Multitask Learning-Driven Physics-Guided Deep Learning Magnetotelluric Inversion

An ongoing trend seeking to incorporate forward modeling, which involves the physical laws of wave propagation, into the network architecture to improve the generalization capability of the deep learning (DL) inversion method has showcased promising applications. However, directly embedding the time-consuming 2-D magnetotelluric (MT) forward modeling solved by conventional numerical algorithms to facilitate physics-guided DL MT inversion, which usually necessitates millions of forward operations during a complete training session, is challenging. Hence, in this work, we develop a physics-guided DL inversion method (PGWNet) by constructing a W-shaped DL model and performing a multitask learning strategy. The DL model consists of one encoder and two decoders, where the two decoders are independent of each other and share the encoder. During the training process, two decoders are first optimized independently by minimizing the model misfit, quantifying the discrepancy between the predicted and labeled resistivity models, and the data misfit, quantifying the discrepancy between the predicted and labeled MT responses, respectively. When model and data misfits backpropagate to the encoder, they are combined to jointly optimize the encoder. Moreover, to ensure practical application effect, this work builds a set of random synthetic resistivity models with gradually varying resistivity values to delineate realistic subsurface structures. We substantiate the developed PGWNet inversion method using synthetic and actual MT data and benchmark it against a fully data-driven DL inversion method and the conventional least-squares regularization inversion method. It is anticipated to promote the practicability and applicability of the DL inversion method in practical MT prospecting scenarios.