Interpolated fast and computational-efficient multidimensional singular spectrum analysis (I-FMSSA) for compressive simultaneous-source data processing
Bibliographic record
Abstract
We present a fast and computational-efficient algorithm for simultaneous data deblending and source interpolation that honors true source positions. The algorithm adopts the projected gradient descent method with a denoiser that acts as a projection to iteratively deblend and reconstruct shots from an irregular-grid onto a regular grid. An interpolation operator is used to link irregular-grid acquisition with the regular-grid output. A fast and computational-efficient multidimensional singular spectrum analysis (FMSSA) is adopted as the projection operator to speed up the low-rank step of the MSSA filter estimation. Compared with the nearest neighbor interpolation (binning) strategy, the proposed interpolated-FMSSA (I-FMSSA) method performs deblending and reconstruction with a minimal computational burden. In essence, we propose an algorithm that allows one to simultaneously deblend and reconstruct shot positions. Hence, one can achieve extra acquisition savings by blending sources and source decimation.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".