Separation of simultaneous sources acquired with a high blending factor via coherence pass robust Radon operators
Bibliographic record
Abstract
ABSTRACT We have developed an iterative method for simultaneous source separation (deblending) suitable for data acquired with a high blending factor. Our technique adopts the robust sparse Radon transform to define a coherence pass operator that is used in conjunction with the steepest-descent method to guarantee solutions that honor simultaneous source records. We find that an important improvement in convergence is attainable when the coherence pass projection is derived from a robust sparse Radon transform. This is a consequence of having an iterative deblending algorithm that applies intense denoising to erratic blending noise in its initial iterations. The coherence pass robust Radon operator acts as a data projection operator that preserves coherent signals and annihilates incoherent blending noise right from the start of the iterative process. We compare the algorithm with its nonrobust version and find that a coherence pass nonrobust Radon operator will only achieve high-quality results for acquisitions with a moderate blending factor.
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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 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".