Separation and Shot Interpolation of Simultaneous-Source Data with Interpolated Mssa (I-Mssa)
Bibliographic record
Abstract
Summary We present an algorithm for simultaneous data deblending and source interpolation. The algorithm adopts the projected gradient descent method with a denoiser that acts as a projection to iteratively deblend and reconstruct shots onto a regular grid. We study two problems. Data are assumed to lie on a regular grid with missing shot positions in the first problem. These data were obtained by simple nearest neighbour interpolation (binning). The Multichannel Singular Spectrum Analysis (MSSA) filter is used as the denoiser. In the second case, we honour true spatial shot positions and adopt Interpolated MSSA (I-MSSA) as the denoiser. We show the superior performance of the deblending and reconstruction algorithm when the I-MSSA projection is adopted. In essence, we propose an algorithm that allows one to 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".