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Implicit neural representations for 3D gravity inversion
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DOI：10.1016/j.cageo.2025.106082.png)
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Abstract
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• An implicit gravity inversion method is developed, the governing physical laws of gravity is embedded into the network optimization. • The proposed method circumvents the need for large training datasets and eliminates the reliance on manually designed regularization terms. • Explored the potential source of implicit regularization effects from the network architecture. • Evaluated on synthetic and the San Nicolas field dataset, the method achieves fidelity reconstructions and exhibits robust performance.
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Fast 3D inversion of gravity data using solution space priorconditioned lanczos bidiagonalization
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