolas. D a t a are produced from models w i t h and without the keel. T h e difference is also plotted  6.8  144  West-facing cross-section of the magnetic susceptibility rock-model' w i t h the keel shown to the lower left. Four drill-holes are also shown, w i t h i n which synthetic total magnetic field measurements are performed  6.9  145  Synthetic total-field, down-hole, magnetic data produced from a magnetic susceptibility rock-model of San Nicolas. D a t a are produced from models w i t h and without the keel. The difference is also plotted.  Drill-hole A  is just north of the keel and drill-holes B , C , and D are each 50m further north that the previous one so that drill-hole D is 150m north of the keel.  xxi  146  6.10 A perspective view of conductivity rock-model (including keel), cut to show the conductivity structure at -400m south. T h e approximate resistivity values of the model are shown, as is the location of the transmitter that was used i n the field C S A M T survey  149  6.11 Color plot of synthetic real and imaginary E and H fields, at the surface x  y  over the San Nicolas deposit, that emanate from a far, 16Hz, grounded transmitter.  T h e first and second rows show the field components that  would be produced over conductivity models with, and without the keel, respectively (these are plotted on the same scales). T h e t h i r d row of plots show the difference between fields produced w i t h and without the keel for each component. Each plot covers the area over the deposit, as described previously, although the northing and easting values are not shown. . . .  150  6.12 Perspective views of conductivity rock-model showing locations of the transmitters used for forward modeling of electromagnetic data  153  6.13 N o r t h facing cross-sections through a rock-model, which includes the keel, showing the magnitude of current density i n each cell for two, (a) far and (b) near, transmitter locations.  T h e cross-section is located at -400m  north and shows an increase i n current density w i t h i n the keel (the small anomaly to the lower left of each section) from about 5 x l 0 A/m  - 8  to 6 x l 0 ~  7  155  2  6.14 Real part of E surface measurements made for four different transmitter x  source locations over conductivity rock-models of the San Nicolas deposit w i t h and without the keel  156  6.15 Imaginary part of E surface measurements made for four different transx  mitter source locations over conductivity rock-models of the San Nicolas deposit w i t h and without the keel xxii  157  6.16 Synthetic, D C , apparent resistivity, Realsection data produced from conductivity rock-models of San Nicolas. T h e x-locations refer to the m i d point of the receiver dipoles, while the y-axis refers to different transmitter dipole positions; the bottom line corresponds to data collected w i t h the longest transmitter, and the top line corresponds to data collected w i t h the smallest transmitter length.  T h e section is just a representation of  the data and should not be taken as the true structure of the earth. T h e percent difference between data produced from models with, and models without the keel is also shown. Note: the apparent resistivity color-bar is on a log scale w i t h low resistivities (high conductivities) displayed i n red, while the percent difference is plotted on a linear scale  161  6.17 Schematic diagram of dipole-dipole array for one transmitter location, showing the pseudo-section plotting points relative to the electrode geometry. 162 6.18 Synthetic apparent resistivity dipole-dipole pseudo-sections produced from conductivity rock-models of San Nicolas. Pseudo-sections of data produced over conductivity rock models w i t h and without the keel are plotted on a log scale along w i t h the percent difference between the two, which is plotted on a linear scale. T h e plotting convention use is shown i n Figure 6.17.163 6.19 Synthetic apparent chargeability Realsection data produced from chargeability rock-models of San Nicolas. T h e x-locations refer to the midpoint of the receiving dipoles, while the y-axis refers to different transmitter d i pole positions; the b o t t o m line of data being collected w i t h the longest transmitter, and the top line of data being collected w i t h the smallest transmitter length. T h e difference between the two data sets is also plotted. 166  xxiii  6.20 Synthetic apparent chargeability dipole-dipole data produced from chargeability rock-models of San Nicolas. T h e plotting convention used is shown i n Figure 6.17. T h e difference between the two data sets is also plotted. .  168  7.1  Geophysical E x p l o r a t i o n Methodology Flowchart  172  B.l  T h e range of susceptibility values for different rock types at San Nicolas.  187  B.2  Coarse and fine magnetic susceptibility measurements on a section of core from drill-hole S A L 70  B.3  188  Coarse and fine magnetic susceptibility measurements on a section of core from drill-hole S A L 34  B.4  189  Magnetic susceptibility measurements made on core from drill-hole S A L 25. A composite model was produced that was used to constrain inversion of ground magnetic data  B. 5  190  Perspective view of the physical property conductivity model used for test forward modeling i n Section 4.4.1. T h e model has been sliced at -400m N to expose a north-facing cross-section  192  C l  T h e force exerted on mass m i by mass m is i n the direction of r  C. 2  Orientation of transmitter-receiver ( T x - R x ) pairs i n the D i g h e m frequency-  194  2  domain airborne electromagnetic system C.3  198  Diagrammatic sketch of the Questem time-domain electromagnetic system.  T h e transmitter  (Tx) is placed around the aeroplane while the  three-component receiver (Rx) is towed i n a bird behind the plarie. G P S positioning is used to measure the position of the receiver relative to the bird  . . .  XXIV  200  C.4  T h e current i n the transmitter as a function of time for 50% of one cycle. Receiver measurements are made over 15 time-windows during the 16 msec following the current on-time  C.5  201  Diagrammatic sketch of the C S A M T survey layout (modified from Zonge and Hughes)  C.6  202  "Real-section" array geometry used for collection of D C resistivity and I P data i n Section 4.5  C.7  203  T h e voltage response as a function of time, V(t), i n the presence of polarizable material, when a current is suddenly applied and then interrupted.  V =V +V T  D.l  P  205  S  Topographic elevations at San Nicolas. A p a r t from a hill at 6000m east, 1000m north, most of the area exhibits small topographic elevation changes. Outlines of outcropping geology are also shown, along w i t h a hammer and pick symbol that locates San Nicolas at about -1700m east, -400m north.  D.2  G r o u n d gravity observations.  207  T h e data are the original data supplied  by Quantec but w i t h observations on the h i l l removed. T h e topographic map shown i n Figure D . l indicates a h i l l w i t h over 100m of elevation gain. Initial inversions produced a large negative anomaly coincident w i t h the hill.  Observations made on and close to the hill were subsequently  discarded, although the affects of the hill probably persist i n some of the remaining observations i n this area. A constant was also removed from the data i n order to remove a first order regional field. A n outline of outcrop geology is overlain D.3  208  Dighem airborne total-field magnetic data. T h e deposit is identified by a subtle anomaly at -1600m east, -400m north  xxv  209  D.4  Subset of Dighem airborne total-field magnetic data used for inversion. .  210  D.5  Falcon airborne total field magnetic data  210  D.6  Questem airborne total field magnetic data  211  D.7  In-phase and quadrature frequency-domain electromagnetic data from the Dighem system: 466Hz, 1046Hz, and 6495Hz  D.8  212  In-phase and quadrature frequency-domain electromagnetic data from the Dighem system: 7206Hz and 56kHz  D.9  213  Profiles of Z-component d B / d T data for the fifteen time-channels (0 to 18 msec after the transmitter current turn-off) along line 10. T h e upper panel shows data for the first five time channels, the center plot shows data for the next five time channels, and the lower panel shows data for the last five time channels. T h e deposit corresponds to the strong negative response i n the later time channels. Thirty-one lines of data were collected and different orientations but this is the only line that has been presented. 214  D.10 Gradient A r r a y I P data. T h e boundary shows the extension of the survey measurements and an outline of outcropping geology is also shown.  . . .  215  D . l l Reduced gravity data. T h i s gravity data has had a regional signal removed. The resulting gravity data were used for producing the density-contrast models i n Chapters four and five  216  D.12 Total-field ground magnetic data. T h e observation locations are displayed and the gaps where the data have been discarded are clearly visible. . . .  216  D.13 Observed and predicted C S A M T apparent resistivity data. T h e data was collected along three lines, -200m N , -400m N , and -600m N . E a c h data plot is plotted w i t h frequency i n the vertical axis (ranging from 8192Hz at the top to 0.5Hz at the bottom), and easting along the horizontal axis (-2200m E to -800m E )  217 xxvi  D.14 Observed and predicted C S A M T phase data. E a c h data plot is plotted w i t h frequency i n the vertical axis (ranging from 8192Hz at the top to 0.5Hz at the bottom), and easting along the horizontal axis (-2200m E to -800m E )  217  D.15 Observed and predicted "Realsection" I P data.  Five rows of data are  plotted i n each panel. Each row corresponds to a different transmitting dipole length  218  F.l  Synthetic chargeable body  227  F.2  North-facing cross-sections through the conductivity models: A ) 0.01 S / m half-space, B ) 0.1 S / m block i n a 0.01 S / m background, C ) 1 S / m conductive block i n a 0.01 S / m background, D ) 0.1 S / m block and a 40m thick 0.1 S / m overburden i n a 0.01 S / m background  228  F.3  North-facing cross-section through recovered chargeability models  229  F.4  North-facing cross-section through vector model of real component of the electric field  F. 5  G. l  231  North-facing cross-section through vector model of real component of the current density  232  Removal of electrode-effect v i a weighting  236  xxvii  Acknowledgments  I would like to thank my advisor, D r . Doug Oldenburg, for his guidance, motivation, and enthusiasm for this project. His mentorship has made this thesis a very enjoyable and interesting study to be involved with. M y sincere appreciation to former and present Geophysical Inversion Facility members without w h o m this thesis wouldn't be what it is, specifically: Christophe for his endless patience and help w i t h time-domain electromagnetic modeling; R o m a n for his amazing technical support; and C o l i n and E l d a d for their ability to answer my questions at a simplified level of understanding. Other thanks go to Partha, C h a d , Jiuping, Y u j i , Francis, Yaoguo, Peter, L e n , and Steve for their help, input, and friendship. M y thanks to other members and former members of the Department of E a r t h and Ocean Sciences at the University of B r i t i s h C o l u m b i a who have made my time here fun and fulfilling, including: Christina, Chris, Nicolas, K e v i n , P h i l , Charlie, T i m , Dave, James, Daniel, K i m , K a t e , Jeff, Jeff, Andrew, John, Fern, Andy, Stephan, Parisa, John, Steve, M a t t , and of course Jane. Included i n this acknowledgment are the members of the M i n e r a l Deposit Research U n i t . Also, my thanks go to the following companies for their support i n this venture: Newmont G o l d Company, for their encouragement and financial support (specifically: J i m , Rick, Perry, and Colin); Teck-Cominco, for providing an excellent data set w i t h which to work; M i r a Geoscience; Quantec Geoscience; Fugro; B H P - B i l l i t o n ; S J Geophysics; and Kennecott, a l l for helping w i t h issues relating to the data sets. Finally, my thanks to Ursula and Ellen for helping English w i t h his English.  xxviii  Dedication  I would like to dedicate this thesis to my family who support me i n exploring the riches of the world.  xxix  Chapter 1 Introduction  Geophysical techniques can be used to detect and delineate mineral deposits. T h i s has become more evident i n recent years for the following two reasons: 1. T h e recently developed ability to invert geophysical data to recover three-dimensional physical property models. 2. T h e increasing need to explore i n concealed terranes. A s a result, geophysics is providing a larger amount of information t h a n i n the past, which can impact an exploration program. A t this time, it is important to re-evaluate how geophysical inversion can be employed i n an integrated exploration program that includes geological, geophysical, geochemical, and remote sensing components. T h e possible role of geophysics i n an integrated exploration program is outlined by a flowchart i n Figure 1.1. T h e a i m of this thesis is to demonstrate the use of geophysical inversion techniques at various stages of the exploration program. A s an example, the results of inverting geophysical data from the San Nicolas copper and zinc deposit, i n Mexico, are presented. A n exploration methodology is presented that outlines the steps that might be taken i n an exploration program. W i t h i n this larger framework, the role of geophysics is defined. T h e geophysical role is an integrated part of the program w i t h geologic and physical property information being essential for the optimal use of geophysics. T h i s geophysical exploration methodology is followed throughout the thesis from defining an exploration model to delineating the deposit. 1  Chapter 1. Introduction  2  Defining the Geophysical Exploration Model  Exploration Goal: •find economic mineralization  Geologic Constraints; •existing mapping  ,  'historical workings  Geologic  •tectonic setting  | Models:  'descriptive model  [  'genetic model  j  Physical Property Geophysical . Exploration .^i^ Model:  c x p c c t o J  • > Exploration Model I  J  ™1  \ Regional Geologic j Mapping:  I j  I 'Outcrop geology  j  j •Structure "  j  I 'Alteration  \  Regional/Reconnaissance Scale Geophysics:  Collection I  Measurements  sja/depth of target propertfe, contrasts  Existing Data  Survey Design  AMAO (+/- ARAD)  Gravity  Other VLF, MT  Geochemistry •Stream sediments  Modeling . Joint/Simultaneous  Individual Forward  Remote Sensing  Inverse  Forward  Inverse  •Multispectral 'Hyperspectral  Correlation  Conclusions about physical property distributions of the the earth  Geologic Interpretation No Targets  3*  Target Generation  Terminate Exploration Fro gram  Detailed Geologic Mapping:  Exploration Model  Local/Deposit Scale Geophysics:  Geologic Logging 4  |  Survey Design  1  •Stratigraphy •Structure  Additional Physical Property Measurements  Update Geophysical  Update Geologic L Exploration Model j  •  >,  3  i  Gravity  GMAG  EM: CSAMT, UTEM  •  DC/IP  Other: VLF  •Alteration  j Trenching  *  Modeling  •Assays •Alteration mineralogy (X-ray/PIMA)  Forward  Inverse  Forward  Correlation  Conclusions about physical property distributions of the the earth  Geochemistry •soil surveys  If  ....  1  Mineralization Not Intersected  Joint/Simultaneous  Individual  Rock samples  Geologic Interpretation  Locate Drill-hole Physical property core measurements  Terminate  Down-hole methods  Exploration Program  Mineralization Intersected  z  Delineation  Goal Realized: Mineral Resource  Figure 1.1: Geophysical Exploration Methodology Flowchart: Defining the geophysical exploration model.  Chapter 1.  Introduction  3  In this thesis, sophisticated inversion techniques are used to generate digital, physical property models of the subsurface from geophysical data. These techniques are implemented at the regional scale to identify additional areas that might host mineralization, and at the deposit scale i n order to define the deposit and delineate mineralization. Physical property measurements made on drill-core have been integrated i n the inversion modeling at the delineation stage. The models are more easily interpreted t h a n the data, and can be related to geology using physical property measurements of local rocks. Furthermore, different inversion models are combined to establish a more comprehensive understanding of the subsurface.  Interpretations of the inverted models are proposed  based on the geologic information presented i n the thesis. In addition, the San Nicolas deposit is almost entirely bounded to the east by a southwest-dipping fault, which could have been a feeder structure. Mineralization continues to follow the fault at depth i n an unconstrained part of the deposit called the "keel". Modeling has been performed to demonstrate the sensitivity of different survey designs to this deep high-grade ore-zone. T h e remainder of this chapter introduces: (1) the exploration methodology that is followed throughout this thesis, (2) the geologic background of San Nicolas, which is used to produce a geologic model, (3) the physical properties of rock units at San Nicolas, (4) the geophysical exploration model, (5) the data that have been collected, and (6) the tools that are used to model the geophysical data.  1.1  Exploration methodology  M i n e r a l exploration programs commonly employ a staged approach when assessing large amounts of land or data. T h e a i m of this approach is to systematically discern the most  Chapter  1.  Introduction  4  prospective targets from a large amount of land without allowing any economic mineralization to be overlooked. These targets can then be evaluated w i t h a local exploration program and drill-testing. T h e flowchart i n Figure 1.1 starts w i t h an exploration goal and proceeds w i t h defining a geologic exploration model on which a regional scale exploration program is based. T h e geologic exploration model provides exploration criteria, which are an estimate of the essential controls on mineralization (favorable stratigraphy, association w i t h faults, etc.). These criteria are based on descriptive models (including mineralogy) and possibly genetic models that provide information about how the deposit might have formed. A l s o the model can be defined from knowledge about existing deposits i n the area, and their tectonic setting. Geophysical exploration methodology requires a geophysical exploration model. T h i s builds on the geologic model and, w i t h the inclusion of physical property information, defines geophysical exploration criteria. The criteria provide the expected physical properties of the mineralization itself, or physical properties of critical geologic features associated w i t h mineralization. A t the regional scale the aim is to identify smaller, more focused, areas of interest. Different geophysical techniques can be used w i t h survey designs that are tailored to the geophysical exploration model. Modeling of the geophysical data can produce inferences about the physical property distribution of the earth that, along w i t h physical property information, w i l l aid i n regional geologic interpretations.  The regional program may  result i n generation of targets that warrant more detailed investigation. A s shown i n the flowchart, target generation is followed by the local- or depositscale exploration program. The local geophysical program w i l l require different  survey  techniques and designs to those used at the regional scale, and this w i l l be based on an updated geophysical exploration model. A t this stage, geophysical inversion can play a  Chapter  1.  Introduction  5  role i n defining more detailed aspects of the subsurface, such as the depth and size of a b o d y of favorable physical properties, or the geometry of deposit-critical geologic features. A l o n g w i t h physical property information of the local rocks, geophysical inversion models contribute to local-scale geologic interpretation.  T h i s geologic interpretation can result  i n the spotting of drill-holes. T h e iterative nature of an integrated exploration program can continue during drilling. Information from drill-holes or drill-cores can be used to further refine geologic and geophysical exploration models. T h i s w i l l result i n better inversion modeling of the physical property distributions. Subsequently, these models can better guide drilling, which could result i n defining mineralization w i t h fewer drill-holes. Furthermore, surveys can be designed to delineate specific areas of mineralization that may have been intersected, but not completely defined by drilling. T h i s exploration model, while only outlining two stages of the exploration process, allows for several iterations and further focusing of a program through the use of feedback paths. W i t h i n the flowchart, emphasis is placed on how geophysics and inversion is integrated into such a program, but this is not meant to reflect a reduced importance of the other methods.  It is also acknowledged that the exploration model may be over-simplified;  however, it does demonstrate how geophysics has been used at San Nicolas.  1.2  San Nicolas  San Nicolas, owned by Teck Corporation and Western Copper Holdings L t d . , is an unmined, volcanic-hosted, massive sulphide deposit containing ore-grade copper and zinc w i t h associated gold and silver. It is located i n Zacatecas State, Mexico (Figure 1.2), a region that, until the discovery of San Nicolas and the nearby E l Salvador deposit,  Chapter  1.  Introduction  was unrecognized as having potential to host such deposits  6  [Johnson et al., 2000] . T h e 1  reserve estimates are 72 million tonnes grading 1.4% copper and 2.27% zinc, making it the largest massive sulphide deposit yet discovered i n Mexico, and a world class deposit.  1.2.1  Geology  Discovery of economic ore is of primary importance. A l t h o u g h we hope to detect massive sulphide mineralization using geophysics directly, the geologic environment must be considered i n order to effectively use geophysical information. A n understanding of the regional geology can focus exploration efforts i n more favorable structural and stratigraphic settings, thus expediting the whole exploration process. The association of mineralizat i o n w i t h geologic features that could be identified using geophysics, such as: favorable horizons, plutons, faults, shallow intrusions, rhyolite domes, and alteration zoning, aid i n producing informed exploration decisions. Discrimination of geologic features that could be mistaken for sulphide ore is also an important part of the exploration process. Furthermore, knowledge of the local geology, and the associated physical property values, allow for expected geophysical contrasts to be determined, and appropriate geophysical methods to be applied. The geophysical data that have been collected is not only used for direct detection of massive sulphide ore, but for providing information that can help form a geologic model of the area. A background to the geologic setting and deposit genesis is given below, while more detailed regional- and local-scale geologic information is given at the beginning of C h a p ters 3 and 4 respectively. 1  The area has been known mostly for the historically worked, epithermal silver and gold deposits.  Chapter  Introduction  1.  UNITED  w K  STATES  .'I  Gulf of Mi Zacatecas State 1  'ft Pacific  ;  Mexico City*'.  ' BELIZE  MEXICO Zacatecas 0 8  tt  \ fUATKMAlf & S9ft8 Natie*jai Geograpiwc Society  OAlitlllA  .  •  SOOI National Caographir Socsty  50 m!  '  i SO k m  DURANCO  T AM4UI  IANAJUATO  ;?AS  •" • '  Figure 1.2: M a p of Mexico (upper panel) and Zacatecas State (lower panel) showing location of San Nicolas.  Chapter  1.  Introduction  8  Geologic setting T h e paleotectonic regime i n which the San Nicolas deposit formed is a transition from a back-arc basin to an arc, and possibly a fore-arc setting , as documented by Daniel2  son [Danielson, 2000]. T h e deposit is hosted i n marine volcanic and sedimentary rocks, locally known as the Chilitos Formation, of Upper Jurassic to Lower Cretaceous age, [Johnson et al., 2000]. Mineralization is predominately found along a time-line  that oc-  curs between the formation of rhyolitic lava domes and the deposition of mafic extrusives above. These rocks include: mafic, intermediate, and rhyolite flows and shallow intrusions, felsic tuffs, volcanoclastic sediments, mudstone, chert, and limestone. T h i s package of rocks was subsequently accreted to N o r t h America; possibly i n the mid-Cretaceous. Northeast-directed thrusting of the Late Cretaceous to E a r l y Tertiary resulted i n the juxtaposition of the Chilitos formation against carbonates and siliciclastic sedimentary rocks.  Throughout the region, the rock assemblages are intruded by  Late Cretaceous to E a r l y Tertiary granitic plutons, and are unconformably overlain by Tertiary aged felsic volcanic flows and tuffs, [Johnson et al., 2000]. N o r t h - , northwest-, and west-striking faults cut the assemblage; the age and displacement of these faults are unknown, although they could be synvolcanic extensional or postvolcanic strike-slip faults, [Johnson et a l , 2000].  A section of rocks, which includes the Chilitos Forma-  tion, has also been uplifted to form a horst, w i t h i n which the San Nicolas deposit if found.  Tertiary volcanic rocks have intruded along the northwest-striking faults, and  along north-striking faults at the west of the horst. T h e deposit, itself, is bounded to the southwest by the northeast flank of a rhyolite A back-arc to fore-arc setting refers to relative locations within an island arc system. Island arcs are a chain of islands, such as the Aleutian Islands, that form at the convergent margin of two oceanic plates. They are often associated with deep oceanic trenches, and volcanic activity. A fore-arc setting would exhibit compressional tectonic forces, while a back-arc setting exhibits tensional tectonic forces. 2  Chapter  1.  Introduction  9  dome, and by a southwest dipping fault to the northeast. T h e deposit is copper-rich towards the b o t t o m w i t h a high-grade polymetallic cap at the top. Also, the sulphide is granular and fragmented i n the lower zone and laminated i n the upper zone, typical of VMS  deposits. T h e two zones could have different physical properties that geophysics  could help differentiate. Silicification and barite are present along the fault that bounds the deposit to the northeast. Extensive hydrothermal alteration of the host rocks is also observed; this could affect physical properties of the rocks.  Deposit Genesis Massive sulphide deposits are often formed i n the proximity to plate margins [Lydon, 1988]. Mineral-rich hydrothermal fluids, either originating from a magmatic source or circulating i n the sediments and being driven by the heat from a magmatic source, react w i t h sea water and deposit sulphide minerals. These deposit types are usually quite small and the duration of mineral deposition short-lived. Several deposits can be found i n the same area often i n association w i t h submarine volcanic flows and sediments [Lydon, 1988]. San Nicolas, although probably formed by a similar process, is larger t h a n usual and was possibly formed i n a tectonic environment not usually associated w i t h massive sulphide deposits. T h i s makes San Nicolas an unusual massive sulphide deposit, and provides a new and exciting type of exploration target for this region. Consequently, the regional potential for discovering other deposits is high because exploration programs i n this area have not previously focused on this type of deposit.  Geologic Exploration Model T h e m a i n features of the deposit that could be critical to similar deposits being found comprises the geologic exploration model. A t San Nicolas this could include the following:  Chapter  1.  Introduction  10  1. Favorable host stratigraphy between the rhyolitic and mafic volcanics that corresponds to a  time-line  2. Association of the deposit w i t h a quartz-rhyolite dome complex. 3. T h e shallow depth of the Chilitos formation due to the presence of a horst. 4. Faults that may have provided conduits for hydrothermal fluids.  1.3  Physical Properties  Geophysics can help define the physical property distribution of the subsurface.  This  is useful when objects can not easily be investigated firsthand, and have physical properties that distinguish them from their surroundings.  Estimates of physical property  measurements are also needed to relate inversion results to geology. Massive sulphides are commonly known to have contrasting values i n density, conductivity, chargeability, and possibly magnetic susceptibility. These physical properties stem from the minerals which constitute the ore. In the case of San Nicolas, the ore is primarily made up of pyrite, chalcopyrite, and sphalerite, w i t h some magnetite (not a sulphide mineral, although frequently associated w i t h sulphide assemblages), and possibly pyrrhotite. T h e total sulphide content is greater than fifty percent by volume w i t h i n the m a i n ore body. F r o m the mineralogy described above, the deposit is expected to have the following physical attributes: 1. H i g h density (a m i n i m u m of 3.8 g/cc [Ward, 1966]) 2. H i g h magnetic susceptibility (due to the presence of magnetite and pyrrhotite) 3. H i g h conductivity (this variable property often depends on the connectivity of the  Chapter  1.  11  Introduction  Rock T y p e  Density (g/cm ) 2.3 2.7  Magnetic Susceptibility (S.I. x l O " ) 0-5 2  Resistivity (Ohm-m) 80  Chargeability (mV/V) 10-30 30-80  3.5  10  20-30  200+  2.4 2.4  0-1 0  100 100+  10-20 30-70  3  Tertiary Breccia Mafic Volcanics Sulphide Quartz rhyolite G r a p h i t i c Mudstone  3  20  Table 1.1: Estimated physical properties for the five major rock types encountered at San Nicolas. T h e massive sulphide has contrasting values i n a l l physical properties apart from resistivity. A low resistivity value of 20 O h m - m for sulphides, similar to the Tertiary breccia, makes detection of the deposit more difficult when using some geophysical methods.  conducting paths ) 3  4. H i g h chargeability due to the presence of metallic minerals [Telford, Geldart, and Sheriff, 1990c] A p a r t from the sulphide ore, the geological units that are of significant interest are: the Tertiary overburden, which is conductive and contains some susceptible units; a 4  mafic volcanic unit that is magnetically susceptible; and dense mafic intrusions. T a ble  1.1 provides physical property information for the major rock types found at S a n  N i c o l a s , while A p p e n d i x B contains a complete review of physical property values used 5  to constrain the models and interpret the inversion results. many sulphides, excluding sphalerite, are semiconductors with conductivities often above 1000 S/m in their pure form [Sherman, 1997] 3  Conductivity (CT [Siemens per meter]) is the inverse of resistivity (p [Ohm-meters]). 5  The density of the sulphide has been reduced due to the presence of semi-massive sulphide material.  Chapter  1.4  1.  Introduction  12  Geophysical Exploration Model  Based on the physical property values and the geologic exploration model we can define a geophysical exploration model (Figure 1.1).  For San Nicolas this can be defined as  follows: 1. Bodies w i t h contrasting density, chargeability, conductivity and magnetic susceptibility w i l l be good candidates for mineralization. 2. In combination, the physical property contrasts can be used to differentiate between mineralization and shallow intrusives that may be just dense and magnetic. 3. T h e density contrast between the host volcanic rocks and the overburden w i l l help define the horst, which is important i n bringing the favorable horizon near to the surface.  1.5  Data  M a n y geophysical data have been collected over San Nicolas, and the surrounding area. D a t a were collected at San Nicolas during the last five years, and provided to U . B . C . by Teck Corporation. T h e data used i n this study include: airborne electromagnetic and magnetic data, gravity, ground magnetic, C S A M T  (controlled source audio-frequency  magnetotelluric), I P (induced polarization) data. T h e locations of these data sets are shown i n Figure 1.3. Plots of the data are presented i n A p p e n d i x D along w i t h a summary of survey specifications for each data set (Table D . l ) . In order to limit the number of plots in the m a i n text I present only: (1) physical property inversion models, (2) geologic p l a n maps and sections, and (3) interpretative figures. Geological information, as interpreted from drill holes, is also used to demonstrate the success of inversions performed, and i m p l y the benefits of inversion methods i n exploration. Unless stated otherwise, a l l coordinates  Chapter 1.  Introduction  13  are given i n meters and are denned on a local San Nicolas grid system. T h e conversion between the local grid system and the U T M Zone 13, N A D 27 system that was employed, is given i n A p p e n d i x A .  1.6  Modeling  Geophysical modeling involves relating, or mapping, data (a numerical representation of a physical signal that has been measured or observed) to a numerical representation of a physical property distribution w i t h i n the earth, known as a model. T h e mapping procedure can occur i n b o t h directions.  1.6.1  Forward modeling  Forward modeling is a procedure i n which a unique set of synthetic geophysical data are calculated based on a known physical property distribution. A s well as being a n inherent part of the inversion process, forward modeling plays an important role i n designing geophysical surveys, and testing geologic scenarios.  1.6.2  Inverse modeling  Inversion is the process of constructing physical property models of the earth based on information contained i n geophysical data. Unlike forward modeling, the inversion problem is often ill-posed. A n infinite number of models could have produced the data. We are able to choose one model by only considering those w i t h simple structural characteristics. Inversion models are generally much easier to interpret than the original data and provide a superior understanding of the subsurface. A description of the inversion procedure, as it applies to this thesis, is given i n Chapter 2 and the inversion parameters used to generate the physical property distributions presented are given i n Table E . l .  Chapter  1.7  1.  Introduction  14  Outline of the thesis  The following provides a synopsis of each chapter. Having already defined the geophysical exploration model, the following chapters (apart from Chapter two, which introduces geophysical inversion) proceed through the geophysical exploration steps while emphasizing the role of inversion. T h e work is presented w i t h i n the context of San Nicolas.  1.7.1  Inversion methodology  Chapter two provides an overview of the inversion process. T h i s is needed i n order for the inversion models to be given their correct context regarding the information they supply to the geologist.  1.7.2 The  Regional-scale modeling regional scale for this thesis is defined by the coverage of gravity and airborne  magnetic and electromagnetic data. T h i s large-scale region is shown by the upper plot of geology w i t h data boundaries i n Figure 1.3.  Not all of the airborne magnetic and  electromagnetic, and reconnaissance I P data sets cover such a large area as the regionalscale coverage, so an intermediate-scale is introduced and shown i n the center plot i n Figure 1.3. Inversions presented i n Chapter three are performed at either the regional or intermediate scales. In the context of this thesis, only inversion models of geophysical data are used to answer relevant regional geologic questions and focus exploration efforts.  1.7.3  Deposit-scale modeling  T h i s is the scale at which anomalies are explored and deposits are found, or grossly delineated. More detailed surveys are carried out on the best areas and targets that were identified at the previous stage of the exploration process. It may be, that work carried  Chapter  Introduction  1.  15  15000  ^  \  10000  \  \  5000  e o -5000'  .A  \\  L o c a l Grid North  \  \  UTM Grid North  *  \  M f—-  \  \  \  '  V  Regional Scale  -10000.  Gravity Data  -15000 -1(1000 ;  Dighem Electromagnetic and Magnetic Data -5000  0  5000  Easting (m)  10000  15000'  5000  Intermediate Scale Questem Electromagnetic and Magnetic Data  E cn  c.  Falcon Magnetic Data  E o  Subset of Dighem Magnetic Data Gradient Array IP Data  -5000 -10000  -5000  Easting (m)  5000  Deposit Scale Ground Magnetic Data Realsection IP\DC resistivity and CSAMT Data  X -2500  -1500  San Nicolas  Easting (m)  Figure 1.3: D a t a coverage over the San Nicolas deposit and the surrounding area. T o p : the regional-scale area w i t h data coverage and outcrop geology (legend shown). Center: Intermediate-scale data coverage shown on top of outcrop geology. B o t t o m : deposit-scale data coverage. T h e size of the rock-hammer symbol approximately corresponds to the extents of the surface projection of the San Nicolas deposit.  Chapter  1.  Introduction  16  out on a more regional scale is sufficient to identify a discrete target, but more detailed information about the deposit is needed to determine whether it is worth drilling w i t h one or five drill-holes, or at a l l . Alternatively, drilling may already have commenced and the geologist may require information about the geometry of the deposit before drilling the next hole. These scenarios provide the motivation behind the deposit-scale inversions of gravity, magnetic, electromagnetic, and induced polarization data that are presented in Chapter four.  T h i s work is performed at the scale depicted by the lower plot i n  Figure 1.3.  1.7.4  Detailed modeling using drill-hole information  Chapter five addresses the use of a priori information i n refining the physical property models constructed at the local-scale. Density and magnetic susceptibility measurements provide information about the earth that is independent of the data.  Including this  information i n the inversion process forces only models that are consistent w i t h known physical property distributions to be constructed.  Such a priori information could be  used to help spot a second drill-hole based on the results of the first, at the early stages of drilling, or could help model what is below a drilled out portion of a deposit at the end of the first phase of drilling. Furthermore, inversions w i t h a priori information could be used to incrementally update physical property models w i t h new information as each drill-hole is completed and logged. T h e evolving physical property models could help guide a d r i l l program that would potentially result i n less drilling needed to delineate the mineralization or conclusively test the anomaly.  1.7.5  Detection of deep ore: survey design  Forward modeling is an important tool that generates synthetic geophysical data based on physical property distribution models. Chapter six employs forward modeling to examine  Chapter  1.  Introduction  17  the success (based on signal-to noise estimates) of different survey designs i n detecting the keel and any extensions of mineralization that may exist.  Forward modeling can  also be used to test the geophysical response of different geological scenarios against field data.  1.8  Summary  T h e flowchart of Figure 1.1 provides the framework around which this thesis is based. T h e flowchart is presented at the beginning of each results chapter i n order to set the scene and provide an exploration context for the results. E q u i p p e d w i t h knowledge of the geologic environment, physical properties, and i n version methods, geophysical inversions are performed on data from San Nicolas. T h e inversion results demonstrate the assistance that geophysical inversion can provide to geologists regarding a variety of issues that range from regional exploration to detection and delineation of deep ore. Understanding the geology, and providing physical property models w i t h a geologic context, are essential at a l l stages of the exploration process and are important to this study. Geologic information is also used to determine the success of using geophysics alone to define the deposit.  Chapter 2 Geophysical Inversion  2.1  Introduction  T h i s chapter aims to construct a framework of understanding about the inversion process so that important issues such as non-uniqueness and data misfit are appreciated, and the inversion models are given their correct context regarding the information they supply to the geologist. Geophysical inversion produces physical property models from geophysical data, whereas forward modeling produces data from a physical property model of the earth. Several factors influence the inversion procedure and, therefore, the outcome of the model. These factors include: (1) estimating data errors, (2) using different reference models, (3) choosing smoother models or smaller model types, and (4) choosing a different balance between honoring the data and introducing features i n the model. T h i s chapter w i l l briefly address the aforementioned issues, along w i t h providing a synthetic example, i n order to demonstrate the construction of the physical property models presented i n this thesis.  