Solid Earth Discuss., https://doi.org/10.5194/se-2019-4 Manuscript under review for journal Solid Earth Discussion started: 15 January 2019 c Author(s) 2019. CC BY 4.0 License. Bayesian geological and geophysical data fusion for the construction and uncertainty quantification of 3D geological models Hugo K. H. Olierook1, Richard Scalzo2, David Kohn3, Rohitash Chandra2,4, Ehsan Farahbakhsh2,4, Gregory Houseman3, Chris Clark1, Steven M. Reddy1, R. Dietmar Müller4 5 1School of Earth and Planetary Sciences, Curtin University, GPO Box U1987, Perth, WA 6845, Australia 2Centre for Translational Data Science, University of Sydney, NSW 2006 Sydney, Australia 3Sydney Informatics Hub, University of Sydney, NSW 2006 Sydney, Australia 4EarthByte Group, School of Geosciences, University of Sydney, NSW 2006 Sydney, Australia Correspondence to: Hugo K. H. Olierook (
[email protected]) 10 Abstract. 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 data. Here, we demonstrate a Bayesian methodology to fuse geological field observations with aeromagnetic and gravity data to build robust 3D models in a 13.5 × 13.5 km region of the Gascoyne Province, Western Australia. Our approach is validated by comparing model results to independently-constrained 15 geological maps and cross-sections produced by the Geological Survey of Western Australia. By fusing geological field data with magnetics and gravity surveys, we show that at 89% of the modelled region has >95% certainty. The boundaries between geological units are characterized by narrow regions with <95% certainty, which are typically 400–1000 m wide at the Earth’s surface and 500–2000 m wide at depth.