Science, Faculty of
Earth, Ocean and Atmospheric Sciences, Department of
DSpace
UBCV
Postlethwaite, Benjamin
Master of Science - MSc
University of British Columbia
It has been suggested that processes driving crustal formation in the Archean and Proterozoic were dissimilar and produced crusts with unique bulk properties and average thicknesses. The calibration of models based on evolving mantle fractionation or mantle convection style require accurate estimates of the geological and geophysical properties of crustal provinces to better constrain the details of crustal formation. Fifteen years of publicly accessible teleseismic data from all available Canadian seismic stations are binned in horizontal slowness and deconvolved into receiver functions. We apply a stacking method to retrieve estimates of the bulk crustal velocity ratio V P /V S and thickness H from these data under the assumption of locally 1-D structure. We also investigate modifications to this approach that can allow discrimination of V S and V P under certain conditions. Bootstrap error analysis is performed for each station dataset and subsets of these measurements are compared with results for matching stations from previous studies. Analysis of results in conjunction with additional velocity estimates from active source seismic studies and a seismic property database affords improved constraints on bulk geological composition of the Canadian landmass that are used to evaluate competing models of crustal formation.
Seismic velocities andcomposition of the CanadiancrustbyBen PostlethwaiteB.Sc., The University of British Columbia, 2011A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF SCIENCEinThe Faculty of Graduate and Postdoctoral Studies(Geophysics)THE UNIVERSITY OF BRITISH COLUMBIA(Vancouver)May 2014c© Ben Postlethwaite 2014AbstractIt has been suggested that processes driving crustal formation in the Archean and Proterozoicwere dissimilar and produced crusts with unique bulk properties and average thicknesses.The calibration of models based on evolving mantle fractionation or mantle convection stylerequire accurate estimates of the geological and geophysical properties of crustal provincesto better constrain the details of crustal formation. Fifteen years of publicly accessibleteleseismic data from all available Canadian seismic stations are binned in horizontal slownessand deconvolved into receiver functions. We apply a stacking method to retrieve estimates ofthe bulk crustal velocity ratio VP/VS and thickness H from these data under the assumptionof locally 1-D structure. We also investigate modifications to this approach that can allowdiscrimination of VS and VP under certain conditions. Bootstrap error analysis is performedfor each station dataset and subsets of these measurements are compared with results formatching stations from previous studies. Analysis of results in conjunction with additionalvelocity estimates from active source seismic studies and a seismic property database affordsimproved constraints on bulk geological composition of the Canadian landmass that are usedto evaluate competing models of crustal formation.iiPrefaceTeleseismic data discussed in chapter 2 section 2.1 was processed using Python scripts writtenby myself and a set of shell scripts based off previous work by Michael Bostock. Deconvolutionalgorithms and coordinate transformation algorithms are likewise interpreted from previouswork by Michael Bostock. Suggestions by David Snyder to investigate signals of the ConradDiscontinuity lead to the analysis of crustal discontinuities in chapter 3 and chapter 4. NikChristensen provided invaluable support during the discussion on crustal composition inchapter 4 section 4.2. Specifically, Nik providing assistance in the interpretation and useof laboratory velocity measurements (Christensen, 1996) and rock chemical analyses (LeMaitre, 1976). His suggestions on crustal compositional literature and recommendations forthe layout of figure 4.2 were critical to this discussion. Michael Bostock provided advice andeditorial recommendations in all chapters of this thesis.iiiTable of ContentsAbstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iiPreface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iiiTable of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ivList of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viList of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1 Geology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Previous work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 Data and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62.1 Teleseismic data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62.2 Vp/Vs method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82.3 Additional data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133.1 Comparisons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133.2 Full grid search analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143.3 Crustal discontinuities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163.4 Regional bulk crustal parameters . . . . . . . . . . . . . . . . . . . . . . . . 20ivTable of Contents4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 224.1 Crustal reflectivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 224.2 Crustal composition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32vList of Tables3.1 Multi-study comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143.2 Regional seismic parameter comparison . . . . . . . . . . . . . . . . . . . . . 214.1 Bulk crustal composition comparison . . . . . . . . . . . . . . . . . . . . . . 28viList of Figures2.1 Seismic broadband station locations . . . . . . . . . . . . . . . . . . . . . . . 72.2 Teleseismic reflection phase diagram . . . . . . . . . . . . . . . . . . . . . . . 93.1 Stacked receiver functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163.2 Misfit contours and kinematic curves . . . . . . . . . . . . . . . . . . . . . . 173.3 Comparison of P wave velocity estimates . . . . . . . . . . . . . . . . . . . . 183.4 Station depth profiles and SVD analysis . . . . . . . . . . . . . . . . . . . . 194.1 Normalized station depth profiles and SVD analysis . . . . . . . . . . . . . . 234.2 VP and VS estimates for lithologies and regions . . . . . . . . . . . . . . . . . 264.3 Normalized crustal composition comparison . . . . . . . . . . . . . . . . . . 