2.2 2.2.1  Inversion Basics Data  W h a t are the data? T h i s may seem like a basic question, but it is one that needs to be explicity answered i n order for an inversion to be successful. D a t a include not only the actual d a t u m values, such as the strength of a magnetic field or the voltage between  18  Chapter 2. Geophysical Inversion  19  two electrodes, but, an error associated w i t h each d a t u m (where possible), locations of transmitters and receivers, orientations of instruments, data normalizations or changes i n units, and detailed knowledge of any processing that is applied to the data. These are all crucial elements that define what the datum is and how it is connected to the spatial distribution of physical property values w i t h i n the earth. Other information that can be thought of as a form of data could be physical property measurements made directly on the earth or any geologic knowledge that is pertinent to the region of the earth that produced the geophysical data. In the context of this thesis I deem such information to be a priori, that is information known prior to inverting the geophysical data. We can use these data to constrain and guide inversions.  2.2.2  Discretization and forward modeling  T h e earth is a continuous medium having physical property values everywhere.  We  can describe the physical properties of the earth as a continuous function of position, m(x, y, z). W h e n modeling, we usually simplify the earth through discretization, that is, dividing the earth into many cells whose physical property values are constant and whose size is smaller t h a n the resolving power of the experiment or survey. For the inversions presented i n this thesis, the cells are cuboidal and aligned w i t h an orthogonal coordinate system. A 3 D grid system or mesh defines the discretization of the model (Figure 2.1). T h e earth is now represented by a column vector m = ( m i , m%,... , mM)  T  of length M  (the number of cells), where each element of the vector corresponds to a the physical property value of a different cell i n the model. Forward modeling then consists of solving a system of equations to predict the responses at each observation location based on the physical property distribution of the discretized earth. For a linear system of equations, the data are calculated by performing a matrixvector multiplication. T h e matrix notation of the discretized forward problem is stated  Chapter 2.  Geophysical Inversion  20  d = Gm,  (2.1)  where G is the forward operating m a t r i x (of size N x M ) , m is a column vector comprised of M model parameters, and d is a column vector of length N, containing the observations, or data. For the inversion problems addressed i n this thesis, the number of data N is much less than the number of model parameters M.  2.2.3  Non-uniqueness and the Model Objective Function  In the inverse problem we have the data (d) and a forward operator (G), and we want to find the model (m). However, as w i t h any data set, we are limited to dealing w i t h a finite number of inaccurate observations. T h i s means there are many more model parameters than data, which makes the system under deter mined. Also, we don't want to produce a model that w i l l exactly reproduce the observed data, as this w i l l also reproduce noise. Therefore the data don't constrain a l l of the model parameters uniquely, and there are an infinite number of models that, when forward modeled, will reproduce, or fit, the d a t a to a specified degree.  Chapter 2. Geophysical Inversion  21  In order to chose one model from the infinite many that fit the data, we select the type of model we desire using a model objective function (sometimes called a model norm). The inverse problem is posed as an optimization problem by minimizing the model objective function subject to the requirement that the recovered model produces data that adequately resembles the original observations. W h e n a model objective function is suitably designed the recovered model has simple structural characteristics that should emulate geology. Flexibility should be built into the model objective function so most simple geologic situations can be accommodated.  The model objective function for a  continuous 3D model rn(x, y, z) can be written as:  =  a J s  w (m(x,y,z)  - m(x,y,  a  z) ) dv  + a  2  ref  f  2  +  dy  ^^1)  oyj^  )  d  v  +  a  d  v  +  a  J  x  w  x  fdm(x,  ^  dm(x,y,z)\  2  dx 2  :  * J  v  z  [  w  * { — z ( W  z  )  d v  ,  (2.2)  w i t h the equivalent matrix notation written as: $m= ||W (m-rn m  )|| . 2  r e /  (2.3)  T h e first term i n equation 2.2 measures the closeness of the model to a reference model  (m(x, y, z) f), re  while the following terms measure the smoothness of the model i n the x,  y, and z directions. Coefficients a , a , a , and a are used to weight the different parts s  x  y  z  of the model objective function relative to each other. T h i s enables the model objective function to recover models that are close to the reference model, or preferentially smooth in one direction, by only changing one or two parameters. Spatially dependent weighting functions w , w , w , and w are used to weight the importance of one model parameter s  x  y  z  versus another. The reference and weighting functions allow for a priori  information to  be incorporated into the inversion that may come from other geological or geophysical sources. Bounds can be implemented to limit the range of values a cell can exhibit and  Chapter  2.  Geophysical  Inversion  22  dip information can also be included [Li and Oldenburg, 2000b]. m a t r i x notation of the model objective function, where W  m  E q u a t i o n 2.3 is the  is a weighting m a t r i x of size  M x M that includes all the weighting and derivative functions of the model objective function, and || • || is the l<i norm.  2.2.4  Signal-to-noise a n d d a t a misfit  Signal-to-noise plays an important part i n inversion because, no matter how good the field work and instrumentation, the data always contain noise. It follows that we do not wish to produce a model that can exactly reproduce the data as it would also reproduce the noise (known as over-fitting  the data). Unfortunately noise levels are often unknown  or only approximately known, but some estimate as to either absolute or relative noise levels is usually required. In order to measure how well the model response, or predicted data, fit the observations, we design a data misfit function as stated below.  (2.4) W (d - d  p r e  d  where N is the number of data, d°  bs  is the i  th  observed datum,  d a t u m generated by forward modeling the model, and of the i  th  datum, and is included i n  (2.5)  )  df  re  is the i  th  predicted  represents the standard deviation  when the expression is written using m a t r i x  notation (Equation 2.5). We assume the noise is Gaussian i n nature, has zero mean, and is non-correlative. T h i s being said, if noise levels are well estimated, the data misfit should be approximately equal to the number of observations  « N). W h e n data have been over  fit  «  N),  the misfit is too small, and models often display erroneous structural complexity. T h i s complexity is needed to fit noise i n the signal and has no reflection on the structure of  Chapter  2.  Geophysical  Inversion  23  the earth. If the data misfit is too large (<3>d »  N), the model often lacks structure  signifying that not all of the information that the data contain about the earth has been extracted. In practice, several inversions are generally needed i n order to produce a model that has some structure, as defined by signal i n the data, but not too much structure or complexity, as originating from noise i n the data. Estimating the correct noise levels often comes down to the experience of the user. Obviously, the better the signal-to-noise ratio and the noise estimates of the data, the better the inversion model.  2.2.5  Optimization  The inversion procedure is stated as the following optimization problem: find a model that minimizes the model objective function subject to fitting the data to a specified tolerance. T h e objective function is comprised of the data misfit and the model objective function (Equation 2.6), w i t h (3 representing a trade-off, or regularization, parameter. M i n i m i z a t i o n of the objective function will generate the model.  (2.6) Choosing the regularization parameter is key to producing a model that has the maxi m u m amount of information about the earth while containing the m i n i m u m amount of erroneous structure that relates to noise i n the data. W h e n noise levels are known, the regularization parameter can be determined using the discrepancy principle [Hansen, 1998]. Methods that can be applied when noise levels are unknown include: (1) generalized cross-validation ( G C V ) , which is based on the principal that a good model would be able to predict a missing d a t u m  1  [Haber, 1997], (2) the L-curve criteria that searches  For the generalized cross validation method to be most effective, a sense of the relative size of data errors is necessary, although the actual magnitude of the error is not needed. 1  Chapter  2.  Geophysical  Inversion  24  for the corner of the curve when data misfit and model objective function values are plotted for different values of the trade-off parameter , b o t h of which are described by 2  Hansen [Hansen, 1998], and (3) manually performing inversions w i t h different noise estimates. In practice it is often useful to apply G C V or L-curve methods to get an idea of the approximate magnitude of the errors, and then use the discrepancy principle to slightly increase the misfit.  2.2.6  Appraisal  W h e n a model is generated v i a the inversion procedure it is often desirable to determine how good the model is, that is, how well does it represent the earth? A s geophysics is usually a m i n i m a l l y invasive technique we don't tend to dig up the earth, but revert to the data and model to answer this question. W h a t is really being asked is: what can be concluded about the earth, given the data? A s it is already known that the constructed model is not the only one that w i l l fit the data, a range of models that fit the data could be generated.  Construction of many models using inversion could be quite time  intensive, although a rudimentary exploration of this model space is a good idea. T h i s can be accomplished by changing the model objective function. T h e user soon gets an idea of features i n the model that don't change (which are therefore probably required i n order for the model to fit the data) and features that do change (which are not constrained by the data). It is important to be aware that the model may not define the earth well in a l l places, and over-interpretation of features that are not constrained by the data is O n a log-log scale, a plot of the data misfit (<£<,) verses model norm (4> ) for different values of the trade-off parameter (j3) produces a curve which resembles the letter L. In the upper part of the curve, a decreasing value of /3 will produce large decreases in data misfit for small increases in model norm. Conversely, along the lower part of the curve, as (3 decreases further a small decrease in data misfit will correspond to a large increase in model complexity, as measured by the model norm. A model that corresponds to the point of maximum curvature will have a reasonable amount of complexity but should not over fit the data. 2  M  Chapter  2. Geophysical  Inversion  25  possible. A s an alternative to defining how good the model is, hypothesis testing can be performed to confirm, or eliminate, geological scenarios of interest. Other, more sophisticated methods of appraisal, which are not used i n this thesis, might include analysis of averaging functions [Backus and G i l b e r t , 1968] and point spread functions of the model resolution matrix [Menke, 1984].  2.3  A synthetic example  In order to demonstrate the inversion process, and the level of understanding that is needed i n order to gain the most from an inversion result, a synthetic example has been devised. A density-contrast model is used to forward model synthetic, verticalcomponent, gravity data. The data are contaminated w i t h noise and then inverted using  GRAV3D  (Appendix E.2) to recover a density-contrast model.  g/cm  3  Figure 2.2: Perspective view of synthetic density-contrast model. T h e model has been volume rendered so that cells w i t h density contrasts less t h a n l g / c m have been removed. 3  Chapter  2.3.1  2.  Geophysical  Inversion  26  The data  To start, the data are generated by making measurements over a three-dimensional density contrast distribution (Figure 2.2), much like we would i n the field. T h i s is performed using a forward modeling procedure that is described i n A p p e n d i x C l .  Figure 2.2 shows  a volume-rendered, perspective view of the synthetic density contrast model. T h e model consists of 7200 cells of size 10m x 10m x 5m. The feature we wish to model is a 40m x 40m  x 4 0 m cube located 20m below the origin w i t h a density-contrast of 1 g / c m . T h e 3  cube is hosted i n a background of 0 g / c m density-contrast. 3  Observed Gravity Data 400 data  Easting (m)  Figure 2.3: Surficial synthetic gravity observations w i t h 3% + 0.0005 m G a l r a n d o m noise added. Four hundred synthetic gravity measurements were made at the surface on a regular 10m grid. T h e data were contaminated with 3% + 0.0005 m G a l r a n d o m noise (Figure 2.3) and standard deviations were assigned equal to 3% of the magnitude of each d a t u m value. The  m i n i m u m standard deviation was limited to 0.0005 m G a l .  Chapter  2.3.2  2.  Geophysical  Inversion  27  Presentation and interpretation of inversion models.  M i n e r a l deposits and the geologic structures can exhibit very three-dimensional geometries. T h e geology of San Nicolas and the surrounding area is no exception, and much confidence is gained when we are able to model the earth i n three-dimensions.  Models  often contain complicated 3D structures of varying physical property values that are difficult to present i n a document. In this thesis 3D models are presented using a combination of perspective view images, plan views at different elevations, and cross-sections.  Visu-  alization tools such as: coloration based on physical property values, shading, volume rendering, and view angle are factors that can change what is perceived when viewing a model. A s a result, an attempt is made to objectively convey the salient features of each model w i t h one or two images. For comparison between models, north-facing crosssections at -400m north, and west-facing cross-sections at -1700m east are frequently presented. Furthermore, i n order to address non-uniqueness issues, information may be distilled from the analysis of several models generated using different objective functions. Care is needed when making inferences about geology from an inversion model, and when the models are discussed i n the text the reader may assume the features have been appraised using the methods outlined i n Section 2.2.6.  2.3.3  Recovered models  T h e inversion problem is solved by minimizing Equation 2.6.  T h e solution depends  on the definition of the model objective function ( $ ) , the data misfit m  and the  regularization parameter (B) that controls the trade-off between misfit and structure. T h e following sections show how changing the desired misfit or alpha coefficients i n the model objective function effects the recovered model. Examples of changing the reference model and assigning bounds on some of the model cells are also shown.  Chapter  2.  Geophysical  Inversion  28  g/cm  3  Figure 2.4: Perspective view of recovered density-contrast model. T h e model is viewed from the southeast (local coordinate system), and is volume-rendered w i t h a cut off' at 0.3 g / c m . 3  Figure 2.5: North-facing cross-section through recovered density-contrast model w i t h location of true model shown.  Chapter  2.  Geophysical  Inversion  29  A generic model For this first example the errors are assigned appropriately and so we have an estimate of the desired misfit. A model is recovered that fits the data to a value equal to the number of data  = TV). Figure 2.4 shows a perspective view of the model recovered.  T h e model has been volume-rendered using a cut-off value of 0.3 g / c m . T h e view angle 3  is the same as that used i n Figure 2.3, but the coloration has been changed. A northfacing cross-section through the model is shown i n Figure 2.5 w i t h the outline of the true model also displayed. A s can be seen from these two images, the recovered model has a concentration of anomalous density coincident w i t h the location of the true model. T h e recovered model seems smeared out relative to the true model, especially at depth, producing an anomaly of larger volume, and, as a result, lower density contrast values. T h i s is because the model objective function was designed to generate a model that is close to a reference density-contrast model of 0 g / c m , and have smooth features. 3  The  density values of the recovered model are smaller than the true model; however, the t o t a l mass w i t h i n the model is the same.  Effects of over- or under-fitting the data Figure 2.6 shows north-facing cross-sections through the true model and seven recovered density contrast models from inversions of the synthetic gravity data.  E a c h model is  overlain w i t h an outline of the true model, and a l l of the models are plotted on the same colour scale (0 to 0.5 g/cc). For comparison, Figures 2.6(a) and 2.6(b) show cross-sections of the true and recovered model w i t h $d = TV respectively. Figure 2.6(c) shows a model chosen w i t h the data misfit one tenth of the number of data. Because the correct noise levels were assigned to the data, the model over-fits the data.  In comparison to F i g -  ure 2.6(b), where the data misfit was appropriate, the density-contrast values are slightly  Chapter  2.  Geophysical  Inversion  30  (a) True density-contrast model  (c) Model over fits the data: $  d  (b) Model fits the data: $ d = N  = O.liV  (d) Model under fits the data: $  (e) Model smoother i n z-direction:a > ct ,ct z  x  (f)  y  Model smoother  a ,a x  (g) Reference model is half of true model  y  d  = 5N  i n horizontal direction:  > a  z  (h) Half of model bounded  Figure 2.6: North-facing cross-sections through true model and seven recovered models for seven different inversions. A l l of the inversion models are shown on the same color-scale w i t h dark blue representing 0 g / c m and below, and magenta representing 0.5 g / c m and above. T h e original model, an outline of which is shown on a l l the recovered models, is shown at a different scale. 3  3  Chapter  2.  Geophysical  Inversion  31  closer to the value of the true model. However, erroneous artifacts, represented by more complex structures, are produced i n the recovered model as the noise i n the data manifests itself. Conversely, the density contrasts are lower when the data misfit is too large, as shown by the model i n Figure 2.6(d). T h i s model was produced w i t h a misfit of five times the number of data. T h e under-fitting arises because the model objective function has more control over the model produced when the data misfit is larger, resulting i n a smaller and smoother model w i t h more simple structure.  If noise levels were t r u l y  unknown then b o t h of these models would be an equally viable a representation of the earth. In such cases determining how well the data have been fit becomes more difficult. GCV  and L-curve methods can be useful i n such cases as the models are chosen based  on methods other than data misfit.  Effects of changing the model objective function Figures 2.6(e) and 2.6(f) show the results of altering the model objective function to produce models smoother i n the vertical direction and smoother i n the horizontal directions, respectively.  T h i s is done simple by changing the relative values of the a coefficients  in equation 2.2. A s can be seen, the model shown i n figure 2.6(e) is elongated i n the vertical direction, which extends the anomaly at depth, while the model shown i n figure 2.6(f) is truncated at depth but has broader horizontal extents.  B o t h models still  have a concentration of mass i n the vicinity of the true model. T h e models also b o t h fit the data to the same degree ( $  d  = N) so, i n the absence of any external biases, b o t h  are an equally reasonable representation of the earth. T h i s is a demonstration of nonuniqueness. Together, the models produced represent a very brief exploration of model space.  Chapter  2. Geophysical  Inversion  32  Including a priori information A s mentioned i n section 2.2.1, other forms of information (such as that from a d r i l l hole) can be incorporated into the inversion. If we assume that half of the cube is known to exist prior to inverting the data, we can use this information directly i n the inversion. In the first case a reference model is constructed that is 1 g / c m where the cube is known 3  to exist, and 0 g / c m everywhere else. T h e result of using the reference model w i t h the 3  same parameters (data misfit and a coefficients) as those used i n the original inversion (figure 2.5) is given i n figure 2.6(g). T h e recovered model is slightly improved compared to the one generated without the reference model i n that the anomaly doesn't have values extending to such depths. T h i s result could be changed further by altering the relative weighting of each cell to the reference model using the weighting m a t r i x i n equation 2.3. T h i s would allow regions of the model that are known prior to inverting the data to be weighted more strongly w i t h respect to the reference model t h a n other cells.  An  alternative method is to restrict the range of values a cell can assume. In figure 2.6(h) a model was constructed using this method. For the part of the cube known to exist, the model was restricted to ranges between 0.9 and 1.1 g / c m , for the rest of the model 3  space the bounds were set from 0 to 1 g / c m . Most of the mass is concentrated i n the 3  correct place. It seems evident that this technique has produced a very good model that is the most representative of the true model. T h i s demonstrates how constraining the model i n one area can improve the model i n the other areas.  2.4  Conclusions  Inverting geophysical data to construct physical property models can allow for greater understanding of the subsurface. However, i n order to get the most from the inversion process the following issues must be appreciated: (1) the data have to be fully understood  Chapter 2. Geophysical Inversion  33  and, where possible, errors should be assigned; (2) fitting the data to an appropriate degree can be difficult when errors are unknown; (3) users should be aware of non-uniqueness and the role that the model objective function plays; and (4) significant  understanding  of the m a i n model features can be obtained through rudimentary exploration of model space. The issues addressed above, and i n the synthetic example, underlie the importance of accurate data and the need for well-designed surveys that constrain models leaving as little r o o m for ambiguity, and non-uniqueness, as possible.  Chapter 3 Regional Scale Modeling  3.1  Introduction  T h i s chapter demonstrates the use of regional geophysical inversion i n an exploration program.  T h e goals of the regional geophysical program have been determined by the  geologic and geophysical exploration models of Chapter one. T h i s chapter presents the results of inverting regional geophysical data provided to U . B . C . by Teck Corporation, and includes a preliminary geologic interpretation based on physical property values and geologic information. The steps that are followed i n this chapter are w i t h i n the context of the exploration model, and are shown by shaded boxes i n Figure 3.1. Extensive Tertiary and Quarternary cover makes exposure of the bedrock limited. A s well as obscuring the sulphide-hosting Chilitos Formation, this has resulted i n a limited understanding of the regional geology and hampers exploration for V M S deposits. Geophysical methods have been applied i n order to shed light on the regional geology and search for massive sulphides underlying the overburden. Regional-scale inversion of gravity, magnetic, electromagnetic,  and IP data is per-  formed i n order to generate areas for detailed follow-up exploration.  The: generation  of target areas is carried out by identifying physical property distributions that fit the expected response from massive sulphide deposits and by assigning regional geologic context to physical property features observed i n the models. B o t h of these approaches can help focus exploration efforts.  34  Chapter 3. Regional Scale Modeling  35  Geophysical Exploration: Regional Scale Modeling  Exploration Goal: •find economic mineralization  Geologic Constraints: •existing mapping •historical workings  Geologic  •tectonic setting  Models: *H  'descriptive model 'genetic model  Geophysical .  => Exploration Model  Exploration  e x p e c t o d l t f 2 e  ,  Physical Property; Measurements  ( f c p U l o f t a l 8 ( ! l  . p h y ^ properties contrasts  Model:  Regional Geologic Mapping:  Existing Data  Regional/Reconnaissance Scale Geophysics:  •Outcrop geology •Structure •Alteration  Survey Design  Data Collection  Gravity  AMAQ (+/- ARAD)  Other. VLF. MT  Geochemistry 'Stream sediments  Modeling' ,  Individual  •Forward  Remote Sensing  Inverse  Joint/Simultaneous  Forward  Inverse  •Multi spectral 'Hyperspectral  Correlation  ^1  j -|«J  Geologic Interpretation  No Targets  of the the earth  j -4  Target Generation  Terminate Exploratioo Program Update Geophysical  Update Geologic Exploration Model  I  Detailed Geologic Mapping: 'Stratigraphy •Structure  Geologic Logging A  Exploration M o d e l  Local/Deposit Scale Geophysics:  Additional Physical Property Measurements  uTK. |r1\  Survey Design  Data Collection  Gravity  EM: CSAMT, UTEM  •Aheration  Trenching  4  Modeling Individual  Rock samples •Assays  Forward  Joint/Simu Itaneous Forward  Inverse  •Alteration mineralogy (X-ray/PlMA)  Correlation  Conclusions about physical property distributions of the the earth  Geochemistry •soil surveys  Geologic Interpretation  Mineralization Not Intersected  Locate Drill-hole Physical property core measurements  Terminate  Down-hole methods  Exploration Program  Mineralization Intersected Delineation  Goal Realized: Mineral Resource  Figure 3.1: Geophysical Exploration Methodology Flowchart: Regional Scale Modeling.  Chapter  3. Regional  Scale  Modeling  36  In the first instance, correlation methods are applied to three-dimensional models to determine regions of favorable combinations of physical property values. T h i s involves a rudimentary spatial correlation of cells i n inversion models that exhibit physical property values comparable to the physical properties of massive sulphide, as determined i n section 1.3. In the second instance, geologic features are interpreted from models, and the relation of features to mineralization is determined by using conceptualized geologic models. Interpretation of the models is performed while keeping i n m i n d the notions of nonuniqueness and fitting the data. Ideally, the two methods w i l l complement each other by generating fewer, but better, exploration targets than when one method is used i n exclusion. T h i s approach w i l l hopefully result i n a more efficient use of exploration resources.  3.2  Regional geology  A regional map showing mapped surficial geology is presented i n Figure 3.2.  Apart  from the geological setting that was described i n Chapter 1, and the direct detection of massive sulphides, features that are of regional interest i n this chapter include: (1) the varying thickness and physical properties of the overburden, (2) the location of shallow mafic intrusions, (3) the extent of a horst block, and (4) widespread fault systems and how they might relate to mineralization. Some of these features w i l l undoubtedly be related to each other because faults define the boundaries of the horst, and overburden thickens away from the horst.  T h e scenarios mentioned above represent a subset of  the many possible geologic attributes of the area that maybe related to mineralization. However, the geologic conceptual model considered i n this thesis assumes that the Chilitos formation is where we expect to discover deposits like San Nicolas, and faults play an  Chapter  3. Regional  Scale  Modeling  37  Qal Tert Breccia Mst/Lst Mafic Vol MaficAnt Vol Qtz Rhyolite Graphitic Mst  Dlas -5000  -10000  -15000 I -10000  1  -5000  ' 0  1  5000  1  10000  1  15000  Easting (m)  Figure 3.2: Plan-view of regional geologic outcrop at San Nicolas. Modified from Johnson et al., 2000. Most of the region is overlain by Quarternary alluvium. Inset shows a key to the stratigraphic units.  Chapter  3. Regional  Scale  Modeling  38  important role i n delivering mineral-laden hydrothermal fluids to areas where deposition occurs. P r o x i m i t y to heat sources could also be significant to the creation of deposits.  (1)  Overburden:  T h e overburden varies i n thickness throughout the region, and can change even on a local scale. A n increased thickness i n overburden could produce a gravity variation that could mask the response of high density sulphides, while false anomalies could be caused by a thinning overburden.  A w a y from the deposit the overburden also contains large  volumes of Tertiary volcanic tuff that has high magnetic susceptibility . 1  Identifying  the overburden, even w i t h varying properties, plays an important part of geophysical regional exploration by allowing false anomalies, and features that could potentially mask a deposit, to be determined. In addition, the possible propagation through the overburden of reactivated, deeper faults can give clues about the underlying structure.  (2) Mafic Intrusions: A small, intrusive, mafic body outcrops to the northeast of the deposit (shown on F i g ure 3.2). T h e mafic material has a high density and, due to the high iron content of mafic rocks, could also have high magnetic susceptibility values. These intrusives have the potential for producing physical distributions similar to massive sulphide concentrations when using gravity and magnetic methods. Other methods are needed i n order to discriminate between false anomalies and possible mineralization. Large, magnetically susceptible, intrusive bodies could also be present at depth. It is Another consideration to the physical characterization of the overburden involves the interaction of water. Seasonal changes in the level of the water table may change the conductivity, and IP responses have been observed coinciding with the boundary between areas of contrasting hydraulic.permeability within the overburden. Both of these phenomena, while not being entirely explained or understood in this thesis, may have the potential for producing anomalous responses that could be mistaken for responses from mineralized bodies. 1  Chapter  3. Regional  Scale  Modeling  39  expected that the deposit is proximal to a former heat source that would have driven the circulation of hydrothermal fluids. T h e relationship between a heat source and the deposit maybe very complicated and could depend, among other things, on the availability of fluid conduits. Nevertheless, it is hoped the identification of large intrusive bodies w i l l make the regional picture more complete and allow geologists to make their own hypotheses about the importance of such a feature.  (3) Horst: The oldest exposed rocks i n the region are Upper Triassic phyllite, slate, quartzite and marble of the Zacatecas Formation ([Danielson, 2000]). T h e host rocks of the Chilitos formation lay unconformably on these metamorphic rocks. These rocks outcrop to the north and south of the deposit on a large horst-block of upthrust geologic section. T h e formation of the horst is assumed to be more recent than the deposition of the volcanic rocks of the Chilitos Formation, and more recent than the genesis of the deposit. T h e proximity of favorable host rocks to the surface throughout the horst makes defining the limits of the horst intrinsic to the exploration process. T h e gravity data show high values where these rocks outcrop and it is expected that these data w i l l be instrumental in defining the horst.  (4) Faults: Finally, the data imply the presence of several similarly orientated fault sets; faults that may have played a role i n the circulation of hydrothermal fluids. If a fault can be shown to be related to the formation of the deposit, then identification of similar faults becomes an important part of exploring the surrounding area for more deposits. Regional inversions can help define these faults and determine their association w i t h the deposit. It is hoped that airborne E M data will give insight into fault orientations nearer the surface, while  Chapter  3.  Regional  Scale Modeling  40  gravity and magnetic methods could give information about faults at depth . W h i l e the 2  existence of faults w i l l define the extents of the horst, faults internal to the horst are also of interest.  3.3 The  Introduction to the inversion results. following sections describe the results of inverting the regional geophysical data  sets. T h e o r y and methodology behind each data type is contained i n A p p e n d i x C , and descriptions about the inversion programs used are i n (Appendix E ) . T h e m a i n features of each inversion model are described and, where appropriate, geologic information is inferred i n an attempt to address the issues mentioned above. Information pertaining to each data set is provided i n Table D . l , while Table E . l shows the inversion parameters used for each inversion. Every attempt is made to provide a clear image of the feature or features being discussed i n the text although it is realized that this may not be achieved when only a few images of each model are provided. Interpretations are made from the model displayed and also from the numerous other models that were generated w i t h different, yet just as reasonable,  3.4  model objective functions and data misfit values.  Density-contrast models  Quantec Geofisica de Mexico and Geociencias Consultores collected gravity data at San Nicolas i n 1998. Traditional corrections, including instrument drift, tidal, and Bouguer, were applied to the data by Quantec; however, no terrain corrections were included The inversions of the gravity and magnetic data are designed, through implementation of the model objective function, to produce small or smooth models. Ideally a blocky model is sought/when the aim is the definition of faults. However, physical property transitions in the models may still be indicative of fault contacts and their orientation. 2  Chapter  3. Regional  Scale  Modeling  41  because of the generally low topographic relief i n the majority of the survey area (Figure D . l ) . Neglecting the terrain correction is probably valid except i n the vicinity of a h i l l located at 7000m East and -1000 N o r t h on the local coordinate system. After an i n i t i a l inversion it was realized that data i n and around the hill were erroneous. T h e decision was made to discard them. T h e resultant data, further altered by removing their mean value, are shown i n Figure D.2. Surface gravity data contain relatively strong information about lateral variations i n density but they don't have any inherent depth resolution. Therefore a depth distribution comes from incorporating an additional depth-weighting into the inversion. T h e weighting procedure has been developed through the use of synthetic forward modeling and inversion [Li and Oldenburg, 1998b]. GRAV3D,  a program developed by the U B C -  G I F , was used for inverting the gravity data. A brief overview of this program is given i n A p p e n d i x E . 2 . For the inversion, each datum was supplied w i t h a location and assigned a standard deviation of 0.01 m G a l . Topographic relief was included i n the inversion procedure. Table E . l provides more details about the inversion parameters, as it does for all of the inversion results presented i n this thesis. The m i n i m u m data misfit that was achieved, without producing too much complexity i n the model, was about twenty five times the number of data. T h i s number seems larger t h a n expected even if the data errors were assigned inappropriately . T h e reason for this 3  may reside w i t h the discretization of the model. In order to model such a large volume, w i t h i n a reasonable amount of time, a cell size of 250m by 250m by 100m was used for the majority of the model. T h e data were collected on intervals ranging from 100m to 50m. A s a result, shallow density variations that may have been detected by the data, would E r r o r s of 0.01 m G a l magnitude correspond to the normal operating sensitivity of most gravimeters. Applying gravity corrections (Instrument, Bouguer, etc.) to the data does not usually introduce significant errors that would degrade the accuracy of the data. 3  Chapter 3.  Regional Scale  Modeling  42  not be modeled by a cell of uniform density-contrast and the model w i l l not be able to fit the data.  However, it is expected that large, regional-scale features are acceptably  modeled.  3.4.1  Regional gravity inversion results  A plan-view of the regional density-contrast model is shown i n Figure 3.3. A l l the cells that have a density-contrast of 0.1 g / c m or lower have been removed. Outcrop geology 3  is overlaid and a boundary shows the extent of data coverage. A cut-off of 0.1 g / c m is 3  somewhat arbritrary because the transition from areas of low density-contrast to areas of high density-contrast is smooth. However, this does show the distribution of the denser materials, and the western edge of the dominant north-south feature moves only slightly when the cut-off value is varied. Outcropping rocks that correspond to the Chilitos and Zacatecas Formations roughly coincide w i t h the surface projection of the northern half of a large dense north-south body, which approximately extends from -5,000m to 2,000m east, and -10,000m to 10,000m north. T h e continuation of this high density feature to the south, together w i t h knowledge of the regional geology, suggests the rocks persist under the Tertiary and Quarternary overburden to about -10,000m north. T h i s large feature probably represents uplifted geologic section and is interpreted as a horst.  A north-  south trending string of high density anomalies seems to mark the western edge of the horst. For the southern half of the horst, the east and west edges show a north-northeast orientation. T h i s orientation is consistent w i t h a mapped fault located between -750m east, 0m north, and 1,500m east, 7,000m north, which is also shown i n Figure 3.2). Four northwest-southeast  linear anomalies are also present east of the m a i n horst  feature. Westerly projections of these linear features coincide w i t h breaks and inflections along the east and west edges of the horst, suggesting a relationship between the features. T h e relationship described above can be explained w i t h northwest-southeast  faults that  Chapter 3. Regional Scale Modeling  43  15000  10000  *>  t  5000  fc«*  s t  £  o  \  sz  o -5000  IfogJi a «  HI \l ^  \  |K  •  i  —*\ r\  \  /•  -  •r'  » y  -^ /  -10000  -15000 -10000  -5000  0  5000  10000  15000  Easting (m)  0.05  0.175  g/cm  3  0.3  Figure 3.3: P l a n view of the regional density contrast model w i t h cells below a value of 0.1 g / c m invisible. Outcrop geology is overlaid, and the boundary of the gravity survey is represented by a purple line. A l l coordinates are i n the local grid system. 3  Chapter 3. Regional Scale Modeling  11  Figure 3.4: West-facing cross section of the regional density contrast model w i t h cells below a value of 0.1 g/cc invisible. T h i s cross-section is located at -1600m east and shows an interpreted horst. laterally off-set sections of the horst.  