Marine towed streamer data reconstruction based on compressive sensing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 it