Authors generated and released 3,000 unlabeled field and 4,000 labeled synthetic seismic datasets for global shelf-edge clinothems to enable deep learning for automated seismic stratigraphic interpretation.
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physics.geo-ph 2years
2026 2verdicts
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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.
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Massive-scale unlabeled field and labeled synthetic seismic datasets of global shelf-edge clinothems
Authors generated and released 3,000 unlabeled field and 4,000 labeled synthetic seismic datasets for global shelf-edge clinothems to enable deep learning for automated seismic stratigraphic interpretation.
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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.