Bibliographic record
Abstract
Abstract "Blended" or simultaneous source seismic acquisition has had a dramatic impact on the quality and productivity of land seismic acquisition but its application offshore has been comparatively limited to date. For towed streamer operations the principle benefit is to improve data quality by increasing the fold of the acquired data by reducing the shot spacing. There is little, if any, reduction in data acquisition time and hence no reduction in costs. For ocean bottom seismic applications, however, the situation is very different - survey durations can be almost halved with a very modest increase in costs by firing more than one source into the receiver spread "simultaneously." In this paper we will describe the acquisition of what is believed to be the world's largest ocean bottom survey, more than 2200 square km, using two blended sources and a very large receiver spread - more than 4200 ocean bottom receiver nodes. By firing each source wholly independently on a pseudo-random distance basis not only is blended source residual noise reduced but also operational efficiency is improved since downtime on one source vessel has no impact on the other. Since ocean bottom data are extensively used for production and development applications to provide wide azimuth data in congested producing fields the usability of blended sources for 4D or timelapse is critical and this will be examined in the presentation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".