The linear features themselves, could be faults  containing higher density materials. A west-facing cross-section located at -1600m east shows a side view of the horst (Figure 3.4). T h e image shows an east projection of material between -1,600m and 3,000m east that has a density-contrast of greater t h a n 0.1 g / c m . T h e image shows 3  that the horst, as defined by the density cut-off, is not continuous. In fact three sections of the horst could be interpreted, w i t h the two most southerly sections being joined at depth and the northern section being larger and deeper. northwest-southeast  T h i s agrees w i t h the idea of  faults being present, as the faults could have been responsible for  changing the elevation of the rocks as well as offsetting them laterally. Figure 3.5 shows the inferred locations of faults i n the area based on evaluation of this model. The straight trace of the northwest-southeast faults, and the cross-cutting of the east and west flanks of the horst, imply an age relationship between faults i n the  Chapter 3. Regional Scale Modeling  45  15000  -10000  -15000 -10000  -5000  5000  10000  15000  Easting (m)  0.05  0.175  g/cm  3  0.3  Figure 3.5: Surface projection of the regional density-contrast model w i t h interpreted faults overlaid. Northwest-southeast trending faults are shown i n red, while north-northeast trending faults are shown i n black. T h e mapped fault is shown i n yellow. M a t e r i a l w i t h a density-contrast of less than 0.1 g / c m has been removed. 3  Chapter 3. Regional Scale  Modeling  area, w i t h the north-northeast fault being older than the northwest-southeast  46  faults.  Figure 3.6 shows a horizontal slice through the regional density contrast model at an elevation of 1700m (400m below the surface). T h e m a i n features are the linear trends of high density that correspond w i t h the east and west edges of the horst and northwestsoutheast trending linear features. It is interesting that high densities would be associated w i t h faults. A n explanation for this could be the presence of higher density material w i t h i n faults. T h i s could be representative of igneous rocks that were intruded along the fault. A n anomaly located at -1,600m east and -400m north represents the deposit.  This  is a small anomaly and selecting this feature over the others would be difficult. Based on the interpreted faults that are shown i n Figure 3.5, the deposit lies w i t h i n a coherent block of bedrock. T h i s suggests that any faults associated w i t h the genesis of the deposit are not detected and are probably older than the formation of the horst. Searching for density anomalies that are not coincident w i t h younger, interpreted faults seems like a reasonable way to search for massive sulphides. However, other interpretations might place faults directly through the deposit. A north-facing cross-section through the regional density-contrast model is shown i n Figure 3.7. T h e negative density-contrast located near the surface at 6,000m east is i n the vicinity of the hill where data were removed prior to inverting, and is thought to be an artifact created by not terrain correcting the data around the h i l l . T h e deposit is shown as a discrete density high near the surface. N o significant depth extent to the deposit has been modeled. Another isolated density high is shown at about 4,000m east, this is one of the northwest-southeast linear features. A lateral change i n density contrast at -6,000m east represents a fault, which is probably representative of the near vertical western flank of the horst block. T h e eastern flank of the horst block maybe dipping to the east.  Chapter  3. Regional  Scale Modeling  47  Figure 3.6: Horizontal slice through the regional density-contrast model at an elevation of 1700m (400m below the surface at the location of San Nicolas). A n outline of geology outcrops is overlaid, and the extent of data coverage is shown by a purple line.  Chapter  3. Regional  Scale  Modeling  48  San Nicolas  5000 Q.  Q  10000 -10000  -5000  0  5000  10000  15000  Easting [m]  -0.4  0.05  g/cm  3  0.3  Figure 3.7: N o r t h facing cross-section through a regional density contrast model at a northing of -400m (coincident w i t h the deposit).  Chapter 3. Regional Scale Modeling  49  A t depths greater than 4 kilometers, a large amount of higher density material is present i n the model, and it is coincident w i t h the bounds of the horst block as defined from the shallower parts of the model. T h e deep large high density features,  needed  to explain the high gravity anomalies shown i n the data, could be uplifted limestone, mudstone, or metamorphic rocks of the Zacatecas Formation. A l l of these rocks could have higher densities than the overlying volcanic and sedimentary units. In addition, the deep-rooted, large bodies at depth could represent large intrusive bodies rich i n mafic material and high i n density.  3.4.2  Summary of regional gravity results  T h e density contrast model is consistent w i t h the previously interpreted geology and has contributed to regional geological understanding w i t h the following interpretations: (1) the delineation of a horst block, (2) the interpretation of faults below the overburden that cross-cut the horst (3) a possible discrimination of discrete high density anomalies similar i n nature to the deposit, and (4) the identification of deep-rooted bodies that could represent large amounts of sedimentary or intrusive igneous rocks, or b o t h .  3.5  Magnetic susceptibility models  T h i s section addresses the the inversion of regional magnetic data. Total-field magnetic data is inverted w i t h the a i m of producing a three-dimensional magnetic susceptibility distribution of the earth. Background information on the magnetic method is given i n A p p e n d i x C . 2 . Airborne methods are employed at San Nicolas to provide generalized geologic information on large areas i n a short period of time as well as identify anomalous regions of high susceptibility that might indicate mineralization. T h e data are displayed i n A p p e n d i x D . Three data sets are inverted and subsequently  Chapter  3. Regional  Scale  Modeling  50  combined to form a single susceptibility distribution model of the subsurface. Surface magnetic data also have no inherent depth resolution and so, as w i t h the gravity inversion, depth weighting is needed.  3.5.1 The  Data three airborne magnetic data sets include:  1. D i g h e m collected airborne magnetic data while also collecting frequency-domain electromagnetic and radiometric data. T h e Dighem data has the most extensive coverage of the three data sets. Northwest-southwest trending features that can be interpreted from the regional density-contrast models are also apparent i n F i g ure D . 3 . T h i s texture betrays the regional structural setting. A subset of observations from the Dighem data set was chosen for inversion (Figure D . 4 ) . 2. T h e airborne magnetic data, shown i n Figure D.5 were acquired by B H P at the same time as airborne gravity-gradient data were being recorded w i t h their Falcon system. 3. Fugro collected airborne magnetic data (Figure D.6) while collecting time-domain electromagnetic data w i t h their Questem system. The coverage and survey specifications for each data set are slightly different although similar features are observed. T h e deposit is represented i n the data by a subtle magnetic high at about -1600m east and -400m north, and the response from highly magnetic Tertiary tuffs are seen to the north west of the deposit. A l l of the data sets had regional fields removed prior to inversion and standard deviations of l O n T were assigned to each datum.  Chapter  3. Regional  Scale  (a) Dighem susceptibility model.  Modeling  (b) BHP model.  susceptibility  5000  5000  m 1 -5000 -5000  -5000  0  xirrs.1.  (c) Fugro model.  0 Easting (m)  Easting (m)  susceptibility  xKTS.I.  (d) Composite susceptibility model.  Figure 3.8: Comparison of individual magnetic susceptibility inversion models w i t h composite model of mean values. T h e models are shown i n plan-view looking at a depth of 300m (within the depth of the deposit).  Chapter  3.5.2  3.  Regional  Scale  Modeling  52  Composite susceptibility model  Initially an attempted was made to invert all three d a t a sets simultaneously. T h e results from this inversion were not successful; this is possibly due to the different regional fields that were removed prior to inverting. Consequently each data set was inverted separately. A single susceptibility model was produced from the three individual inversion models by placing them all on the same mesh and calculating mean value of each cell. Volumes of the model that do not lie directly below the data coverage have been removed. Figure 3.5.1 shows a comparison between the three individual models and the resulting model.  3.5.3  Regional susceptibility model analysis  Figure 3.9(a) shows a plan view of the regional magnetic susceptibility model at a depth of 3 0 0 m . A t this depth the deposit is modeled as a discrete anomaly located at about 4  -1700m east and -500m south (labeled). However, features w i t h the highest susceptibility include two elongated regions to the west and northwest of the deposit (between -4000m and -2000m east, and -500 and 750m north), and a discrete anomaly located at -1500m east and 2000m north. T h e two elongate features correspond w i t h out-cropping Tertiary breccia.  T h e other anomaly to the north (located at -1500m east and 2000m north)  corresponds w i t h out-cropping mafic volcanics. Except for a small anomaly located at -2000m east and -2000m north, other mafic volcanic outcrops have low susceptibilities. Several other low magnitude anomalies of unknown origin are also observed at this depth. A t a depth of 1200m, as shown i n Figure 3.9(b), four large features are observed. A n anomaly at -1500m east and 2000m north is probably an extension of the anomaly seen at 300m depth, and could represent an intrusive body. A large anomaly is observed at this depth, located between -4000 and -2000m, and -1000m and -3000m. It is interesting 4  The deposit is approximately located between depths of 200m and 500m.  Chapter  -10000  3. Regional  Scale  -5000  Modeling  53  _ „ , , Easting (m)  0  5000  xlO" S.I. 3  8  (a) Composite regional susceptibility model: 300m below the surface. 5000  pi'' 11  cn c  'sz tl  o  i  P  1  -5000 -10000  -  5 0 0 0  Easting (m)  0  5 0 0 0  xlO" S.I. 3  (b) Composite regional susceptibility model: 1200m below the surface.  Figure 3.9: Plan-view of horizontal slices through the composite magnetic susceptibility model. T h e upper panel shows a slice at 300m depth, while the lower panel shows a slice through the model at 1200m depth.  Chapter  3. Regional  Scale  Modeling  54  to note that a shallow ring of small, moderate ( 2 x l 0 ~  3  S.I.) susceptibility concentra-  tions coincides w i t h the edge of this body (Figure 3.9(a)). T h i s volume of anomalous susceptibility could be a deep rooted intrusion, and the shallower anomalies may have originated from this deep source.  Alternatively, this susceptible body might represent  magnetic units below the Chilitos Formation. T h e anomaly located between -4000 and -2000m, and -1000m and -3000m also has a distinct northern edge that is orientated i n a northwest-southeast direction.  This  orientation corresponds to those interpreted from the regional density-contrast model. T h i s suggests that it was i n place prior to a faulting event or its emplacement  was  controlled by an existing fault. T h e western edge of this susceptible b o d y is also distinct but is likely an artifact of combining the three separate susceptibility models. Anomalous susceptibility distributions have also been modeled at -6000m east, 0m north, and -2500m east, 3000m north. B o t h of these are constructed to fit incomplete anomalies at the edge of the data coverage.  T h e y could also represent deep intrusive  bodies or magnetic sequences w i t h i n the Zacatecas Formation. Some of the features mentioned above are observed i n the north- and west-facing cross-sections of Figure 3.10. T h e north-facing cross-section is located at -400m south and corresponds to the location of the deposit. T h e deposit is clearly represented by a discrete susceptibility anomaly (labeled). T h e anomaly near -4000m east is the southern boundary of the Tertiary tuffaceous unit. T h e large anomaly i n the far west of the crosssection, between depths of 100m and 2500m, corresponds to the most westerly anomaly seen i n figure 3.9(b). T h i s anomaly does not extend to great depths although it might extend beyond the modeled volume. T h e west-facing cross section shows the deposit which is connected to a smaller magnetic feature to the north.  T h e high susceptibility concentration at -1500m east and  2000m north is clearly seen on this cross-section, along w i t h the large deep b o d y to the  Chapter  3. Regional  Scale  Modeling  55  San Nicolas  Q-  "a 5000 -10000  5000 Easting (m)  xl0 S.I. 3  N  Northing (m)  5000  Figure 3.10: N o r t h - and west-facing cross-sections (upper and lower sections respectively) through the magnetic susceptibility model generated by combining inversion results from Dighem, Fugro, and B H P airborne magnetic data.  T h e north-facing cross-section is  positioned at -400m south, while the west-facing cross-section is positioned at -1600m east.  Chapter 3. Regional Scale Modeling  south.  56  T h i s large, most southerly anomaly is the only anomaly that persists below a  depth of 2500m and continues to the bottom of the mesh at a depth greater t h a n 5000m.  3.5.4  Geologic interpretation  Mafic rocks make up part of the volcanic section w i t h i n which the deposit was formed. T h e anomalies observed i n the mafic volcanics could represent concentrations of magnetite or pyrrhotite associated w i t h massive sulphides, as seen at San Nicolas. A l t e r n a tively, the mafic rocks may naturally exhibit a higher magnetite content . 5  T h e Tertiary volcanic breccia, while predominately being comprised of subordinate volcanic debris, contain some ash-flow tuffs that are magnetically susceptible . 6  These  tuffs are responsible for the elongate anomalies seen i n figure 3.9(a). T h e extention of the susceptibility values to the north of the deposit may be indicative of an extension of mineralization and warrants further investigation. T h e large susceptibility features seen at depth could indicate large intrusive bodies. T h i s is especially true when the relationship between some of the deeper and shallower anomalies are considered. It seems unlikely that thick sections of susceptible, unaltered volcanic rocks reside at depth. Evidence for the alteration or metamorphism of rocks on a regional scale comes from the rocks of the Zacatecas Formation, which underly most of the region. Some linear features can be seen i n the model, such as an approximate north-northwest line of susceptibility anomalies, which includes the deposit, (seen i n figure 3.9(a)), and well defined boundaries to some features that share the same orientation. These linear Volcanic rocks in the San Nicolas region generally exhibit subdued magnetic susceptibility values, possibly due to the effects of wide spread alteration. Less altered volcanic rocks could posses higher magnetic susceptibility values. 5  D r i l l hole Sal-133 intersected Tertiary breccia to a depth of more than 400m [Jansen, 2000]. Susceptibility measurement made on the core exceed 2 x l 0 ~ S.I. between a depth of 200m and 300m (Appendix B.3.1). 6  3  Chapter  3. Regional  Scale  Modeling  57  features might represent faults although collaborative information would be necessary to confirm this.  3.6  Regional conductivity models  Airborne electromagnetic data are used to determine the regional conductivity structure of the subsurface and are usually acquired contemporaneously w i t h airborne magnetic data. T h e development of airborne electromagnetic systems has been hand-in-hand w i t h the exploration of massive sulphides as the targets are usually very conductive and can be hosted i n quite resistive rocks. Dighem's frequency-domain system and Fugro's Questem time-domain system were employed at San Nicolas and modeling of b o t h these data sets was performed.  3.6.1  Frequency-domain data  A n overview of the frequency-domain, airborne electromagnetic method is given i n A p pendix C.3.1. T h e overview describes the nature of the data and A p p e n d i x E.5 gives a description of  EM1DFM,  the algorithm that is used to invert frequency-domain E M data  to recover a one-dimensional conductivity distribution at each station. T h e data cover a very large area (Appendix D ) and it was hoped that a regional conductivity model of the earth could be produced to complement the 3 D density contrast and magnetic susceptibility information that was obtained by inverting the gravity and magnetic data. Unfortunately the inversions have shown that the data are not sensitive to conductive features below the overburden. T h i s lack of sensitivity was determined by performing several inversions w i t h different reference models and observing the depth at which the model reverts to the value of the reference model. B o t h G C V and L-curve methods of choosing the appropriate misfit were used and generate similar outcomes. T h i s suggests  Chapter 3. Regional Scale  Modeling  58  that inappropriate error assignments do not contribute to the shallow penetration. T h e depth at which the data were deemed insensitive to the model was about 150m. T h e reason for the observed sensitivity of this system is probably because of the high overburden conductivity, the frequencies employed, and the geometry of the system. Insight into the observed depth of penetration can be explained by considering the skin depth for a plane wave i n a homogenous medium [Ward and Hohmann, 1987] . For a 7  30 O h m - m medium at the lowest frequency of the system (466 H z ) , the skin depth is approximately 127m. T h i s implies that the signal has dramatically deteriorated by the time it passes through the conductive over burden and reaches the volcanic sequence. A t higher frequencies the signal w i l l be even more deteriorated by passing through the overburden. Aside from the lack of penetration, the survey is useful i n that it covers a large area and provides detailed information about the thickness of the overburden. A d d i t i o n a l l y features can be interpreted i n the overburden that might be the manifestation of deeper structures.  After the I D inversion models were produced an interpolation routine was  used to construct a 3 D m o d e l . T h e 3D models are presented below i n the same manner 8  that the 3 D density and magnetic susceptibility models are presented, although it should be kept i n m i n d that they have been produced by stitching together many I D conductivity models. The skin depth (<5) is the depth at which a sinusoidal electric field (from a planar source in a wholespace) diminishes to about 37% of it's original strength, and is approximately stated as 5 = 503 [m], 7  where p is the resistivity, and / is the frequency [Ward and Hohmann, 1987]. In the Dighem system we are not dealing with plane waves but a dipolar source. Also the geometry between source and receiver influences depth of penetration. However, this approximation can give an idea of the strength of different frequency signals in different mediums. The interpolation routine uses triangulation to assign values to cells at each depth. The routine was applied to the Logio of the conductivity values in order to reduce homogeneity artifacts. 8  Chapter  3. Regional  Scale Modeling  59  15000  \ 10000  <*•#•:» o  5000 «  0  .£  O  0  TV \  -TO)  o  z  *  '"'  1  s  -5000  il  )  -10000  -15000 -10000  -5000  0  5000  10000  15000  Easting (m)  1 0 0  Ohm-m  2000  Figure 3.11: Horizontal slice 60m below the surface of the 3D resistivity model generated by interpolating between lines of I D models inverted from D i g h e m frequency-domain airborne electromagnetic data.  Chapter  3. Regional  Scale  Modeling  60  A horizontal slice through the stitched conductivity model is shown i n Figure 3.11. 9  T h e slice is at a depth of 60m below the surface and it clearly maps out the outcropping geology w i t h high resistivity values, and the overburden w i t h low resistivity values. O u t cropping volcanic units of the Tertiary overburden exhibit low resistivity values. T h i s is especially significant i n the south, where a tuffaceous unit outcrops. N o anomaly is detected i n the vicinity of the San Nicolas deposit. A l t h o u g h this model doesn't seem that useful i n the direct search for massive sulphides, the method can be used as a reconnaissance mapping tool for determining the extent of cover and inferring regional structures. Linear features seen i n the model have the same orientation as structures observed i n the regional density contrast model. Specifically, three discrete resistive regions are defined that correspond well w i t h the limits of the horst (as defined by the density contrast model). Northwest-southeast trending linear resistivity contrasts are seen that may also relate to the structural nature of the region.  3.6.2  Time-domain data  A i r b o r n e time-domain electromagnetic ( T E M ) surveys are used at the regional or reconnaissance stage of exploration programs. T h e y often enjoy more success t h a n frequencydomain systems at resolving the conductivity structure of the earth below conductive overburdens, and can be used to detect conductors as well as delineate structures. T h i s section details the inversion of such a data set collected by Fugro i n 1998. A cursory investigation is also made into the possible cause of negative data that is observed at late time-channels. T i m e - d o m a i n electromagnetic data were collected using Fugro's Questem system. T h e nature of the data and how they are collected is discussed i n A p p e n d i x C.3.2, while The conductivity (a) models are presented as resistivity (where p = 1/<T) for consistency with models presented later in the thesis. 9  Chapter  3. Regional  Scale  Modeling  61  the specifications particular to this survey are given i n Table D . l . T h e x-, y-, and zcomponents of the rate of change of the magnetic field ( ^ ) are measured as a function of time, but only the vertical component is considered here. series (  d B  ^  One stacked time-decay  measured i n fifteen time-windows) is collected for each station. E a c h time-  decay series was inverted using TEM1D  to recover a layered-earth conductivity model.  The resulting I D models were combined, using an interpolation routine, to produce a 3 D conductivity structure of the earth. A n overview of TEM1D  is given i n A p p e n d i x E . 6 .  A horizontal slice through the "stitched" conductivity model at a depth of 200m is presented i n Figure 3.12. T h e 3D conductivity model shows the distribution of conductive overburden and maps out the outcrops quite effectively, similar to the frequency-domain results presented i n Section 3.6.1. However, not much structure is modeled below the overburden, and the absence of a conductive b o d y i n the vicinity of the deposit is conspicuous. It was expected that data collected w i t h this system would be sensitive to conductivity structure below the overburden , and would probably result i n a more deeply resolved 10  inversion model than that recovered from the inversion of the D i g h e m frequency-domain data. T h e reason for this inconsistency might lie i n how standard deviations were assigned to the data to account for the "noise". The depth of the peak amplitude of a decaying and propagating, impulsive, electromagnetic planewave, in a whole-space, at a given time, can be stated as: z = f ^ ) [m] [Ward and Hohmann, 1987]. This is sometimes referred to as the diffusion depth. Although this depth would be different for a layered earth, and a magnetic dipole source, as used in the Questem survey, this gives a general idea of the penetration of time-domain electromagnetic systems. For an overburden with a conductivity of 20 Ohm-m, the diffusion depth is 280m for late times, which is well below the top of the deposit. 10  Chapter  3. Regional  Scale  Modeling  62  Figure 3.12: P l a n view of a slice through the 3-D resistivity model generated by interpolating between lines of I D models from the inversion of Questem time-domain airborne electromagnetic data. T h e slice (at a depth of 200m below the surface) shows, i n red, the thicker areas of overburden that continue to this depth. T h e underlying, more resistive, volcanic rocks are represented by cooler colors.  Chapter  3. Regional  Scale  Modeling  63  Negative data A significant amount of negative data i n late time-channels, initially thought to be noise, was present throughout the survey. Large errors were assigned to these data. Assigning these errors had the effect of making the inverted models insensitive to the negative data, but also to structures at depth. A s a result, the resulting model only detects the shallow i  feature, namely the overburden. Sixty percent of the stations contained negative data, usually at late time-windows. However, sixteen percent of the stations contained negative data from time-windows that correspond to earlier times (time-channels 8 to 11); channels that usually contain little noise. Furthermore, these negative values persisted locally from station to station and appear to be above the noise levels, particularly over the deposit. A line of Questem, z-component data is presented i n Figure 3.13, along w i t h a total magnetic field profile, and the results of inverting the data using TEM1D.  A significant negative z-component  response is observed over the deposit i n time-channels 10 to 15 (annotated w i t h a number 1). Smaller depressions that are possibly above the noise level, are also observed at late times (labeled w i t h 2, 3, and 3). Line-by-line inspection of the whole data set showed that no other late-time response was as compelling as that seen over the deposit. A n example of time-decay data that exhibit negative values for one station over the deposit is provided i n Table 3.1 and plotted i n Figure 3.14. A l t h o u g h this time-decay series is from over the deposit, it's characteristics are typical of other time-decay series in that it exhibits a faster decay at late time and then changes sign. T h e negative data are part of a coherent decay pattern observed i n the time-decay series. It is strongly suspected that the negative data are a real part of the signal and are not noise. Such negative data are commonly referred to as IP effects.  Negative data occur  when the rate of change of the magnetic field, as measure by a receiver coil, opposes  Chapter  3. Regional  Scale  Modeling  til  D Channel 10 Channel 11 Channel 12  Channel 13 Channel 16 • •  43840 43830 law fieis MaQnafict 43820  !nl  43810 43800 43790  Resistivity Model  E.  500-  Ohm-m  -2000 -3000  Approximate location o f San Nicolas deposit  -1250 Easting [m] 2300  Northing |m]  Figure 3.13: Vertical component, time-channel data, flight height, t o t a l field magnetics, and resistivity inversion model for line 20110 of the Questem data. T h e data are presented as parts-per-million of the peak z-component of the waveform, w i t h channels 1 and 15 corresponding to the earliest and latest times respectively. T h e inversion model is produced by "stitching" together I D models. T h e location of the deposit corresponds to a coherent pattern of negative data values i n time-channels 12 through 15 (1), and a magnetic high. Other regions of negative data that could be above the noise level are also observed (2, 3 and 4).  Chapter 3. Regional Scale Modeling  Time Channel 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15  W i n d o w center time (msec) 0.375 0.531 0.688 0.922 1.234 1.625 2.094 2.641 3.344 4.281 5.531 7.094 9.047 11.469 14.359  65  z-component  (MV) 0.080425279 0.038734626 0.022706049 0.012765264 0.006915682 0.003740238 0.002087504 0.001168190 0.000640473 0.000316427 0.000087868 -0.000008127 -0.000083297 -0.000070091 -0.000042156  z-component (ppm) 15834.6 7626.3 4470.5 2513.3 1361.6 736.4 411.0 230.0 126.1 62.3 17.3 -1.6 -16.4 -13.8 -8.3  Table 3.1: Questem time-decay ( ^ ) data for one station located over the deposit. T h e center time of each time channel is given (time after turn-off of p r i m a r y field) along w i t h the z-component data i n micro-volts and i n ppm, as normalized by the peak z-component of the p r i m a r y field.  Chapter 3. Regional Scale Modeling  1.E-06 -I 0.1  a  I  I  I  I II  66  II | 1  I  I  I  I I I II|  Time [msec]  10  I  I  I  I M i l ) 100  Observed data: positive A Observed data: negative  Figure 3.14: Logarithmic plot of Questem vertical-component time-decay data given i n Table 3.1. Positive data are shown by squares, and negative data (the last four) are shown w i t h triangles. T h e data are from over the deposit and show a typical decay pattern for stations that contains negative data.  Chapter  3. Regional  Scale  Modeling  67  that of the primary field. T h i s could be caused by geometric effects distorting the field, or by the presence of polarizable material that reverses the field.  In addition, for I D  conductivity structures w i t h realistic ranges of magnetic susceptibility, negative data can only be produced when polarizable is present material [Weidelt, 1982]. Flis, Newman, and H o h m a n n [Flis et al., 1989] have also shown I P effects i n T E M data to be caused by polarizable, or chargeable, material at the surface. A s the massive sulphides at San Nicolas are expected to be chargeable, further investigation is performed on the negative data, through the use of I D forward modeling.  3.6.3  Forward modeling the Questem data  Forward modeling is performed i n an attempt to reproduce negative data that are observed at late times i n the Questem data set. T w o investigations are undertaken; one to determine if the geometry of the earth can produce the negative data, and one to determine the role of polarizable material i n producing these data.  Forward modeling a 3D conductivity structure The  affect of geometry is investigated by forward modeling synthetic Questem data over  a three-dimensional conductivity structure. T h e conductivity model is generated by digitizing geologic cross-sections and assigning conductivity values to different rock types based on physical property measurements (Appendix B . 4 ) . T h e conductivity sections are interpolated to produce a three-dimensional model (Figure B . 5 shows a north-facing cross-section through the conductivity model at -400m south). EH3DTD A p p e n d i x E.9) was used to collect synthetic ^f100m  (described i n  data at twenty four stations, spaced  apart along one east-west line, over the conductivity structure.  T h e same sur-  vey parameters (transmitter-receiver geometry, wave-form, time-channels) were used to collect the synthetic data as those employed by the Questem system.  Chapter  3. Regional  Scale  Modeling  68  A l l of the synthetic data were positive. Moreover, the synthetic time-decay data over the deposit exhibit a slower decay at late times, which is i n contrast to that observed i n the field data. T h e decay-rate is also slower than that observed over a half-space. T h i s result suggests that the negative data were not due to the geometry of the subsurface.  ID forward modeling of polarizable material T h e role polarizable material might play i n producing negative data is investigated by forward modeling one-dimensional, layered-earth structures that contain polarizable layers . 11  A I D forward modeling program was available that uses the Cole-Cole [Pelton et al., 1978] model to represent complex conductivities . Models were produced i n an attempt to 12  match time-decay data observed at one station over the deposit. T h e time-decay chosen for forward modeling analysis was recorded w i t h the receiver located at -1600N and -500N and is representative of many of the decay curves over the deposit. T h i s data is presented i n Table 3.1 Figure 3.14. T h e observed data show a reversal i n polarity at the twelfth time-channel (about 7 msec). Figure 3.15 shows observed and predicted time-decay data and the corresponding real In this case, polarization refers to a relaxation mechanism at the pore-scale that occurs when alternating, or time-dependent currents are present. Polarizable material has frequency-dependent, or complex, conductivity. 11  Complex conductivity is introduced in the model by using the empirical Cole-Cole model. This is a simple relaxation model that allows conductivity to change with frequency based on a few parameters. An equation for the Cole-Cole model is given below: 1 2  where Z(u>) is the frequency-dependent impedance, to is the frequency, Ro is the zero-frequency resistivity, c controls the rate at which the relaxation occurs, r is a time constant that determines when the relaxation occurs, and m is the chargeability, and relates the zero-frequency resistivity to the resistivity at infinite frequency.  Chapter  3. Regional  Scale  Modeling  69  1200  (a) Observed and predicted time-decay data. The predicted data are from the TEM1D inversion.  (b) TEM1D inverted conductivity model.  (c) Observed and predicted time-decay data for conductivity model with additional conductor at depth.  (d) Conductivity model with additional conductor,at depth.  Figure 3.15: Observed and predicted time-decay data, and corresponding I D conductivity models for Questem station located over the deposit. Frequency-independent conductivity layers do not reproduce the observed negative data.  Chapter  3. Regional  Scale  Modeling  70  (frequency independent) one-dimensional conductivity models. Figure 3.15(a) shows the observed field data and predicted data from the inversion using TEM1D. T h e predicted data fit the early time data very well, negative signals are not produced at late times. A s a result the overburden is well represented i n the model, but most depth information is not realized. Based on the expected conductivity structure of the deposit from physical property measurements, a conductive layer of 200 O h m - m was introduced into the model between 200m and 400m depth.  T h i s is shown i n Figure 3.15(d), and the data resulting from  this model is shown i n Figure 3.15(c). The introduction of a conductor into the model increases the magnitude of the data, and a worse fit to the observations is produced. W h e n the conductive layer at depth is assigned a complex  (frequency-dependent)  conductivity, the data values are reduced, negative data are produced, and the character of the observed time-decay is emulated.  T h e results from two complex conductivity  models are shown i n Figure 3.16. M o d e l " A " (Figure 3.16(b)) consists of the one-dimensional real conductivity distribution that was recovered from the inversion w i t h a layer of complex conductivity between depths of 200m and 400m. T h e conductivity of the overburden was also decreased to 100 O h m - m i n order to more closely fit the early-time data. T h i s model produces data (Figure 3.16(a)) that emulate the observed time-decay data.  T h e predicted late-time  data are negative and have the same decay pattern as the observed data. T h e early-time data are also a good fit. M o d e l " B " (Figure 3.16(d)) consists of the one-dimensional real conductivity distribu t i o n that was recovered from the inversion w i t h a layer of complex conductivity between depths of 350m and 480m. T h i s model produces predicted data (Figure 3.16(c)) that also emulate the observed time-decay data. T h e predicted late-time data are negative and have the same decay pattern as the observed data. T h e late time data also have a better  Chapter  3. Regional  Scale  Modeling  71  10000 1000  ? E 100  V  c=0.4 m=0.7 T=0.005  10  O •>  Time [msec] • Observed data: positive Observed data: negative • Predicted data: positive Predicted data: negative 1  10  200  "35  (a) Observed field data and predicted time-decay data for complex conductivity model A.  400  600  800  1000  1200  Depth [m]  s  (b) Complex conductivity model A. A layer of complex conductivity (Cole-Cole parameters are shown) is present between depths of 200m and 400m.  10000 1000  c=0.785 m=0.8 r=0.005  E  £ 100  .c O  * >  1  T i m e[ m s e c ]  10  10  Observed data: positive ; Observed data: negative |4 Predicted data: positive « Predicted data: negative  (c) Observed field data and predicted time-decay data for complex conductivity model B .  "95  200  400  600  800  1000  1200  Depth [m] (d) Complex conductivity model B . A layer of complex conductivity (Cole-Cole parameters are shown) is present between depths of 350m and 480m.  Figure 3.16: Observed and predicted data, and I D conductivity models for Questem station located over the deposit. A layer w i t h complex (frequency-dependent) conductivity (based on the Cole-Cole dispersion model), positioned at the approximate depth of the deposit, can produce data that has the same character as the observations. The conductivity of the overburden was also decreased i n an attempt to more closely fit the early-time data.  Chapter  3. Regional  Scale  Modeling  72  fit to the observations than those i n Figure 3.16(a). However, the early-time data do not fit the observed data so well. The Cole-Cole parameters were manipulated to produce data that resemble the observations.  A l t h o u g h the data have not been fit exactly, the results seem acceptable  for only having introduced one conductive layer. T h e Cole-Cole parameters used were Ro = 200 O h m - m , c = 0.4, r = 5 msec, and m = 0.7, for model " A " , and R  0  = 100  O h m - m , c = 0.785, r = 5 msec, and m = 0.8, for model " B " . T h e parameters correspond quite well w i t h numbers determined by Pelton [Pelton et al., 1978] for massive sulphides, although values of c less than 0.55 are expected. It is not unreasonable to expect that, by slightly changing the conductivities and Cole-Cole parameters at different depths, a model can be found that fits the data w i t h i n the expected noise levels and has chargeable material at depth.  Discussion on inverting complex conductivity data Since a forward modeling is available that produces time-decay data from complex conductivities, it might seem reasonable to use this to invert the data. However, there are issues that should be considered before this is undertaken. First the appropriateness of the Cole-Cole model should be demonstrated. Pelton [Pelton et a l , 1978] has shown that many sulphide minerals cannot be described by one set of Cole-Cole parameters alone. The use of two or more Cole-Cole models is necessary to fit laboratory observations. Secondly, inverting for more unknowns could introduce more  non-uniqueness into  the problem. T h a t is, each layer of complex conductivity w i l l have its own frequency dependence, and will be characterized by four parameters, a, m , c, and r . In contrast, each frequency-independent layer needs only one parameter (a) for full characterization. T h i s means that the number of unknowns w i l l increase by a factor of four if only one Cole-Cole model is employed.  Chapter  3. Regional  Scale  Modeling  73  Alternatively, so as not to be restricted by any parameterized  frequency-dependent  models, real and imaginary parts of the complex conductivity could be inverted for at each frequency. R o u t h (1999) investigated this for multiple grounded sources i n order to remove coupling effects form I P data. However, data from one source may not contain enough information to constrain b o t h depth and frequency-dependent information. Negative data are i n the noisiest part of the time-decay (late times). These data may not be sufficient to resolve between different combinations of the Cole-Cole parameters. In such cases, additional information would be needed to constrain or eliminate some of the parameters from the inversion.  Better constraints could come from the data  collected w i t h T E M systems w i t h increased sensitivity and better sampling of the decay series. Alternatively, rock samples i n the area may reveal only small variations i n some of the Cole-Cole parameters, thus allowing them to be constrained or removed from the inversion altogether. T h e discussion so far has only dealt w i t h one-dimensional complex conductivity structures. It is possible that no combination of complex conductivity layers w i l l fit the data. T h e combined effect of three-dimensional geometries and complex conductivities is unknown, but may be necessary i n order predict some decay-series.  3.6.4  Summary of regional conductivity methods.  B o t h frequency-domain data and early-time, time-domain data are able the model the overburden quite well, and regional structures that manifest themselves i n the overburden structure can be identified. W h i l e it is not expected that the frequency-domain data can  provide much more information than that which has already been modeled, the  time-domain data has significant potential for providing more information about subsurface.  the  Forward modeling has shown that the decay response produced over the  deposit could be explained w i t h a buried, polarizable conductor.  