Two-dimensional deep learning magnetotelluric inversion

Abstract Deep learning (DL) inversion methods have showcased promising applications in solving geophysical inverse problems. In this work, we develop a DL model (SwinTUNet) to enable accurate reconstruction of the nonlinear mapping between the network inputs and outputs and achieve two-dimensional (2-D) magnetotelluric (MT) inversion. SwinTUNet is built by employing the strong Swin Transformer as the backbone network and adopting the UNet architecture. Moreover, to create a training dataset that aligns with practical MT inverse problems and ensuring effective applications, we design and generate a set of random synthetic resistivity models with gradual-varying resistivity values. Inversion examples demonstrate that the proposed SwinTUNet inversion method holds a great promise in promoting the applicability of DL inversion methods in realistic MT prospecting scenarios.