29viiChapter 1IntroductionThe availability of broadband teleseismic data covering most regions in Canada affords theopportunity to study changes in crustal composition and structural patterns on a broadscale. We collect and process this teleseismic data and interrogate the resulting dataset forsignals from the Mohorovicˇic´ and Conrad discontinuities to constrain crustal composition andevolution. Patterns in the seismic signature of the upper and lower crust have been notedin previous work and are an expression of, and window into, the processes driving crustalgrowth and differentiation. We then combine the teleseismic dataset with active sourceseismic results, rock unit velocity measurements and a dataset of matching rock chemicalcompositions. By exploiting these data sets we obtain velocity ratios for tectonic regimes inCanada that constrain crustal composition . The analysis of both structure and compositionof the Canadian crust provides new insights into competing hypotheses for crustal evolution.1.1 GeologyThe Canadian continental lithosphere is composed of at least fifteen large geological provincesrepresenting crustal evolution from the early Archean to the present accretionary growth andorogenic deformation in the Western Cordillera. The Precambrian Canadian Shield and thebounding platforms of North America’s interior comprise the cratonic core of Laurentia(Hammer et al., 2010). Several distinct geological provinces brought together during a seriesof Paleoproterozoic orogens make up the Canadian Shield (Hoffman, 1988, Figure 1). Thelargest of these provinces, and the world’s largest Archean craton, is the Superior Province.Bounded by Proterozoic orogenic belts, the western and southern Superior is composed11.1. Geologyof roughly east-west trending geological subprovinces that include the granite-greenstoneterranes of the Abitibi Greenstone Belt, metasedimentary gneisses, high-grade gneisses andplutonic complexes assembled during the period between 3-2.6 Ga (Thurston et al., 1991).In the northwest of the Superior Province on the eastern shores of the Hudson Bay liesthe Nuvvuagittuq greenstone belt that recent geochronological work has dated at 4.28 Gamaking it the only known remnant of Hadean crust preserved on Earth (O’Neil et al., 2011).To the east lies the younger, 1.2-0.98 Ga, Grenville orogen, consisting largely of Su-perior and Southern Province rock reworked during Alpine-Himalayan like continental col-lision (Eaton et al., 2006). North of the Superior Province and separated by the Paleo-proterozoic Trans-Hudson Orogen are the Rae and Hearne domains which together formthe western extent of the Churchill Province. Studies of the Rae suggest a Paleoarcheanand Mesoarchean basement whereas the Hearne comprises granite-and-greenstone terranesof ages 2.7Ga (Thompson et al., 2010). The Slave Province is a relatively small Archeangranite-greenstone block lying to the northwest of the Churchill province. Its western por-tion is the 4.0 Ga remnant of a larger Archean continent, whereas the eastern Slave forms ayounger (2.6 Ga) paired accretionary prism and island arc (Kusky, 1989).To the west of the Superior is the extensive Interior Platform overlying the southernextensions of the Trans-Hudson Orogen, Hearne Domain and Alberta Orogen. The Inte-rior Platform is largely Archean reworked during the Paleoproterozoic with bore-hole sam-ples indicating amphibolite to granulite-grade Proterozoic metamorphism (Hammer et al.,2010). Extending west from the Interior platform, the Western Cordillera developed in apassive margin setting on and adjacent to late Archean and Paleoproterozoic crystallinebasement. Between the middle of the Jurassic and Paleocene intense metamorphic, plutonicand uplifting events occurred as the result of Paleozoic and younger intraoceanic-arc accre-tionary events. The Coast Mountains evolved during the late Cretaceous as a wide magmaticarc composed largely of granitic rock (Clowes et al, 1995). Although there is considerablevariation in structure and tectonic history, roughly half of the Cordillera is underlain by21.2. Previous workProterozoic-margin crust that forms a crustal-scale de´collement beneath interleaved and du-plexed crust (Hammer et al., 2010). The rich tectonic tapestry of the Canadian continentalcrust affords an excellent opportunity to study variations in bulk crustal properties withgeological age.1.2 Previous workOur knowledge of the continental crust in Canada, in particular its thickness and structurein depth, has advanced greatly through Lithoprobe, a now complete national geoscienceproject spearheaded by active-source seismic profiling (Clowes et al., 1999). The full suite ofLithoprobe seismic profiles form a near-continuous transect from the Pacific to the AtlanticOcean, and has enabled many insights into crustal structure and genesis. Recent summarystudies utilizing Lithoprobe data include the analysis of Eaton (2005) which considersseismic observations in light of several hypotheses for the origin of the continental Moho:relic Moho, underplating, metamorphic front and regional de´collement. Seismic evidence ofpreserved island arc Moho in Cascadia supports the first hypothesis whereas the presence ofhigh-velocity, lower-crustal xenoliths in southern Alberta favours thick mafic underplating.Eclogite mineral assemblages forming a deep crustal root below the Grenville Front lendsupport to the third hypothesis, that at least in some places, continental Moho forms by large-scale eclogitization of the lower crust. Finally, seismic imaging of subhorizontal reflectionsseparated by dipping reflection fabrics beneath the southern Canadian Cordillera suggestsdecoupling of the upper crust from the mantle during deformation and is taken as evidencefor the fourth hypothesis, that an overprinted Moho may serve as a subhorizontal regionalde´collement. A subsequent study by Cook et al.(2009) argues that the continental Moho hasvaried and complex origins and that its imprint evolves through the effects of temperature,pressure, deformation and fluid migration.In another recent summary study of Lithoprobe data, Hammer and Clowes (2011) dis-31.3. Summarycuss broad structural characteristics of the Canadian crust and note that Archean cratonsand their boundaries contain structures similar to those found in Proterozoic and Phanero-zoic orogens. They also discuss the lack of evidence for significant crustal disruption frommagmatic intrusion related to subduction, post-orogenic extension or orogenic collapse. Theauthors remark upon the striking uniformity in Moho depth that is imaged between 33 kmand 43 km depth along 20,000 km of Lithoprobe transects, despite the diversity of overly-ing crust. Both Cook et al. (2009) and Hammer and Clowes (2011) raise the possibility that,in some circumstances, the Moho and the base of seismic reflectivity do not represent thepetrological crust-mantle boundary but rather eclogitized lower crust that is seismically