W h i l e other models  Chapter  3. Regional  Scale  Modeling  74  might also be able to explain the observed data, and cannot be ruled out, the concept of detecting massive sulphides w i t h this data becomes conceivable. A benefit of accounting for polarizable material would be i n modeling non-polarizable, conductive bodies more accurately because this would effectively remove I P effects that come from geologic noise. U n t i l an inversion routine that models complex conductivity is available, it is likely the majority of the data can only be inverted to define the overburden, and complex conductivity forward modeling analysis could be applied to select parts of the data.  3.7  Chargeability models  A t San Nicolas, gradient-array and "Real-section" array configuration I P data were acquired by Quantec.  A n overview of how data are generated using the I P method is given  in A p p e n d i x C . 6 . T h e gradient-array data is used for regional modeling as it covers a relatively large area (Figure D.10). T h e large chargeability anomaly, indicated by a strong positive anomaly at -1600m east, -400m north, is associated w i t h the deposit. There is no doubt that this is valuable information regarding the existence of an ore-body.  In  fact, this was the data that prompted the discovery of San Nicolas. In the gradient array configuration, the current electrodes are located outside of a rectangular area to be surveyed and the I P potential data are collected using a roving-receiver dipole w i t h i n the rectangular area. T h e data are generally used only as a mapping tool because detailed depth information cannot be obtained without multiple transmitter locations. However, the similarity of gradient array I P data to magnetic field data allows for the data to be inverted using MAG3D  through which depth information is introduced w i t h sensitivity  weighting. A description of how gradient I P data can be inverted using MAG3D  is given  in A p p e n d i x F , along w i t h an example. Inversion parameters such as noise estimates and cell sizes are provided i n Table E . l .  Chapter  3. Regional  Scale  Modeling  75  Figure 3.17: Plan-view of a slice at 300m below the surface through the chargeability model generated by inverting gradient-array I P data as magnetic d a t a using MAG3D. T h e deposit is the largest chargeable body detected by the survey. T h e extents of the survey are shown by the red outline and outcrop geology is shown i n black.  Chapter  3. Regional  Scale  76  Modeling  Q. OJ TD  5000 -10000  5000 Easting (m)  0 50001 -5000  L_  _J 5000  0 Northing (m)  Figure 3.18: N o r t h - and west-facing cross-sections (upper and lower sections respectively) through the chargeability model. The chargeable anomaly is well defined yet extends below the elevation of the deposit, as defined by drilling.  Chapter  3. Regional  Scale  Modeling  77  Figures 3.17 and 3.18 show the modeled distribution of chargeable material on slices through the deposit i n plan and cross-sectional views. A s expected from the data, the anomaly corresponding to the deposit is the largest feature seen i n the model. In p l a n view, the anomaly seems to be concentrated slightly south of the deposit and has extensions of lesser chargeable material to the east and the southwest (the eastern extension of chargeability anomaly is also shown i n the north-facing cross-section).  T h e deposit  does have mineralized extensions to the east and southwest, and although the elevation of this mineralization does not quite correspond w i t h the chargeability model, the model maybe sensitive to these areas. O f course, the distribution of chargeable material may not be one-to-one w i t h ore. Chargeable minerals could have been deposited w i t h i n the surrounding host-rocks that might result i n anomalies peripheral to the deposit. T h e modeled concentration of chargeable material also extends deeper than the extent of the deposit, as defined by drilling.  T h i s is probably a result of having no depth  constraints w i t h i n the data themselves.  However, the overall results suggest that the  assumptions made i n order to invert the data using MAG3D,  were appropriate i n this  case. A cluster of smaller chargeability anomalies are located to the northeast of San Nicolas centered at about 500m east and -200m north. T h i s is i n the vicinity of E l Salvador, a smaller lense of massive sulphide hosted w i t h i n the same volcanic r o c k s . T h e only 13  other anomaly of significant magnitude is a small, shallow discrete b o d y located at about -1200m east and -2800m north. T h i s anomaly has a magnitude of about 20 msec and is located on the edge of the survey. T h i s anomalous region of chargeable material coincides w i t h regions of anomalous density and susceptibility and does contain mineralization. A n analysis of this anomaly is presented i n the next section — physical property correlation. 13  E1 Salvador was discovered prior to the discovery of San Nicolas.  Chapter  The  3. Regional  Scale  Modeling  78  only anomalies that have been modeled correspond to mineralization. However,  away from this area, chargeability anomalies have been associated w i t h some of the Tertiary rocks. In a more widespread survey, other methods such as gravity would be needed to distinguish between mineralization and Tertiary rocks.  3.8  Physical property correlation  The regional physical property models that have been generated contain three-dimensional structures that are, hopefully, easier to interpret and relate to geology t h a n the original data.  However, combining multiple sources of information, i n order to target areas of  favorable physical properties, becomes more involved. A s a result, models are evaluated in three-dimensions using two simple spatial correlation methods. For each correlation method the physical property models are first imported on to a common mesh.  This  allows for direct correlation between cell values of different property types. For the first method, cut-off values for the physical properties are determined based on the expected physical property values and the recovered values over San Nicolas. For the density-contrast model a cut-off of 0.13 g / c m is used and a cut-off value of 0.0026 S.I. is 3  used for the regional susceptibility model. T h e models have cells removed that correspond to values below these cut-off levels. A s a result only the cells w i t h values above the cut-off remain. These relate to volumes of high density-contrast and magnetic susceptibility. A model is then generated, on the same mesh, that represents the intersection of regions of high density-contrast and high magnetic susceptibility. T h e resulting cut-off  correlation  model should be useful i n locating more massive sulphides because it represents regions w i t h density and magnetic susceptibility values similar to that of the deposit. Figure 3.19 shows pro jection-to-surf ace plan-views of density contrast, magnetic susceptibility, and correlation models of the upper 1000m. O n l y the upper 1000m of the  Chapter  3.  Regional  Scale  Modeling  79  5000  -5000 -10000  -5000  •sir  -5000 0  -10000  5000  -5000  Easting (m)  (a) Cells with a density contrast greater than 0.13 g/cm  II  \J  1/  I  Q  I  5000  (b) Cells with magnetic susceptibility greater than 0.0026 S.I.  3  5000  0 Easting (m)  0  r  t!  o .  -5000 -10000  -5000  0  .  N  ~r\ ( 5000  Easting (m) (c) Cut-off correlation model: regions high in density and magnetic susceptibility.  Figure 3.19: Plan-views of density contrast, magnetic susceptibility, a n d cut-off correlation models. T h e upper images show the projection-to-surface of cells, w i t h i n the first 1000m from the surface, that exhibit high density contrast and magnetic susceptibility values. T h e lower image shows regions that are high i n b o t h density and susceptibility. The image is a projection to surface of the volume intersection of the upper models. A blue border shows the extents of the correlation analysis.  Chapter  3. Regional  Scale  Modeling  80  earth is considered, because finding ore below that depth is not w i t h i n the objective of most exploration programs.  Cells below a value of 0.13 g / c m  3  i n the density contrast  model are invisible, as are cells below a value of 0.0026 S.I. i n the magnetic susceptibility model. T h e cut-off  correlation model (Figure 3.19(c)) shows five separate anomalies,one  of which coincides w i t h the deposit at -1600m east, -400m north.  T h e most  northern  anomaly lies coincident w i t h out cropping mafic volcanics. T h e small (one model cell), shallow anomaly about 750m to the west-southwest of San Nicolas, upon field inspection, proved to be a small unmapped outcrop of unaltered intrusive mafic material. T h e large anomaly to the west is the only dense-susceptible body that extends beyond a depth of 1000m. T h i s is the intersection of the large deep intrusive features seen i n the regional density-contrast and magnetic susceptibility models. The remaining anomaly is located at -1300m east, -2700m north w i t h i n the  first  100m from the surface, and is coincident w i t h the chargeability anomaly mentioned i n section 3.7. Three drill-holes have been completed each to a depth of 450m around, but not directly into, this anomaly. O f the three drill-holes only one encountered mineralization similar to that found at San N i c o l a s . If the chargeability model was correlated 14  w i t h b o t h the susceptibility and the density models, the resulting three-way  correlation  model would contain only two anomalies; one being the deposit and the other being this small occurrence of mineralization. T h i s is an important result and demonstrates how the inversion models can be used together to find mineralization. However, the known mineralization at E l Salvador has not been found w i t h this method. T h e method presented above has been successful i n reducing the number of targets, however, it is dependent on the cut-off values and doesn't indicate whether one area is more dense or susceptible than another. Consequently, a second correlation method 14  Hole SAL-61 intersected 0.7% copper at 430m depth and 0.2% zinc at 207m.  Chapter  3. Regional  Scale  Modeling  81  was employed. For the second method, the density-contrast and magnetic susceptibility models are normalized to each of their range of values. T h i s means that high density and high susceptibility regions will have a value of one, while low density and susceptibility values w i l l be represented by zero. T h e product of these models is then calculated. T h e resulting model (Figure 3.20) w i l l have high values that corresponds to high density and susceptibility values. Figure 3.20(a) shows a perspective view of the product of normalized density-contrast and magnetic susceptibility values. A g a i n , the number of anomalies has been significantly reduced w i t h this method. T h e model has similar features to that shown w i t h the previous correlation method, however one important difference is present. A volume has been produced to the northwest of the deposit that corresponds to very high magnetic susceptibility but only moderate density-contrast values.  T h i s is a l i m i t a t i o n of this  method, however most of the other anomalies produced are from a combination of the two physical properties. T h e deposit along w i t h a dense mafic intrusion to the north and a deep intrusion to the south have all been modeled. In addition, the small anomaly, located at -1000m E -2700m N , which also corresponds to the chargeability anomaly, is also present. B o t h of the correlation methods work well at San Nicolas because a l l of the physical properties considered give high values over the deposit. If low or intermediate physical property values are of interest then further manipulation of the models would be required.  3.9  Conclusions  Inversion of regional geophysical data sets has produced large-scale physical property distributions of the earth. Together w i t h physical property values and an understanding of the geologic setting, these models have been interpreted and given a geologic context.  Chapter  3. Regional  Scale  Modeling  82  North^f  3500  (a) Northwest-facing perspective view of density-susceptibility correlation values. The model has been volume-rendered at a value of 0.2.  (b) Projection-to-surface of density-susceptibility correlation values within the first 1000m. Cells with a value of less than 0.2 have been removed. A blue border shows the extents of the correlation analysis.  Figure 3.20: Normalized density-susceptibility regional correlation values. The values are the product of normalized regional density-contrast and magnetic susceptibility models.  Chapter  3. Regional  Scale  Modeling  83  T h i s interpretation would be much harder, if not impossible, without considering threedimensional physical property distributions. Geologic features such as the extents of a horst, the detection and definition of a deep rooted intrusion, the identification of faults of two different ages, and the detection of shallow bodies that could be mafic intrusions have a l l contributed to the regional geologic understanding of the area. In addition to the regional geologic interpretation, the models have been correlated in order to directly detect mineralization. T h e correlation of density and susceptibility models results i n a reduced number of regions that might warrant further investigation. W h e n combined w i t h results from the inversion of gradient array chargeability data only two anomalous regions would remain, b o t h of which contain mineralization. T h i s method of selecting targets would hopefully be as effective i n areas further from the deposit as it is here. False anomalies can occur from a l l of the methods used, as a result there isn't one combination of physical properties that w i l l detect mineralization without missing some that is already known. A s a result different priorities of targets could be determined. These could be based on the different intersections of dense, magnetic, and susceptible material. If a comprehensive conductivity model could be constructed for the same region the correlation results could change and a better discrimination method might result. W i t h i n the context of our exploration program, regional inversion modeling of geophysical data has been successful i n targeting areas for detailed work based on geologic interpretations and direct detection methods. A n integrated geophysical approach has been shown to be most efficient at selecting targets. W h e n combined w i t h results from other exploration methods such as geological mapping, and geochemical surveys, the target selection process could be even more refined. In addition, the knowledge obtained from this work can be used to update the exploration models and thus refine exploration directives.  Chapter 4 Deposit Scale Modeling  4.1  Introduction  T h e work presented i n chapter three was successful i n directly detecting mineralization and contributing to the regional geologic picture. F r o m the correlation methods presented possible targets could be identified.  G r o u n d surveys are now conducted i n order to  provide more information about these targets. The flowchart i n Figure 4.1 shows how the work presented i n this chapter is integrated into the second part of an exploration program, and builds on the knowledge already obtained. T h i s chapter deals w i t h detailed geophysical modeling of the deposit, using finely discretized meshes, for further physical property characterization and enhanced definition. Results from the regional modeling are used for removal of regional trends from the gravity data prior to inversion, and for constructing reasonable starting and reference models when inverting the other data. A l o n g w i t h the gravity and gradient-array IP data, which was used for the regional modeling, more localized data sets are considered, including ground magnetics, C S A M T , and realsection-array  IP.  T h e goal is to invert the data to see how much information about the deposit can be recovered. Unlike our example exploration program, we are i n the position to check the inversion results of this chapter w i t h geologic sections that have been interpreted from drill-hole information. However, the models are constructed through inversion of the geophysical data alone.  84  Chapter 4. Deposit Scale Modeling  Exploration Goal: •find economic mineralization  85  Geophysical Exploration: Deposit Scale Modeling  \ Geologic Constraints:  j  existing mapping  |  •historical workings  Geologic  •tectonic setting  Models:  •descriptive model •genetic model  -> Exploration Model  Geophysical  . e x p e c t gije/depth o f target  Exploration  .physical properties contrasts  Model: Regional Geologic Mapping: •Outcrop geology  Physical Property Measurements Existing Data  Regional/Reconnaissance Scale Geophysics:  Survey Design  •Structure •Alteration  AMAG (+/- ARAD)  Collection I Gravity  AEM  II IP | |  Other: VLF, MT  Geochemistry •Stream sediments  Modeling Joint/Simultaneous  Individual Forward  Remote Sensing  Inverse  Forward  Inverse  •Multi spectral •Hyperspectral  Correlation  Conclusions about physical property distributions of the the earth Geologic Interpretation No Targets  Target Generation  Terminate Exploration Program  Update Geophysical Exploration Model  Update Geologic Exploration Model  I  Detailed Geologic Mapping: •Stratigraphy •Structure •Alteration  Geologic Logging  5  Local/Deposit Scale Geophysics: Data Collection  Additional Physical Property Measurements  Survey Design r  I  Gravity  GMAG  EM: CSAMT, UTEM  DC/IP  Other VLF  Trenching Modeling Joint/Simultaneous  Individual  Rock samples •Assays •Alteration mineralogy (X-ray/PlMA)  Inverse  Forward  Inverse  Correlation  Conclusions about physical property distributions of the the earth  Geochemistry •soil surveys  j  Mineralization Not Intersected  Geologic Interpretation  Locate Drill-hole Physical property core measurements  Terminate E i p l o ration  Down-hole methods  Program  Mineralization Intersected Delineation  Goal Realized: Mineral Resource  Figure 4.1: Geophysical Exploration Methodology Flowchart: Deposit scale modeling.  Chapter  4.1.1  4. Deposit  Scale  Modeling  86  Local geology  Figure 4.2 shows the local geology i n the vicinity of the deposit as interpreted from drilling. T h e deposit is hosted i n a series of interwoven mafic and felsic volcanic rocks of the Upper Jurassic to Lower Cretaceous Chilitos Formation, which lie unconformably over graphitic mudstones of the Zacatecas Formation. T h e deposit is almost entirely bounded to the east by a southwest-dipping fault, which could have been a feeder  structure.  Mineralization continues to follow the fault at depth i n an unconstrained part of the deposit referred to as the keel. T h e deposit is flanked by a thick package of rhyolites to the west that contains a steeply dipping north-south fault (not shown on this section). The  volcanic succession that envelops the deposit is overlain by a Tertiary-aged breccia  overburden, which varies i n thickness from 50 to 150m. T h e breccia includes tuffs and clasts derived from the underlying volcanics. Outcrops of the breccia have been mapped to the northwest, but an overlying t h i n veneer of Quaternary a l l u v i u m is present i n the vicinity of the deposit.  Hydrothermal alteration, which may have caused a change i n  physical property values, is prevalent throughout the deposit and surrounding geology. Figure 4.3 shows a simplified stratigraphic section as interpreted i n the vicinity of the deposit.  Introduction to inversion results The  models constructed by inverting local-scale geophysical data are presented i n the  following sections. O n l y the models are presented; the data are displayed i n A p p e n d i x D . The  models are presented i n plan, cross-sectional, and perspective views i n much the  same way as Chapter 3. T h e geologic sections shown i n Figure 4.2 are shown w i t h each inversion result i n order to evaluate the success of the inversion. A l l of the inversion parameters used are provided i n Table E . l .  Chapter 4. Deposit Scale Modeling  -2000  87  -1600  -1200  Easting (m) (a) North-facing geologic cross-section located at -400m north.  -800  -400  0 Northing (m)  (b) West-facing geologic cross-section located at -1700m east.  Figure 4.2: Geologic cross-sections through the San Nicolas deposit, modified from sections provided b y Teck C o r p . T h e main geologic units are labeled.  Chapter  4. Deposit  Scale  Modeling  88  Stratigraphic Section Alluvium  Quaternary  |  Tuff, Volcanoclastic Breccia  Tertiary  S  Sedimentary - Mudstone, Limestone  Cretaceous  u/c  U. Jurassic » L. Cretaceous  Mafic Volcanics Mafic to Intermediate Flows & Breccia Massive Sulphide  =3 s w  ,5  Rhyolite Flows and Breccias u/c •  Graphitic Mudstones  Figure 4.3: Simplified stratigraphic section i n the vicinity of the S a n Nicolas deposit, modified from [Danielson, 2000].  Chapter  4.2  4. Deposit  Scale  Modeling  89  Density  Inverting the gravity data on a local-scale requires a two-pass procedure. First, a "regional" field is removed from a subset of the original data, and secondly, the residual data are inverted on a finely discretized mesh, centered on the deposit. T h e removal of regional signal from the local data is undertaken i n order to remove signal i n the data that doesn't originate from the localized region of interest, thus allowing the inversion to be concerned w i t h only the deposit and the immediately surrounding volume.  4.2.1 The  Regional Removal regional density contrast model created i n Chapter 3 is used to generate a regional  gravitational field over a 1.8 k m x 2.4 k m area centered on the deposit. T h i s is done by calculating the response of the regional density contrast model w i t h a volume removed (Figure 4.4).  T h e removed volume encompasses the deposit and two smaller, dense  bodies. T h e resulting regional signal is subtracted from the original observations and residual data are produced that can be used for the detailed inversion (Figure D . l l ) . L i and Oldenburg [1998a] describe removal of regional magnetic fields using this technique.  4.2.2  Inversion of Residual Gravity Data  D a t a errors of 0.01 m G a l were assigned to the residual gravity data and the 3 D inversion was carried out w i t h GRAV3D  using the parameters listed i n Table E . l . T h e observed 1  data are reproduced very well by responses from the detailed density contrast model (Figure 4.5) and the errors that were assigned to the data seem to have been appropriate. The regional density contrast model used to create the regional response is based on a mesh with large cells (100m x 100m x 50m). Consequently, this is a coarse representation of the earth, and the regional gravity field contains small inaccuracies that will propagate through to the residual gravity data. In order to account for these inaccuracies, padding cells are used in the inversion of the residual gravity. x  Chapter  4. Deposit  Scale  Modeling  90  Figure 4.4: Perspective view, facing north-west, of regional density-contrast model used for calculation of the regional signal. The volume that has been removed contains the deposit and two smaller high density anomalies, and is the volume on which the more detailed inversion is focused. The central density contrast anomaly represents the San Nicolas deposit and correlates well w i t h the dense massive sulphides. This is an improvement over the regional model probably because the deposit is defined w i t h small cells, and discretization is not affecting the results. Figure 4.6 shows cross-sections of the density-contrast model overlain w i t h geologic boundaries.  T h e centroid of the anomalous density coincides w i t h the San  Nicolas deposit and corresponds well w i t h the dense massive sulphides.  T h e lateral  dimensions of the m a i n b o d y of the deposit are reasonably well defined, and the density anomaly doesn't continue to the surface, as it does i n the regional inversion. Large density-contrast values are present above the expected elevation of the deposit, and neither the keel nor the t h i n extension of sulphides to the east have been modeled i n this inversion. However, this is a useful result when one considers the lack of depth information contained i n the data, and locating a drill-hole based on this model would  Chapter  4. Deposit  Scale  Modeling  91  035  Figure 4.5: South-east perspective view of the detailed, density-contrast model shown as a volume-rendered isosurface plot using a cut-off of 0.18 g/cc. Cells w i t h density contrasts less than this value are invisible.  Chapter  4. Deposit  Scale  Modeling  92  lead to a successful intersection of the ore-zone. T w o other density anomalies are also well defined by the model. T h e north-eastern anomaly was drilled and intersected 400m of intrusive, mafic rock. Field inspection of the western anomaly revealed similar mafic intrusives outcropping at the surface.  4.3  Magnetic Susceptibility  Finely sampled, magnetic field data were available for detailed modeling i n the form of ground magnetic data. A base-level of 44,000 nano-Tesla was removed from the 614 diurnally corrected data as a processing step prior to inverting.  In addition, several  data i n the center of the survey that were contaminated w i t h cultural noise, such as a fence or steel-cased drill hole, were discarded. T h e 3D magnetic inversion was performed using MAG3D  and the predicted data were i n good agreement w i t h the  observations  (Figure D.12). A volume-rendered representation of the 3D susceptibility structure is shown i n F i g ure 4.7.  T h e cut-off value is 5 xlO-3 S. I. A distinct b o d y of higher susceptibility is  modeled. T h e majority of the susceptibility high is coincident w i t h the deposit, however, high values continue to the north (as seen i n the data and the regional model). A good correlation between magnetic susceptibility values and geology i n the vicinity of the deposit can be seen i n the north-facing cross section presented i n Figure 4.8. T h e high magnetic susceptibilities associated w i t h the deposit align well w i t h the boundaries of the sulphide body. A concentration of high susceptibility is present i n the western half of the deposit. A s w i t h the regional magnetic susceptibility model (Figure 3.10), there appears to be a continuation of moderately susceptible material to the east and below the deposit. Mafic rocks below the ore-bounding fault zone are less altered, and may be the source of  Chapter  4. Deposit  Scale  Modeling  93  (a) North-facing cross-section through density-contrast model located at -400m north. Geology from Figure 4.2(a) is overlain.  700  I  -800  Northing [m]  1 0  (b) West-facing cross-section through density-contrast model located at -1700m east. Geology from Figure 4.2(b) is overlain.  Figure 4 . 6 :  Cross-sections through deposit-scale density-contrast  Nicolas deposit.  m o d e l at the  San  Chapter  4. Deposit  Scale  Modeling  91  ,0.008 0 00667 0 00533 0 004 0.00267  10.00133 0  S.I.  Figure 4.7: Perspective view of the detailed, magnetic susceptibility model. this susceptible material. There is no indication of the keel or the eastern extension of sulphide i n this model. T h e result is a smoothed version of the true susceptibility values, and it does not contain highly detailed structural information, particularly at depth. A reasonable magnetic susceptibility model has been recovered from the ground magnetic data, however, it appears that obtaining information about the location of sulphides from the model is not a straight forward exercise. Evidence from physical property measurements ( A p p e n d i x B.3.1) show that magnetic minerals, while i n i t i a l l y expected to be associated w i t h sulphides, are not distributed throughout the deposit.  Moreover, they  can also be present i n host rocks and might have been deposited b y hydrothermal events not associated w i t h the deposit itself. T h i s might explain the h i g h susceptibility values that persist to the north, and those that persist to the east and below the deposit, and the lower values that are modeled i n the eastern half of the deposit.  Chapter  4. Deposit  Scale  Modeling  95  (a) North-facing cross-section through susceptibility model located at -400m north. Geology from Figure 4.2(a) is overlain.  Figure 4 . 8 : Cross-sections through deposit-scale magnetic susceptibility m o d e l at the San Nicolas deposit.  Chapter  4.4  4. Deposit  Scale  Modeling  96  Conductivity Data  Three lines of controlled source audio-frequency magneto-telluric ( C S A M T ) data were collected over the San Nicolas deposit. The C S A M T method is an electromagnetic technique that uses a grounded dipole current source . A t San Nicolas, C S A M T data were 2  collected w i t h the receiver lines orientated perpendicular to perceived geologic strike. It is not obvious from the observed apparent resistivity or phase data (Figures D.13 and D.14) that a conductive ore deposit is present.  Ohm-m  Figure 4.9: Perspective view of resistivity model created by interpolating between lines of "stitched" I D models. T h e inversion has successfully discriminated between the overburden and the deposit.  Apparent resistivity and phase data are inverted to recover a one-dimensional, resistivity model beneath each station. The one hundred and eighty I D models were "stitched" together to form a 2 D model along each of the three lines of data. A n interpolation routine was used to fill i n the volumes between the lines and a three-dimensional resistivity 2  A n overview of the CSAMT method is given in Appendix C.4.  Chapter  4. Deposit  Scale  Modeling  97  model was produce. A volume-rendered image of the model, w i t h an isosurface cutoff of 30 O h m - m , is shown i n Figure 4.9. The large conductive feature at depth is the deposit. It is separated from a conductive overburden by an intervening layer of higher resistivity. T h e cross-section of the resistivity model, shown i n Figure 4.10, shows that the deposit boundaries interpreted from the inversion agree reasonably well w i t h those inferred from drilling.  Easting [m]  F i gure 4.10: Cross section of resistivity model w i t h geology. The resistivity model was produced by "stitching" together 60 I D inversion models.  4.4.1  Testing the 3 D resistivity model  In order to test the validity of the one-dimensional inversion, the "stitched" inversion models and a 3D physical property m o d e l were forward modeled using EH3D 3  (see A p -  pendix E . 8 ) . Apparent resistivity and phase data were calculated from electric and magnetic field measurements and are plotted i n Figure 4.11. T h e forward-modeled data from The physical property model was generated by digitizing the geologic sections, assigning conductivity values based on the physical property measurements, and interpolating between the sections to create a 3D model (Appendix B.5). 3  Chapter  4. Deposit  Scale  Modeling  98  the inversion models replicated the original data quite well at higher frequencies (Figure 4.11(b) - left plots). A t low frequencies, the 3D conductivity model produced data that came closer to the observations than the "stitched" inversion models (Figure 4.11(a) - right plots). T h i s result is plausible when one considers the geometry of the deposit and the volume of the subsurface that low frequency measurements w i l l sample. A t low frequencies the measurements w i l l sample a larger volume, including the 3 D deposit, and this information is hard to represent i n only one dimension. Conversely, at high frequencies the part of the subsurface that the measurement has sampled w i l l be more one-dimensional.  Since the high frequency data are sensitive to shallow features, this  suggests that structure i n the upper region of the "stitched" inversion models is valid, and the structure of the physical property model is more representative of the deposit at depth. Discrepancies between the observed data and the forward modeled physical property data possibly arise due to inaccurate assignment of conductivity values. The forward modeling mentioned above suggests that this data set might produce a model w i t h greater resolution at depth if it were to be modeled i n three dimensions. W i t h the inversion codes available, the raw electric and magnetic field data, from which the apparent resistivity and phase data were derived, are needed i n order to invert this data in 3 D . Unfortunately, at this time, these raw data have not been recovered. O f specific interest i n a 3D inversion would be detection of the deep high-grade keel. A sensitivity analysis on the detectability of the keel using different C S A M T and C S E M (Controlled Source Electromagnetic) survey designs is presented i n Section 6.4.  4.5  Chargeability  In this section "Real-section" array I P data is simultaneously inverted along w i t h a subset of the gradient array data i n order to produce a chargeability model that has more detail,  Chapter  4. Deposit  Scale  Modeling  99  1 -D Inversion Model  Physical Property Model  1000  ^ a; 30 600  200 -2500  Easting  -500  -2500  Easting  -500  (a) 8Hz data  1-D Inversion Model 1000  i s  ^3 a*  1  1  r  ;  Physical Property Model  S 30 600  5S  g  200 -2500  Easting  -500  -2500  Easting  -500  (b) 256Hz data  Figure 4.11: Comparison of C S A M T field data w i t h forward modeled d a t a from the stitched 1-D inversion resistivity model(left plots) and a physical property resistivity model (right plots). The field observations are displayed i n red while the modeled data is shown i n blue. A t 8Hz (upper plots), the physical property model fits the field data well while the 1-D inversion model fits the field data better at a frequency of 256Hz (lower plots).  Chapter  4. Deposit  Scale  Modeling  100  256Hz  -2000  8Hz  -1600  -1200  Easting (m) Figure 4.12: North-facing, cross-sectional diagram of the geology at S a n Nicolas showing the skin-depths associated w i t h propagating plane-waves at frequencies of 256Hz and 8Hz. T h e skin-depths were calculated for a 25 O h m - m whole space. T h e higher frequency signal samples geology that is much more "one-dimensional" t h a n that sampled by the low frequency signal.  Chapter  4. Deposit  Scale  Modeling  101  and better depth information, than the regional model that was produced using  MAG3D  in Section 3.7.  4.5.1  Chargeability data  In real-section surveys, the voltages from consecutive dipoles are measured and plotted in a pseudosection format.  The current transmitter electrodes straddle the potential  electrode array and the transmitter electrode spacing is continually reduced to change current flow i n the subsurface. D a t a are recorded only at those potential electrodes lying interior to the transmitters. The configuration is a modified version of that employed i n gradient-array surveys but should have better depth information due to the increase i n number, and varied spacing of the transmitter electrodes. T h e calculated chargeability is plotted beneath the receiver dipole and each row of the resultant pseudosection-type plot corresponds to a different location of the current electrodes. T h e final plot has an inverted appearance compared to pseudosections  obtained  w i t h more traditional pole-dipole or dipole-dipole plots. The Realsection plots for San Nicolas are shown i n Figure D.15. It is apparent from the data that a chargeable b o d y is present, but these pseudosections provide no tangible information about the depth of the target. T h a t can only be obtained through inversion. The inversion procedure for chargeability is a three-step process. First the resistivity of the model volume needs to be calculated.  Next, the sensitivities needed for the IP inversion are calculated using  the resistivity model. Lastly, the I P data are inverted using IPINV3D  to recover a 3-D  chargeability model. The details for the IP inversion are provided i n Table E . l .  4.5.2  The resistivity model  D C resistivity data are collected at the same time as IP data and, as codes are available to invert these data i n 3D, they are usually used to construct the resistivity model needed  Chapter  4. Deposit  Scale  Modeling  102  for the sensitivity calculation when inverting IP data. A t San Nicolas, these data do not produce a resistivity model that corresponds to the expected resistivity distribution. A surficial conductive layer was modeled that resembles the overburden, but no conductive b o d y is recovered i n the vicinity of the deposit. One possible explanation could be that the currents emitted from the transmitters do not penetrate into the volcanic rocks and are only concentrated w i t h i n the more conductive overburden; i n other words, taking the p a t h of least electrical resistance. T h i s would seem to make sense, although some currents would have to penetrate to the deposit i n order for a chargeability response to be recorded (assuming the deposit is the only chargeable source) . 4  O n l y an approximate conductivity structure is needed for the sensitivity calculations, so chargeability inversions were performed using sensitivities calculated from the inversion of D C resistivity data.  T h i s produced a chargeability model that corresponded well  w i t h the deposit, but seemed to have layered artifacts corresponding to the Real-section transmitter locations. However, other, more accurate, conductivity models were available; the conductivity model produced from the inversion of C . S . A . M . T . data, and a model constructed by digitizing the geologic sections and assigning conductivities based on physical property measurements. B o t h of these conductivity models were used to construct sensitivities for the inversion of the IP data. T h e resulting models did not have the artifacts observed before. T h e model produced using the C S A M T conductivity model is presented i n this section as it contains no apparent artifacts and also is derived only from geophysical data collected at the surface. T h e phenomenon! of a disconnected conductive and chargeable body, lying below a conductive overburden, producing a negligible D C resistivity yet observable IP response has been observed before and is documented by Brescianini et al. [Brescianini et al., 1992]. 4  Chapter  4.5.3  4. Deposit  Scale  Modeling  103  Removal of electrode effects  W h e n inverting I P data using IPINV3D, especially data that have limited inherent depth information, such as gradient-array data, accumulations of chargeable material are often observed around the transmitter electrodes.  T h i s "electrode-effect" often leads to the  recovery of bodies w i t h smaller chargeability values than expected. For this reason, an empirical weighting function is applied i n the inversion of the San Nicolas data that penalizes against chargeability values near the transmitter electrodes. T h e weighting function is included i n the spatially dependent weighting matrix (w ) introduced i n E q u a t i o n 2.2. s  It is designed to strongly weight the model close to a reference model of zero at the transmitters.  A p p e n d i x G shows how this weighting function has been derived using  synthetic models. The  result of implementing this weighting function on the model is shown i n F i g -  ure 4.13. Most of the surficial chargeability has been reduced. T h e magnitude of chargeability material at the anomaly that corresponds to the deposit has doubled from about 25 msec to 50 msec. A n alternative method to using this weighting function would be to weight the sensit i v i t y m a t r i x used i n the inversion. T h i s could make cells near the transmitter locations less sensitive to the data, and cells further away more sensitive to the data.  4.5.4  The chargeability model  Figure 4.14 shows a perspective view of the recovered chargeability model. T h e spatial distribution of the high chargeability values agrees quite well w i t h the location of the deposit.  T h e high chargeability values are off-set slightly to the south i n relation to  the deposit (this is also seen i n the observed real-section data i n Figure D . 15).  The  high chargeability values present at the surface are probably remaining artifacts of the  Chapter  4.  Deposit Scale  -3000  -2500  104  Modeling  -2000  -1500  -1000  -500  0  Easting [m] (a) Plan-view of recovered chargeability model at the surface. The black circles show the location of the transmitters; these locations correspond with most of the high chargeability material that has been recovered at the surface.  -3000  -2500  -2000  -1500  -1000  -500  0  Easting [m] (b) Plan-view of recovered chargeability model at the surface. The black circles show the location of the transmitters. Much of the surficial chargeable material that was modeled has been subdued with a weighting function.  Figure 4.13: Plan-views of the recovered chargeability models at the surface. T h e high chargeability values that correspond w i t h electrode positions have been reduced through the use of a weighting function.  