One-dimensional magnetotelluric parallel inversion using a ResNet1D-8 residual neural network

• Published in: Computers & geosciences 2023-11, Vol.180, p.105454, Article 105454
• Main Authors: Ling, Weiwei, Pan, Kejia, Ren, Zhengyong, Xiao, Wenbo, He, Dongdong, Hu, Shuanggui, Liu, Zhengguang, Tang, Jingtian
• Format: Article
• Language: English
• Subjects: algorithms; China; data collection; Deep learning; electrical conductivity; Generalization ability; Magnetotelluric method; neural networks; Parallel technology; Residual neural network
• ISSN: 0098-3004; 1873-7803
• DOI: 10.1016/j.cageo.2023.105454

Deep Learning is an effective tool to invert the underground electrical conductivity structure. In this study, we build a new 8-layer residual neural network (ResNet1D-8) for audio magnetotelluric (AMT) data inversion based on the deep learning theory. In terms of the network structure, the degradation of the model with the increase of depth is effectively avoided by adding shortcut connections. Meanwhile, adding a batch normalization layer greatly improves the model training speed and network generalization ability. This study uses the parallel technology to quickly generate millions of samples, which effectively reduces the computational time and provides a large number of high-quality samples for deep learning model training. Compared with the simulated annealing algorithm, the neural network model in this paper has the advantages of high reliability, short inversion time, and strong model generalization ability. Moreover, we add Gaussian noise to the data of testing samples, and inversion results show that the model has good robustness. The inversion test is carried out on the field measured AMT data set collected in the Dachaidan area of Qinghai Province, China. The results show that the ResNet1D-8 residual neural network model established in this paper can effectively invert the underground electrical structure. •A new residual neural network model (ResNet1D-8) for 1-D MT inversion is constructed.•A uniform stochastic model and parallel techniques are used to rapidly generate samples.•The generalizability and robustness of ResNet1D-8 are verified through numerical experiments.