Improving streamer data sampling and resolution via nonuniform optimal design and reconstruction
Bibliographic record
Abstract
The cross-line sampling is usually poor for narrow-azimuth streamer data and shallow imaging is unavoidably affected by acquisition footprint. Guided by compressive sensing (CS), we developed a non-uniform optimal design principle which favors CS-based data reconstruction. Combining both techniques, we can achieve much denser sampling and higher unaliased bandwidth at the same acquisition cost. Compared with conventional survey design, the implementation of nonuniform design requires minimal changes to filed operations. A 3D towed streamer survey with non-uniform optimal design was conducted over a producing field in Asia Pacific. We use examples from this survey to illustrate the increased sampling and extended bandwidth after data reconstruction, and the resulting uplift to the final imaging quality. Presentation Date: Tuesday, September 26, 2017 Start Time: 1:50 PM Location: 360A Presentation Type: ORAL
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".