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Bayesian geological and geophysical data fusion for the construction and uncertainty quantification of 3D geological models

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Geoscience Frontiers 2021年第1期12卷 479-493页

作者： Hugo K.H.Olierook Richard Scalzo David Kohn Rohitash Chandra Ehsan Farahbakhsh chris clark.steven m.reddy R.Dietmar müller School of Earth and Planetary Sciences Curtin UniversityGPO Box U1987PerthWA6845Australia Centre for Translational Data Science University of SydneyNSW2006SydneyAustralia Sydney Informatics Hub University of SydneyNSW2006SydneyAustralia EarthByte Group School of GeosciencesUniversity of SydneyNSW2006SydneyAustralia Timescales of mineral Systems and John de Laeter Centre Curtin UniversityGPO Box U1987PerthWA6845Australia School of mathematics and Statistics University of New South WalesSydneyNSW2052Australia Department of mining Engineering Amirkabir University of Technology(Tehran Polytechnic)TehranIran

Traditional approaches to develop 3D geological models employ a mix of quantitative and qualitative scientific techniques,which do not fully provide quantification of uncertainty in the constructed models and fail to optimally weight geological field observations against constraints from geophysical ***,using the Bayesian Obsidian software package,we develop a methodology to fuse lithostratigraphic field observations with aeromagnetic and gravity data to build a 3D model in a small(13.5 km×13.5 km)region of the Gascoyne Province,Western *** approach is validated by comparing 3D model results to independently-constrained geological maps and cross-sections produced by the Geological Survey of Western *** fusing geological field data with aeromagnetic and gravity surveys,we show that 89%of the modelled region has>95%certainty for a particular geological unit for the given model and *** boundaries between geological units are characterized by narrow regions withm wide at the Earth's surface and 500-2000 m wide at ***~4 km depth,the model requires geophysical survey data with longer wavelengths(e.g.,active seismic)to constrain the deeper *** Obsidian was originally built for sedimentary basin problems,there is reasonable applicability to deformed terranes such as the Gascoyne ***,modification of the Bayesian engine to incorporate structural data will aid in developing more robust 3D ***,our results show that surface geological observations fused with geophysical survey data can yield reasonable 3D geological models with narrow uncertainty regions at the surface and shallow subsurface,which will be especially valuable for mineral exploration and the development of 3D geological models under cover.

关键词： Capricorn orogen machine learning Bayesian inference markov chain monte Carlo Solid earth mineral exploration

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