Joint source deblending and reconstruction for seismic data
Bibliographic record
Abstract
The goal of simultaneous shooting is to acquire better seismic data more quickly at lower total cost. Effective source deblending techniques provide us with one of the tools for accomplishing this goal. The use of compressive sensing theory gives us another tool by helping to increase the effective spatial bandwidth of our acquired data. Seismic surveys designed to collect both optimally sampled and blended data can reduce acquisition costs and significantly improve image quality. In this paper, we consider a joint deblending and reconstruction problem using the framework of a synthesis-based basis pursuit denoising model. The combination of a “deblending” operator together with a “restriction” operator leads to a joint inversion in which the data are both deblended and reconstructed at regular sampling intervals. Our inversion model can be further constrained by down-weighting the evanescent portion of the wavefield. We illustrate our method using both synthetic and real data examples simulating continuous-time recording under ocean bottom node (OBN) settings.
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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 | 0.000 |
| 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 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".