A 3-D Magnetotelluric Inversion Method Based on the Joint Data-Driven and Physics-Driven Deep Learning Technology
Weiwei Ling, Kejia Pan, Jiajing Zhang, Dongdong He, Xin Zhong, Zhengyong Ren, Jingtian Tang
Abstract
The conventional magnetotelluric inversion method is subject to the influence of the initial model, which leads to an unstable inversion process and a tendency to get trapped at local optimal solutions. In contrast, deep learning technology relies on its powerful non-linear fitting capability and can construct complex non-linear mappings directly from observation data (input) to model (output). In recent years, it has received extensive attention from researchers. Due to the difficulties in creating a sufficiently large dataset and performing extensive neural network training, most current magnetotelluric inversion methods for geophysical exploration remain limited to one-dimensional (1D) or two-dimensional (2D) scenarios. To the best of our knowledge, for deep learning-based three-dimensional (3D) magnetotelluric inversion, currently there is no reported work in the literature. In this work, we propose a 3D magnetotelluric inversion method based on deep learning technology. By designing a neural network architecture for 3D structures (MT3D-Net), we achieve an end-to-end mapping from the network input to output. To alleviate the excessive dependence of the network on the training set, we introduce a joint weighted loss function based on data-driven and physics-driven method, allowing the network to follow the physical constraints of magnetotelluric data during the training process and thus more reasonably guide the update of network parameters. Numerical experiments show that this method combines the advantages of traditional and data-driven inversions, significantly improving the stability and accuracy of magnetotelluric inversion. The proposed method has been successfully applied to synthetic models and measured field data, and has good application prospects.