in-distinguishable from the upper mantle. If this is the case, the seismic Moho would representthe top of a metamorphic front and would be shallower than the crust-mantle boundary.The Lith5.0 revised crustal model presented by Perry et al. (2002) was developed fromcrustal thickness estimates taken from Lithoprobe seismic profiles. This work updates theglobal Crust5.1 model (Mooney et al, 1998) and emphasizes broad crustal features of theCanadian crust such as relatively thin Archean Shield regions (32.5-40.0 km) as comparedto the thick (>42.5 km) Phanerozoic crust in the south and west of the continental craton.1.3 SummaryIn this study, we examine the additional constraints that analyses of teleseismic receiverfunctions place upon Canadian bulk crustal properties when combined with available activesource results. It is widely appreciated that receiver functions are sensitive primarily tocrustal thickness and Poisson’s ratio (or equivalently VP/VS) (Zhu and Kanomori, 2000),and that the latter quantity provides some ability to discriminate among possible bulk com-positions (Zandt and Ammon, 1995). The constraint on Poisson’s ratio and the greateraveraging performed by low frequency (0.04-3 Hz) scattered teleseismic waves versus highfrequency (10-40 Hz) P-waves employed in active source studies render the two techniques41.3. Summarysomewhat complementary. Our data set comprises the majority of teleseismic data availablefor the Canadian landmass including both permanent and temporary deployments. Many ofthe temporary deployments are described by Bostock et al. (2010). Three previous studiesemploying similar methodologies but with more regional emphases deserve special mention:an examination of crustal thickness and VP/VS variation in the Grenville Orogen (Eaton etal., 2005), a structural and compositional characterization of the Superior Province (Dar-byshire et al. 2006) and a crustal evolution analysis of the Hudson Bay region (Thompsonet al., 2010). Results from these studies will serve as benchmarks for our expanded effort.In the following sections we describe the data and methods employed to interrogate theCanadian crust. We proceed to present the major results beginning with a comparisonbetween our seismic parameter estimates and the estimates from three studies mentionedabove for identical seismic stations. We then present the results of a modified approach toour parameter estimation method and follow this with a presentation of the resulting crustalproperty estimates. First presented are the average crustal discontinuity and structuralpatterns across Canada. Second, we take a tour through those geological provinces for whichsufficient data exists, focusing on regional variation and characterization. We conclude witha discussion on the implications for our understanding of the composition and evolution ofthe Canadian crust.5Chapter 2Data and MethodsOur analysis of bulk Canadian continental crust is based on estimates of crustal propertiesthat can be accessed via seismic techniques, namely seismic wave velocities (or velocityratio) and crustal thickness. This study draws on three sources for these crustal properties.The first and primary source is teleseismic receiver function (hereafter RF) analysis. Theremaining two datasets are Crust 1.0, a widely published statistical average of similar regionswith a global one degree resolution, and a compilation of preprocessed active source data(Mooney, 2012). Both Crust 1.0 and the active source compilation are used to qualify andcompare with the primary dataset.2.1 Teleseismic data setThe primary data utilized in this study are RF processed teleseismic P wave seismogramsoriginating from more than 700 earthquake sources. These earthquakes occurred between2000 and 2012 and were recorded on subsets of 343 broadband seismic stations distributedacross Canada. Seismic stations are selected from all available regional and national networksincluding CNSN, POLARIS, FedNor and Chasme. Seismograms are included for analysisif they fall within a 30◦-100◦ epicentral distance window. Quality control is performed byadmitting only those seismograms with sufficiently high signal to noise ratios to permit visualobservation of the dominant P wave energy and with sufficiently impulsive first arrivals toallow arrival onsets to be accurately measured. After selection and filtering more than 80,000seismograms are available for further processing. Seismic stations contributing to the datautilized in this study are shown in figure 2.1.62.1. Teleseismic data setProcessed Seismic StationsPolitical ProvincesAppalachian OrogenArctic PlatformBear ProvinceChurchillChurchill ProvinceCordilleran OrogenGrenville ProvinceHudson Bay LowlandsInnuitian OrogenInterior PlatformNain ProvinceGrenville ProvinceSlave ProvinceSouthern ProvinceSt. Lawrence PlatformSuperior ProvinceFigure 2.1: Map showing major Canadian geological provinces and seismic stations con-tributing to the data discussed in this study.The first stage of processing requires the transformation of teleseismic data into receiverfunctions. In general terms, this transformation involves deconvolving an approximation ofthe earthquake source from horizontal recordings of ground motion (Langston, 1979). Theresulting waveforms contain discrete pulses corresponding to S waves scattered from sub-surface discontinuities including the base of the crust, or Moho. Confident identification ofthese peaks is required to estimate crustal parameters, and improved results can be obtainedby transforming the displacement seismogram into its P and S contribution based on a 1-Dmodel of propagation. This procedure is accomplished by first rotating the N and E coordi-nates into radial and transverse directions and then performing a wave field decompositionusing the radial and vertical channels (Kennett, 1991). The direct P arrival of the signal onthe resulting P wave component is used as an approximation to the source function becausethe P component impulse response for teleseismic P approximates a delta function. Thiswindowed source estimate is deconvolved from the S wave component computed from thewave field decomposition.We employ an L2, multichannel, frequency domain approach to perform the deconvo-lution that has the advantage of computational efficiency and does not require a priori72.2. Vp/Vs methodassumptions concerning noise levels in the data. More specifically, we employ a simultane-ous deconvolution of N seismograms sharing a similar slowness to compute a single impulseresponse or receiver function r(t).r(t) = F−1 (G(ω)) = F−1[ ∑Nn Sn(ω)P∗n(ω)∑Nn Pn(ω)P∗n(ω) + δ], (2.1)where F−1 is the inverse Fourier transform, Sn represents the nth S wave