Fast dual-domain reduced-rank algorithm for 3D deblending via randomized QR decomposition
Bibliographic record
Abstract
ABSTRACT We have developed a fast dual-domain algorithm based on matrix rank reduction for separating simultaneous-source seismic data. Our algorithm operates on 3D common receiver gathers or offset-midpoint gathers. At a given monochromatic frequency slice in the ω-x-y domain, the spatial data of the ideal unblended common receiver or offset-midpoint gather could be represented via a low-rank matrix. The interferences from the randomly and closely fired shots increased the rank of the aforementioned matrix. Therefore, we could minimize the misfit between the blended observation and the predicted blended data subject to a low-rank constraint that was applied to the data in the ω-x-y domain. The low-rank constraint could be implemented via the classic truncated singular value decomposition (SVD) or via a randomized QR decomposition (rQRd). The rQRd yielded nearly one order of processing time improvement with respect to the truncated SVD. We have also discovered that the rQRd was less stringent on the selection of the rank of the data. The latter was important because we often had no precise knowledge of the optimal rank that was required to represent the data. We adopted a synthetic 3D vertical seismic profile and a real seismic data set acquired at the North Viking Graben to test the performance of the proposed source separation algorithm. The proposed algorithm effectively eliminated the interferences while preserving the desired unblended signal. Especially for the synthetic vertical seismic profile example, experiments were evaluated under different survey time ratios. Our tests indicated that the proposed method could save up to 90% of acquisition time under a self-simultaneous source acquisition scenario.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".