Chapter 4. Deposit Scale Modeling  105  Figure 4.14: Perspective view of the volume-rendered chargeability model with a cut-off value of 40 msec. electrode-effect mentioned above, although they could be real. B y viewing a north-facing cross-section (Figure 4.15), one can see a concentration of chargeable material centered on the south-west dipping fault. A n exploration of model-space was conducted by changing the inversion parameters and the chargeable material around the fault was consistent for all models produced. The fault zone is known to contain semi-massive sulphides and stringer zones, which contain high-grade veins mixed in with host rock. It might be possible that this fault zone could be more chargeable than other parts of the deposit.  4.6  Conclusions  A l l of the models produced at the deposit-scale build on the understanding obtained at the regional scale. The density model has been refined, through removal of a regional signal and finer discretization, in order to produce an anomaly that better represents the deposit. A much more detailed magnetic susceptibility model has also been produced.  Chapter 4. Deposit Scale Modeling  -2200  106  _  8 0 0  Easting [m]  (a) North-facing cross-section through chargeability model located at -400m north. Geology from Figure 4.2(a) is overlain.  (b) West-facing cross-section through chargeability model located at -1700m east. Geology from Figure 4.2(b) is overlain.  Figure 4.15: Cross-sections through deposit-scale chargeability model at the San Nicolas deposit.  Chapter 4. Deposit Scale Modeling  107  However, the new model raises questions about the relationship between the distribution of magnetic minerals and massive sulphides at San Nicolas.  T h e distribution of  magnetic material is offset from the distribution of ore i n what can be described as an over-printing effect. T h i s over-printing effect suggests that the magnetic content of San Nicolas was produced after the deposition of the sulphides i n a later hydrothermal event. Layered-earth inversions of the C S A M T data have produced a conductivity model that differentiates between the deposit and the overburden. W h e n the models are "stitched" together to produce a 3D model, they have been shown to fit the data well at high frequencies. T h e simultaneous inversion of gradient-array data w i t h "Real-section" I P data has produced a chargeability model that defines the deposit well, and has high values colocated w i t h the m a i n south-east dipping fault zone. These models were a l l constructed by inverting the geophysical data alone, and information i n the form of physical property measurements and geologic interpretations have only been used to demonstrate the success of these models, and the validity of the techniques that produced them. T h i s work clearly demonstrates how geophysics can be used as part of a more focused exploration effort to spot drill-holes.  A l l of these methods have proved successful i n  defining the deposit at the correct depths and lateral extents. Furthermore," the models start to produce more detailed information regarding the geometry of the deposit. T h e gravity inversion seems to be sensitive to the keel and the chargeability model produces increased values around the fault-zone. T h e next step i n the exploration program would be to use information that might result from drilling these well defined targets.  T h e next chapter addresses this i n an  attempt to further refine the models and delineate more ore by including physical property information directly i n the inversion procedure.  Chapter 5 Detailed Modeling: Incorporating a priori information  5.1  Introduction  Inversion of surface geophysical data to produce deposit-scale physical property models has been successful i n recovering the general features of the San Nicolas deposit (as shown i n Chapter 4). These models could have been used to successfully direct a drilling program that would have resulted i n the discovery of San Nicolas. T h e next stage i n the exploration process is to further delineate any mineralization that may have been found.  Geophysical inversion can play a role at this delineation scale, as outlined by  the shaded elements of the flowchart presented i n Figure 5.1. If a target is drill tested, and whether or not mineralization is intersected, each drill-hole can provide additional information that can be used to further refine the inversion models. T h e inclusion of this data-independent  information w i l l help eliminate models inconsistent w i t h known  geology that fit the data [Scales and Tenorio, 2001] T h e additional information can be i n the form of analysis performed on the drillcore itself, down-hole geophysical measurements, or even interpreted geology that can be assigned physical property values (this last option would obviously not provide as much information unless the physical properties were very constrained before hand).  These  updated models can, i n turn, guide further drilling. T h i s iterative process can expedite In a similar maimer additional information would be an important addition to inversions performed on in-mine data sets where mine geologists or mining engineers might require detailed information about parts of the earth where drilling has not yet reached. Again, the inclusion of known physical property information would constrain the result and focus the inversion on areas of interest. 1  108  Chapter 5. Detailed Modeling: Incorporating a priori information  109  a drilling program and more effectively define a resource. The  extensive drilling program that was completed at San Nicolas (Figure 5.2 shows  the location of drill-holes that make up over 25,000m of d r i l l i n g ) presents a n opportunity 2  to improve u p o n the deposit-scale models by including additional information about the subsurface i n the inversion process. The  idea of including drill-hole information to constrain a n inversion seems entirely  acceptable, however, some major issues are raised when this put into practice. T h e issues include how to incorporate detailed point measurements i n a large-scale model, and how to perform inversions that are consistent w i t h the drill-hole information. T h i s chapter presents a first look at these issues. T h e results demonstrate the potential benefits that may  arise from including information from one drill-hole, and those that may arise from  including information from many drill-holes.  T h e process of generating these models  identifies potential research areas.  5.1.1  A priori information at San Nicolas  A s well as providing geologic information about the subsurface, core samples were measured for density and magnetic susceptibility physical property values. Density measurements were made o n sulphides from most drill-holes i n order to estimate mine tonnages. The  susceptibility measurements were made by myself during a visit to S a n Nicolas i n  February of 2001. Over one thousand magnetic susceptibility measurements were made on core samples from thirteen drill-holes. T h e measurements were made o n a l l rock types in the local geologic section. These physical property measurements are considered to be a source of explicit a priori information, that is, information that can provide direct knowledge about the physical The drill-hole notation of SAL represents Salvador—the San Nicolas discovery came from a drilling program that was initiated on the E l Salvador project. 2  Chapter 5. Detailed Modeling: Incorporating a priori information  Exploration Goal: *flnd economic mineralization  i > | |  Geologic Constraints: 'existing mapping •historical workings 'tectonic setting  Geologic Models:  Geophysical Exploration: Using Drill-hole Information  -descriptive model 'genetic model  Physical Property  | . "Outcrop geology •Structure •Alteration  Measurements  Geophysical . Exploration .physical properties contrasts Model: e x p e c t e d 5 i z e / d e p t h o f t e l g e t  => Exploration Model  j Regional Geologic j Mapping:  110  Regional/Reconnaissance Scale Geophysics: Collection  Existing Data  Survey Design  Onyity  A M A G (+/• A R A D )  Other V L F , MT  Geochemistry •Stream sediments  Modeling Joint/Simultaneous  Individual Inverse  Remote Sensing •Multispectral •Hyperspectral  Correlation  Conclusions about physical property distributions of the the earth  Geologic Interpretation No Targets  Terminate Exploration Program  Target Generation  ^ ii Update Geologic j - j Exploration Model j Local/Deposit Detailed Geologic Mapping; •Stratigraphy •Structure •Alteration  Geologic Logging 4  Additional Physical Property Measurements  Update Geophysical Exploration Model  Survey Design  Scale Geophysics: Data Collection  in.  1  1  1  Gravity  GMAQ  E M : CSAMT. U T E M  1  1  DC/IP  Other: V L F  Trenching  •  Modeling Joint/Simultaneous  ^Individual  Rock samples •Assays •Alteration mineralogy (X-ray/PlMA)  Forward  Inverse  Forward  Correlation  : Conclusions about physical property distributions of the the earth :  Geochemistry •soil surveys  Geologic Interpretation  Mineralization Not Intersected  Locate Drill-hole Physical : property core measurements  :  Terminate Exploration Program  Down-hole methods Mineralization Intersected  x  Delineation  Goal Realized: Mineral Resource  Figure 5.1: Geophysical Exploration Methodology Flowchart: Using drill-hole information.  Chapter 5. Detailed Modeling: Incorporating a priori information  111  property distribution prior to inverting the data. For this study, a priori information is incorporated i n the inversion of gravity and magnetic data by directly constraining the range of values model cells can assume. For the inversion of gravity and magnetic data, the a priori information is used i n two stages. First, information from one drill-hole that intersects the deposit is used to constrain the inversion. T h i s is done to determine if constraining part of the deposit can improve definition to other parts of the deposit. Secondly, a l l the available drill-hole information is included. T h i s is performed i n an attempt to identify any sulphide ore that has not been already drilled. If the physical property distribution of the deposit is well accounted for, it is hoped other parts of the model might be more accurately represented, thus improving the chances of finding more ore. Including a priori information i n these two scenarios demonstrates the approaches that might be used at different stages of a drilling program.  5.2  Inversion of gravity observations  Density measurements were made on core samples of the sulphide ore, and some of the host rock material, that came from a l l of the drill-holes that intersected sulphide ore, as well as a few other holes. T h i s is performed for reserve estimates, however, it provides an excellent source of information for constraining inversions. Measurements were made about every 1.5m along the core and a plot of density verses depth, w i t h geology and assay results, for drill-hole S A L - 3 1 is shown i n Figure 5.3. T h e higher density values clearly correspond to the massive sulphides , which have an average value of about 4.3 3  g/cm , while the density of the mafic rocks below the deposit are about 2.8 3  g/cm . 3  The copper and zinc assay results show a change in ore-grade with depth, having larger concentrations of zinc at the top of the deposit and more copper present towards the base. The copper assay values seem to display a negative correlation with density; this probably reflects the relative concentrations of pyrite and chalcopyrite. 3  Chapter  Detailed  5.  Modeling:  Incorporating  a priori  information  112  0 -100  t  -200  4  f  -300  O) O  -400  f  f C  ^  r  <4 f  ^  W  ^  -500 -  ^  -600 -  €  €'  *  4 _c£  y .1*  -700 -800 -2200  -2000  -1800  -1600  -1400  -1200  -1000  Easting [m] Figure 5.2: Plan-view of drill-hole locations. Density measurements on core from drill-hole S A L - 3 1 are used to set bounds on the model values i n Section 5.2.2 (shown by a red square), while measurements made on core from all of the drill-holes shown (apart from S A L - 9 0 and S A L - 9 1 ) are used to provide a priori density contrast information i n Section 5.2.3. Susceptibility measurements made on drill-cores from S A L - 6 9 are used to set bounds i n Section 5.3.3 (shown by a green triangle), while bounds are assigned i n Section 5.3.4 based on magnetic susceptibility values made on cores from thirteen drill-holes that are denoted by a blue diamond.  Chapter  5.  Detailed  Modeling:  Incorporating  a priori  information  113  Discussion of bounds The  density measurements are used to constrain the density inversion model through  the use of bounds that limit the range of values a cell can assume.  T h e thought of  bounding a 25m x 25m x 25m cell from values measured along drill-core that is five or six centimeters i n diameter raises questions about the reliability of the process. There is obviously a discrepancy between the information we have and the information we require i n order to constrain the inversion. T h i s discrepancy is b o t h a scale issue, and an issue relating to lateral inhomogeneities. The  information recorded i n fine detail by the core measurements cannot be ade-  quately represented by one cell value. In order to alleviate this problem we have two options. Either we can reduce the size of the cells or we can t r y to scale-up the d r i l l hole information. Wherever possible it would be better to reduce the size of the cells so there is less inconsistency i n scale between drill-hole information and the model. A smaller cell w i l l probably contain a smaller range of values as sampled by a drill hole, and w i l l probably be less likely to have large lateral inhomogeneities. Unfortunately, the size of the model being used at San Nicolas was already large (close to half a million cells) so the decision was made to scale-up the drill-hole information. In doing so we lose valuable information.  5.2.1  Setting bounds on the density-contrast model  To bound a model, we require upper and lower bounds for each model cell. Drill-core density measurements provide very detailed and localized information (values every 1.5m) whereas cells w i t h i n the density-contrast model exhibit constant values throughout 25m x 25m x 25m volumes. Therefore, processing of these core measurements is necessary in order for this information to be consistent w i t h the density-contrast model, and to  Chapter  5.  Detailed Modeling:  Incorporating  a  Density [g/cc]  Zinc [%]  Copper [%] p  1  114  information  priori  2  :1  4  5  10  20  3.0  100  200  a Q  1  300  Massive Sulphide  400  Quartz Rhyolite and Mafic Volcanics 500  500  Figure 5.3: Drill-hole SAL-31. Displayed as a function of depth from left to right: 1) Geology, 2) density (g/cm ), 3) percent copper, and 4) percent zinc. The average density of the sulphide is 4.2 g/cm and the density of the rocks below the deposit is 3  3  approximately 2.8  g/cm . 3  Chapter  5. Detailed  Modeling:  Incorporating  a priori  information  obtain the m i n i m u m and m a x i m u m density-contrast bounds for each model cell.  115  Fur-  thermore, the core measurements are absolute density values, while the model values are density contrasts relative to an unknown background value. T o that end, inversions were conducted w i t h a background value removed from the core density measurements. Considering the volcanic host rocks, it is expected that a background value of between 2.35 g/cm  3  One  and 2.5 g/cm  3  is reasonable.  method of setting bounds on a model cell could be to assign the m i n i m u m and  m a x i m u m core density values that are located w i t h i n the model cell. It doesn't seem realistic, however, to expect the cell, which has a consistent value throughout, to exhibit these extreme values; a cell value closer to the mean core-density might be more probable. The core-density measurements were sampled regularly throughout the vertical extent of each cell, and from these measurements the mean density value from the core was determined. T h e upper and lower bounds were calculated b y adding and subtracting a small density variation from the mean. This variation could be based on the standard deviation of the measurements.  However, the uncertainty i n the result would decrease  as more samples are used. For independent random variables, the uncertainly associated w i t h the mean would be the standard deviation divided by the square-root of the number of samples. A l t h o u g h , as more samples are used the likelihood of them being independent w i l l probably reduce. In addition, other factors have to be taken into account. W e are only sampling the cell at one position i n x and y. T h e drill-hole would sample about three parts-per-million of the model cell (a five centimeter diameter drill-hole intersecting a 25m x 25m x 25m model cell) and, although we can consider vertical density variations w i t h i n the cell at one horizontal location, we know nothing about lateral density variations w i t h i n the cell. W e might expect horizontal inhomogeneities to exist that could increase the range of values that the cell might have to represent. A s a result, the difference between the upper and  Chapter  5. Detailed  Modeling:  Incorporating  a priori  information  116  lower bounds would probably increase. A p a r t from a knowledge of the geological setting, we have know way of knowing the possible homogeneities that might exist close to a d r i l l hole. A t San Nicolas, the variation from the mean value ranged from 0.05 g/cm  3  g/cm  3  to 0.2  for different inversion runs. In addition, i f the core-measurements only populated  half of the cell height, the variation from the mean was doubled relative to cells w i t h core-measurements throughout the cell; and cells w i t h no core-measurements were assigned upper and lower density-contrast bounds of 5 g/cm  3  and 2 g/cm  3  respectively.  These values might be a bit small although, based on the discussion above, it is hard to accurately determine these bounds at such large scales. T h i s is one area where further investigation is needed.  Implementing the bounds Once values for the upper and lower bounds have been determined, they are used as inputs i n the inversion process. GRAV3D  uses a logarithmic barrier method, directly i n -  corporated i n the objective function, to implement bounds on the model during inversion. A n equation for the objective function w i t h the barrier term is given i n E q u a t i o n 5.1. T h i s function is minimized for subsequently decreasing values of the A while keeping the regularization parameter ((3) constant. W h e n A reaches zero the resulting model honors the a priori information provided.  M  d?(A)  = $ + /JS d  TO  - 2A Y$*iPi  - Pf ) n  + (P? ln  aX  - Pi)].  where: M is the number of model cells, pj is the density of the j  p-rain  a  r  e  tfiQ upper and lower bounds the of the j  th  th  cell respectively.  cell, and p™  C - ) 5  ax  1  and  Chapter  5. Detailed  Modeling:  Incorporating  a priori  information  117  For the subsequent inversions presented, unless otherwise stated, the same inversion parameters where used as those presented i n Section 4.2.2 and Table E . l . T h a t is, the same noise levels, reference model, and choice of regularization parameter are used.  5.2.2  A priori density values from drill-hole SAL-31  Using the method described above, density values from drill-hole S A L - 3 1 were used to constrain the deposit scale density-contrast model. T h e bounds implemented assume a background density value of 2.5 g/cm , and constrain the model along a vertical column 3  of cells, from 150m to 475m depth. T h e bounded cells correspond mostly w i t h massive sulphides, and about twenty-five meters of interlayered, quartz rhyolite and mafic volcanics. T h e inversion result, displayed as east-west, and north-south cross-sections i n Figure 5.4, shows an improvement over the results of inverting the gravity data.without a priori information. T h e anomaly that corresponds w i t h the sulphide ore has a higher density-contrast throughout most of the deposit, not just around the bounded cells (the density contrast increases from 0.3 g / c m to 0.4 g / c m 75m away from bounded cells). 3  3  These higher density-contrast values make the anomaly less spread out, and are consistent w i t h expected values and a priori information. T h e model has also been well constrained along the fault that marks the lower extent of the deposit, and there is much less mass contained i n the model below the deposit than previously modeled. Furthermore, the small density-contrast anomaly that was thought to correspond w i t h the keel, in the inversion results presented i n Section 4.2.2, is again present. The model is more affected by the introduction of a priori information from drill-hole S A L - 3 1 . Changes over the original inversion are observed vertically above and below, and laterally adjacent to the bounded cells. T h i s modeling suggests that high density material is not present below the fault, which is i n contrast to the previous gravity inversion result that had a more significant continuation of high density-contrast values at depth.  Chapter  5. Detailed  Modeling:  Incorporating  a priori  information  118  700 -800  Easting [m]  (a) Initial density model: north-facing crosssection with geology overlaid.  o rs-  Northing [m]  (b) Initial density model: west-facing crosssection with geology overlaid.  g/cc 0.35  700 Easting [m]  (c) Model bounded using density values from drill-hole SAL-31: north-facing cross-section with geology overlaid.  -800  Northing [m]  (d) Model bounded using density values from drill-hole SAL-31: west-facing cross-section with geology overlaid.  Figure 5.4: N o r t h - and west-facing cross-sections through recovered deposit-scale density-contrast models. T h e upper cross-sections show the results from the original deposit-scale gravity inversion, described i n Section 4.2.2. The lower cross-sections show the results of inverting the gravity data w i t h the model bounded using the density measurements made on core from drill-hole S A L - 3 1 . Geologic contacts have been overlaid.  Chapter  5. Detailed  Modeling:  Incorporating  a priori  information  119  A w a y from drill-hole, the model has not been significantly changed, and the next section addresses the results of incorporating a l l the available drill-hole information i n an attempt to further improve the model, and determine the density structure below, and to the side of where drill-holes have sampled the earth.  Summary of using a single borehole to constrain a gravity inversion T h e models presented above show the results of inverting gravity data w i t h additional information obtained from a single drill-core. T h e results have improved over the previously inverted model and now we investigate the use of multiple drill-hole information.  5.2.3  A priori density values from all drill-holes  Figure 5.5 shows the north-facing interpreted geology section at -400S w i t h the location of drill-holes. These drill-holes, along w i t h many others to the north and south, are used to bound the density-contrast model during the inversion of gravity data, by providing explicit a priori density information. T h i s is performed i n order to account for the density distribution of the deposit and, hopefully, enable other parts of the model to be more accurately defined, particularly any extension to the keel. T h e same method of bounding the model cells as described previously, is employed. Different inversions were performed using background densities of 2.35 g/cm  2,  g/cm , 3  and bounds calculated w i t h deviations of between 0.05 g/cm  3  above and below the mean value for each cell.  and 2.5  and 0.2  g/cm  3  A l l the results were very comparable,  showing no major differences, and the results of using a background density of 2.35 g/cm  3  w i t h a 0.1 g/cm  3  deviation from the mean are presented i n Figure 5.6.  A s expected, the deposit is now very well defined by the density-contrast model, and the density structure matches the interpreted geology. Also, the density anomaly that corresponds w i t h the deposit exhibits even greater density-contrast values, which is  Chapter  5. Detailed  Modeling:  Incorporating  a priori  information  120  Figure 5.5: North-facing cross-section of interpreted geology w i t h drill-hole locations. Density measurements made on core from the drill-holes were used to b o u n d model cells during the inversion of gravity data. O n l y the drill-holes along line -400m S are displayed, although many more were used to bound the model.  Chapter  5. Detailed  Modeling:  Incorporating  a priori  information  121  consistent w i t h the a priori information. Below the penetration depth of the d r i l l holes the model is very uniform; giving more credence to the absence of high-density sulphides at depth. A p a r t from where a priori information has been explicitly incorporated, the keel has not been well defined as a distinct density-contrast structure. T h e smooth, deep, south-west extension to the density anomaly seen i n the previous inversions of this data has been replaced by a few cells of high density-contrast. In addition there is no extension of the keel present i n this model. T h i s does not mean the keel terminates at the limit of the drilling.  T h e result merely indicates a model can exist that doesn't exhibit an  extension to the keel, and meets the following requirements:  (1) it fits the data to a  reasonable degree, (2) it is consistent w i t h the a priori density information provided, and the way i n which it has been implemented.  Summary of using multiple borehole to constrain a gravity inversion Density contrast models have been produced by inverting gravity data while constraining the range of values some of the models can exhibit. T h i s has been useful i n defining the deposit, but more importantly accounting for the mass of the deposit w i l l increase the resolution away from the drill-holes, especially below the deposit.  In this  density anomaly that was observed when drill-hole information was not included virtually disappeared when the drill-hole information was included. The  recovered model does seem to be discontinuous i n some areas. T h i s is mainly  due to the fact that i n cells next to those that are bounded, the bounds are opened right up to accept almost any reasonable density value. Constraining cells next to those that contain drill-hole information is a philosophical one and relates back to the issue of lateral inhomogeneities that are expected i n the geology. A discussion is presented at the end of this chapter that deals w i t h some of the issues regarding the discontinuous nature of the recovered models.  Chapter  5. Detailed Modeling:  Incorporating  a priori  122  information  g/cc I 0.35  800-2200  700 -800  Easting [m]  (a) Initial density model: north-facing crosssection with geology overlaid.  Northing [m]  (b) Initial density model: west-facing crosssection with geology overlaid.  g/cc •  0.76  700  800  -800 Easting [m]  (c) Density-contrast model bounded by all drillholes: north-facing cross-section with geology overlaid.  Northing [m]  (d) Density contrast model bounded by all drillholes: west-facing cross-section with geology overlaid.  Figure 5.6: Cross-sections through recovered deposit-scale density-contrast models. T h e upper cross-sections show the results from the original deposit-scale gravity inversion, described i n Section 4.2.2. The lower cross-sections show the results of inverting the gravity d a t a w i t h the model bounded by density measurements made o n core from a l l drill-holes. Interpreted geologic contacts are shown on each cross-section. Note: the lower cross-sections are shown w i t h a range of density-contrast that is different from those previously presented.  Chapter  5.3  5. Detailed  Modeling:  Incorporating  a priori  information  123  Magnetic Susceptibility  Inversion of ground magnetic data has produced a model that shows part of the deposit is magnetically susceptible, parts of the deposit are void of susceptible material, and some rocks outside of the deposit are also likely to be magnetically susceptible. Over one thousand magnetic susceptibility measurements, made on core samples from thirteen drill-holes, confirm the complexity of the susceptibility distribution (Appendix B.3.1). Figure 5.7 shows magnetic susceptibility values along w i t h geology for core from the four drill-holes: S A L - 2 5 , S A L - 3 4 , S A L - 4 7 , and S A L - 7 0 . T h i s drill-hole information, and that from other drill-holes, is used to constrain the inversion of ground magnetic data i n an attempt to improve upon the existing models. Firstly, measurements from drill-hole S A L - 6 9 are included, and secondly, a l l the available measurements from the drill-holes are included.  5.3.1  Core Measurements  A K T - 9 magnetic susceptibility meter was used to make measurements on drill-core samples at San Nicolas. T h e instrument gets 90% of its signal from the first 20mm of the sample, can be calibrated for core measurements, and has a m a x i m u m sensitivity of lxl0~  5  S.I. units. T h e instrument was also calibrated i n free air before each reading.  Several test measurements showed that the susceptibility distribution w i t h i n the deposit could dramatically change from less than l x l O  - 4  to over 0.1 S.I. w i t h i n a few  centimeters. A s time constraints did not permit very fine sampling of the core, I took readings approximately every 3 meters i n rocks that were expected to exhibit low susceptibilities, every meter i n rocks w i t h high or variable susceptibilities, and several readings were collected either side of faults and geologic contacts. Tests were conducted over different rock types to see how effectively this sampling characterized the finer susceptibility  Chapter  5. Detailed  SAL-25  Modeling:  Incorporating  SAL-34  a priori  information  SAL-47  124  SAL-70  Figure 5.7: Magnetic susceptibility core measurements and geology for drill-holes S A L - 2 5 , S A L - 3 4 , S A L - 4 7 , and S A L - 7 0 . Note: the upper-most geologic unit is the Tertiary breccia, although it may appear a similar colour to the sulphide ore, which is shown i n red. Reference the geologic section and recognize it as an interpretation.  Chapter  5. Detailed  Modeling:  Incorporating  a priori  information  125  distribution, the results of which are presented i n A p p e n d i x B.3.1.  5.3.2  Bounding the magnetic susceptibility model  T h e core susceptibility measurements are used to set upper and lower bounds on model cells, which are implemented using the logarithmic barrier method. A s w i t h the density measurements, many core susceptibility readings were used to make up one value for each model cell around which bounds were centered. Unlike the density measurements, however, no background level need be removed prior to including this data i n the inversion. A l s o the samples were not evenly collected along the core. T h e uneven sampling had to be taken into account when calculating the composite susceptibility value to use for each cell ( A p p e n d i x B.3.2). Upper and lower bounds were centered around the composite susceptibility value. A s w i t h the density measurements, the magnitude that the upper and lower bounds vary from the composite value is debatable. Most of the core susceptibility values were around 0.1 x 10~ S.I. w i t h some values as high as 1 x 1 0 3  S.I..  - 3  S.I. and a few as high as 100 x 10~  T h e value that was chosen to vary the bounds by was 5 x l 0 ~  4  3  S.I., however, more  information might be contained i n the inversion by using a bound that was related to the core measurements themselves.  5.3.3  Magnetic inversion using a priori information from SAL-69  Constraining the inversion of ground magnetic data by using bounds on the susceptibility model has produced a model of the San Nicolas deposit that has significant differences w i t h respect to that which was produced without a priori information. Figure 5.8 shows a comparison between the magnetic susceptibility models recovered w i t h and without a priori information from S A L - 6 9 .  Chapter  5. Detailed  Modeling:  Incorporating  a priori  information  126  W h e n the model is bounded, the concentration of susceptible material i n the west part of the deposit exhibits higher susceptibility values, occupies less volume, and is tightly constrained between the depths of 275m and 350m. Moderate (2-3 x l 0 ~  3  S.I.)  susceptibility values are still present below the deposit and the north extension of the susceptibility anomaly is also unaffected by including the a priori information.  5.3.4  Magnetic inversion using a priori information from all drill-holes  A priori susceptibility information was used from a l l twelve drill-holes measured i n the inversion of ground magnetic data. T h e results show a susceptibility distribution that is concentrated i n the southwest part of the m a i n ore-zone, the keel, and to the northwest of the delineated deposit. Figure 5.9. T h e additional a priori information helps constrain the susceptibility distribution w i t h i n the main ore-zone to a small volume that is located against the rhyolite dome to the west and the southwest dipping fault to the northeast. Also, high susceptibility values have been introduced i n the keel that have not been previously modeled. These high susceptibility values i n the keel are introduced through the presence of drill-hole information but persist i n cells next to the drill-holes where the model was not constrained. T h i s suggests that drill-hole information is improving the model i n cells that are not bounded by drill-hole information. A continuation of high susceptibility values persist to the north and moderately high values are present below the deposit. Less-altered, mafic volcanic rocks could be responsible for the susceptible concentrations below the deposit. Different inversion models were constructed using different alpha coefficients to smooth the model i n different directions.  T h e results show that the effect the bounded cells  have on laterally surrounding cells is dependent on the relative amount of horizontal (x-component and y-component) weighting that is introduced. T h i s could be used to construct more coherent models when geologic stratigraphy can be assumed.  Chapter 5. Detailed Modeling:  Incorporating  a priori  information  xl O S.I. 3  Easting [m]  xlO'S.I.  -800  (a) Initial magnetic susceptibility model: northfacing cross-section  127  Northing [m]  (b) Initial magnetic susceptibility model: westfacing cross-section  SAL-69 x!0> S.I.  700 Easting [m]  -800  (c) Magnetic susceptibility model bounded by SAL-69: north-facing cross-section  Northing [m]  (d) Model bounded by SAL-69: west-facing cross-section  Figure 5.8: N o r t h - and west-facing cross-sections through recovered deposit-scale magnetic susceptibility models. The upper cross-sections show the results from the original deposit-scale magnetic susceptibility inversion, described i n Section 4.3. T h e lower cross-sections show the results of inverting the magnetic data w i t h the model bounded by magnetic susceptibility measurements made on core from drill-hole S A L - 6 9 . A n outline of geology interpreted from drill-holes is overlaid on each section.  Chapter 5. Detailed Modeling:  Incorporating  a priori  information  128  xl O S.I. 3  700 Easting [m]  -800  800  (a) Initial magnetic susceptibility model: northfacing cross-section  Northing [m]  (b) Initial magnetic susceptibility model: westfacing cross-section  xlO'S.I.  700 Easting [m]  -800  (c) Magnetic susceptibility model bounded by thirteen drill-holes: north-facing cross-section  -800  Northing [m]  (d) Model bounded by thirteen drill-holes: westfacing cross-section  Figure 5.9: N o r t h and west-facing cross-sections through recovered deposit-scale magnetic susceptibility models. The upper cross-sections show the results from the original deposit-scale magnetic inversion, described i n Section 4.3. T h e lower cross-sections show the results of inverting the magnetic data w i t h the model bounded by magnetic susceptibility measurements made on core from twelve drill-holes. Geologic contacts as interpreted from drill-holes are overlaid on each section.  Chapter  5.4  5. Detailed  Modeling:  Incorporating  a priori  information  129  Discussion on the discontinuous nature of the models  W h e n examining Figures 5.4(c), 5.4(d), 5.6(c), 5.6(d), 5.8(c), 5.8(c), 5.9(c), and 5.9(d), it seems apparent that models produced w i t h a priori information are somewhat blocky and discontinuous. T h i s is consistent w i t h the a priori information that reflects discrete geologic boundaries, but it is not consistent w i t h the smooth requirements of the model objective function. In fact, the blockish nature of the bounded model is counteracting the efforts of the model objective function to produce a smooth model. A more consistent way  of producing a model would be to have b o t h the model bounds and the model  objective function acting i n concert; b o t h seeking a model of the same type. T h i s could be achieved by changing the bounds to be smooth, or by changing the model objective function to allow blocky models. In this case, changing the bounds would be dishonoring the a priori information, so the appropriate alternative would be to favor blocky models. T h i s can be accomplished i n the following two ways: (1) the W , W , and W weighting x  y  z  matrices could be changed so the model doesn't have to be smooth i n the vicinity of sharp discontinuities, as determined from the a priori data, and (2) blocky, or piecewisecontinuous, models could be sought through the use of the L\ n o r m i n the model objective function, as discussed i n [Farquharson and Oldenburg, 1998].  5.5  Conclusion  T h e introduction of a priori information into an inversion can improve the resulting model. For the potential field examples presented here, the results have improved around where drill-hole information has been included, and below the known parts of the deposit. A l l of the results produced fit the data to the same degree to that used i n Chapter 4. W i t h the incorporation of information from one drill-hole, expected physical property values were increased over much of the deposit. T h e density contrast was reduced below  Chapter  5. Detailed  Modeling:  Incorporating  a priori  information  130  the deposit, while the susceptibility distribution was constrained to a range of depths w i t h i n the deposit. T h i s demonstrates how a priori core measurements could be used i n the early stages of drilling to help direct subsequent holes. Incorporating all a priori information has produced models that define the deposit, i n the case of the density-contrast model, and define a localized concentration of susceptibility i n the western part of the deposit, i n the case of the susceptibility model. The a priori information is useful is defining the deposit so that parts of the signal represented by the data can be accounted for, and areas that have not been drilled may be more accurately modeled. The gravity inversion has shown a decrease i n density values below the deposit that suggests there is no high density sulphide ore below the level of drilling. None of the models presented exhibit an extension to the keel but the data may not be sensitive to such a small, and deep, body (the sensitivity of the data to the keel is addressed i n Chapter 6. T h e issues involved w i t h incorporating drill-hole information have been presented and only a first pass solution has been formulated. It is understood that the work presented here represents a preliminary attempt to resolve these issues but also demonstrates the potential benefits than come about if drill-hole information can be included. Issues that still require investigation include: 1. A priori information can originate from finely sampled measurements made on drillcore. T h i s information is processed i n order for it to be consistent w i t h the large model cell volumes that are used to constrain the inversion using bounds.  This  processing is not trivial. Errors are introduced that relate to the sampling size of the cells, variations of physical property w i t h i n a cell along a drill hole and lateral variations that can only be assumed. In addition, an estimate of this error is needed to determine upper and lower limits of the bounds. It should also be realized that  Chapter  5. Detailed  Modeling:  Incorporating  a priori  information  131  the finely sample drill-hole information is itself a coarse representation of the actual susceptibility distribution w i t h i n the earth (Appendix B.3.1). 2. Removal of regional gravity signals and assignment of appropriate background density values are issues that need to be addressed when using density information i n gravity inversions. 3. T h e recovered models that have been bounded have a blocky appearance.  This  could be due to the inappropriate application of a smooth component model objective function when a block model is known to exist. Methods of recovering models that are consistent w i t h known geology has a greater scope than just incorporating bounds i n a model. We have to address how the model is constructed.  