Integrating Physical Information Constraints for Magnetotelluric Data-Driven Inversion

Abstract

Deep learning (DL) techniques have been tentatively explored for geophysical inversion, but most inversion networks currently rely on a purely data-driven mode, where the training process is highly dependent on the data sets and lacks physical information constraints. In this article, we propose a physically constrained, data-driven magnetotelluric (MT) inversion algorithm that jointly optimizes the model and forward response by incorporating data misfit into the loss function. However, integrating traditional forward operators into modern DL frameworks presents challenges in computational efficiency and gradient propagation. To address this, we develop a high-precision DL-based forward operator to compute MT response misfit, enabling seamless integration and accelerated training. Test results show that over 95% of the relative errors in forward responses are within ±1%. An attention-based deep residual network (ADRN) is then employed to map MT responses to the 1-D geoelectric model, with the pretrained forward operator imposing physical constraints during inversion training to enhance generalization. Inversion results from both simulated data and marine MT data from the South Yellow Sea show that the physically constrained, data-driven inversion method improves robustness compared to the traditional purely data-driven mode. It effectively reduces the data mean square error (MSE) by approximately 70% on average while achieving nearly the same model misfit, thereby providing more reliable technical support for geophysical exploration.