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| Semi-supervised learning inversion of data derived from high-resolution electrical resistivity tomography based on forward modeling constraints |
LI Guo-Hao1,2,3( ), LYU Yu-Zeng1(), DONG Yi-Fan1, YU Hai-Tao2,3 |
1. College of Earth Sciences, Guilin University of Technology, Guilin 541004, China
2. Guangdong Provincial Academy of Building Research Group Co., Ltd., Guangzhou 510500, China
3. Guangdong Construction Engineering Quality & Safety Testing Head Station Co., Ltd., Guangzhou 510500, China |
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Abstract Supervised deep learning inversion relies on labels of subsurface media for training. However, the measured data from electrical resistivity tomography usually lack such labels. Supervised learning based on significant synthetic data generated through forward modeling fails to obtain reliable inversion results for complex data outside the training set. This study proposed a semi-supervised learning inversion method based on forward modeling constraints, combining with labeled and unlabeled data for training, to enhance the inversion accuracy and the capability to characterize complex geological structures. The inversion results of synthetic data demonstrate the strong generalization capability of the proposed method. The inversion results of measured data indicate that compared to conventional least-squares inversion, semi-supervised inversion provides higher resolution and particularly more accurate characterization of vertical lithological boundaries. Overall, the proposed method offers a novel approach for the inversion of the data derived from high-resolution electrical resistivity tomography.
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Received: 21 October 2024
Published: 22 July 2025
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Semi-supervised learning inversion flowchart
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DualChannel architecture
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DualChannelUNet architecture
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Synthetic electrical dataset model
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Change curve of quantitative evaluation indicators
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Synthetic data inversion result comparison
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Field data and inversion results of the Shaodong water exploration project
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