A fast rank-reduction algorithm for 3D deblending via randomized QR decomposition
Bibliographic record
Abstract
Summary This paper illustrates an inversion approach based on matrix rank reduction that separates simultaneous source data. The algorithm operates on each common receiver gather of a multidimensional data set. We propose to minimize the misfit between the observed data and blended predicted data subject to a low-rank constraint that is applied to the data in the frequency-space domain. The low rank constraint can be implemented via the classical truncated Singular Valued Decomposition (tSVD) or via a new randomized QR decomposition (rQRd) method. Compared to the tSVD, rQRd significantly improves the computational efficiency of the method. In addition, the rQRd algorithm is less stringent on the selection of the rank of the data. This is important as we often have no precise knowledge of the optimal rank that is required to represent the data. We adopt a synthetic 3D VSP data set to test the performance of the proposed deblending algorithm. Through tests under different survey time ratios, we show that the proposed algorithm can effectively eliminate interferences caused by simultaneous shooting.
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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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".