seismogram, Pnis the corresponding windowed P wave seismogram, ∗ denotes complex conjugation and δ isthe regularization parameter controlling the trade off between model smoothness and datamisfit. The parameter δ which is chosen programatically by minimizing the general crossvalidation function GCV (δ) (Golub et al., 1979) is given byGCV (δ) =∑Nn∑Mm (Sn(ωm)− Pn(ωm)G(ωm))2(NM −∑Mm X(ωm))2 , (2.2)whereX(ωm) =∑Nn Pn(ωm)P∗n(ωm)∑Nn Pn(ωm)P∗n(ωm) + δ, (2.3)and ωm is the mth discrete frequency. All resulting receiver functions, r(t), are filteredbetween 0.04Hz and 3.0Hz.2.2 Vp/Vs methodA well tested and widely published method for extracting the ratio of P velocity VP toS velocity VS, hereafter referred to as R, and crustal thickness (or depth to Moho) H, isoutlined by Zhu and Kanamori (2000), hereafter ZK. This method exploits the dependenceof these parameters on the differential arrival times between the S wave reflected phasesPs, PpPs, PsPs, PpSs and PsSs and the direct P wave arrival Pp (Fig 2.2). Note thatPsPs and PpSs are kinematic analogues, such that the energy for these two phases arrive82.2. Vp/Vs methodPpPsPpSs + PsPs P PsP-waveS-waveMohoFigure 2.2: Schematic diagram illustrating geometry of phases for the velocity contrastrepresenting the Mohosimultaneously for a 1-D model. For a range of slowness values, p, the differential arrivaltimes, t(p), trace moveout curves for each phase arrival given bytPs(pn) =HVP(√R2 − V 2P p2n −√1− V 2P p2n)(2.4)tPps(pn) =HVP(√R2 − V 2P p2n +√1− V 2P p2n)(2.5)tPss(pn) =2HVP√R2 − V 2P p2n (2.6)where pn is the slowness for the nth receiver function. Since strong reflected phases occurat strong velocity contrasts, the Moho, the boundary targeted by ZK, tends to be wellrepresented on most RF’s.The travel time equations, as employed by ZK, assume a value for crustal P wave ve-locity, VP , that in practice will trade-off to some degree with crustal thickness H. In ourimplementation of the ZK approach, each station is assigned a VP value corresponding tothe Crust 1.0 value for the corresponding 1◦ containing cell. In specific cases where data are92.2. Vp/Vs methodbeing compared to previously published results for quality control, VP values are chosen tomatch the values in the published study.RFs are stacked along trial moveout curves for a range of candidate models of R and Hto generate a fitness function s(H,R). Within the stacking procedure, each phase is assigneda weight to account for the expected relative amplitudes of the phases for typical velocitycontrasts, with the direct conversion Ps usually characterized by the highest quality signalfollowed by PpPs and PpSs. We chose weights of w1 = 0.5, w2 = 0.3 , w3 = −0.2 forthe Ps, PpPs and PpSs phases, respectively. A negative weight for the combined PpSsand PsPs phases is required as the polarity of the signal is reversed. Semblance weighting(Eaton, 2006) is employed to reduce the effect of spurious large amplitude noise in the data.The semblance function assigns a weight between zero (incoherent noise) and one (coherentsignal). The stacking function is therefore defined ass(H,R) =3∑m=1SmN∑n=1wmrn(tm) (2.7)whereSm(H,R) =(∑Nn=1 rn(tm))2∑Nn=1 r2n(tm)(2.8)is the semblance weight for the mth phase, time tm is calculated from the corresponding traveltime function for a given H, R pair as a function of slowness pn and N is the total numberof receiver functions. Multiplying by the semblance weighting sharpens the stacked image,s(H,R), and results in better resolution when discriminating between different models. Thefunction s(H,R) can be thought of as a transformation that maps times of the Ps, PpPsand PpSs+ PsPs phases into R and H space as positive bands, via Eq. 2.7. Constructiveinterference where these bands intersect produces a maximum in the stacking function. Themodel, R and H, that produces this maximum provides the best estimate for the bulk crustalparameters for a given seismic station.102.3. Additional dataA limitation of the ZK approach is the requirement of an initial VP estimate. In principalwe may compute the stacking function Eq. 2.7, as a function of all three seismic parameters,s(H,R, VP ). Taking advantage of modern multicore systems with reasonable quantities ofdata (fifty to a few hundred RFs) and a moderate search space (n3 parameter candidateswhere n ≈ 150) the computational time makes this method easily scalable to hundreds ofstations. This “full grid” approach is analyzed to determine the viability of recovering VP .The trade-off between VP and H (and other parameter combinations) and error resultingfrom data quality are quantified for both the full parameter search and ZK methods bybootstrap resampling (Efron and Tibshirani, 1986). An estimate for the error is calculatedin a bootstrap resampling approach by taking the standard deviation of 1024 parameterestimates each produced with randomly selected RFs, with replacement.2.3 Additional dataWe supplement receiver function estimates of crustal properties with data from controlledsource experiments collected and compiled from Geological Survey of Canada (GSC) refer-ences by Walter Mooney (personal communication, 2012). The active source data provideVP and a few VS estimates for hundreds of locations across Canada, many along transectsthat cross major geological boundaries, faults and discontinuities. Some of these data arelocated within reasonable (<100 km) proximity to seismic stations used in this study andcan be used to compare with the VP and VS estimates resulting from the RF full gridsearch.The Crust 1.0 model (Laske et al., 2013) employs many of these same active source datato provide regular, complete coverage across all of Canada. The model includes estimates ofVP and VS information with depth at 1◦ resolution in latitude and longitude. In addition toemploying local active source data Crust 1.0 is also constrained by statistically aggregateddata from geographically distant but geologically similar regions. It therefore affords asomewhat independent dataset suited for comparison with the crustal estimates calculated112.3. Additional datain this study.12Chapter 3Results3.1 ComparisonsThree previous studies have provided parameter estimates for R and H for subsets of theseismic stations considered here. The most recent study (Thompson et al., 2010) focuses onthe northern Canadian Shield and employs both the linear stacking approach used by ZKas well as a phase weighted method. A 2007 study of the Superior Province (Darbyshireet al, 2007) and a study of the Grenville Orogen (Eaton et al., 2006) utilize the ZK +semblance approach outlined in section 2.2. Comparisons between crustal estimates of R forparticular seismic stations are made for those values with