Improving shallow water MWD: Offshore Canada case study
Bibliographic record
Abstract
Summary Data from towed streamer surveys in shallow water environments are usually contaminated by different orders of sea floor reflections called water-layer-related multiples (WLRMs). The model-based water-layer demultiple (MWD) method effectively removes WLRMs. The initial development of 2D MWD was followed by a 3D implementation to better handle out-of-plane WLRMs. This 3D implementation was further improved by using a selective-input strategy to handle the inconsistency of the high-frequency multiple patterns between adjacent sail lines and shots. Time-variant aperture scheme is proposed based on multiple contribution gathers (MCGs) to include sufficient large apertures, avoid aliasing problems, and compensate for apex shifting. Using narrow azimuth towed streamer (NATS) data from the Hibernia field, we demonstrate the benefit of selective-input MWD with time-variant apertures for attenuating WLRMs in shallow water data.
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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 | 0.001 | 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) | 0.001 | 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".