Crooked-line 2D seismic reflection imaging in crystalline terrains: Part 1, data processing
Bibliographic record
Abstract
Abstract For cost and access reasons, most of the seismic reflection data collected in crystalline terrains have been acquired by 2D crooked-line profiling. When the survey geometry is significantly irregular and the geologic structures have cross-profile dip, several standard 2D imaging procedures severely underperform. As a result, reflection signal is poorly aligned across individual common midpoint (CMP) gathers, and much is lost during the CMP stack. To improve imaging, either the methods used to align signal before stack need to be modified or more tolerant methods of combining trace signals than the standard CMP stack need to be applied. Because a high-fold 2D crooked-line profile is really a 3D survey of a swath of terrain around the processing line, better signal alignment before CMP stacking may be achieved by revisiting the traveltime equation and including the cross-dip terms into the moveout calculations. Therefore, in addition to the correction of NMO and in-line dip moveout (DMO), we also locally compute and subsequently remove cross-dip moveout (CDMO). This requires a procedure for estimating the amount of cross-dip associated with each local reflection event. Stacking after the successful removal of the CDMO yields what we call an optimum cross-dip stack—a seismic section that is significantly more complete and informative than the standard stack. Alternatively, amplitude stacking appears to be more robust to residual time anomalies. When little or no cross-dip information can be extracted from the 2D crooked-line data, we use it as a last resort to obtain a section that contains more structural information than the standard stack.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".