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Uncertainty estimation for a geological model of the Sandstone greenstone belt, Western Australia; insights from integrated geological and geophysical inversion in a Bayesian inference framework

J. Florian Wellmann, Miguel de la Varga, Ruth E. Murdie, Klaus Gessner and Mark Jessell
Uncertainty estimation for a geological model of the Sandstone greenstone belt, Western Australia; insights from integrated geological and geophysical inversion in a Bayesian inference framework (in Characterization of ore-forming systems from geological, geochemical and geophysical studies, Klaus Gessner (editor), Tom G. Blenkinsop (editor) and P. Sorjonen-Ward (editor))
Special Publication - Geological Society of London (October 2017) 453 (1): 41-56

Abstract

The spatial relationship between different rock types and relevant structural features is an important aspect in the characterization of ore-forming systems. Our knowledge about this geological architecture is often captured in 3D structural geological models. Multiple methods exist to generate these models, but one important problem remains: structural models often contain significant uncertainties. In recent years, several approaches have been developed to consider uncertainties in geological prior parameters that are used to create these models through the use of stochastic simulation methods. However, a disadvantage of these methods is that there is no guarantee that each simulated model is geologically reasonable - and that it forms a valid representation in the light of additional data (e.g. geophysical measurements). We address these shortcomings here with an approach for the integration of structural geological and geophysical data into a framework that explicitly considers model uncertainties. We combine existing implicit structural modelling methods with novel developments in probabilistic programming in a Bayesian framework. In an application of these concepts to a gold-bearing greenstone belt in Western Australia, we show that we are able to significantly reduce uncertainties in the final model by additional data integration. Although the final question always remains whether a predicted model suite is a suitable representation of accuracy or not, we conclude that our application of a Bayesian framework provides a novel quantitative approach to addressing uncertainty and optimization of model parameters. Supplementary material: Trace plots for selected parameters and plots of calculated Geweke statistics are available at https://doi.org/10.6084/m9.figshare.c.3899719


ISSN: 0305-8719
Coden: GSLSBW
Serial Title: Special Publication - Geological Society of London
Serial Volume: 453
Serial Issue: 1
Title: Uncertainty estimation for a geological model of the Sandstone greenstone belt, Western Australia; insights from integrated geological and geophysical inversion in a Bayesian inference framework
Title: Characterization of ore-forming systems from geological, geochemical and geophysical studies
Author(s): Wellmann, J. Floriande la Varga, MiguelMurdie, Ruth E.Gessner, KlausJessell, Mark
Author(s): Gessner, Klauseditor
Author(s): Blenkinsop, Tom G.editor
Author(s): Sorjonen-Ward, P.editor
Affiliation: Rheinisch-Westfaelische Technische Hochschule Aachen, Graduate School Aachen Institute for Advanced Study in Computational Engineering Science, Aachen, Germany
Pages: 41-56
Published: 20171026
Text Language: English
Publisher: Geological Society of London, London, United Kingdom
References: 77
Accession Number: 2017-090209
Categories: Structural geologyApplied geophysics
Document Type: Serial
Bibliographic Level: Analytic
Annotation: Includes appendices
Illustration Description: illus. incl. 1 table, geol. sketch map
S28°15'00" - S27°45'00", E119°00'00" - E119°30'00"
Secondary Affiliation: Geological Survey of Western Australia, AUS, AustraliaUniversity of Western Australia, AUS, Australia
Source Note: Online First
Country of Publication: United Kingdom
Secondary Affiliation: GeoRef, Copyright 2019, American Geosciences Institute. Reference includes data from The Geological Society, London, London, United Kingdom
Update Code: 201747
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