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Enhanced Geologic Modeling with Data-Driven Training Images for Improved Resources and Recoverable Reserves Open Access


Other title
Multiple Point Statistics Simulation
Linear Opinion Pool
Geologic Domains
Type of item
Degree grantor
University of Alberta
Author or creator
Silva Maureira, Daniel A
Supervisor and department
Deutsch, Clayton (Civil and Environmental)
Examining committee member and department
Leung, Juliana (Civil and Environmental)
Apel, Derek (Civil and Environmental)
Boisvert, Jeff (Civil and Environmental)
Musilek, Petr (Electrical and Computer Engineering)
Coimbra Leite Costa, Joao Felipe (Federal University of Rio Grande do Sul)
Department of Civil and Environmental Engineering
Mining Engineering
Date accepted
Graduation date
Doctor of Philosophy
Degree level
Deterministic geologic modeling methods accurately characterize large-scale continuous features of geological phenomena, but often fail in reproducing their inherent short-scale variability. The opposite is the case with stochastic methods that lack large-scale continuity yet contain reasonable short-scale variability. Both methods are limited in their ability to account for a balanced amount of geologic variability. Integrating both large and short scale geologic elements properly improves the prediction of mineral resources and reserves. This thesis develops a methodology that improves the characterization of the geologic variability in mineral deposits. The central idea is to combine deterministic and stochastic geologic interpretations and transfer the essential geological features into geostatistical models. The multiple point statistics (MPS) simulation method is suitable for this task. This technique utilizes training images for extracting and then reproducing complicated geomorphological features in the models. The method has been adapted to integrate information from different images. Generally, training images are designed based on conceptual models of the geologic phenomena; in this work, deterministic and stochastic geologic representations are used as data-driven training images, one comes from modeling the categories by an implicit geologic approach, and another comes from the application of conventional sequential indicator simulation (SIS) method. Such data-driven training images permit a robust inference of spatial structure from reasonably spaced drillhole data. This work establishes the principles to integrate multiple training images through a scheme of data integration for conditional probabilities known as a linear opinion pool. A methodology for calibrating the contribution of each training image is developed based on the variability at the available drillholes. A measure of multipoint entropy along the drillholes is matched by the combination of the two training images. The resulting calibrated models integrate geologic features from both training images, reproducing the correct underlying continuity and variability of the deposit, and reducing misclassified ore/waste material. Practical implementation of the methodology shows improvement in the predicted profit relative to classical geostatistical approaches.
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
Citation for previous publication
D.A. Silva and C.V. Deutsch, A Multiple Training Image Approach for Spatial Modeling of Geologic Domains, Mathematical Geosciences, Volume 46, Issue 7, pp 815-840 DD.A. Silva and C.V. Deutsch, CIM Journal, Vol. 6, No. 3, pp 137-148

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