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When building 3D models of the subsurface, reconciling several geological and geophysical data of diverse nature, resolutions, coverage, or sensitivity, is challenging, both numerically and petrophysically. In this work, we propose a workflow for mapping selected geological features and characterise their uncertainty using a Bayesian Estimate Fusion algorithm. Different datasets such as 1D probabilistic models derived from geophysical data, drillholes 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 in the cover, the Tregolana Shale, using 1D magnetotelluric (MT) and 1D Airborne Electromagnetic (AEM) probabilistic models, drill holes and surface geology; (2) we map the depth to basement using 1D probabilistic MT models, drill holes and interpreted structural information. Our results show that the different resolution, data sampling, depth of investigation and reliability of the utilised 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 characterisation and reduce uncertainty when mapping the location of features of interest, but also permit the testing of 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. [ABSTRACT FROM AUTHOR]

In this paper, we use a new workflow to substantiate the characterization of a prominent, deep sediment conductor in the hyperextended Bjørnøya Basin (SW Barents Sea) previously identified in smooth resistivity models from 3-D deterministic inversion of magnetotelluric data. In low-dimensionality environments like layered sedimentary basin, 1-D Bayesian inversion can be advantageous for a thorough exploration of the solution space, but the violation of the 1-D assumption has to be efficiently handled. The primary geological objectives of this work is therefore preceded by a secondary task: the application of a new machine learning approach for handling the 1-D violation assumption for 21 MT field stations in the Barents Sea. We find that a decision tree can adequately learn the relationship between MT dimensionality parameters and the 1-D–3-D residual response for a training set of synthetic models, mimicking typical resistivity structures of the SW Barents Sea. The machine learning model is then used to predict the dimensionality compensation error for MT signal periods ranging of 1–3000 s for 21 receivers located over the Bjørnøya Basin and Veslemøy High. After running 1-D Bayesian inversion, we generated a posterior resistivity distribution for an ensemble of 6000 1-D models fitting the compensated MT data for each 21 field stations. The proportion of 1-D models showing ρ < 1 Ω·m is consistently beyond 80 per cent and systemically reaches a maximum of 100 per cent in the Early Aptian–Albian interval in the Bjørnøya Basin. In hyperextended basins of the SW Barents Sea, the dimensionality compensation workflow has permitted to refine the characterization of the deep basin conductor by leveraging the increased vertical resolution and optimal used of MT data. In comparison, the smooth 3-D deterministic models only poorly constrained depth and lateral extent of the basin anomaly. The highest probability of finding ρ < 1 Ω·m is robustly assigned to the syn-tectonic Early Aptian–Albian marine shales, now buried at 6–8 km depth. Based on a theoretical two phase fluid-rock model, we show that the pore fluid of these marine shales must have a higher salinity than seawater to explain the anomaly ρ < 1 Ω·m. Therefore, the primary pore fluid underwent mixing with a secondary brine during rifting. Using analogue rift systems in palaeomargins, we argue that two possible secondary brine reservoir may contribute to deep saline fluid circulation in the hyperextended basin: (1) Permian salt-derived fluid and, (2) mantle-reacted fluid from serpentinization. [ABSTRACT FROM AUTHOR]