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Bayesian geological and geophysical data fusion for the construction and uncertainty quantification of 3D geological models
Authors:Hugo K.H. Olierook  Richard Scalzo  David Kohn  Rohitash Chandra  Ehsan Farahbakhsh  Chris Clark  Steven M. Reddy  R. Dietmar Müller
Affiliation:School of Earth and Planetary Sciences, Curtin University, GPO Box U1987, Perth, WA, 6845, Australia;Timescales of Mineral Systems and John de Laeter Centre, Curtin University, GPO Box U1987, Perth, WA, 6845, Australia;Centre for Translational Data Science, University of Sydney, NSW, 2006, Sydney, Australia;Sydney Informatics Hub, University of Sydney, NSW 2006, Sydney, Australia;EarthByte Group, School of Geosciences, University of Sydney, NSW, 2006, Sydney, Australia;School of Mathematics and Statistics, University of New South Wales, Sydney, NSW, 2052, Australia;Centre for Translational Data Science, University of Sydney, NSW, 2006, Sydney, Australia;EarthByte Group, School of Geosciences, University of Sydney, NSW, 2006, Sydney, Australia;School of Mathematics and Statistics, University of New South Wales, Sydney, NSW, 2052, Australia;Department of Mining Engineering, Amirkabir University of Technology(Tehran Polytechnic), Tehran, Iran;School of Earth and Planetary Sciences, Curtin University, GPO Box U1987, Perth, WA, 6845, Australia;EarthByte Group, School of Geosciences, University of Sydney, NSW, 2006, Sydney, Australia
Abstract:Traditional approaches to develop 3D geological models employ a mix of quantitative and qualitative scientific techniques, which do not fully provide quantifica...
Keywords:Capricorn orogen  Machine learning  Bayesian inference  Markov chain Monte Carlo  Solid earth  Mineral exploration
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