Integrating U-net into full-waveform inversion for salt body building: A challenging case
Bibliographic record
Abstract
Full-waveform inversion (FWI) applied to regions with large salt bodies often fails without a good initial model, long offsets, and low frequencies. To address this limitation, a previous study embedded U-Nets within a multi-scale FWI to assist in building salt bodies by flooding and unflooding. This approach showed successful results on salt bodies with relatively smooth structures. In this abstract, we test this framework on a more challenging salt body inversion case. We extract seismic velocity slices from the Tiber field in the Gulf of Mexic (GOM) region, where the top of the salt body exhibits a distinctive ”U” shape, like a canyon, with significant variations in depth ranging from 2 km to 6 km. To enable the generalization of neural network, we design training samples that include 1D velocity models with variations in the salt top ranging from 2 km to 6 km. Meanwhile, we use stronger total-variation regularization in FWI to reduce false flooding. Preliminary results from synthetic data indicate that this approach has reasonable potential for complex salt inversion.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".