Introduces a staged pretrain-to-alignment workflow for geophysical AI that improves relative geologic time estimation across global field surveys despite limited labels and domain gaps.
Current state and future directions for deep learning based automatic seismic fault interpretation: A systematic review
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Pretrain-to-alignment learning paradigm to improve geophysical AI applicability under scarce field labels and synthetic-to-field gaps: A case study of relative geologic time estimation in global shelf-edge clinothems
Introduces a staged pretrain-to-alignment workflow for geophysical AI that improves relative geologic time estimation across global field surveys despite limited labels and domain gaps.