Oral Presentation Sub22 Conference

When building 3D models of the subsurface, reconciling several geological and geophysical data of diverse nature, resolutions, coverage or sensitivity, is challenging, from a numerical and petrophysical point of view. In this work, we propose a workflow for mapping selected geological features and characterize their uncertainty using a Bayesian Estimate Fusion algorithm. Different datasets such as probabilistic models derived from geophysical data, drill holes and geological data are combined to produce probabilistic maps of selected geological boundaries, relying on petrophysical and geological assumptions. Leveraging large, high-quality geophysical datasets acquired in the eastern Gawler Craton in South Australia, we demonstrate the applicability of our approach with two examples: 1) we map in 3D the top of a stratigraphic unit located in the cover, the Tregolana Shale, using magnetotelluric (MT), Airborne Electromagnetic (AEM), drill holes and surface geology; 2) we map the depth to basement using MT, drill holes and interpreted structural information. Our results show that the different resolution, data sampling, depth of investigation and reliability of the different datasets can be combined in a complementary fashion, overcoming their respective limitations, to find solutions/models that satisfy all the datasets. We show that probabilistic workflows permit to characterize and reduce uncertainty when mapping the location of features of interest, but also permit to test geological hypotheses against other geophysical and geological data. These types of models are valuable to better characterise, interpret and conceptualise the subsurface, enabling better exploration targeting and supporting efforts to discover new mineral deposits.