Enabling full-waveform inversion to recover salt bodies in challenging conditions: A field data application
Bibliographic record
Abstract
Summary Full-waveform inversion (FWI) fails to converge when starting with a poor initial model, especially for complex structures such as salt models. Building a good initial salt model requires manual interpretation from seismic images, which is time-consuming and depends on human decisions. Also, for FWI to have a stable convergence, the seismic data should have low frequencies and long offsets to penetrate the deeper structure and build an accurate salt body. Here, we automatically reconstruct the salt body with the aid of deep learning in a 2D vintage field data from the Gulf of Mexico area that lacks frequencies below 6 Hz and have a maximum offset of 4.8 Km. We perform FWI starting from a linearly increasing model in a multiscale approach. We train three 1D U-net networks that are applied subsequently after each FWI frequency scale: the first two U-nets are responsible for salt flooding and improving the top of the salt, and the third one is to unflood the salt to the base. The inversion result reconstructs a large salt body structure, although the data lacks low frequencies and long offsets.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".