MT2DInv-Unet: A two-dimensional magnetotelluric inversion method based on deep learning technology

Abstract

Traditional gradient-based inversion methods usually suffer from the problems of falling into local minima and relying heavily on initial guesses. Deep learning methods have received increasing attention due to their excellent nonlinear fitting ability. However, given the recent application of deep learning methods in the field of MT inversion, there are currently challenges associated with achieving high inversion resolution and extracting sufficient features. We develop a neural network model (called MT2DInv-Unet) based on the deformable convolution for two-dimensional MT inversion to approximate the nonlinear mapping from the MT response data to the resistivity model. The deformable convolution is achieved by adding an additional offset to each sample point of the conventional convolution operation, which extracts hidden relationships and allows the flexible adjustment of the size and shape of the feature region. Meanwhile, we design the network structure with multi-scale residual blocks, which effectively extract the multi-scale features of the MT response data. This design not only enhances the network performance but also alleviates issues such as vanishing gradients and network degradation. The results of synthetic models show that the proposed network inversion method has stable convergence, good robustness and generalization performance, and performs better than the fully convolutional neural network (FCN) and U-Net network. Finally, the inversion results of field data show that MT2DInv-Unet can effectively obtain a reliable underground resistivity structure, and has a good application prospect in MT inversion.