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Computing geologically consistent models from geophysical data

University of British Columbia

In this thesis an attempt is made to develop a methodology by which the information provided by downhole physical property logs can be leveraged to assist in the creation of constraints for the inversion of surface geophysics. I first motivate the research with an introduction to the utility of downhole physical property logging, including an overview of the diverse methods and data which can be acquired. Background information is also provided on statistical classification techniques and the UBC-GIF (University of British Columbia Geophysical Inversion Facility) inversion formulation so that the methodology can be properly understood. The introduced methodology differs from previous attempts at incorporation of a priori information since it applies statistical classification of in situ physical property measurements (as opposed to physical property values inferred from geology) as the basis for constraints. Statistical classification, combined with the iterative nature of the scheme, act to propagate the information from the downhole physical property logs through-out the model with minimum user input required. This automated approach reduces the potential for bias from unsupported constraints, while maximizing the integration of the classification results. The methodology is explained, and then demonstrated on three simple illustrative models. The results from these demonstrations are compared against unconstrained inversion, and the strengths and shortcomings of the methodology are discussed.

Text

2011-12-02

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/39442

Geophysics

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/