T h e difficulties i n implementing drill-hole information accurately i n inversions suggests that actual down-hole measurements might be better suited to producing inversion models. In such cases all the data w i l l be consistent w i t h each other. In summary, this chapter has addressed the methods that might be employed to incorporate drill-hole information i n an inversion. T h i s study reveals important aspects that require further investigation.  However, removing ambiguities from an inversion model  by constraining a model could have major benefits.  Superior models can be produced  through the use of just one drill-hole. T h i s can lead to more accurate spotting of drillholes i n an iterative exploration program.  Chapter 6  Forward Modeling and Survey Design: Detecting the keel  6.1  Introduction  Chapter five began looking at the delineation of mineralization. T h a t work is continued here.  In the context of an exploration program (Figure 6.1) we address the issue of  delineation by trying to conduct surveys that are adequately sensitive to the target. In the case of San Nicolas, detecting a deep ore zone involves modification of survey designs. T h e m a i n objective of delineation is to define the major features of the deposit. A t San Nicolas a major feature is the keel. T h e keel makes up about one sixth of the volume of the deposit, is located below and to the west of the m a i n ore-zone, and was found through drilling. Despite the success of inverting surficial geophysical data at San Nicolas to define the deposit, the keel was not readily identified (although possibly by the gravity inversion). T h e p r i m a r y reason for this is that the signal (anomalous field) from the keel is generally small. We quantify these signals through forward modeling. A s w i l l be shown, some of the data collected at San Nicolas were probably not adequate to detect the keel. T h i s work leads naturally to the question: " W h a t data are required to find the keel?" A s a result, the focus of this chapter is i n two parts. Firstly, the signal from the keel is quantitatively evaluated. T h i s is performed through forward modeling of synthetic physical property models w i t h and without the keel. T h e difference between the two responses is compared w i t h assumed noise levels i n order to  132  Chapter 6. Forward Modeling and Survey Design: Detecting the keel  133  determine if the response from the keel is observable. T h e second focus of this chapter is to obtain data that are substantially influenced by the keel. For gravity and magnetic data synthetic measurements are made down-hole. T h e simulations provide insight about the increase i n signal strength as readings are made closer to the keel. For electromagnetic and D C / I P data (active source methods), there is much more flexibility for designing an informative survey. T h i s is because b o t h sources and receivers can be placed close to the keel i n order to increase signal strength. T h e methodology for designing optimum surveys is a separate research project; one that is not addressed i n this thesis. However, computing the response of the keel for simple survey design changes is performed. T h i s may provide a foundation on which a more comprehensive analysis of the survey design problem cab be addressed. F i n d i n g these small ore-zones is more difficult i n the absence of a large amount of 1  known sulphide, as it becomes harder to instigate follow-up, detailed exploration. A s a result, a method of detecting these from the surface becomes important. Information about the keel is obtained from geology sections that were interpreted from drill-holes. T h i s information, along w i t h linear interpolation methods, is used to construct physical property models, or rock-models (digitized on 25m x 25m x 2 5 m cells), on which forward modeling can be conducted. The m a i n ore-zone, containing massive sulphide, semi-massive sulphide, and sulphide stringer zones, is modeled as having a volume of about seventeen million meters cubic meters, and is located between a depth of 175m and 425m. T h e keel, on the other hand, is modeled as having a volume of about three m i l l i o n cubic meters (one sixth the size of the m a i n zone), and is located between depths of 425m and 550m. T h i s helps put i n perspective the different signals that might come from the keel and the main-ore zone, and suggests that different surveys maybe Smaller amounts of ore, that are not economically feasible to mine on their own, could be economically viable because of the mining infrastructure that might be in place due to San Nicolas. 1  Chapter 6. Forward Modeling and Survey Design: Detecting the keel  Geophysical Exploration: Survey design for the delineation of deep ore  Exploration Goal: •find economic mineralization  s \ | I  Geologic Constraints: 'existing mapping 'historical workings 'tectonic setting  1 Geologic Models:  'descriptive model •gene* mode' -> Exploration Model  I  } Regional Geologic | Mapping: | 'Outcrop geology | •Structure | 'Alteration  134  Physical Property Measurements  Geophysical ,tXpK^ size/depth of target Exploration .physical properties contrasts Model:  Regional/Reconnaissance Scale Geophysics: Data Collection  Existing Data  Survey Design  A M A O {H- A R A D )  Gravity  AEM  IP  Other V L F . M T  Geochemistry •Stream sediments  Modeling Joint/Simultaneous Forward  Remote Sensing •Multi spectral •Hyperspectral  Forward  Inverse  Correlation  Conclusions about physical property distributions of the the earth  No Targets  Geologic Interpretation  1-  Target Generation  Terminate Exploration Program  I  Detailed Geologic Mapping: •Stratigraphy •Structure •Alteration  Geologic Logging  *  Additional Physical Property Measurements  Update Geophysical Exploration Model  Update Geologic Exploration Model Local/Deposit  iSurvey Designs  Scale Geophysics:  1  Data Collection j  EMrCSAMT.UTHM  Gravity  1  1  DOU>  Other V L F  Trenching  A k  Modeling Individual  Rock samples •Assays •Alteration mineralogy (X-ray/PlMA)  Joint/Simultaneous  Forward  Conclusions about physical property distributions of the the earth  Geochemistry •soil surveys  Y  Geologic Interpretation  Mineralization Not Intersected  Locate Drill-hole Physical property core measurements  Terminate Exploration Program  Down-hole methods Mineralization Intersected Delineation  Goal Realized: Mineral Resource  Figure 6.1: Geophysical Exploration Methodology: Survey design  Chapter  6.  Forward  Modeling  and Survey  Design:  Detecting  the keel  135  -1100  Figure 6.2: Size and position of the keel relative to the deposit, needed i n order to detect the keel.  6.2  Gravity Observations  Inversion of gravity observations i n Section 4.2 have produced a model that, along w i t h approximately defining the main ore zone, had a distribution of higher density-contrast values i n the vicinity of the keel. T h i s suggests that the data contain information about the keel above the noise level. In this section the magnitude of the gravitational response of the keel is investigated by performing synthetic gravity surveys on digitized density rock-models. T h e results are compared to the estimated instrument sensitivity and noise levels i n order to see i f gravity readings could be used to constrain a n inversion model of the keel. A synthetic survey is conducted that is similar to that conducted i n the field, and several down-hole gravity surveys are also performed. A s we are not able the change the mass or location of the keel, the only way to  Chapter  6. Forward Modeling and Survey Design: Detecting  the keel  136  increase the signal from the keel is to decrease the distance from the receiver to the keel. To that end, synthetic down-hole gravity measurements are made i n order to determine how near to the keel a drill-hole need pass for a discernable signal to be observed.  6.2.1  Simulation of gravity field observations  T w o sets of synthetic gravity data are made on different density rock-models. One model contained density distributions of the m a i n ore-zone and the keel, while the other model had density distributions of only the main ore-zone. B y comparing gravity observations made on each model one can determine the magnitude of the response of the keel. T h i s response is compared w i t h estimated noise levels of between 0.02 and 0.05 m G a l .  In  order to confidently distinguish the response of the keel i n the data, signal-to-noise levels of greater t h a n 3 are assumed to be needed, therefore, a gravitational response from the keel of greater t h a n 0.06 m G a l is required. T h e 3D density rock-model was created by digitizing geologic cross-sections provided by Teck, assigning density values to the rock units, and interpolating between the 2D sections. T h e density value assigned to each geologic unit is determined from the measurements made on the core, but small density variations w i t h i n each unit are not taken into account (a constant value of 4 g / c m is assigned to the massive sulphide ore). T h e 3  density rock-model, which covered only a limited volume, was then embedded w i t h i n a background of 2.5 g / c m  3  material. T w o density rock-models were constructed (Fig-  ure 6.3); one w i t h the keel, as digitized from the geologic sections, and one w i t h a l l density values below a depth of 425m replaced by w i t h values of 2.5 g / c m . Replacing 3  the density values below 425m effectively removes the keel from model and, although some minor density variations are also changed during this process, it is assumed that the keel represents most of the anomalous mass at these depths. Five hundred and eighty-five synthetic gravity observations were made on the surface  Chapter  6.  Forward  Modeling  and Survey  Design:  Detecting  the keel  g/cm  137  3  3.15  (a) Perspective view of density rock-model showing an east-west section at -400m north.  I 1 I  4  g/cm  3  (b) Perspective view of density rock-model with the keel removed. The model is cut at -400m north to reveal an east-west cross-section. Figure 6.3: T h e two density rock-models that were used to generate synthetic gravity measurements.  Chapter 6. Forward Modeling and Survey Design: Detecting the keel  138  of each density rock-model. T h e observations, shown i n Figure 6.4, have a station spacing of 50m and a line spacing of 100m, which is similar to some of the more closely spaced readings taken at San Nicolas.  T h e difference between the observations made on the  model w i t h the keel and those made on the model w i t h the keel removed is shown i n Figure 6.4(c).  T h e difference ranges i n magnitude from 0.004 to 0.12 m G a l and, i n  comparison to estimated noise levels (between 0.01 and 0.05 m G a l ) , the response from the keel gives a discernable response. Assuming the densities were appropriately assigned to the rock-model, this has the important implication that information specific to the keel could be extracted from surface gravity data, and is consistent w i t h the inversion model, which exhibits an extension to the deposit where drilling indicates the presence of the keel.  6.2.2  Synthetic, downhole, gravity observations  Accurate determination of the keel's density distribution would likely come from inverting gravity observations that contain information about the keel significantly above noise levels. If we assume that noise levels are constant, the only other option would be to increase the gravitational response of the keel. Because we are dealing w i t h a potential field, this can only be done by making observations nearer to the source; we do this by performing down-hole measurements.  T h e use of drill-holes i n geophysical surveys is a  logical progression of survey design at San Nicolas because of the extensive drilling that has occured, and although down-hole gravity measurements are not often carried out i n the minerals i n d u s t r y , they have the potential to produce data w i t h low noise levels 2  because of the quiet environment i n which they are made. Six synthetic drill holes were used to collect down-hole gravity measurements, at 10m Down-hole gravity measurements are not often performed due to the expense of the instrumentation and the possibility of losing it down a hole. 2  Chapter  6. Forward Modeling  I •21S3  I  -1867  i -1550 EastiriE(m)  i -1233  i -917  and Survey Design: Detecting  i -600  mGal -2600  (a) Synthetic gravity data produced from density rock-model.  ( -2183  . -1867  139  the keel  I  I 1550 Esstine(m)  I 1233  -917  (b) Synthetic gravity data produced from density rock-model without keel.  Eattlng (m)  (c) Difference between synthetic gravity data with keel and synthetic gravity data without keel.  Figure 6.4: Synthetic gravity data produced from a density rock-model of San Nicolas. D a t a are produced from models w i t h and without the keel. T h e difference is also plotted.  Chapter  6.  Forward  Modeling  and Survey  Design:  Detecting  the keel  140  intervals, to a depth of 650m on the rock-model. Figure 6.5 shows the locations of these drill-holes plotted on a west-facing cross-section through the density rock-model.  ABCDEF  Northing [m]  Figure 6.5: West-facing cross-section of density rock-model. T h e m a i n ore-zone is represented by the higher density values i n the top half of the model while the keel is represented by the anomaly i n the lower half of the model. The locations of six drill-holes, w i t h i n which synthetic gravity measurements are made, are also shown. Drill-hole A is 25m north of the keel and each subsequent drill-hole is positioned 25m further north than the last one. T h e difference between the gravitational response of the model w i t h the keel and the model w i t h the keel removed is shown for measurements made down the six drill-holes in Figure 6.6.  Drill-hole A is located on the north edge of the keel and, at a depth  of between 450m and 500m, which corresponds to the depth of the keel, the vertical gravitational response changes from 0.7mGal to about -0.5 m G a l . T h i s response is very much above the expected noise levels, and would definitely indicate an anomalous mass. A s measurements are made down drill-holes further to the north, the response from the  Chapter  6.  Forward  Modeling  and Survey  Design:  Detecting  the keel  141  G, [mGal]  A  B  C  D  E  F  Figure 6.6: Down-hole plots of synthetic gravity data made i n six drill-holes. T h e data are the difference between the response of a density rock-model w i t h the keel, and a model w i t h the keel removed. Note the change i n horizontal scale for plots C , E , and F .  Chapter  6.  Forward  Modeling  and Survey  Design:  Detecting  the keel  142  keel is seen to smooth out and decrease i n value to + / - 0.05 m G a l i n d r i l l F , 250m n o r t h of the keel. T h e magnitude of response from the keel i n drill-hole F is close to the expected noise levels. It is likely that gravity data collected i n drill-holes further t h a n 150m  6.2.3  from the keel would have difficulty i n detecting the keel from the noise.  Summary of gravity forward modeling.  A simulation of the observations made i n the field show that surface gravity data could contain sufficient information for the keel to be determined. T h i s result adds credence to the inversion results obtained i n Section 4.2. W i t h assumed noise levels, down-hole measurements should be made w i t h i n about 150m of the location of the keel.  6.3  Magnetic Observations  Inversion of the ground magnetic data was not successful i n modeling the keel, even though magnetic susceptibility measurements made on drill-core show the keel to have high susceptible values. Forward modeling of magnetic data is performed to determine if the field ground magnetic data that were inverted i n Section 4.3 could detect the keel. In addition, down-hole synthetic measurements are made i n an attempt to increase signal levels of the magnetic response of the keel, and determine how near such down-hole measurements need be i n order to detect the keel. Forward modeling is conducted on a magnetic susceptibility rock-model. A s outlined in A p p e n d i x B , the susceptibility rock-model is constructed using interpreted geologic sections and assigned magnetic susceptibility values based on core measurements . 3  A  second susceptibility model is constructed which is the same as the one just described, Even though core susceptibility measurements show that the distribution susceptible material.isn't very predictable within the deposit, it does seem to be consistently high within the keel, especially along the fault. 3  Chapter  6.  Forward  Modeling  and Survey  Design:  Detecting  the keel  143  except a l l susceptibility values below 425m are replaced w i t h a value of zero. T h e difference between the data collected on each of these models w i l l be used to determine the magnitude of the response of the keel. For the field ground magnetic data a noise level of 2 n T was used during the inversions. T h i s resulted i n a model that corresponded w i t h the deposit yet was devoid of overly complex structures.  Based on this information, a  signal level of about 5 n T is expected from sources i n order for them to be adequately discerned from the noise.  6.3.1  Simulating the ground magnetic field data  Five hundred and eighty-five surface measurements of the total magnetic field are calculated over b o t h susceptibility rock-models. T h e inducing field strength and orientation was approximately that i n which the original field data were collected. T h e synthetic data are shown i n Figure 6.7 along w i t h a plot of the difference between the two. T h e response from the keel (as depicted by the plot of the difference between the two data sets) ranges from about -1 to 3 n T i n magnitude. T h i s signal level is above a noise level of 2 n T , but only exhibits a signal-to-noise ratio of 1.5; smaller than that required for confident detection.  6.3.2  Synthetic downhole magnetic observations  So how can magnetic field observations be used to detect and model the keel?  The  observations need to contain information about the keel above the expected noise levels. A s the source of the magnetic field cannot be moved nearer the receiver, the receiver is moved nearer the source; below the surface. Synthetic magnetic field observations are made below the surface i n four drill-holes that extend to a depth of 650m. T h e drill-holes, shown on a cross-section of the susceptibility rock-model i n Figure 6.8, are spaced 50m apart and are located at -1800m west, and from -350m north (against the northern edge  Chapter  Forward  6.  Modeling  and Survey  Design:  Detecting  585 data, I • 50.6, D = -13.4  I  I -2183  -1867  t .1550 Easting (m)  i -1233  i  i -937  144  585 data, I • 50.6, D • -13.4  HT -600  (a) Synthetic magnetic data produced from susceptibility rock-model.  ==:±:=  .................x^-r...................  7 5 0  I •2500  the keel  lioo.  i •2500  . . . . . . i „ , -t.. . . • « * . . . . . . . t  i -2183  t -1867  i • 1560 East Ins (m)  i • 1233  i -917  i -600  | •_  nT  (b) Synthetic magnetic data produced from susceptibility rock-model without keel.  (c) Difference between synthetic magnetic data with keel and synthetic magnetic data without keel.  Figure 6.7: Synthetic magnetic data produced from a susceptibility rock-model of S a n Nicolas. D a t a are produced from models w i t h and without the keel. T h e difference is also plotted.  Chapter  6. Forward Modeling  and Survey Design: Detecting  the keel  145  of the keel) to -200m north. Sixty-six measurements are down the hole to a depth of 650m. T h e measurements are made every 10m.  A B C D  Figure 6.8: West-facing cross-section of the magnetic susceptibility rock-model w i t h the keel shown to the lower left. Four drill-holes are also shown, w i t h i n w h i c h synthetic total magnetic field measurements are performed.  T h e total field magnetic response for observations made down-hole o n b o t h susceptibility models are shown i n Figure 6.9(a). The response from the model w i t h the keel is shown as a dashed line, while the response from the model without the keel is shown as a solid line. T h e difference between the two responses are plotted i n Figure 6.9(b). T h e signal that emanates from the keel is easily observed i n data from drill-hole A at a depth of between 450 and 500m. The observations made down drill-holes at more northern locations show the signal from the keel decreasing to a magnitude of about + / -  15nT  at a distance of 150m from the northern edge of the keel. A c c o r d i n g to the noise and signal-to-noise levels prescribed above, this response should be sufficient to detect the keel.  Chapter  6.  Forward  Modeling  and Survey  Design:  Detecting  the keel  146  Total magnetic field (nTJ  I I I..I-1 1 1 I I Lil I I A B  I I Lil I i C  I I Ul I i D  (a) Down-hole, synthetic total magnetic field data. For each hole the response from the model containing the keel is shown by a dashed line, and the response from the model without the keel is shown by a solid line. Total magnetic field [nT]  f U  i »i i  A  i S!  s . i J  B  S  is  =  C  a  8  i  i  .  •  •  D  (b) Difference between down-hole data collected on a model with the keel and those collected on a model without the keel. This is equal to the response from the keel alone. Note the change of scale for plots from drill-holes C and D.  Figure 6.9: Synthetic total-field, down-hole, magnetic data produced from a magnetic susceptibility rock-model of San Nicolas. D a t a are produced from models w i t h and without the keel. T h e difference is also plotted. Drill-hole A is just north of the keel and drill-holes B , C , and D are each 50m further north that the previous one so that drill-hole D is 150m north of the keel.  Chapter  6.3.3  6.  Forward  Modeling  and Survey  Design:  Detecting  the keel  147  Summary of magnetic forward modeling  Forward modeling of total field magnetic data over a susceptibility rock-model of San Nicolas has produced results that suggest noise levels are too high for surface data to detect the keel. T h i s result is consistent w i t h the inversion results that d i d not model the keel. Also, drill-hole data collected at a distance of 200m from the keel w i l l still contain information about the keel that is sufficiently above assumed noise levels.  6.4  C S A M T / C S E M observations  One-dimensional inverse modeling of C S A M T data collected over San Nicolas has, when the models are 'stitched' together, successfully produced a conductivity structure of the subsurface that defines the ore-body and the Tertiary overburden. A conductivity distrib u t i o n that models the keel, however, was not recovered. T h r o u g h the forward modeling presented i n Section 4.4.1 it has already been shown that 3D modeling is probably necessary i n order to recover a l l the information contained i n the C S A M T data, especially information about structure at depth encoded i n lower frequency data (0.5 H z to 32 Hz).  B u t , even if this data were to be modeled i n three dimensions, would it contain  information about the keel that is above expected noise levels? In order to address this question, and examine ways of increasing signal-to-noise levels of the response from the keel, forward modeling of C S A M T data w i t h different transmitter-receiver layouts is performed over a conductivity rock-model of San Nicolas. A l t h o u g h the field C S A M T data were collected at fifteen frequencies ranging from 0.5 H z to 8192Hz, forward modeling is only presented for a frequency of 16Hz. T h i s frequency is chosen because the plane-wave skin-depth for this frequency, w i t h i n a 50 O h m - m half-space (a reasonable approximation for the conductive environment at San Nicolas), is about 900m. Therefore, signals at this frequency would probably contain sufficient  Chapter  6.  Forward  Modeling  and Survey  Design:  Detecting  the keel  148  information about the keel. O f course, as the geometry of the survey changes, the planewave approximation w i l l become less valid, but an investigation into the fields generated at 16Hz may give some insight into the problem of detecting the keel using the C S A M T method. W h e n inverting the field data, standard deviations of between 2 and 5 percent were assigned to the apparent resistivity data, and between 1 and 2 degrees were assigned to the phase. For this sensitivity study of the keel, apparent resistivity and phases are not calculated, rather the data remain i n terms of magnitudes of electric and magnetic field components. For the purpose of drawing conclusions here, it is assumed that noise levels of greater t h a n 5 percent are necessary for a coherent signal from the keel to be useful i n detecting the keel.  6.4.1  Simulated C S A M T observations  In a similar manner as before, rock-models were made for conductivity structures w i t h and without the keel. T h e rock-model was embedded i n a half-space of 150 O h m - m material and a perspective view of the conductivity rock-model (with keel), cut at -400m south to reveal the deposit, is shown i n Figure 6.10. T h e location of the transmitter (grounded at each end) as used i n the field C S A M T survey is also shown. Receiver locations were centered over the deposit along lines -600m, -400m, and -200m north.  Using  EH3D  (Appendix E.8), electric and magnetic fields were modeled for the conductivity models due to a current source of 1 A m p , oscillating at 16Hz. A s w i t h the field survey, real and imaginary parts of the x-component electric field ( . E ^ a n d y-component magnetic field (H ) y  were measured (an orthogonal coordinate system is orientated w i t h the local  coordinate system). These measurements were made at 264 stations located at the surface on a regular, 50m x 50m grid between -1000m and -2150m east, and -150m and -650m north.  Chapter  6.  Forward Modeling  and Survey Design: Detecting  the keel  149  Figure 6.10: A perspective view of conductivity rock-model (including keel), cut to show the conductivity structure at -400m south. The approximate resistivity values of the model are shown, as is the location of the transmitter that was used i n the field C S A M T survey.  Chapter  6.  Forward  Modeling  and Survey  E Re [V/m] x  Design:  Detecting  E Im [V/m]  the keel  150  H Re [A/m]  x  H Im [A/m]  y  y  ox  With keel  e | © Easting  Without keel  -2  6 x 1  °  -2.5  -2  . -1.5 x10  -2  7  -1.5 . x10  5  -7  -6  6  x10  Difference  . x10 4  9  "  "  2  1 8  x10  0  -1  -0.5  -8 X 10  0  Figure 6.11: Color plot of synthetic real and imaginary E and H fields, at the surface over the San Nicolas deposit, that emanate from a far, 16Hz, grounded transmitter. T h e first and second rows show the field components that would be produced over conductivity models with, and without the keel, respectively (these are plotted on the same scales). T h e t h i r d row of plots show the difference between fields produced w i t h and without the keel for each component. E a c h plot covers the area over the deposit, as described previously, although the northing and easting values are not shown. X  Y  Chapter  6.  Forward  Modeling  and Survey  Design:  Detecting  the keel  Plan-view, color plots of real (Re) and imaginary (Im) parts of E  151  x  and H , y  for a l l  stations, are shown i n Figure 6.11. T h e first row of plots shows the fields that would be produced over a conductivity structure of the deposit that includes the keel, the second row of plots shows those that would be produced if the keel were absent, and the t h i r d row of plots displays the difference. The difference the keel makes to the real part of the E field ranges from - 5 x l 0 x  - 9  to 1 0 x l 0 ~ . W h e n compared to the actual measured values, 9  we can see this is less than one percent of the total signal. T h i s signal level w i l l be most likely saturated by noise, and therefore not useful for keel detection. Similar observations are made for the remaining field component measurements, where the effect of the keel is about two percent of the imaginary part of the E  field, and less t h a n one percent of  x  b o t h the real and imaginary parts of the H  y  component.  T h i s forward modeling suggests that, for a grounded current source oscillating at 16Hz,  noise levels much smaller than five percent would be needed for the keel to be  modeled even if the information contained i n the E  x  and H  y  field components can be  extracted through 3D inverse modeling. The analysis for other frequencies, or for other E and H components has not been carried out, so it is difficult to draw general conclusions. Furthermore, only relative signal magnitudes and noise levels have been considered, however, absolute signal-to-noise levels are also important — if more current were to enter the ground the magnitudes of the electric and magnetic fields would be larger and, assuming that noise levels remain the same, and a coherent response from the keel above the noise levels could be measured. It w i l l probably be difficult to detect the keel using a plane wave source, that is a transmitter a large distance away from the target. Greater signal is needed for detection and this can come about through a change i n survey design. T h e next section looks at bringing the source i n closer to the target i n order to increase the signal.  Chapter  6.4.2  6.  Forward  Modeling  and Survey  Design:  Detecting  the keel  152  C S A M T / C S E M survey design  A n o t h e r way to increase the signal from the keel would be to change the design of the survey.  In contrast to potential field methods, whose sources are fixed, active-source  methods, such as C S A M T , allow for the repositioning of sources as well as receivers . 4  In addition, many more components of the resulting fields can be measured, including real and imaginary parts of x-, y-, and z-components of electric and magnetic fields, and different source frequencies can also be employed. Accordingly, there are many more variables to consider when determining survey design, and optimization methods can be used to design the appropriate surveys given a specific target. G l e n n and W a r d (1976), Boerner and West (1989), and Maurer and Boerner (1998) address o p t i m i z a t i o n of electromagnetic survey designs. For this study, however, only simple survey-design changes are considered, for a single frequency of 16Hz, i n an attempt to increase the response of the keel by increasing the current-density w i t h i n the keel . T h e forward modeling is 5  performed i n order to demonstrate the types of surveys that could be conducted i n order to detect the keel, rather than conclusively show the best survey design for the job. B y bringing the current source, or transmitter, closer to the deposit, it is expected that more current w i l l enter the deposit, and therefore larger signals can be measured. Consequently, the signal from the keel should also be larger and the absolute response from the keel may become higher than the noise levels.  Figure 6.12 shows the four  transmitter locations that are used for the forward modeling. T h e characteristics or each transmitter setup is described below.: When sources are used that generate primary electric and magnetic fields that are not planar in nature at the receiver locations, the survey is considered to be a controlled source electromagnetic survey (CSEM), and no longer a controlled source audio magneto-telluric survey (CSAMT). 4  An increase in current density within the keel is expected to result in larger magnitude secondary fields emanating from the keel. 5  Chapter  (a)  Far  6.  Forward  Modeling  and Survey  l o c a t i o n of t r a n s m i t t e r ; as u s e d i n the  Design:  Detecting  (b)  Near  the keel  153  l o c a t i o n of t r a n s m i t t e r .  field survey.  (c) T r a n s m i t t e r w i t h one e n d below the deposit  (d) T r a n s m i t t e r l o c a t e d w i t h one e n d w i t h i n the  down a drill-hole.  deposit d o w n a d r i l l - h o l e .  Figure 6.12: Perspective views of conductivity rock-model showing locations of the transmitters used for forward modeling of electromagnetic data.  Chapter  6.  Forward  Modeling  and Survey  Design:  Detecting  the keel  154  1. Figure 6.12(a) shows the far grounded transmitter location that was used i n the field survey. T h i s is 1.6 k m long, parallel to the perceived geologic strike, and is 2.5 k m north of the center of the deposit (-400m N ) . 2. For the near transmitter location, the source remains grounded on the surface and is brought closer to the deposit (Figure 6.12(b)). T h e transmitter is now located along line -200m N (very close to the deposit), and is still 1.6 k m long. 3. Figure 6.12(c) shows a very different transmitter location. One end of the transmitter is located at the surface just north of the deposit (-1600m E , -200m N ) and one end is located i n a drill-hole below the deposit at a depth of 525m at -1600m E , -400m N . 4. T h e final transmitter location is shown i n 6.12(d). T h i s is also located w i t h one electrode at the surface and one down a drill-hole, but now the electrode is grounded w i t h i n the deposit at a depth of 325m.  A plot of current density magnitudes for models cells on a north-facing cross-section through the deposit is shown i n Figure 6.13.  A n increase of current densities i n the  keel of more t h a n one order of magnitude results from decreasing the distance to the transmitter. T h e current density w i t h i n the keel increases i n magnitude to over l x l O A/m  - 5  for the down-hole source locations (not shown), which is an increase of more t h a n  2  two orders of magnitude over the original field survey. The  new transmitter locations have increased the currents w i t h i n the keel as well as  the rest of the deposit. Synthetic measurements of E and H are now made at the surface x  y  for a l l three transmitter locations on conductivity structures with, and without, the keel. The  following figures show the measurements made w i t h these transmitter locations  and  compare them w i t h the results of the simulated field observations (Section 6.4.1).  Chapter 6. Forward Modeling and Survey Design: Detecting  the keel  155  Easting [m]  Eosttng [m] Keel  Keel  (a) Magnitude of current density through a north-facing cross-section of the rock-model at -400m north for the far transmitter location (2500m north of the deposit) used in the field survey.  (b) Magnitude of current density through a north-facing cross-section of the rock-model at -400m north for the near transmitter (at the northern edge of the deposit) location.  Figure 6.13: N o r t h facing cross-sections through a rock-model, which includes the keel, showing the magnitude of current density i n each cell for two, (a) far and (b) near, transmitter locations. T h e cross-section is located at -400m north and shows an increase i n current density w i t h i n the keel (the small anomaly to the lower left of each section) from about 5 x l 0 " to 6 x l 0 ~ A / m . 8  7  2  Four figures are presented; one for each part (real and imaginary) of the E  x  and  H  y  components. W i t h i n each figure there are four columns of plots; each column represents one transmitter location. E a c h column of plots contain synthetic readings calculated for: (1) a conductivity structure w i t h the keel, (2) a conductivity structure without the keel, and (3) the difference between the two. Each plot is for receivers at the surface and covers an area over the deposit which extends from -2150m to -1000m east, and -150m to -650m north (the northing and eastings are not shown). T h e plots shown for observations w i t h all of the transmitter locations, w i t h and without the keel, are presented at the same scale (given by a color bar to the left), while the difference plots, for a l l transmitter locations, are presented at a different color-scale. A s shown i n Figure 6.14, the real part of the E  x  component of the electric field  measured at the surface increases by two orders of magnitude when the transmitter  Chapter  6.  Forward  Far source  Modeling  and Survey  Near source  Design:  Detecting  DH: source below deposit  ox c  -  o  lxltr* Easting  the keel  156  DH: source within deposit  mm  1 Surface Real E, data with keel  "WW  xlu"  IBB  4  -1  V/m Surface Real E, data without keel  lxlO  in  9  Difference of surface Real E, data  I  1  xlO  6  1-1  Figure 6.14: Real part of E surface measurements made for four different transmitter source locations over conductivity rock-models of the San Nicolas deposit w i t h and without the keel. x  Chapter 6. Forward Modeling and Survey Design: Detecting  157  the keel  location is brought close to the deposit. T h e difference, between measurements made on conductivity structures w i t h and without the keel, increases by just under two orders of magnitude when the transmitter is brought near to the deposit, whereas it increases by nearly three orders of magnitude when one end of the transmitter is placed near the keel, down a drill-hole. Also, the response from the keel seems larger when the transmitter is placed below the deposit, as opposed to within the deposit. A similar result is shown for the imaginary part of the x-component of the electric field at surface receiver locations. W h i l e the observed signal increases b y about two orders of magnitude when the distance to the transmitter is decreased, the difference between measurements made w i t h and measurements made without the keel is only significant when one end of the transmitter is located below the surface.  Far source  Near source  DH: source below deposit  DH: source within deposit 1  Easting  Surface Imaginary E„ data with keel  xlO  1  1-1 V/m Surface Imaginary E, data without keel  l xlO  Difference of surface Imaginary E data  7  ftl.5  x  Figure 6.15: Imaginary part of E  X  surface measurements made for four different trans-  mitter source locations over conductivity rock-models of the San Nicolas deposit w i t h and without the keel. V e r y similar results are also observed for surface measurements of the magnetic field,  Chapter  and  6.  Forward  Modeling  and Survey  Design:  Detecting  the keel  158  are summarized below. For the three transmitter locations close to the deposit,  the real part of the H  y  field increases by about two orders of magnitude, while the  imaginary part increases by just under two orders of magnitude. T h e difference between H measurements made on conductivity rock-models w i t h and without the keel increases y  by two orders of magnitude only when one end of the transmitter is grounded close to the keel, down a drill-hole.  6.4.3  Summary of C S A M T / C S E M forward modeling  Forward modeling has shown that current densities, and the resulting electromagnetic fields, a l l increase when the transmitter is brought closer to the deposit. T h e response of the keel relative to the total signal observed also increases, but does not exceed one percent of the total signal. However, w i t h these elevated field strengths, the noise levels may remain close to their original level, and the signal-to-noise could be improved. The results presented are for two field components (E  x  and H ) at one frequency (16 y  Hz). These components are measured i n C S A M T surveys because they have m a x i m u m coupling w i t h the propagating plane-wave source w i t h the transmitter geometry used, and therefore w i l l produce the strongest signals. A s other survey geometries are considered, the source does not approximate a plane wave, and the orientation of m a x i m u m signal is harder to determine.  Therefore, other field components should be measured so a l l  information is recorded. In addition, the aspect of placing the receiver locations down a drill-hole has not been presented. T h i s would probably result i n an increase i n signalto-noise response of the keel depending on the proximity of the drill-hole to the keel. Finally, the response of the keel at different frequencies might also show different results. The preliminary study made on the design of C S A M T surveys, targeted at detecting the keel, clearly demonstrates the scope of this problem. A s such, definite conclusions cannot be made regarding the detectability and definition of the keel w i t h  CSAMT  Chapter  6.  Forward  Modeling  and Survey  Design:  Detecting  the keel  159  techniques, apart from stating that the range of survey designs available probably includes one that could provide data to sufficiently model the keel.  6.5  D C resistivity  Inversion of D C (direct-current) resistivity measurements, made using the 'Realsection' array configuration, has not been successful i n identifying the San Nicolas deposit. T h i s may be due to current channeling i n the conductive overburden, or the small number of transmitter locations (more transmitter locations seem to have a greater constraint on conductivity distributions at depth). Even so, forward modeling is performed i n order to quantify how sensitive the survey is to the keel, and to determine if other survey designs might provide more information about the keel. In order to determine if the signal from the keel is above noise levels, the noise levels must be known, however, only estimates of the noise are available. Noise levels of five percent were assigned to the D C data for inverting, and, although the recovered models didn't seem very successful, noise levels between two and eight percent typical for such surveys . For a better understanding of the noise levels, instrument sensitivity and specific field conditions would have to be taken into consideration.  