Magnetotelluric Inversion Based on Double-Layer Convolutional Neural Network

Abstract

A double-layer neural network combining a fully convolutional network (FCN) and U-Net is introduced to improve the accuracy of 2.5-D magnetotelluric (MT) inversion. The initial model obtained through the Bostick inversion method is randomly transformed to generate the dataset for the training of the convolutional neural network (CNN). The training input consists of the apparent resistivity obtained through the forward modeling of transformed models, while the output represents the resistivity of those same models. The proposed method has the local optimization of the neural network inversion method and narrows the range of network optimization by employing an initial solution obtained from the Bostick inversion method. The results of the inversion experiments, including an actual measurements, demonstrate a significant enhancement in the accuracy of the inversion results when employing the neural network method. This demonstrates the efficiency of neural networks in solving 2.5-D magnetotelluric (MT) inversion problems.