corresponding standard errors ofless than ±0.06. This error threshold is chosen empirically as that which best partitions thosereceiver function stacks with visually recognizable energy along phase moveout curves fromthose where lack of data or poor quality make these moveout curves difficult to distinguish.For each study under comparison, data are reprocessed using the VP chosen by the studyauthors. For the 2006 and 2007 studies the ZK + semblance method is used whereas forthe Thompson et al. study the linear stacking ZK approach is employed. Both correlationcoefficient and mean difference are provided in Table 3.1 as measures of similarity.All three studies reported H estimates that correlate strongly with the crustal thicknessparameters computed here. The data from the northern Canadian Shield shows strongcorrelation in both H and R. The lower correlation observed in estimates of R than H is notunexpected as the range of R is relatively narrow and small changes (≈ 2%) are physicallymeaningful. A corollary of this is greater sensitivity of R to differences in procedure such133.2. Full grid search analysisCorr. Coeff. RMS differenceStudy Authors Region Num. Stns H R H(km)RThompson et al.(2010)N. CanadianShield29 0.97 0.70 0.78 0.017Darbyshire et al.(2007)SuperiorProvince10 0.95 0.43 1.71 0.037Eaton et al. (2006) Grenville Oro-gen26 0.90 0.61 1.03 0.033Table 3.1: Comparison of R and H estimates with three published studiesas the choice of deconvolution algorithm and selection and scaling of data. The results fromThompson et al. (2010) also correspond most closely to the parameter estimates computedhere, with the mean difference for both parameters being less than half of the averagedbootstrap error computed in this study. Results published by Eaton et al. (2006) showreasonable correlations in both R and H and a mean difference for both parameters thatis roughly equal to the averaged uncertainty in the data. Parameter estimates from theSuperior Province (Darbyshire et al., 2007) show the lowest correlation in R as well as thehighest mean difference in both parameters. These differences are well over the averagedcomputed error, and cannot be easily explained.3.2 Full grid search analysisWe now proceed to compare our results computed using the ZK approach with those fromthe full grid search. We note that the velocity ratio R has a correlation between the twodatasets of 0.76 whereas H has a correlation of 0.51. The low correlation between crustalthickness estimates can be explained by the strong trade-off between H and VP through thequantitytPps − tPs2=tPss2− tPs =HVP√1− p2V 2P ).As a consequence, contours of the misfit surface s(H,R, VP ) are highly extended in H - VP143.2. Full grid search analysisspace (Figure 3.2a), contributing to the low correlation in H between the two methods. Inother words, the ratio H/VP is better constrained than either of the two parameters H, VPindividually.For all but the cleanest stations, the trade-off between H and VP renders recovery ofVP unfeasible using the full grid approach. A comparison between two stations, ULM, thestation with the highest quality RFs, and DORN, a station exhibiting poorer resolution ofthe reflected phases Pps and Pss, illustrates this difficulty. RFs are arranged vertically andordered by increasing slowness, shown in figure 3.1. Cross sections of s(H,R, VP ) througheach coordinate plane are displayed for ULM and DORN in figure 3.2. The lower resolutionapparent in the H - VP cross section for DORN translates into a bootstrap error of VP ±0.64km/s. Station ULM, with cleaner data, has less trade-off between H and VP as indicatedby the corresponding cross section and the lower bootstrap error of VP ± 0.16km/s.Active source experiments located near broadband seismic stations provide independentcrustal thickness and P wave velocity estimates to test against data derived from the fullparameter search method. We select stations with low error in VP and active source ex-periment locations within a 100 km radius. If several active source experiments are locatedwithin this radius, estimates are averaged. Four stations (ARVN, TYNO, ULM and WHY)meet these criteria and the parameter estimates are presented in figure 3.3. Estimates com-puted from the full grid approach are roughly 0.25 km/s higher than the active source data.This difference is consistent across all four stations and is exemplified by a 0.99 correlationcoefficient, suggesting possible bias. However, the limited number of data means this bias ispoorly constrained.There is lower correlation between the crustal thickness estimates, though this is largelyattributed to an approximate 10 km difference between the active and passive source valuesfor station ARVN. This discrepancy may result from low signal to noise levels at this stationalthough both the ZK method and a published study (Thompson et al., 2010) provideestimates of 41 km for ARVN, much nearer the full grid search value of 42 km. Despite153.3. Crustal discontinuitiesFigure 3.1: Receiver functions stacked vertically for stations DORN and ULM. ULM featuresstronger moveout on the reflected arrivals that results in smaller uncertainties in resultingparameter estimates.lower correlation, the apparent bias towards higher values computed from teleseismic data isalso maintained for H, and is not unexpected given the dependence of travel times in 2.4-2.6on H/VP .3.3 Crustal discontinuitiesWe now consider the average crustal structure across Canada. We produce a depth profilefor each station by stacking receiver functions along traveltime curves corresponding to arange of values in H (0 to 50 km in increments of 100 metres) using the estimates for Rcomputed via the ZK + semblance method. The resulting profiles contain energy at depthscorresponding to major velocity discontinuities and are shown in figure 3.4 (left) arranged inorder of increasing crustal thickness. By assembling all depth profiles into a matrix, we maydecompose the data via SVD into their principal components. Figure 3.4 (right) containsthe first five singular vectors or modes normalized to unit magnitude that account for 78% ofthe variance in the profiles. Higher modes correspond to diminishing contributions to total163.3. Crustal discontinuitiesFigure 3.2: 2D slices through the 3D misfit volume of s(H,R, VP ) for stations DORN (a-c)and ULM (d-f). ULM has very clearly defined signals whereas DORN is representative ofdata containing lower SNR. Contours of the misfit surface are highly extended along moveoutcurves for the VP and H pairings for both DORN and ULM in a) and d), revealing the strongtrade-off between the two parameters.173.3. Crustal discontinuities6.06.26.46.66.87.07.27.47.6Vp [km/s]Full