2D magnetotelluric inversion based on ResNet  

作　　者：LiAn Xie Bo Han Xiangyun Hu Ningbo Bai 

机构地区：[1]Institute of Geological Survey,China University of Geosciences,Wuhan,430074,China [2]Hubei Subsurface Multi-Scale Imaging Key Laboratory,School of Geophysics and Geomatics,China University of Geosciences,Wuhan,430074,China [3]Key Laboratory of Geological Survey and Evaluation of Ministry of Education,China University of Geosciences,Wuhan,430074,China

出　　处：《Artificial Intelligence in Geosciences》2023年第1期119-127,共9页地学人工智能（英文）

基　　金：co-funded by the National Natural Science Foundation of China(No.42220104002,42174095,U1812402,and 41630317);the Opening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education(No.GLAB2022ZR10);the Fundamental Research Funds for the Central Universities,China University of Geosciences(Wuhan).

摘　　要：In this study,a deep learning algorithm was applied to two-dimensional magnetotelluric(MT)data inversion.Compared with the traditional linear iterative inversion methods,the MT inversion method based on convolutional neural networks(CNN)does not rely on the selection of the initial model parameters and does not fall into the local optima.Although the CNN inversion models can provide a clear electrical interface division,their inversion results may remain prone to abrupt electrical interfaces as opposed to the actual underground electrical situation.To solve this issue,a neural network with a residual network architecture(ResNet-50)was constructed in this study.With the apparent resistivity and phase pseudo-section data as the inputs and with the resistivity parameters of the geoelectric model as the training labels,the modified ResNet-50 model was trained end-to-end for producing samples according to the corresponding production strategy of the study area.Through experiments,the training of the ResNet-50 with the dice loss function effectively solved the issue of over-segmentation of the electrical interface by the cross-entropy function,avoided its abrupt inversion,and overcame the computational inefficiency of the traditional iterative methods.The proposed algorithm was validated against MT data measured from a geothermal field prospect in Huanggang,Hubei Province,which showed that the deep learning method has opened up broad prospects in the field of MT data inversion.

关 键 词：MAGNETOTELLURICS 2D inversion Residual network Deep learning 

分 类 号：F42[经济管理—产业经济]