Summary
In this article, we use a new workflow to substantiate the characterization of a prominent, deep sediment conductor in the hyper-extended Bjørnøya Basin (SW Barents Sea) previously identified in smooth resistivity models from 3D deterministic inversion of magnetotelluric data. In low dimensionality environments like layered sedimentary basin, 1D Bayesian inversion can be advantageous for a thorough exploration of the solution space but the violation of the 1D 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 1D 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 1D-3D 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 to 3000 s for 21 receivers located over the Bjørnøya Basin and Veslemøy High. After running 1D Bayesian inversion, we generated a posterior resistivity distribution for an ensemble of 6000 1D models fitting the compensated MT data for each 21 field stations. The proportion of 1D models showing ρ < 1 Ω.m is consistently beyond 80% and systemically reaches a maximum of 100% in the Early Aptian - Albian interval in the Bjørnøya Basin. In hyper-extended 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 3D 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 to 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 paleomargins, we argue that two possible secondary brine reservoir may contribute to deep saline fluid circulation in the hyper-extended basin: (1) Permian salt-derived fluid and, (2) mantle-reacted fluid from serpentinization.