Gridsearch estimateProximal active source estimateCrust 2.0 estimate2 std dev BootstrapARVNULMTYNOWHYStations30323436384042444648H [km]Figure 3.3: Top, P wave velocity estimates computed from the full grid search methodcompared to active source estimates and values from Crust 1.0. Bottom, crustal thicknessestimates computed from the full grid search method compared to active source estimatesand values from Crust 1.0.183.3. Crustal discontinuitiesFigure 3.4: Left, station depth profiles. Right, the singular vectors (principal components) ofthe station depth profile matrix. Each principal component represents a mode that capturessome part of the total variance in the dataset. Vertical lines are the axis each profile andvector is normalized about. A grey horizontal window highlights a region of low energy.193.4. Regional bulk crustal parametersvariance. Each vector is normalized to unit magnitude. The first mode is effectively theaverage depth profile across all stations and captures 50% of the variance in the dataset. Itfeatures a maximum near 38.7 km that may be taken as a measure of station average Mohodepth. The second and third modes are dominated by a high amplitude shallow structures(e.g. sedimentary layers) within the top 10 km and Moho variability between 30 and 46 km. The fourth and fifth modes account for a combined 11% of the variance in the dataset,and contain energy at upper-mid crustal depths in the 10 km to 20 km range. All 5 modesdisplay relatively little energy within a depth window roughly between 20 km and 30 km.3.4 Regional bulk crustal parametersWe proceed to consider crustal thickness H and velocity ratio R estimates for stations withassigned R bootstrap errors below ±0.06 in a geographic context (figure 2.1). To compensatefor the uneven distribution of seismic stations, weights are applied to both R and H estimatesbefore averaging over a given region. Weights are calculated by projecting station locationsonto a 2D plane using the Albers equal-area conic projection. A Voronoi diagram is thencomputed using all projected station locations. The ratio between the Voronoi cell surfaceencompassing each station and the total area of the convex hull bounding region is employedas that station’s weight.Table 3.2 displays the results of our regionalization in R and H. The weighted averagecrustal thickness for all stations (Canada) is 37 km, about two kilometres thinner than theaverage for the Canadian Shield. Crust 1.0 provides a slightly thicker crustal average forCanada (38.1 km) and a slightly thinner average for the Shield (38.3 km). The Slave Provinceaverages are similar to those for the Churchill province with a crustal thickness and seismicvelocity ratio comparable to Crust 1.0. Moving south and east from the Churchill Provincethrough the Superior Province into the Grenville Province, we note an increase in the ZKR values as well as crustal thickness H. The weighted R average is 1.73 in the Churchill203.4. Regional bulk crustal parametersZK Crust 1.0 Active SourceRegion H(km] R H[km] R VP [km/s] VP [km/s)Canada 37.0 1.75 38.1 1.74 6.41 6.33Shield 39.2 1.74 38.3 1.74 6.47 6.42Slave 38.2 1.74 38.0 1.73 6.47 6.44Churchill 38.9 1.73 37.9 1.74 6.46 6.39Superior 39.7 1.74 38.9 1.74 6.49 6.44Grenville 41.7 1.78 39.7 1.74 6.50 6.48Table 3.2: Comparison of seismic velocity and crustal thickness estimates for Canada andselected geological provinces.Province increasing slightly to 1.74 in the Superior and jumping to 1.78 in the Grenville. In asimilar fashion, the weighted average of H increases from 38.9 km in the Churchill Provinceto 39.7 in the Superior Province and 41.7 km in the Grenville Province. The southeasterntrend towards increasing values is also expressed in weighted averaged active source VP dataas well as the Crust 1.0 VP data. The Crust 1.0 model is characterized by slightly thinnercrust though the southward thickening trend is noticeable. The trend in the seismic velocityratio is not, however, as apparent.21Chapter 4Discussion4.1 Crustal reflectivitySeismic refraction and reflection studies of continental crust have afforded valuable con-straints on the processes governing crustal evolution. Early efforts to characterize continen-tal crust invoked a 2-layer, slab model comprising lower velocity upper crust and a highervelocity lower crust (Conrad, 1925; Jeffreys, 1926). Evidence supporting this model wasoriginally presented by Conrad (1925) which cites observations of P arrivals propagatingalong a mid-crustal interface similar to but shallower than the deeper crust-mantle interfaceimaged by Andrija Mohorovicˇic´ in 1909 (Litak and Brown, 1989). Interpretations of seismicdata invoking both the crustal slab model and the Conrad Discontinuity, as the mid-crustalinterface became known, persisted in the literature but were increasingly criticized as be-ing too simplistic, in the case of the slab model, or, lacking consistent evidence (Litak andBrown, 1989) for the Conrad Discontinuity. Technological improvements in reflection seis-mology allowed researchers to further argue for a complex heterogeneous crust and proposemodels wherein smooth random lateral inhomogeneities are superimposed on simple velocitygradient models to reproduce the range of seismic observations (Litak and Brown, 1989;Mereu and Ojo, 1981).Despite diminishing support in recent years for the Conrad Discontinuity as a universalcrustal feature, the division of the crust into seismically distinguishable upper and lowercomponents remains a common assertion. In their review, Mooney and Brocher (1987) arguefor a model of the continental crust separated into a seismically reflective lower crust overlain224.1. Crustal reflectivityinterpolated station depth profilesDepth [km]5101520253035404550 profile stackFigure 4.1: Left, vertical station depth profiles interpolated to a common Moho. Right,normalized stack of depth envelopes, grey line shows meanby transparent upper crust. Their physical interpretation is that of a poorly laminated uppercrust dominated by features of short length scale that contrasts with a lower crust composedof thin (∼ 100 m) lamellae of significantly longer horizontal length scale. The inference of alaminated lower crust has since found support in studies of regions with extended or thicker,warmer crust (e.g. Ito, 1999; Mayer et al., 1997; Meissner et al., 2006).The lower energy in the 20-30 km depth window noted in Section 3.3 for the stationprofiles given in Figure 3.4 (left) provides some supporting evidence for seismically distinctupper and lower crustal layers. To better compare results across stations, we normalize eachdepth profile such that the Moho falls on a common ordinate (Figure 4.1, left) and