6.5.1  Simulation of Realsection resistivity observations  T w o conductivity models are used for the forward modeling; one contains the structure of the keel and one does not. A synthetic Realsection survey was conducted over b o t h 6  of these models, the results of which are presented as apparent resistivity values i n F i g ure 6.19. T h e survey, which has a layout as described i n A p p e n d i x C.6, was conducted ^Realsection is the name given to a type of modified gradient array survey that was devised by Quantec Geoscience, and employs several transmitter dipoles of different lengths. When plotted on a section, the resulting picture merely represents the data and not the true electrical structure of the earth.  Chapter  6.  Forward  Modeling  and Survey  Design:  Detecting  the keel  160  over the deposit on line -400m north and involves measuring the potential difference between two receiver electrodes.  T h i s potential difference is the actual measurement  made i n the field, but apparent resistivities are commonly calculated using the  following  equation, [Telford, Geldart, and Sheriff, 1990b].  where A; is a geometric factor that is determined by the position of the transmitter and receiver electrodes, V is the potential difference measured w i t h the receiver dipoles, and / is the transmitted current. T h e synthetic Realsection data are plotted i n Figure 6.16 and show a response from the keel that is about one percent of the total signal.  T h i s amplitude is below the  expected noise levels (5 %). A n o t h e r survey design is employed to see if the response from the keel can be i n creased.  T h e dipole-dipole survey design is chosen.  Dipole-dipole arrays are usually  more sensitive to lateral variations i n conductivity, and, although the depth penetration may not be as great as the Realsection survey, it is hoped that a greater sensitivity to the keel is observed. Dipole-dipole arrays also employ many more transmitter locations and therefore can provide better constraint of inversion models at depth.  6.5.2  Forward modeling of dipole-dipole resistivity observations.  A dipole-dipole array was used to collect synthetic resistivity data over two rock-models of the San Nicolas deposit; one that includes the keel, and one that does not. T h e dipoledipole array employed has thirteen 100m transmitter dipole locations, each w i t h five 50m, and five 100m, associated receiver dipoles. For display purposes, resistivity values are plotted at the intersection of two forty-five degree lines that lead down from the center  Chapter  6.  Forward  Modeling  and Survey  Design:  Detecting  the keel  161  | 40 Ohm-m  I 77.5  I 150 •2275  -2033  •1792  -1550 -1308 Easting (m)  -2275  (a) Synthetic line of Realsection apparent resistivity data collected over a conductivity rockmodel with the keel.  -2275  -2033  -1792  -2033  -1792  -1550 Easting (m)  -1308  -1067  (b) Synthetic line of Realsection apparent resistivity data collected over a conductivity rockmodel without the keel.  -1550 -1308 Easting (m)  -1067  -825  (c) Percent difference between synthetic Realsection apparent resistivity data with keel and synthetic apparent resistivity data calculated without keel.  Figure 6.16: Synthetic, D C , apparent resistivity, Realsection data produced from conduct i v i t y rock-models of San Nicolas. T h e x-locations refer to the midpoint of the receiver dipoles, while the y-axis refers to different transmitter dipole positions; the b o t t o m line corresponds to data collected w i t h the longest transmitter, and the t o p line corresponds to data collected w i t h the smallest transmitter length. T h e section is just a representat i o n of the data and should not be taken as the true structure of the earth. T h e percent difference between data produced from models with, and models without the keel is also shown. Note: the apparent resistivity color-bar is on a log scale w i t h low resistivities (high conductivities) displayed i n red, while the percent difference is plotted on a linear scale.  Chapter  6.  Forward  Modeling  and Survey  of the transmitter (I) and receiver (v ) x  Design:  Detecting  the keel  162  dipoles, as shown i n Figure 6.17. T h i s pseudo-  section is a convenient way of plotting the data but does not represent the structure of the earth. T h e values are interpolated and displayed i n Figure 6.18.  surface  Figure 6.17: Schematic diagram of dipole-dipole array for one transmitter location, showing the pseudo-section plotting points relative to the electrode geometry.  T h e synthetic dipole-dipole data collected using the two conductivity rock-models show apparent resistivity values that range from 25 to 75 O h m - m . T h e signal from the keel, as determined by calculating the difference between the data collected over the two models, has resistivity contrast values ranging from -2 to 0.3 O h m - m . T h i s response is up to 2.75 % of the magnitude of the total signal, which suggests that this dipole-dipole array configuration is more sensitive to the keel that the Realsection array, however, it is still below the assumed noise level of 5%.  6.5.3  Summary of DC resistivity forward modeling  W h i l e inverse modeling of D C Realsection resistivity data hasn't resulted i n an appropriate conductivity model of San Nicolas, the data have been shown to be sensitive to the presence of a conductive keel. Unfortunately, the response from the keel is below expected noise values and so other survey designs are sought i n order to increase the signal-to-noise.  Chapter  6.  Forward  Modeling  and Survey  Design:  Detecting  the keel  163  25  I  •2394  2136  1879  1622 Easting (m)  1365  1107  -850  -2394  (a) Pseudo-section of synthetic, dipole-dipole, resistivity data collected over a rock-model that includes the keel.  -2136  1879  1622 Easting (m)  1365  1107  Ohm m  -850  (b) Pseudo-section of synthetic, dipole-dipole, resistivity data collected over a rock-model without the keel.  I 32.75  1  %  1.25 -0.25  •2394  -2136  1879  1622 Easting (m)  1365  1107  850  (c) Percent difference between synthetic dipole-dipole resistivity data with keel and synthetic dipoledipole data without keel.  Figure 6.18: Synthetic apparent resistivity dipole-dipole pseudo-sections produced from conductivity rock-models of San Nicolas. Pseudo-sections of d a t a produced over conductivity rock models w i t h and without the keel are plotted o n a log scale along w i t h the percent difference between the two, which is plotted on a linear scale. T h e plotting convention use is shown i n Figure 6.17.  Chapter  6.  Forward  Modeling  and Survey  Design:  Detecting  the keel  164  A traditional dipole-dipole survey design was chosen as an improvement over the Realsection array i n detecting the keel. In changing from a Realsection array to a dipoledipole array, the response from the keel has been shown to increase by almost three-fold (from 1% of the total signal to 2.75%). Furthermore, if this data were to be inverted it is expected that the dipole-dipole data would constrain the model better at depth than the Realsection data. Unfortunately, the response from the keel is still below estimated noise levels so other survey designs are required, and down-hole methods may prove necessary in order to receive a coherent signal from the keel. T h e increase i n response from the keel demonstrates an improvement i n survey design, but comes at the expense of more field work. Five transmitter and eighty-five receiver dipoles were used i n the Realsection survey, verses thirteen transmitter and one hundred and t h i r t y receiver dipoles for the dipole-dipole survey. O p t i m i z a t i o n of D C resistivity surveys for a given target is a very broad subject due to the many different transmitter-receiver geometries that can be employed.  The  work presented i n this section introduces the subject of D C resistivity survey design w i t h two-dimensional surveys over a three-dimensional object, but investigations into three-dimensional designs would provide more complete results.  6.6  Chargeability  Realsection I P data collected at San Nicolas shown i n Figure D.15, show apparent chargeability values up to 24 msec. Physical property measurements suggest the deposit as a likely source of the signal, and inversion of this data, along w i t h gradient array I P data, has shown a distribution of anomalously chargeable material that corresponds well w i t h the deposit (Section 4.5). A l t h o u g h the keel was not modeled through inversion of the I P data, a concentration of chargeable material is observed along the fault-zone that bounds  Chapter  6.  Forward  Modeling  and Survey  Design:  Detecting  the keel  165  the deposit to the east. It is possible that these chargeability values persist w i t h i n the keel and I P surveys could be used to detect the keel. A s a result, forward modeling of IP data is used to determine the sensitivity of I P data to the presence of the keel. Synthetic I P data are calculated using D C I P F 3 D — a forward modeling routine that first calculates the primary voltages over a conductivity distribution, and then calculates the secondary voltages based on the chargeability of the material. A n overview of the forward modeling method is given i n A p p e n d i x C.6. A chargeability rock-model is created from digitizing geologic cross-sections and assigning values to the rock units based on physical property values. A m a x i m u m value of seventy-five milli-Volts per V o l t is assigned to the massive-sulphide. A s before, syn7  thetic observations are made over two chargeability models; one w i t h the keel and one without. T h e difference between the two sets of observations w i l l indicate the sensitivity of the survey technique to the presence of the keel. T w o survey designs are examined for sensitivity to the keel: the Realsection survey design that was used i n the field, and the dipole-dipole survey design that was used i n Section 6.5.2. T h e success of a survey i n providing information about the keel depends on the strength of the signal from the keel and the noise levels encountered during the survey. Standard deviations of about 3 percent of the apparent chargeability plus a base level of 1 m V / V were assumed for the inversion of apparent chargeability data, and resulted i n a reasonable model w i t h some structure, but without too much noise. Therefore, a signal from the keel would have to be above this level i n order for it to add information to the inversion model. Here, the apparent chargeability is calculated by taking the ratio of secondary voltages to primary voltages. 7  Chapter  6.6.1  6. Forward Modeling  and Survey Design: Detecting  the keel  166  Simulating Realsection IP field observations.  Synthetic apparent chargeability data are first calculated for the survey layout that was used i n the field.  I  6  mV/V  mWV 2.5  -2275  -2033  -1792  -1550 Eastmg (m)  -1308  -1067  -825  (a) Synthetic line of Realsection apparent chargeability data collected over chargeability rock-model with the keel.  -2275  -2033  -1792  -1550 Easting (m)  -1308  -1067  -825  (b) Synthetic line of Realsection apparent chargeability data collected over chargeability rock-model without the keel.  (c) Difference between synthetic realsection apparent chargeability data from a chargeability rockmodel including the keel and synthetic apparent chargeability data from a chargeability rock-model without the keel.  Figure 6.19: Synthetic apparent chargeability Realsection data produced from chargeability rock-models of San Nicolas. T h e x-locations refer to the midpoint of the receiving dipoles, while the y-axis refers to different transmitter dipole positions; the b o t t o m line of data being collected w i t h the longest transmitter, and the top line of data being collected w i t h the smallest transmitter length. T h e difference between the two data sets is also plotted.  T h e synthetic Real-section array apparent chargeability d a t a are displayed i n F i g ure 6.19. T h e difference between data collected on models w i t h a n d without the keel range between -0.15 a n d 0.3 m V / V . T h e difference is below the prescribed noise level and so, if the noise levels are appropriate, this survey design would not be useful i n identifying the keel.  Chapter  6.  Forward Modeling  and Survey Design: Detecting  the keel  167  In an attempt to increase the chargeability signal from the keel, a dipole-dipole array configuration is examined using forward modeling methods. The results from this study are presented below.  6.6.2  Synthetic dipole-dipole chargeability observations.  Synthetic dipole-dipole I P surveys, conducted on two chargeability models (one w i t h the keel and one without), employ the same array configuration used i n the D C resistivity survey described i n Section 6.5.2.  The data, displayed as apparent chargeabilities i n  Figure 6.20, show values that range from -0.5 to 6 m V / V . The response of the keel is shown i n Figure 6.20(c) as the difference between data calculated using a chargeability model that includes the keel, and one that does not.  T h i s difference has values that  range from -0.5 to 1.1 m V / V . These data do not show a response from the keel that is above the noise level, however, they are larger than the response from the keel that was calculated for the Realsection I P data.  6.6.3  Summary of chargeability forward modeling  Forward modeling of apparent chargeability data over a chargeability rock-model, which is thought to represent that present at San Nicolas, shows that it is unlikely Realsection I P data contain information about the keel that is above the expected noise levels. Therefore, insight into the presence of the keel is not expected from inversion of this data. Forward modeling of dipole-dipole chargeability data shows that an increase i n response from the keel can be achieved, however, the amplitude is still below the expected noise levels. If the chargeability values have been appropriately assigned to the rockmodel and the expected noise levels are also approximately correct, then detecting the keel using I P methods may have to involve reducing the distance from the keel to either  Chapter 6.  •2394  -2136  Forward Modeling and Survey Design: Detecting the keel  1879  1622 Easting (m)  1365  1107  850  •2394  (a) Pseudo-section of synthetic dipole-dipole apparent chargeability data collected over a chargeability rock-model with the keel.  -2136  1879  1622 Easting (m)  -1365  168  -1107  850  (b) Pseudo-section of synthetic dipole-dipole apparent chargeability data collected over a chargeability rock-model without the keel.  °°. °\ **, \ "'. "", \ °\ *•, \ °% *',  I mV/V 0.3  1-0.5  2394  2136  1879  1622 Easting (m)  1365  1107  -850  (c) Difference between synthetic dipole-dipole apparent chargeability data from a chargeability rockmodel including the keel and synthetic apparent chargeability data from a chargeability rock-model without the keel. Figure 6.20: Synthetic apparent chargeability dipole-dipole data produced from chargeability rock-models of San Nicolas. The plotting convention used is shown in Figure 6.17. The difference between the two data sets is also plotted.  Chapter 6. Forward Modeling and Survey Design: Detecting  the transmitter, the receivers, or both.  the keel  169  T h i s could be achieved by placing electrodes  (transmitter or receiver electrodes) down a drill-hole. It should also be noted that the secondary voltages measured i n the dipole-dipole survey are about one tenth the magnitude of those measure i n the Realsection survey. T h i s could have implications regarding the signal-to-noise of the response of the keel as lower magnitude observations may be closer to ambient noise and instrument sensitivity levels. T h e examination of two survey layouts hardly constitutes a comprehensive analysis of how to detect the keel using I P methods, however, it has been shown that different types of surveys have different sensitivity to the keel and, should the rock-model values be correct, down-hole methods may be necessary.  6.7  Conclusions  Forward modeling, using physical property rock-models at the deposit scale, has shown that a l l of the data collected at San Nicolas could be sensitive to the presence of the keel, yet only the gravity data would show a response that is significantly above the noise level. T h i s is consistent w i t h the inversion results presented i n Chapter 4, i n which the gravity data produced a density distribution of the deposit that had an extension i n the vicinity of the keel, whereas the inversion of other data d i d not. T h e surveys employed i n the field were designed to detect anomalous structure and bodies of unknown location and geometry, and they were successful i n finding San Nicolas. However, changes to the survey design are needed when deep, small ore-bodies such as the keel are the target. To this end, different survey designs have been examined i n an attempt to increase the observable response of the keel. Insight has been provided about what data should be measured i n order to see the keel.  Chapter 6. Forward Modeling and Survey Design: Detecting  the keel  170  T h e target of the investigation i n a potential-field survey is also the source of the signal. For this reason there is a limit to the signal from the keel that can be measured at the surface. Consequently, down-hole methods may be needed. O n the other hand, changing transmitter-receiver geometries on the surface can improve the magnitude of the response from the keel, when active-source methods are used. However, down-hole methods also prove useful for active source measurements. T h e use of drill-holes i n geophysical surveys at San Nicolas is a logical progression i n the search for deeper ore as many drill-hole already exist. T h i s ties i n w i t h the over-all detailed exploration work that has been the motivation for this chapter. Information from drill-holes can be used to constrain inversion models i n the form of a priori information (Chapter 5 ) , or geophysical data can be collected down-hole. I have shown how b o t h of these methods can increase the level of understanding of the subsurface, and therefore, increase the efficiency of a deposit-scale exploration program. T h e issue of increasing the signal-to-noise of the response from the keel has been addressed by assuming noise levels and t r y i n g to increase the signal based on survey design. For a complete study i n this area the noise should also be investigated. Different sources of noise, and how absolute noise levels, and noise levels relative to the signal, change i n response to different survey designs could also be addressed. A s well as considering different survey designs of the methods used to collect data at San Nicolas, other methods may be more successful i n defining the keel, and investigation into such methods as M M R (magneto-metric resistivity) could prove fruitful.  Chapter 7 Conclusions and Suggestions for Further Work  T h e work completed at San Nicolas has successfully demonstrated how geophysical i n version can be used at various stages of an integrated exploration program. T h i s work has been presented w i t h i n the context of an exploration program that is summarized by Figure 7.1.  •  Geologic and geophysical exploration models were formulated and provided direction for the subsequent work at different stages.  Regional-scale inverse modeling allowed  conclusions to be made about physical property distributions of the earth. These models were given a geologic context through interpret ion using physical property measurements. The results provide important information for finding mineralization, and could have been used to direct follow-up work. Local-scale, forward and inverse modeling, along w i t h physical property analysis, has shown how geophysics can be used to define mineralization well enough to successfully spot drill-holes. T h i s leads to further iterations at the local scale that include using drill-hole information directly i n an inversion, and modifying surveys i n order to help delineation. T h e work completed has demonstrated how geophysical inversion can be used w i t h i n general exploration programs by providing information specific to S a n Nicolas.  171  Chapter 7. Conclusions and Suggestions for Further Work  172  Geophysical Exploration Flowchart  Exploration Goal: •find economic mineralization  Geologic Constraints; •existing mapping •historical workings  J Geologic | Models: *1  •tectonic setting  •descriptive model 'genetic model *=> Exploration Model  Geophysical .  e x p c c t c d  0  Physical Property Measurements  ftarget  Exploration .physical properties contrasts  Model: Regional Geologic Mapping: •Outcrop geology •Structure -Alteration  Existing Data  Regional/Reconnaissance Scale Geophysics:  Survey Design  Data Collection I  Gravity  AMAG (+/- ARAD)  AEM  I  IP  Other VLF, MT  Geochemistry •Stream sediments  Modeling Joint/Simullaneous  Individual  Inverse  Remote Sensing  Forward  Inverse  •Mu hi spectral •Hyperspcctral  Correlation  Conclusions abont physical property distributions of the the earth  Geologic Interpretation  Target Generation Terminate Exploration Program  I  Detailed Geologic Mapping: •Stratigraphy •Structure  Geologic Logging i  Additional Physical Property Measurements  Update Geophysical Exploration Model  Update Geologic Exploration Model Local/Deposit Scale Geophysics: Data Collection  Survey Design 1  1  Gravity  OMAQ  EM: CSAMT, UTEM  1  •  DDIP  Other: VLF  •Alteration  Trenching  4  Modeling Individual'  Rock samples •Assays •Alteration  Forward  mineralogy (X-ray/PIMA)  Joint/Simultaneous Forward  Inverse  Correlation  Conclusions about physical property distributions of the the earth  Geochemistry •soil surveys  Geologic Interpretation  Mineralization Not Intersected  Locate Drill-hole Physical property core measurements  Terminate Exploration Program  Down-hole methods Mineralization Intersected Delineation  Goal Realized: Mineral Resource  Figure 7.1: Geophysical Exploration Methodology Flowchart.  Chapter 7. Conclusions and Suggestions for Further Work  7.1  173  Summary of results  Regional-scale geophysical programs can cover a large amount of land i n a small amount of time. T h e information that they contain can often be more easily interpreted using geophysical inversion. Individually, the inversions can produce a number of targets for selection. In combination, the number of follow-up targets can be greatly reduced, thus focusing an exploration program faster than before. A t San Nicolas, regional-scale modeling has provided information about the extents of a horst, w i t h i n which the deposit is contained. T h e location and geometry of deep intrusives has also been modeled. These intrusives might have been the source of mineralrich hydrothermal fluids and are therefore important for future exploration efforts.  In  addition, faults have been defined that cross-cut the horst and contain tertiary volcanics. Discriminating between these faults and older faults w i t h i n the horst may be useful i n finding structures that control mineralization. Shallow dense and susceptible mafic intrusives have also been identified. C o m b i n i n g the results from several physical property distribution models has significantly reduced the number of targets. A p a r t from the deposit, other areas of mineralization were also detected. T h e potential of airborne E M methods to detect the deposit has also been investigated. W i t h b o t h the frequency-domain and time-domain methods only information regarding the upper 200m of the earth has been recovered.  Forward modeling of the  time-domain data, however, suggest that deeper conductivity information might be recoverable. Local-scale inversions can provide detailed information about the subsurface. T h i s can be used to guide drilling programs. Furthermore, feedback from drilling programs (in the form of physical property measurements on the core, or down-hole surveys) can be used  Chapter  7. Conclusions  and Suggestions  for Further  Work  174  to improve physical property inversion models and thus increase the success of drilling. T h e use of drill-core information i n such an iterative manner has been demonstrated. Deposit-scale modeling at San Nicolas has produced physical property models of the subsurface that correspond well w i t h the extents of the deposit.  T h e results confirm  the ability of geophysics to find the concealed deposit. T h e models produced also have geometric features that emulate those of the deposit. Details such as the extension of susceptibility material to the north and the concentration of chargeable material along the fault are also seen at this scale. Including drill-hole information w i t h i n inversions results i n improved models being constructed. T h e physical property range and the extent of the deposit are much better constrained w i t h the inclusion of one or more drill-holes. Physical property distributions away from the drill-holes also change, especially below the deposit.  T h i s allows the  possibility of more sulphide below the deposit to be a l l but discounted. T h e inclusion of drill-hole information i n an inversion addresses the discrepancy between physical property measurements made on small drill-core samples and the physical property values recovered i n inversion models. T h i s discrepancy comes from inability of a large model cell to adequately represent fine-scale structure. Furthermore, the types of models being constructed have to be consistent w i t h known information, that is smooth models should not be expected when sharp discontinuities have already been found. These issues have been addressed and possible solutions have been put forward, however much more understanding is required. T h e use of forward modeling to evaluate the sensitivity of survey designs has also been demonstrated. T h e availability of forward modeling routines allows for quantitative evaluation of survey designs that can lead to more appropriate surveys being conducted in the future.  W h i l e not addressing the topic of optimizing survey designs, ideas w i t h  which survey design can proceed have been introduced.  Chapter 7. Conclusions and Suggestions for Further Work  175  Forward modeling has shown that most of the geophysical survey designs employed at San Nicolas were probably inadequate to detect the keel. Simple survey design changes are suggested that, when evaluated using forward modeling techniques, show an increased sensitivity to the keel.  7.2  Further work  T h e work completed at San Nicolas has successfully demonstrated how geophysical i n version can be used at various stages of an integrated exploration program. In doing so, many issues have been raised that have not been conclusively resolved. Some of these issues form the basis of suggestions for further work. These are presented below: 1. T h e chargeable nature of the San Nicolas deposit and the forward modeling analysis that was performed, b o t h suggest that information about the chargeability of the earth could be contained i n the Questem, time-domain, airborne, electromagnetic data.  If such information could be recovered from the data this would be prove  valuable for two reasons. Firstly, the ability to search for chargeable bodies from the air could revolutionize regional exploration programs. Secondly, accounting for the chargeable material w i l l allow for more accurate, and possibly more deeply resolved, conductivity models to be generated. So how should these data be inverted? ColeCole parameters along w i t h a conductivity value could be recovered for each layer of a one-dimensional model. However, this would dramatically increase the number of unknowns i n the problem and non-unique combinations of the Cole-Cole parameters may be able to produce the same data. A d d i t i o n a l information may be required to recover unique solutions. T h e data could provide additional information if a l l three components of the magnetic field were to be inverted simultaneously. Furthermore, three-dimensional inversion of these data could provide more information and result  Chapter 7. Conclusions and Suggestions for Further Work  176  i n much more easily interpreted models. Some of these issues are already being addressed by the U B C - G I F group. 2. More accurate modeling of the deposit by inverting surface C S A M T data might have resulted i n less-drilling needed for the mineralization at San Nicolas to be defined. In order to define the deposit more accurately, three-dimensional inversion of C S A M T might prove useful. Forward modeling of C S A M T data suggested that only half of the recovered model accurately reflects the information encoded i n the data, so a full 3D inversion might better define the deposit. 3. For external information to be used to m a x i m u m benefit w i t h i n an inversion, methods of representing fine-scale physical property features i n a large model must be developed. B u t , how can this information be used to constrain an inversion? Is bounding the range of values a model cell can assume the best method, and how much detail should be accounted for? These are questions that certainly need much more investigation before we can produce models that are t r u l y consistent w i t h the earth. 4. T h e subject of survey design has only been briefly addressed i n this thesis.  The  advent of widespread inversion modeling allows for data to be collected that don't have to provide information at face-value. We can start to depend i n inversions to decode the desired information about the subsurface. O p t i m i z a t i o n techniques could be used to design surveys specific to the task-at-hand that w i l l be able to adequately resolve the earth where needed. T h i s w i l l hopefully reduce ambiguities i n the resulting models. Consequently, survey design is an important topic that could receive increased attention i n the future. W h a t does it mean for a survey to be well designed? Does it mean that the data are  Chapter  7. Conclusions  and Suggestions  for Further  Work  177  sensitive to the target, or does it mean that the data can adequately constrain an inversion that w i l l model the target? These are questions that need to be addressed at the survey design stage of each exploration program, and the answers w i l l depend on the type of subsurface information being sought. Other obvious questions that come to m i n d w i t h respect to survey design are the following: (1) How efficient are current survey designs at defining the subsurface? and,  (2) How might current survey designs be improved? T h e answers to these  questions w i l l depend on the application of the method, and may be contained w i t h i n the context of an exploration program, even w i t h i n this thesis. A s better survey design methods are developed, surveys could become much more tailored to the exploration stage at which they are implemented. T h i s would result'in an even more integrated and efficient exploration program.  References  [Backus and Gilbert, 1968] Backus, G . , and Gilbert, F . , The resolving power of gross earth data, Geophysical Journal of the Royal A s t r o n o m i c a l Society, 16, 169-205, 1968. [Brescianini et al., 1992] Brescianini, R . F . , Asten, M . W . , and . 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[Telford, Geldart, and Sheriff, 1990c] Telford, W . M . , Geldart, L . P., and Sherrif, R . E . , Applied  Geophysics,  Chapter 9, second edition, Cambridge University Press, 1990.  [Walker, 1999] Walker, S. E . , Inversion  of EM data to recover 1-D conductivity  and a  geometric survey parameter, U B C masters thesis, 1999. [Ward, 1966] W a r d , S. H . , Introduction  to the search for massive sulphides, M i n i n g Geo-  physics - Case histories, Society of Exploration Geophysics, 1966. [Ward and Hohmann, 1987] W a r d , S. H . and Hohmann, G . , W . , Electromagnetic for geophysical applications  in Electromagnetic  theory  Methods in applied geophysics, vol-  ume 1, ed. Nabighian, M . , Society of E x p l o r a t i o n Geophysics, Tulsa, Oklahoma, p 131-311, 1987. [Weidelt, 1982] Weidelt, P., Response characteristics  of coincident loop transient  magnetic systems, Geophysics, 47, 1325-1330, 1982.  electro-  References  183  [Zonge and Hughes, 1987] Zonge, K . L . and Hughes, L . , J., Controlled frequency magnetotellurics  in Electromagnetic  source  audio-  Methods in applied geophysics, vol-  ume 2, ed. Nabighian, M . , Society of E x p l o r a t i o n Geophysics, Tulsa, Oklahoma, p 713-809, 1987.  Appendix A Coordinate System  M a n y of the data at San Nicolas were collected using different coordinate systems. I n order to compare and analyze different data sets and models w i t h one another, a standard coordinate system was chosen to which all other data were transformed prior to modeling. T h e system chosen was a local coordinate system that has a north orientated about 23 degrees to the east of true north. T h i s coordinate system was chosen because the geologic sections and much of the data provided by Teck were already i n this coordinate system. Drill-hole fences were also orientated w i t h the local grid. T h e airborne data was provided i n the U T M (Universal Transverse Mercator) Zone 13, N A D ( N o r t h A m e r i c a n D a t u m ) 27 coordinate system. A s a result a transformation is presented that was used for converting from U T M (meters) to the local grid system, also i n meters. T h i s is accurate to w i t h i n a few meters at the deposit.  X  L  O  C  =  (cos(-0.36824) * (X  - 192432)) + (sin(-0.36824) * (Y  VTM  UTM  - 2500845)) - 2000 (A.1)  Y  LOC  = -(sin(-0.36824) * (  X  U  T  M  -  192432)) + (cos(-0.36824) * (Y  UTM  - 2500845)) - 400 (A.2)  In the above equation,  XLOC  is the local x-coordinate,  Yhoc is the local y-coordinate, and Y  UTM  XUTM  is the U T M x-coordinate,  is the U T M y-coordinate.  184  Appendix B Physical Property Analysis  B.l  Introduction  Physical property measurements are essential i n order to choose which survey to use and to interpret the results of a survey. T h i s section presents the physical property values that were used i n the work carried out at San Nicolas. T h e physical property rock-models are constructed using these physical property values. These rock-models were used for forward-modeling performed i n the thesis.  B.2  Density  T h e density values that were used for i n this thesis come from density measurements performed by Quantec on a variety of rock-types at San Nicolas, from density measurements that were made mainly on the ore for mine tonnage estimates, and, where specific rock-type information was insufficient, from physical property values found i n Applied Geophysics, [Telford et al., 1990]. Density measurements are summarized below.  B.3  Magnetic Susceptibility  T h e magnetic susceptibility ( M S ) of a volume of rock is a function of the amount of magnetic minerals, (mainly magnetite and pyrrhotite), contained w i t h i n the rock. These measurements can be interpreted to reflect lithological changes and the presence of alteration zones.  185  Appendix B. Physical Property Analysis  Rock-Type  186  Density (g/cm ) 1.8 - 2.2 2.1 - 2.5 2.2 - 2.6 2.5 - 2.9 2.5 - 3.0 3.5 - 4.3 3.0 - 3.5 2.8 - 3.3 2.2 - 2.6 2.3 - 2.6 2.6 - 3.0 2.2 - 2.6 3  Quarternary Alluvium. Tertiary Breccia Volcanoclastic sediments, tuffs, and flows Mafic flows and breccias Mafic flows and sediments Massive sulphide Semi-massive sulphide Sulphide stringer zone Rhyolite flows and breccias Quartz feldspar rhyolite Mafic volcanics and siliceous sediments Graphitic Mudstone  Table B . l : Range of densities for rock-types encountered at San Nicolas.  B.3.1  Magnetic Susceptibility core measurements  Over one thousand magnetic susceptibility measurements were collected on core samples from thirteen drill-holes. These measurements are summerize i n Figure B . l for each rock type.  Reliability of measurements Susceptibility measurements were collected mostly on three to four meter intervals along the drill-core (except i n areas of high susceptibility variation). Tests were conducted on four different sections of core to see how well these coarse measurements sampled the detailed susceptibility features. Results for two test sections of core are presented i n the following plots (Figures B . 2 and B . 3 ) . T h e graphs show the original coarsely-sampled measurements and measurements made at finer intervals. T h e results show that the coarse measurements are probably adequately representative of the true susceptibility  Appendix  B. Physical Property  Tertiary volcaniclastic breccia  Analysis  187  mean •(). 19 mean=l .42  Upper mafic volcanics  464  mean=23  Massive sulphides  „  Quartz rhyolite  mcan=(). 12 mean=0.21  Lower mafic volcanics  mean 0.09 j  Mudstone  O O o  o ©  o o  o o o  log scale [xlO S.L] 3  10  Figure B . l : T h e range of susceptibility values for different rock types at S a n Nicolas.  Appendix  B. Physical Property  Analysis  188  distribution.  Figure B . 2 : Coarse and fine magnetic susceptibility measurements on a section of core from drill-hole S A L 70.  B.3.2  Composite models for bounding inversions  For magnetic susceptibility measurements that were not evenly sampled, the mean would not be a representative value of several readings as it would be bias towards the areas where more readings were taken.  W h e n calculating values to assign large cells from  twenty or so unevenly sampled measurements, each measurement was weighted based on the amount or core that it might represent.  A depth interval was determined for each  measurement that corresponded to the distance between two midpoints. T h e midpoints were half way between the location of the measurement i n question and the locations of the two measurements immediately above and below. E a c h measurement was weighted based on the length of this interval. It is hoped that this is an improvement over the mean but it is realized that this method is still somewhat ad hoc. T h e composite model  Appendix B. Physical Property Analysis  189  Original measurements  1000  GO • r*"i t  100  10 o  1 0.1  '£  0.01  * r-l  o uo  CN IT)  CO  w  Tf  in •<r  in in  CO  in  in  oo in  m  o  CO  <U  o  Depth [m]  CO  Figure B . 3 : Coarse and fine magnetic susceptibility measurements on a section of core from drill-hole S A L 34. produced for S A L 25 using this method is shown i n Figure B . 4 along w i t h the actual measurements.  B.4  Resistivity  O n l y twenty-one resistivity measurements were available for different rock-types at San Nicolas. T h e measurements are summerize below but it should be noted that they may not be representative of the values for each rock-type.  B.5  Chargeability  A s w i t h the resistivity values only twenty-one chargeability values were available. These did, however, sample a l l of the rock-types i n the area.  Appendix  B.  Physical Property  Analysis  190  100  5  10 1  •B * <D  '—'  o_  %  • o  |  0.1  c  e  0  a  » e  pr  »  «  «  8 1  • «r—  «a  <  o  8  o  0.01 100  200 •  Depth [m]  300  400  500  Core M e a s u r e m e n t s — C o r e Composite  Figure B . 4 : Magnetic susceptibility measurements made on core from drill-hole S A L 25. A composite model was produced that was used to constrain inversion of ground magnetic data.  Rock-Type Quarternary A l l u v i u m Tertiary Breccia Volcanoclastic sediments, tuffs, and flows Mafic flows and breccias Mafic flows and sediments Massive sulphide Semi-massive sulphide Sulphide stringer zone Rhyolite flows and breccias Quartz feldspar rhyolite Mafic volcanics and siliceous sediments Graphitic Mudstone  Resistivity (Ohm-m) 30? 15-30 80 80 80 20 20 30 100 100 80 100-200  Table B . 2 : Range of resistivities for rock-types encountered at San Nicolas.  Appendix  B. Physical Property  Analysis  Rock-Type Quarternary A l l u v i u m Tertiary Breccia Volcanoclastic sediments, tuffs, and flows Mafic flows and breccias Mafic flows and sediments Massive sulphide Semi-massive sulphide Sulphide stringer zone Rhyolite flows and breccias Quartz feldspar rhyolite Mafic volcanics and siliceous sediments Graphitic Mudstone  191  Chargeability (msec) 5 - 10 10 - 30 10 - 100 30 - 80 30 - 50 500 400 500 10 •- 20 100 50 -- 80 30 -70  Table B . 3 : Range of chargeabilities for rock-types encountered at San Nicolas.  B.6  Physical property rock-models  A geologic rock-model was constructed by digitizing five geologic sections (four east-west sections and one north-south section). T h e sections were digitized on a 25m x 2 5 m grid and each grid cell was assigned a rock-type based on the predominant content of each cell. In order to construct the physical property rock-models the digitized geology was assigned values based of the physical property data base presented above. T h e n an routine was used to interpolate between sections and construct a three-dimensional model. interpolation routine used triangulation between cells at the same depth.  