Invertible Fourier Neural Operator for Inverse Problem in Magnetotellurics

In Magnetotellurics (MT), limited by the computing efficiency of traditional methods, time-consuming is an urgent research topic for complex models. In the past few years, artificial intelligence has been applied to speed up calculations of partial differential equations. Such artificial intelligence methods are also applied in MT. These MT inversion algorithms are based on designing forward models using artificial intelligence. They are used to replace the traditional forward modeling algorithms for speedup in the inverse algorithms. However, it didn't directly solve the inverse problem. A novel approach with the combination of the invertible neural network and Fourier neural operator is proposed in this paper. Due to the reversibility of the proposed approach, the inverse process is completed by training the forward process in the meantime. In addition, it can perform bidirectional training for higher accuracy of the inverse problem. The experimental results show that the approach can perform both forward simulation and inverse problem prediction well simultaneously.