stackthe absolute values (Figure 4.1, right). The stacked signal has highest amplitude near thesurface, where energy is associated with sedimentary layers, and at the Moho. There is a234.1. Crustal reflectivitysteady and marked decrease in energy over the bottom third of the profile that cannot beascribed to sidelobe structure associated with the Moho pulse. This observation is ostensiblyat odds with the Mooney and Brocher (1987) high reflectivity lower crustal model. More-over, reflection profiles from Lithoprobe transects (Hammer et al., 2010) frequently displaysignificant reflected energy through the crust with some transects including the Western Su-perior transect and the Cassiar in the Western Cordillera displaying pronounced reflectivitywithin the lower crust.We reconcile the teleseismic and active source observations of transparent versus reflectivelower crust by considering the scale of vertical heterogeneity to which the two methods aresensitive. Teleseismic body waves include both direct and reflected conversions that canoriginate from layering with velocity gradients over scale lengths of the order of λ/2 and λ/4,respectively, where λ is the P-wavelength (Bostock, 1999). For 1 Hz teleseismic waves, crustallayering with scale lengths of ∼ 800-2000 m should be resolvable. Higher frequency activesource data (10-40 Hz) employing P-wave sources are sensitive to finer scale vertical structurewith scales of ∼ 75-300 m. We infer then that the vertical scales of lower crustal reflectivityare better represented by scale lengths under ∼ 800 m yielding a lower crustal structurethat is relatively transparent to teleseismic waves but can yield strongly reflective signaturesto near-vertical, active-source seismic profiling. Some authors, for example Mayer et al.(1997) in their study of the Rhine Graben, have employed the term Conrad Discontinuityto refer to an interface or transition between transparent upper crust and reflective lowercrust. It is possible in this context that the higher energy levels between 15 and 20 km onthe 5th principal component in Figure 3.4, are a manifestation of such signals, but that thelower energy levels (< 5%) in this mode would indicate that the Conrad Discontinuity so-defined is at best weakly represented within the Canadian crust. This finding is supported bycrustal velocity models characterized by a near constant velocity gradient such as the gradualtransition from a more silicic upper crust to a more mafic lower crust in shields and platformsobserved by Meissner (1986). It is also supported by the lack of evidence for a consistent244.2. Crustal compositionmid-crustal reflection boundary in lithoprobe data (Hammer et al., 2010). The increasedtransparency of the lower crust would appear to relate to changes in rheology. Studies ofcrustal strength (Jackson, 2002), deformation (Burov and Watts, 2006; Meissner et al., 2006)and the inferred ∼ 100 m lower crustal lamallae reported by Mooney and Brocher (1987)as well as the widely noted reduction in seismicity of the lower crust (Ito, 1990; Meissnerand Wever, 1986) would support this contention; though see Deichmann (1992), Handy andBrun (2004) and Simpson (1999) for competing evidence.4.2 Crustal compositionMean crustal composition is another important parameter characterizing continental crustthough it is important to distinguish it from actual lithological or mineralogical state ofthe crust. It serves as a representative, average quantity that affords constraint on crustalstructure and evolution in space and time. Early attempts at estimating mean crustal compo-sition were largely performed through extrapolations based on the surface sampling of uppercrustal rocks (Rudnick and Gao, 2003). After the plate-tectonic revolution, Taylor (1967,1977), proposed the ‘island arc’ or ‘andesite’ model which posited that bulk crustal chemicalcomposition is equivalent to the average convergent-margin andesite. Several problems withthis theory subsequently emerged; for example, average surficial island arc composition ismore basaltic than andesitic, and any crustal growth via basaltic basal accretion will onlyserve to magnify this compositional contrast. Hence this model cannot account for the in-termediate composition of post-Archean crust. Recognition of these shortcomings led tothe analysis of data from crustal refraction seismic surveys to interrogate middle and lowercrustal levels and place constraints on their composition (Christensen and Mooney, 1995;Rudnick and Fountain, 1995). Unlike these latter studies, which also utilize surface andexposed upper-crust geological information, the present work adopts a bulk crustal seismicanalysis along the lines of Smithson et al. (1981) although, whereas these authors utilize254.2. Crustal composition3.4 3.5 3.6 3.7 3.8 3.9 4.0Vs [km/s]6.06.26.46.66.87.07.27.4Vp [km/s]tonalite gneissparagneissgreenstoneanorthositedioritemafic garnet granulitegranite gneissconstant Vp/VsCanadaSlave ProvinceGrenville ProvinceChurchill ProvinceSuperior ProvinceCanadian ShieldPlatformsOrogensVp/Vs = 1.81Vp/Vs = 1.73Vp/Vs = 1.65Figure 4.2: VP and VS estimates for crustal lithologies represented in the Kapuskasing Uplift(Percival and Card, 1983) and for major geologic provinces as determined in this study.Errors associated with rock velocities are sample deviations from lab measurements (Chris-tensen, 1996). Errors for seismic velocity measurements for geological provinces are calcu-lated from the bootstrap VP/VS error and from the standard deviations from geographicalsampling of Crust 1.0 VP data. The latter values are likely to represent the lower range oftrue variability since averaging has been applied to the Crust 1.0 model.mean crustal VP , this study combines VP/VS from teleseismic measurements with constraintson VP from previous active source studies as represented in the Crust 1.0 model.In Figure 4.2 we plot our area-weighted seismic velocity estimates for the entire Canadiandata set along with area-weighted average estimates for various tectonic subdivisions on agraph of VP versus VS. Also plotted are velocity measurements at 600MPa for major rocktypes exposed in a crustal cross-section of the Superior Province of the Canadian shield (e.g.,Percival and Card, 1983; Percival, 1986; Percival and West, 1994). This exposure, knownas the Kapuskasking uplift, comprises Archean granite-greenstone terranes dominated by264.2. Crustal compositiongranitoid and gneissic rocks, and contains supracrustal assemblages generally metamorphosedfrom greenschist to amphibolite and granulite facies and early plutonic structures composedlargely of tonalites and their metamorphosed equivalents. Tonalites and granodiorites arealso part