Abstract
The inverse problem of magnetotelluric data is extremely difficult due to its nonlinear and ill-posed nature. The existing gradient-descent approaches for this task surface from the problems of falling into local minima and relying on reliable initial models, while statistical-based methods are computationally expensive. Inspired by the excellent nonlinear mapping ability of deep learning, in this study, we present a novel magnetotelluric inversion method based on fully convolutional networks. This approach directly builds an end-to-end mapping from apparent resistivity and phase data to resistivity anomaly model. The implementation of the proposed method contains two stages: training and testing. During the training stage, the weight sharing mechanism of fully convolutional network is considered, and only the single anomalous body model samples are used for training, which greatly shortens the modeling time and reduces the difficulty of network training. After that, the unknown combinatorial anomaly model can be reconstructed from the magnetotelluric data using the trained network. The proposed method is tested in both synthetic and field data. The results show that the deep learning-based inversion method proposed in this paper is computationally efficient and has high imaging accuracy.
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Acknowledgements
The research is sponsored by the National Key Research and Development Program of China (No. 2018YFC1505401), the Research and Development Projects of Sichuan Science and Technology Department (No. 2019YF0460, 2020YGF0303, 2021YJ0031), and the Technology Research and Development Program of China Railway Group Limited (No. CZ01-Key Point-05).
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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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Edited by Prof. Gabriela Fernández Viejo (CO-EDITOR-IN-CHIEF).
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Liao, X., Shi, Z., Zhang, Z. et al. 2D inversion of magnetotelluric data using deep learning technology. Acta Geophys. 70, 1047–1060 (2022). https://doi.org/10.1007/s11600-022-00773-z
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DOI: https://doi.org/10.1007/s11600-022-00773-z