Marine towed streamer data reconstruction based on compressive sensing
Bibliographic record
Abstract
In 2011, ConocoPhillips conducted a 3D towed streamer survey in Barents Sea, with optimized shot locations and cable configurations (NUOS design). The purpose of this unconventional survey was to test the ability of compressive sensing to increase the effective spatial bandwidth of the seismic data. In this paper, we describe an alternating direction method (ADM) combined with a nonmonotone line search technique for seismic data reconstruction. It solves a general analysis-based optimization model derived from compressive sensing. This method is highly robust due to the nature of ADM, and able to quickly approach the global minimum for large-scale problems due to the nonmonotone line search. We applied this method to two acquired data sets in the same area—one with a NUOS design and one with a conventionally towed streamer. The final imaging results show the significant improvement of resolution for both data sets obtained from applying the technology, and inspire future marine survey design and processing.
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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) | 0.000 | 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) | 0.001 | 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".