Batch automated image processing of 2D seismic data for salt discrimination and basin-wide mapping
Bibliographic record
Abstract
ABSTRACT We have explored the technical utility of analyzing massive sets of digital 2D seismic data, collected and processed in dozens of different surveys, conducted more than 25 years ago, using batch, automated and unsupervised pattern recognition techniques to produce a basin-wide map of the top of salt. This workflow was developed for the United States portion of the Gulf of Mexico to detect top-salt boundaries on 2D poststack migrated lines. Texture-based attributes as well as novel, reflector-based attributes were used to discriminate between salt and nonsalt on each seismic line. Explicit measures of accuracy were not calculated because the data are unlabeled, but an assessment of confidence was used to score the boundaries. The depth to the top of the salt was estimated for more than 67% of the study area (278,000 km2 or 107,000 mi2), 17% of the study area had insufficient data for processing and analysis, and 16% of the area did not meet confidence requirements for inclusion. The final results compared well with published maps of salt and the locations of salt-trapped fields. Reliable mapping of salt deeper than 6 s two-way time could not be achieved with this data set and approach because many seismic images had indistinguishable features at this depth. The computing time was greater than linear in the number of lines, but parallelization and changes in hardware configuration could reduce the run time of about three weeks to about three days.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".