Deterministic geophysical inversion suffers from a lack of realism because of the regularization, while stochastic inversion allowing for uncertainty quantification is computationally expensive. In this contribution, we propose to use Bayesian Evidential Learning as an alternative to hydrogeophysical coupled inversion. We demonstrate the ability of the approach to successfully predict a hydrogeological target from time-lapse ERT data in the context of a heat injection and storage experiment.

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