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 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) | 0.000 | 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".