Inverting magnetotelluric data using a physics-guided auto-encoder with scaling laws extension

Artificial neural networks (ANN) have gained significant attention in magnetotelluric (MT) inversions due to their ability to generate rapid inversion results compared to traditional methods. While a well-trained ANN can deliver near-instantaneous results, offering substantial computational advantages, its practical application is often limited by difficulties in accurately fitting observed data. To address this limitation, we introduce a novel approach that customizes an auto-encoder (AE) whose decoder is replaced with the MT forward operator. This integration accounts for the governing physical laws of MT and compels the ANN to focus not only on learning the statistical relationships from data but also on producing physically consistent results. Moreover, because ANN-based inversions are sensitive to variations in observation systems, we employ scaling laws to transform real-world observation systems into formats compatible with the trained ANN. Synthetic and real-world examples show that our scheme can recover comparable results with higher computational efficiency compared to the classic Occam’s inversion. This study not only perfectly fits the observed data but also enhances the adaptability and efficiency of ANN-based inversions in complex real-world environments.