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Physics-embedded deep learning inversion for transient electromagnetic method survey data
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DOI：10.1016/j.cageo.2025.106000.png)
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Abstract
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• An unsupervised deep learning network embedding the physical laws is proposed for TEM inversion. • A loss function incorporating dynamics smoothing constraints is constructed to enhance network training efficiency. • A joint LSTM-Attention network and WOA-MVMD technique is presented to improve the inversion precision.
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