of the TTG (tonalite-trondhjemite-granodiorite) suite which dominates Archaeancratons (Rudnick, 1995). Rock unit velocity measurements are taken from the compilationsof laboratory velocity measurements by Christensen (1996). Dashed lines delimit isocontoursin VP/VS.Seismic velocity estimates for Canada and the Canadian Shield fall approximately midwaybetween those of two lithologies: granite gneiss and diorite. This intermediate or andesiticcomposition is in general agreement with other earlier studies of bulk crustal composition(e.g., Rudnick and Fountain, 1995; Christensen and Mooney, 1995). The Slave, Superior andChurchill provinces possess comparable seismic velocities implying similar mean crustal com-positions, with the Slave province represented as slightly more felsic in average composition.All three provinces cluster about the average value for the Canadian Shield. The GrenvilleProvince, in contrast, displays markedly lower VS and higher VP/VS implying a significantdifference in mean composition. The high VP/VS estimate, 1.77, is similar to the averagedresults from a previous seismic study (Eaton et al., 2005) that included stations of the centralGrenville Orogen, and might be interpreted to indicate that the Grenville province possessesa mean crustal composition that is more mafic than other Shield provinces. This interpreta-tion is consistent with a tectonic model for the the central Grenville Province which suggeststhat the mid-crust has been completely excised locally (Eaton et al., 2005; Rivers, 2008).However, many stations in the south-east of the province also display VP/VS ratios greaterthan 1.78, similar to the high values common in the adjacent St. Lawrence Platform (wherethe mean seismic ratio is 1.79). Moreover, and more importantly, the average VP value forthe Grenville province (a region well sampled by refraction profiles) requires low VS valuesthat are inconsistent with a more mafic composition. Alternative explanations that allowfor both high VP/VS and reduced VP , VS include the possibility that Grenville crust contains274.2. Crustal compositionOxide,wt%Pakiserand Robin-son (1966)Smithson(1978)ChristensenandMooney(1995)Rudnickand Gao(2003)This Work:CanadaThis Work:CanadianShieldSiO2 57.9 63.0 61.7 60.6 64.3 61.9TiO2 1.2 0.7 0.9 0.7 0.6 0.7Al2O3 15.2 15.8 14.7 15.9 15.5 15.9Fe2O3 2.3 2.0 1.9 — 1.9 2.1FeO 5.5 3.4 5.1 6.7 3.3 3.9MnO 0.2 0.1 0.1 0.1 0.1 0.1MgO 5.3 2.8 3.1 4.7 2.2 2.8CaO 7.1 4.6 5.7 6.4 4.3 5.1Na2O 3.0 4.0 3.6 3.1 3.6 3.6K2O 2.1 2.7 2.1 1.8 2.9 2.5P2O5 0.3 — 0.2 0.1 0.2 0.2Table 4.1: Bulk crustal composition comparison from selected sources. Rudnick and Gao(2003) use total Fe expressed as FeO.higher fluid concentrations than that of other tectonic provinces.Since our average seismic velocity estimates for the entire Canadian crust lie on a lineconnecting diorite with granite gneiss (Figure 4.2), we may estimate bulk crustal compositionin proportion to these lithologies as 51% diorite and 49% granite. A similar estimate forthe Canadian Shield is 68% diorite and 32% granite. This lithological ratio is applied tothe chemical compositions of diorite and granite using chemical averages calculated from141 diorite and 197 granite samples compiled by Le Maitre (1976) to produce an estimateof bulk crustal composition. The resulting chemical breakdown is provided in Table 4.1along with selected previous estimates from global crustal studies. Our analysis reveals acrust with marginally higher average SiO2 composition and lower MgO content than theseprevious studies. Figure 4.3 displays the major crustal oxide contributions from the datain table 4.1 normalized to our average Canadian crustal composition. The gray-shadedregion indicates the ±30% range from our reference average Canadian crustal model. Alsoincluded in this figure is our average crustal composition estimate for the Canadian Shield.Its values closely track the global crustal estimates of Christensen and Mooney (1995) andSmithson (1978). These data imply that the extensive platform and orogenic regions of284.2. Crustal compositionSiO2Al2O3FeO MgO CaO Na2O K2O0.51.01.52.02.5Pakiser and Robinson (1966)Smithson (1978)Christensen and Mooney (1995)Rudnick and Gao (1995)This study, Canadian ShieldFigure 4.3: Comparisons of crustal composition from selected studies following Rudnick andGao (2003). All estimates have been normalized to the results computed in this study.294.2. Crustal compositionCanada skew the average composition away from the global estimates of previous studiesthrough incorporation of a higher proportion of granite gneiss. In contrast, the CanadianShield crust aligns more closely with these previous global estimates.Other researchers have employed Poisson’s ratio (Zandt and Ammon, 1995) alone as anindicator of bulk crustal composition, and crustal thickness and P-wave velocity (Durrheimand Mooney, 1991) to constrain models of crustal formation. Zandt and Ammon (1995)report high VP/VS values for Archean Shields and concluded that no systematic differencebetween Archean and Proterozoic crust exists. Their average, VP/VS = 1.84, is much higherthan our weighted average for the Canadian Shield of 1.74. These authors also report alow VP/VS value for Cenozoic-Mesozoic crust (1.73) compared to the corresponding valuefor our data of 1.77. They interpret these high values for older crust and low values foryounger crust as favouring either of two limiting models. In the uniformitarian model, adelamination type process operates to remove mafic lower crust during continental collisionsand is subsequently followed by a stabilizing process involving basaltic underplating. Thismodel implies that younger crust has yet to complete the full crustal genesis cycle. In theirsecond model, Precambrian cratons evolve through unique processes and younger orogensinvolve the recycling and reworking of existing material with little net crustal growth.Whereas Zandt and Ammon present evidence for increasing VP/VS with crustal age,our VP/VS estimates decrease with crustal age. This decrease is, however, slight from 1.76in Proterozoic units to 1.74 in the Archean crust and may not be significant. Similarly,whereas Durrheim and Mooney (1991) argue for thicker (>5km) Proterozoic crust withhigher P -velocity than Archean crust, the crustal thickness data presented in this studyreveals no significant difference between Proterozoic crust (38.9 km) and Archean crust(38.0 km). 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Thesis/Dissertation
eng
Geophysics
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivs 2.5 Canada
Graduate
Seismic velocities and composition of the Canadian crust
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