The  For density  and chargeability values a linear interpolation was used, for magnetic susceptibility and resistivity the Logio of the value was interpolated. T h e resulting models were used for the keel sensitivity and survey design analysis presented i n Chapter 6. Figure B . 5 shows the conductivity physical property rock-model that was used to forward model Questem, C S A M T , and D C resistivity data.  Appendix  B.  Physical  Property  Analysis  192  Figure B . 5 : Perspective view of the physical property conductivity model used for test forward modeling i n Section 4.4.1. The model has been sliced at -400m N to expose a north-facing cross-section.  Appendix C Geophysical methods  M a n y different geophysical methods were used i n the collection of data at San Nicolas. T h i s section gives a brief overview of each method and the underlying physical principles, and also defines the data collected i n each case.  Cl  The gravity method  G r a v i t y surveys involve measuring local irregularities i n the earth's gravitational field w i t h the a i m of using these measurements to determine subsurface density variations. Newton's law of gravitation describes the force of attraction that acts at a distance between two masses. In the example shown i n Figure C l , the force exerted by mass m  2  on m\ [Fix] is expressed as:  Pn =  (Cl) \r - riY 2  where G is the universal gravitational constant (6.672 x 1 0 ~ N m / k g ) , u  are masses, f is the unit vector from m i to m , and f i and f 2  2  2  2  mi to m  2  are displacement vectors  from the origin of m i to m respectively. Furthermore the acceleration, or force per unit 2  mass, due to m at a point, defined by f i can be stated as: 2  m) = 193  (c.2)  Appendix  C.  Geophysical  methods  194  Figure C l : T h e force exerted on mass m\ by mass m  2  is i n the direction of f.  B y rewriting f as j ^ f ^ p - , equation C.2 becomes:  7m (r - r p |f - f J 2  2  (C.3)  3  2  Due to superposition, for a number of point masses the gravitational acceleration can be written as:  9(ri) =  - 7  |fi-fi|  (C.4)  3  and, for a distributed density function pfa), equation C.4 can be stated as the following volume integral:  =  f p(F )(r 2  Jv  2  -Wo  \r2-n\  s  Gravimeters use a mass on a zero-length spring to measure the vertical component of the gravitational field as stated below. The usual unit of measurement of the gravitational field is m G a l , and most ground gravimeters are accurate to w i t h i n about 0.01 m G a l .  Appendix  C. Geophysical  methods  (fi) =  195  - 7  /  Jv  p(r )(z2 - zi)dv 2  \f - r i |  3  (C.6)  2  T h r o u g h the removal of regional fields (the field due to a background density distribution) we can focus on the gravitational field due to density variations w i t h i n our depth of exploration.  (C.7) These data are typically collected at the surface of the earth on a 2D grid, and G P S is often used to accurately locate each gravity measurement to w i t h i n a few centimeters, thus providing information about r i and z\. T h e forward modeling operator used i n the inversion procedure comes from the evaluation of E q u a t i o n C . 7 through discretization of the density distribution.  C.2  The magnetic method  L o c a l variations i n the earth's magnetic field are measured at the surface i n order to gain information about subsurface magnetic susceptibility distributions.  When a  magnetizable b o d y is placed i n an external magnetic field it is magnetized by induct i o n [Telford, Geldart, and Sheriff, 1990a]. T h e amount that the b o d y is magnetized is measured by the magnetization vector J , which is the dipole moment per unit volume. For this work we neglect the possible effects of remanent magnetization and demagnetization. We assume relatively small inducing magnetic fields, i n which case the magnetization vector is proportional to, and is i n the same direction as, the inducing field H, thus:  Appendix  C. Geophysical  methods  196  (C.8)  J = KH,  where H = B/u-o (no being the magnetic permeability of free space), and K is the degree to which a body is magnetized by the inducing field or magnetic  susceptibility.  Most of the minerals which exhibit this type of magnetism are described as  ferrimagnetic.  These minerals have positive and relatively large susceptibilities. In the magnetic experiment the inducing field is the earth's m a i n magnetic field (Ho), which is produced mainly by processes occuring i n the outer core. T h e intensity of the total magnetic field resulting from the superposition of the m a i n field and the secondary field is measured during magnetic surveys (Equation C.2).  B  =  B  +  0  Bc  (C.9)  T h r o u g h processing, the m a i n field is removed and the secondary, or anomalous field is derived. T h e anomalous magnetic field due to a distribution of magnetizable materials can be stated as  [Telford, Geldart, and Sheriff, 1990a]:  S (r ) s  0  = ^  f  4TT JV  \r -r\  (CIO)  0  It is this field (measured i n Teslas, [T]) that is inverted i n order to obtain a subsurface susceptibility distribution, K(T). D a t a are usually presented i n nano-Tesla (nT). T h e strength and orientation of the inducing field (Ho) must be known for inversion of this data.  T h i s is obtained by using the program G E O M A G , written by J o h n M .  Appendix  C. Geophysical  methods  197  Q u i n n of the U S G S . G E O M A G used numerical models of the earth's magnetic field to calculate inclination, declination, total intensity, and their associated rates of change given a location and a date [Quinn et al., 1995].  C.3  Inductive electromagnetic methods  Inductive electromagnetic methods actively energize the ground using a time varying current source. T h e current source produces a magnetic field that interacts w i t h conductive material i n the earth, causing electric fields and inducing currents i n the ground. T h e currents i n the earth also produce a magnetic field. T h e magnetic fields emanating from the current i n the transmitter are termed primary fields, while the magnetic fields emanating from the current i n the ground are termed secondary fields.  B o t h of these  magnetic fields induce a voltage i n the conductive receiver loop but it is the secondary fields that provide information about the conductivity of the subsurface.  C.3.1  Frequency-domain electromagnetic airborne surveys.  T h e D i g h e m frequency-domain airborne system produces an alternating current waveform i n the transmitter loop and operates at several different frequencies. T h e sources and receivers are small loops that are towed behind the aeroplane (away from electromagnetic interference).  In this system the loops are orientated i n two configurations:  horizontal co-planar, and co-axial, as shown i n Figure C . 2 . T h e position of the receiver relative to the transmitter is kept constant throughout the survey. W a r d and H o h m a n n [Ward and Hohmann, 1987], derive an expression for the voltage generated i n the receiving loop when a sinusoidal current waveform is produced i n the transmitter loop and the system is i n the presence of a conductive body:  Appendix  C. Geophysical  methods  198  Figure C . 2 : Orientation of transmitter-receiver quency-domain airborne electromagnetic system.  V = -iun NA(H?{s) Q  ( T x - R x ) pairs i n the Dighem fre-  + # f (w, a, h, s))  (C.ll)  where cu is the frequency, no is the magnetic permeability of free space, iV is the number of turns i n the receiver coil, A is the area of the receiver coil, H^(s)  is the vertical  component of the p r i m a r y magnetic field at the center of the receiver coil (and is a function of the separation, s, between the transmitter and receiver coils), and H^(u>, cr, h, s) is the vertical component of the secondary magnetic field at the center of the receiver coil, which is a function of frequency, conductivity (tv), height above the ground (h), and transmitter-receiver separation. Normalization of this voltage by the transmitter current, and the representation of the signal as a ratio of the voltage generated i n the presence of a conducting b o d y to that generated i n free space, allows the measured value to be written as:  #f(LJ, LT, h, s)  (C.12)  Appendix  C. Geophysical  methods  199  In order to remove the effect of the primary field a bucking coil is used. T h i s is an additional coil that measures the primary field at the receiver and allows the secondary field to be calculated. Because the secondary magnetic field is not i n phase w i t h the p r i m a r y magnetic field, equation C.12 can be represented as inphase and  quadrature  components, and are usually given i n parts per million of the p r i m a r y field.  C.3.2  Time-domain electromagnetic airborne surveys  In the time-domain method, a current is passed through the transmitter loop and abruptly interrupted. According to Faraday's law, this change i n primary field w i l l induce currents i n nearby conductors. A s time progresses the currents diffuse from the surface of the conductors inwards [Nabighian and Macnae, 1987] and, for a conducting half space, downward and outward i n a smoke-ring  effect [Nabighian, 1979]. Currents w i l l persist  and propagate slower i n conductors, and diminish and propagate faster i n resistors. T h e secondary magnetic fields emanating from the induced currents generate an electric field and induce a voltage i n a conductive receiver loop. These voltages are measured as a function of time. A s a result, a slower decay the voltage w i l l indicate the presence of a conductor. T h i s is the classic use of time-domain electromagnetic systems. Measurements are made by integrating the voltage over time-windows following the turn-off of the current source.  T h e absence of a primary field during recording time  (current off-time) allows for small signals to be measured, although some systems also measure during the on-time. T h e data can be presented as: dB/dt  for different times,  the secondary, magnetic field as a function of time normalized by the transmitter current, or as voltages induced i n the receiver loops. The transmitter is six turns of wire making a 186 square-meter loop, which is slung around an aeroplane (Figure C.3). W h e n the peak current of about 500 A m p s is reached the transmitter has a magnetic moment of half a million A m p s per square-meter.  The  Appendix  C. Geophysical  200  methods  Figure C . 3 : Diagrammatic sketch of the Questem time-domain electromagnetic system. T h e transmitter ( T x ) is placed around the aeroplane while the three-component receiver (Rx) is towed i n a bird behind the plane. G P S positioning is used to measure the position of the receiver relative to the bird. current waveform refers to the behavior of the current i n the transmitter as a function of time. T h e Questem system uses a periodic half-sine waveform alternating i n polarity to produce the p r i m a r y magnetic field (shown i n Figure C . 4 ) . T h e current is turned on for 4 msec and recordings are made by the three-component receiver over 15 subsequent time-windows during the 16 msec following the termination of the current pulse.  The  time-windows increase i n w i d t h over the recording period so as to improve signal-to-noise i n later times.  C.4  C S A M T surveys  T h e controlled source audio-frequency magnetotelluric ( C S A M T ) method is an electromagnetic technique that uses a grounded horizontal electric dipole source to directly inject currents into the ground. These currents produce magnetic fields that induce electric fields i n the ground as the signal propagates from the source. It is assumed that the fields are measured far from the source (far-field ) i n which case the incident fields should 1  The far-field zone is defined as the region in which the source-receiver separation is much greater than the skin depth. The skin depth is approximately stated as 6 = 503 [m], where p is the resistivity, :  and / is the frequency.  Appendix  C. Geophysical  methods  201  Figure C.4: T h e current i n the transmitter as a function of time for 50% of one cycle. Receiver measurements are made over 15 time-windows during the 16 msec following the current on-time.  Appendix  C. Geophysical  202  methods  be planar i n nature. Components of the electric and magnetic field are measured at a number of frequencies i n the audio range (0.1 H z to 10kHz) (Figure C.5). Perpendicular, horizontal electric and magnetic field values are used to calculate apparent resistivity and phase change at different frequencies. Zonge and Hughes, [Zonge and Hughes, 1987], provide a detailed description of the C S A M T method and derive an expression for far-field apparent resistivity (Equation C.13).  Pa  E 2nu.f Hi, x  [Sim]  (C.13)  T h e phase difference between the electric and magnetic fields i n E q u a t i o n C.13 is also calculated and is measured i n milli-radians (mrads).  Figure C . 5 : Diagrammatic sketch of the C S A M T survey layout (modified from Zonge and Hughes).  Appendix  C.5  C. Geophysical  methods  203  D C resistivity methods  Resistivity methods involve injecting a current into the ground using grounded electrodes, and measuring the voltage between electrodes i n the current flow (in Volts). T h e current, the voltage, and the geometry of the survey are measured so apparent resistivity values can be calculated.  B  A v, v v v  M  2  3  4  v v v 5  6  7  v  w  8  1 1  \  f  -XFigure C.6: "Real-section" array geometry used for collection of D C resistivity and I P data i n Section 4.5.  For the array configuration show i n Figure C.6 the apparent resistivity calculated from measurement V would be: 2  Pa =  2nVo I  [(rl  ~~ r?) ~ (r3  ~~ ri)]  where V 2 is the measured potential, / is the current applied using electrodes A and B , and r i through r are the distances shown on the diagram. 4  In formulating the forward modeling, we start w i t h O h m ' s law:  J =  1E P  T h e electric field, E, is the gradient of the scalar potential V so we can write:  (CU)  Appendix  C. Geophysical  methods  204  E = - W  (C.15)  W i t h the above equation, and assuming that no charge accumulates ( V • J = 0), we can write an equation that relates the resistivity, the potential and the current sources for a homogeneous medium:  V •( - W ) = /  (C.16)  P  T h i s equation is the basis for the D C resistivity forward modeling that is used i n the inverse modeling — D C I N V 3 D (Section E.7). DCINV3D  constructs a resistivity distrib-  u t i o n that w i l l fit the potential data to a specified degree, given the source locations.  C.6  IP methods  Induced polarization occurs when a current is applied to the earth and there is an accumulation of charged particles i n the pore fluid due to either the presence of metallic minerals, clay minerals or graphite, or restrictions i n the pore itself. T h e ability for a material to accumulate these charges is summarized by its chargeability. A s a current is applied, the voltage measured across a pair of electrodes increases very fast initially ( V p i n Figure C.7) and then more slowly (logarithmically) u n t i l a m a x i m u m voltage ( V T ) is reached at a finite time [Telford, Geldart, and Sheriff, 1990c] . 2  T h i s slow increase i n overvoltage corresponds to a build up of charges either side of a pore restriction, much the same way a capacitor charges up. T h i s is known as electrode polarization and occurs i n the presence of metallic minerals. When the applied current is interrupted, a slow decay is also observed after an initial rapid voltage reduction. 2  Appendix  C. Geophysical  methods  205  A  VP  t Figure C.7: T h e voltage response as a function of time, V(t), i n the presence of polarizable material, when a current is suddenly applied and then interrupted. Vr = Vp + VsMeasurements can be made i n the time-domain or frequency domain. T i m e - d o m a i n measurements made at San Nicolas involved calculating the area under the decay curve between two times (ti and t i n Figure C.7) and normalizing it by the total voltage ( V r ) . 2  In this case chargeability is defined i n Equation C.17, and is measured i n units of time (usually milli-seconds).  V(t)dt  (C.17)  ti  T h e data are converted to secondary voltages ( V s ) and forward modeling is carried out using the D C resistivity forward modeling (Equation C.16), but w i t h the conductivity (a) replaced w i t h CT(1 — 77) [Seigel, 1959b]. Where 77 is the chargeability . T h i s produces 3  large secondary voltage for high chargeabilities and small secondary voltages when 77 is small.  The chargeability, 77 for this formulation is dimensionless and less than one. The apparent chargeability data rj can be the ratio of V s to V p (also dimensionless and usually small). Alternatively M in Equation C.17 could be used (in units of seconds), in which case the modeled values of 77 are also shown in units of time. 3  a  Appendix D  D a t a  D a t a used for the inversions are presented i n this section.  T h e survey specifications  associated w i t h each data set are given i n Table D . l . W h e n inversions are performed the predicted data does resemble the observed data to the specified requirement, and often look identical upon first inspection. D a t a misfits are presented i n Table E . l , however, the predicted data are not displayed themselves.  206  Appendix  D.  207  Data  10000  -15000 -10000  -5000  0  5000  10000  15000  Easting (m)  2220  m  2389  Figure D . l : Topographic elevations at San Nicolas. A p a r t from a h i l l at 6000m east, -1000m north, most of the area exhibits small topographic elevation changes. Outlines of outcropping geology are also shown, along w i t h a hammer and pick symbol that locates San Nicolas at about -1700m east, -400m north.  Appendix  D.  Data  208  15000  10000  -10000  -15000 -10000  -5000  0  5000  15000  10000  Easting (m)  -u  mGal  ll  Figure D . 2 : G r o u n d gravity observations. The data are the original data supplied by Quantec but w i t h observations on the hill removed. T h e topographic map shown i n Figure D . l indicates a h i l l w i t h over 100m of elevation gain. Initial inversions produced a large negative anomaly coincident w i t h the hill. Observations made on and close to the h i l l were subsequently discarded, although the affects of the h i l l probably persist i n some of the remaining observations i n this area. A constant was also removed from the data i n order to remove a first order regional field. A n outline of outcrop geology is overlain.  Appendix  D.  209  Data  -15000 -10000  -5000  0  5000  10000  15000  Easting (m)  -50  nT  50  Figure D . 3 : Dighem airborne total-field magnetic data, The deposit is identified by a subtle anomaly at -1600m east, -400m north.  Appendix  D.  Data  210  Figure D.4: Subset of Dighem airborne total-field magnetic data used for inversion.  Figure D . 5 : Falcon airborne total field magnetic data.  Appendix  D.  Data  Figure D.6: Questem airborne total field magnetic data.  211  Appendix  D.  212  Data  10000  -5000  -15000 -10000  -5000  0  5000  10000  15000  -10000  -5000  0  0  15000  (b) Inphase coaxial 1046Hz  (a) Inphase coplanar 466Hz  -5000  10000  ppm  ppm  -10000  5000  Easting (m)  Easting (m)  5000  10000  15000  -10000  -5000  0  5000  10000  15000  Easting (m)  Easting (m)  ppm  (c) Quadrature coplanar 466Hz  PPm  (d) Quadrature coaxial 1046Hz  Figure D . 7 : In-phase and quadrature frequency-domain electromagnetic data from the Dighem system: 466Hz, 1046Hz, and 6495Hz  Appendix  -10000  D.  -5000  Data  0  213  5000  10000  15000  -10000  -5000  Easting (m)  0  5000  10000  15000  Easting (m)  ppm  ppm  (a) Inphase coplanar 7206Hz  (b) Inphase coplanar 56kHz  15000  -5000  0  5000  10000  Easting (m)  15000  -10000  -5000  0  5000  10000  15000  Easting (m)  ppm  (c) Quadrature coplanar 7206Hz  ppm  (d) Quadrature coplanar 56kHz  Figure D . 8 : In-phase and quadrature frequency-domain electromagnetic data from the Dighem system: 7206Hz and 56kHz  Appendix  D.  Data  214  Questem Z-component data along flight line over the deposit. — Ch. 1 g  -Ch. 2  20000  -  Cin 15000  Ch. 3  - Ch.4 • - Ch. 5 105 118  131  144 157 170 183 196 209 222 235  Ch. 6  -  • - Ch. 7 Ch. 8 Ch. 9 C h . 10  1  14  27  40  53  66  79  92  105  118  131  144  157 170 183  196 209 222 235  C h . 11  -  - C h . 12  C h 13 C h . 14 C h . 15 1 N  14 E  27  40  53  66  79  92 - 105 118  131  144  Station #  157 170  183  196 209 222 235  SW  Figure D . 9 : Profiles of Z-component d B / d T data for the fifteen time-channels (0 to 18 msec after the transmitter current turn-off) along line 10. T h e upper panel shows data for the first five time channels, the center plot shows data for the next five time channels, and the lower panel shows data for the last five time channels. T h e deposit corresponds to the strong negative response i n the later time channels. Thirty-one lines of data were collected and different orientations but this is the only line that has been presented.  Appendix  D.  Data  215  Figure D . 1 0 : Gradient A r r a y I P data. T h e boundary shows the extension of the survey measurements and a n outline of outcropping geology is also shown.  Appendix  D.  Data  216  Figure D . l l : Reduced gravity data. T h i s gravity data has had a regional signal removed. T h e resulting gravity data were used for producing the density-contrast models i n Chapters four and five.  Observed Magnetic Data 614 data. I = 50.6, D = -13.4  •2206  -2005  -1804  .  .  i  1603 Easting (m)  1402  1201  nT -1000  Figure D.12: Total-field ground magnetic data. The observation locations are displayed and the gaps where the data have been discarded are clearly visible.  Appendix  D.  Data  Line  217  Observed  Predicted • 3200 Ohm-m ..li  mati  300  Figure D.13: Observed and predicted C S A M T apparent resistivity data. T h e d a t a was collected along three lines, -200m N , -400m N , and -600m N . E a c h d a t a plot is plotted w i t h frequency i n the vertical axis (ranging from 8192Hz at the t o p to 0.5Hz at the bottom), and easting along the horizontal axis (-2200m E to -800m E ) .  Figure D.14: Observed and predicted C S A M T phase data. E a c h data plot is plotted w i t h frequency i n the vertical axis (ranging from 8192Hz at the top to 0.5Hz at the bottom), and easting along the horizontal axis (-2200m E to -800m E ) .  Appendix  D.  Data  218  Figure D.15: Observed and predicted "Realsection" IP data. Five rows of data are plotted i n each panel. E a c h row corresponds to a different transmitting dipole length.  ix D. Data  219  L  O* CD 0 O  5  E S So g <a  >H E lo g  cn  I9 ^  O "t*  » B U S  ,.br  Q.E ID  - CM  ' K <tf  *  ID  3  3 6 » 3  «3 co  o £ o H ° 'x —  £  D  tf  3  «S g 3  m O  tf  tf  tf  'SEE %g £  ^  *t 0>  18";  QE  E » o .5  B ioE E o 3  Z oD  CB  o «  E  0  O  D  QJ  U  kafe  ,B  k  «3  E  0 «j K S E  £S o <u  Appendix E Inversion  In the body of the thesis the models have been presented without much discussion. Here we provide a brief description of the inversion codes used and the parameters that were used to invert each data set.  E.l  Introduction  T h i s section gives a brief overview of the algorithms used for the inversions performed i n this thesis. A l l of the algorithms were developed by the U B C Geophysical Inversion Facility under the auspices of the J A C I (Joint and Cooperative Inversion of Geophysical and Geologic Data) research consortium.  E.2 GRAV3D  GRAV3D is a collection of algorithms that are used for forward modeling, inversion, and  sensitivity calculations of gravity data. T h e developers of this code give a description of the method i n [Li and Oldenburg, 1998b]. A s described i n Chapter 2 the earth model is discretized into a large number of cuboidal cells of constant density contrast. T h e user can design a range of model objective functions to be minimized, subject to fitting the data, i n order to recover a density contrast distribution. Functionality allows for incorporating a priori information through the weighting of reference models, and bounding the range of values that cells can assume. A depth weighting scheme that counteracts the natural  220  Appendix  E.  Inversion  221  decay of the gravitational source is needed to invert data that has no inherent depth information.  T h i s allows a l l parts of the model to play a role i n the solution, and  therefore introduces depth information into the model.  E.3  MAG 3D  MAG3D  is a suite of algorithms used to invert surface and borehole magnetic responses  due to a three-dimensional susceptibility distribution ([Li and Oldenburg, 1997]). W h e n susceptible material is magnetized by an inducing field, an anomalous magnetic field is produced that is superimposed on the inducing field. T h e anomalous, or induced, magnetic field is inverted to recover the susceptibility distribution. T h e algorithms assume that susceptible values are small (a reasonable assumption for most geologic scenarios w i t h paramagnetic materials), and that no demagnetized or remnantly magnetized material is present. Residual magnetic field observations, locations and error estimates, the inducing field orientation and strength is required for inversions to proceed. T h e objective function minimized i n the inversion allows various types of a priori information to be included. In addition weighting is incorporated to distribute the susceptibility i n depth. Wavelet transforms are used for compression of the sensitivity matrix, allowing for faster run-times. T h e logarithmic barrier method ensures that positive magnetic susceptibility values can be enforced and bounds can be applied.  E.4  CSAMT1D  CSAMT  ID is a near- and far-field 1-D code for recovering conductivity structures from  E and H field components or the derived quantities of apparent resistivity and phase. T h e source electric field is a grounded loop and measurements are made i n the frequency domain.  Appendix  E.5 EM1DFM  E.  Inversion  222  EM1DFM inverts airborne or ground E M frequency-domain data to recover simultane-  ously, or separately, 1-D conductivity and magnetic susceptibility models. T h e source and receivers are represented by magnetic dipoles. T h i s is a fast inversion routine and can be applied to regional data sets ([Farquharson and Oldenburg, 2000]).  E.6 TEM1D  TEM1D is used to invert the vertical component of the time-domain electromagnetic  data to recover a layered earth model. T h e forward modeling is done i n the frequency domain and a frequency-to-time transform performed at each iteration.  E.6.1  Complex forward modeling  A modified version of the T E M 1 D forward modeling code was used to produce the response over a polarizable layered earth. The Cole-Cole parameters are used to define the frequency-dependence of the model.  E.7 DCIP3D  DCIP3D is a suite of algorithms that are used to invert D C resistivity and I P data.  The algorithms can be used i n sequence.  First, DCINV3D  is used to invert D C re-  sistivity data (primary potentials) and produce a resistivity distribution of the earth. Secondly, the resistivity model can be used to calculate sensitivities for the I P inversion ( I P S E N 3 D ) . T h i r d l y I P data (secondary potentials) can be inverted using I P I N V 3 D . T h e model objective function is designed to allow for a priori information to be included.  Appendix  E.8  E.  Inversion  223  EH3D  EH3D is a forward modeling algorithm that computes frequency-domain electromagnetic responses from 3D conductivity and/or susceptibility distributions. T h e conductivity and susceptibility distributions can exhibit large discontinuities. T h e source can be either a grounded electric source, a loop source, or a plane-wave. Real and imaginary parts of electric and magnetic fields due to the source are computed over the whole model for different frequencies.  E.9 EH3DTD  EH3DTD is a forward modeling algorithm that computes time-domain electromagnetic  responses from a given 3D conductivity structure of the earth. A s w i t h the frequencydomain equivalent a number of different sources can be used and the electric and magnetic fields are calculated throughout the whole model region. Different time-dependent waveforms can be used as sources.  MAG3D  MAG3D  MAG3D  MAG3D  GRAV3D  MAG3D  airborne  airborne  Dighem magnetics  BHP magnetics  Fugro airborne magnetics  Gradient-array IP  Local scale gravity  Ground magnetics  862580  338640  426400  446684  222880  4nT  1 msec  1 mGal  3D magnetic susceptibility  3D magnetic susceptibility  3D chargeability  3D densitycontrast 3D magnetic susceptibility  ID layeredearth resistivity  3318  10115  6631  60/stn  o  O  O X  25 x 25 x 25m 25 x 50 x 12.5m  s  u  bO  Ol  a  depth weighting, topography, inclination=50.638, declination=13.47 (local grid), inducing field=44000nT depth weighting, topography, inclination=50.638, declination=13.47 (local grid), inducing field=44000nT depth weighting, topography, inclination=50.638, declination=13.47 (local grid), inducing field=44000nT  2hr  11.5hr  38.5hr  17.75hr  3.2hr  4642  2654  9hr  weighting,  Electrode weighting  depth weighting, topography, inclination=50.638, declination=13.47 (local grid), inducing field=44000nT  depth topography  Inducing current orientation: inclination 0°, declination 90°  depth weighting, topography included  33.2hr  482646  OJ  0 msec  1  6min/stn  o  o CO  100 Ohm-m  co" co  1 - 10,000m  0 msec  \  50 x 50 x 50m  O X CO  o  Q H  2  <  CO  O  117600  eo  1  3D chargeability  bO CO  1182  100 x 100 x 50m  U  in  2-5% apparent resistivity and 1-2 degrees phase 5% + 1 msec  0 S.I. xl0"3  100 x 100 x 50m  E  100 x 100 x 50m  0 S.I. xl0~3  250 x 250 x 100m  Other parameters  Inversion Time  Achieved Misfit  OJ  DCIP3D j  167040  4nT  3D magnetic susceptibility  2566  707625  0.01 mGal  3D densitycontrast  CO  17671  Reference Model o  Cell Size  cn tN CO  "Real-section" and gradient-array IP  |  GRAV3D  Regional Gravity  Number of cells  Noise estimation  CO OO  Recovered model  H  c  Number of Data  CO m CN  CSAMT  Inversion Program  Inversion Description  ix E. Inversion 224  m  OJ  X  OJ OJ  m m  x  a 'to  Appendix F Gradient IP data modeled using  Gradient-array data has been modeled using MAG3D, inverting magnetic field data.  MAG3D  an inverse modeling package for  Similarities between the nature of data allow this.  The  advantage of inverting gradient-array I P data i n this way is that depth information can be introduced i n the model. A s w i t h potential field data, depth information is not contained i n these data. Here we show why this method is valid, and test it w i t h some synthetic examples. MAG3D  generates 3-D susceptibility distributions based on the following forward  modeling of the anomalous magnetic field from susceptibility distributions, K, i n an inducing field [Li and Oldenburg, 1997]:  (F.l) where r is the point of observation of the magnetic field. T h i s conservative field is derivable from the scalar magnetic potential [Grant and West, 1965], which is written as:  (F.2) Gradient-array I P data can be interpreted and modeled as magnetic potential field d a t a because of the dipole source of b o t h fields. Seigel [Seigel, 1959a] describes the overvoltage effect associated w i t h a metallic conducting sphere i n an electrolyte as equivalent  225  Appendix  F.  Gradient IP data modeled  using  MAG3D  226  to a dipolar source whose moment is proportional to the current density vector (j). T h e —>  —*  dipole source has a volume current moment of M = —mj, where m is the chargeability. T h e potential due to a distribution of current dipoles can be written as: 1  1  ATTCT  (F.3)  r—  where o is the conductivity of the medium. Expressions of potentials i n equations F . 2 and F . 3 have the same form if the conductivity and electric field are uniform.  The  negative of the data is used, as the dipoles would be aligned i n antiparallel w i t h the inducing current, so the inducing field is set antiparallel w i t h respect to the position of the current electrodes.  F.l  Four synthetic examples  A synthetic example was used to test the use of MAG3D  i n this manner. It is expected  that the conductivity of the deposit is anomalous w i t h respect to it's surroundings, so several synthetic experiments were carried out w i t h different conductivity distributions i n order to determine if this method is valid for use at San Nicolas. A 100 msec chargeable cuboid was generated from 20m x 20m x 10m cells positioned between -60m and 60m east, -60m and 60m north, and 80m and 120m depth (Figure F . l ) . T h e b o d y is hosted i n a background of 0 msec chargeability. A current of 1 A m p was transmitted through grounded electrodes at two locations straddling the target (-600m east, 0m north and 600m east, 0m north). Eighty-four gradient I P measurements were made using 12, 40m dipole locations on seven lines centered around the chargeable body. DCIPF3D,  which is a forward part of the DCIP3D  package, was used to generate these measurements. were used to model this data (Figure F.2):  inversion  Four different conductivity models  Appendix  F.  Gradient IP data modeled using MA G3D  227  Figure F . l : Synthetic chargeable body. 1. a half-space of 0.01 S / m 2. a 0.1 S / m conductive b o d y of the same size and location as the chargeable b o d y i n a background of 0.01 S / m 3. a 1 S / m conductive b o d y of the same size and location as the chargeable b o d y i n a background of 0.01 S / m 4. a 0.1 S / m conductive b o d y of the same size and location as the chargeable b o d y i n a background of 0.01 S / m w i t h a 40m thick conductive overburden of 0.1 S / m (This model is thought to most closely resemble the conductivity distribution at San Nicolas.) T h e four synthetic data sets (one for each conductivity model) were contaminated w i t h random noise w i t h a mean magnitude equal to 3% of value of the data. E a c h d a t u m was assigned an appropriate standard deviation (3% of each datum) and the data sets were inverted using MAG3D.  A n incident field w i t h 0 degrees inclination, 90 degrees  declination, and 1 n T field strength was used.  Appendix  F.  Gradient IP data modeled using  MAG3D  228  Figure F . 2 : North-facing cross-sections through the conductivity models: A ) 0.01 S / m half-space, B ) 0.1 S / m block i n a 0.01 S / m background, C ) 1 S / m conductive block i n a 0.01 S / m background, D ) 0.1 S / m block and a 40m thick 0.1 S / m overburden i n a 0.01 S / m background.  Appendix F. Gradient IP data modeled using  01  50  MAG3D  mV/V  229  100  Figure F.3: North-facing cross-section through recovered chargeability models. Figure F.3 shows the recovered chargeability models for the four models used. As expected, the half-space conductivity model gives the best results; the recovered values are closest to the true model value of 100 m V / V . As the conductivity increases, the recovered model deteriorates. The model with the conductive overburden is the worst. This is possibly because only a small amount of current was able to penetrate to the depth of the target. (A smaller current density will produce a smaller dipole source). In all cases there is good localization of the anomaly.  F.2  Modeling the electric field  As well as being affected by the conductivity structure, the electric field also must also be uniform in order for the treatment of gradient array IP data as potential field data. EH3D  was used to forward model the electric field generated by the transmitter for each  Appendix  F.  Gradient IP data modeled using  MAG3D  230  of the conductivity models. The results are presented i n Figure F.4, which shows the vector representation of the real part of the electric field for the north-facing cross-section at -400m north. T h e colors represent the magnitude of the field, and the line is orientated i n the direction of the field vector w i t h the white tip being forward. T h e electric field is distorted by interactions w i t h the conductivity structure.  It is  not surprising to see the best recovered chargeability results coming from the uniform conductivity and electric field distribution, while the less successful results are obtained from conductivity structures that distort the electric fields. A l t h o u g h the electric field and conductivity structure isn't too anomalous i n the case where a conductive overburden is added, the current is concentrated near the surface (Figure F . 5 ) . T h i s current channeling reduces the signal relative to the noise, which is reflected i n smaller amplitude data, and results i n a recovered model that is reduced i n magnitude.  Appendix  F. Gradient IP data modeled using  MAG3D  231  Appendix  F. Gradient IP data modeled using  MAG3D  232  Appendix  F.3  F.  Gradient IP data modeled  using MA G3D  233  Summary  Gradient array I P data can be regarded as a potential field, and can be treated as pseudo-magnetic data if the conductivity structure over which the data are collected isn't anomalous enough to distort the electric field. Conductivity distributions that have contrast of two orders of magnitude still recover a useful chargeability model. Inversion of this data using MAG3D  can produce good chargeability models as the data is weighted  i n order to provide depth information. In the case of San Nicolas the conductivity isn't too anomalous although the overburden has a detrimental effect on the data.  Appendix G IP electrode weighting  W h e n inverting I P data using DCIP3D,  accumulations of high chargeability values are  sometimes observed coincident w i t h the location of the transmitting electrodes.  This  electrode-effect is even more significant when the data have a small number of transmitter electrodes. T h e data collected at San Nicolas used the gradient-array, and "Realsection" array electrode configurations; b o t h of which have a small number of transmitter electrodes. A s a result, the electrode-effect is observed i n the inversion models. T h i s section introduces an ad-hoc weighting procedure that can be used to reduce the electrode-effect. T h e weighting is invoked using the w weighting m a t r i x i n E q u a t i o n 2.2. s  T h e value of these weights are simply increased i n the vicinity of the electrode. T h e cells around the electrode w i l l be preferentially weighted closer to the reference model, which is set as zero. A weighting distribution is sought that smoothly decreases from a high value at the electrodes to a background weighting value of one away from the electrodes. T h e electrode weighting must effectively suppress charge accumulations around the electrodes but must also decay fast enough so as not to influence the true chargeability distribution. T h e following set of conditions was used to empirically develop a weighting function.  when w  m  when w  m  234  > c, < c.  (G.l)  Appendix  G. IP electrode  where, w  235  is the weighting value assigned to the m  component of the diagonals of  th  m  the m a t r i x w  (Equation 2.2), n refers to the n  th  s  of transmitter electrodes, r m  weighting  transmitter electrode, N is the number  is the distance from the n  th  m  n  electrode to the center of the  cell, and a, b, and c are all empirically derived constants, a increases the weighting  th  value, b determines the rate at which the weight decays away from the electrode location, and c determines the value the weighting function can obtain before the default weights are used. The weighting around each electrode is superimposed to form a weighting function that exhibits hemispheres of high values below each transmitter electrode, a's of one, two, and three were applied to the synthetic model. A value of 100,000 was used for a, and a background value of one was assigned once the weights were less t h a n 5 (the values assigned to c) (this cut-off was used to reduce the size of the volume that is effected by the electrode weights). Weighting functions were tested for a number of simple synthetic chargeability models w i t h different values of b. A value of 2 produced results that seemed to have the least artifacts. T h e results of using this weighting function is shown i n Figure G . The effect of implementing the weights not only reduces the high values around the electrodes but also increases the magnitude of the anomaly. T h i s increase helps the model achieve values closer to that of the true model. Limitations of this weighting function are obvious. T h i s weighting method might not be appropriate if the electrode were to be placed i n chargeable material. Also when the electrode is very close to the center of the cell the method might produce undesired effects. A n alternative would be to construct a sensitivity based weighting function. T h i s would make parts of the model less sensitive to the data and therefore allow them to be closer to the reference model.  Appendix  G. IP electrode  236  weighting  1000  1000  Easting [m]  Easting [m]  (a) Synthetic chargeability model.  (b) Inversion model  1000  Figure G . l : Removal of electrode-effect v i a weighting.  

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