Successive Deep Perceptual Constraints for Multiphysics Joint Inversion

Deep learning techniques have been used to enhance the joint inversion process of dc resistivity and seismic travel time data. Specifically, we introduce deep perceptual losses (DPLs) derived from a pretrained edge detection network. These losses play a pivotal role in enforcing structural constraints during the training of encoder-decoder networks for model prediction. Our approach is based on the assumption of structural similarity, meaning that we presume a shared structure between pairs of resistivity and velocity models, without necessitating prior relationships between the property values. We provide an exhaustive exposition of our joint inversion framework, elucidating the network construction and training dataset design. Numerical examples demonstrate our DPL-based method outperforms the conventional separate inversions and cross-gradient-based joint inversion in view of recovered property values and boundaries of the anomalous bodies. Furthermore, our approach retains the traditional workflows of separate inversions, endowing our framework with the flexibility to expand into multiphysics joint inversion scenarios. To illustrate this adaptability, we incorporate induced polarization data into the framework, further validating its efficacy.