Sparse inversion of the Radon coefficients in the presence of erratic noise with application to simultaneous seismic source processing
Bibliographic record
Abstract
In recent years, efforts have been made in designing simultaneous-source strategies that permit to save seismic acquisition costs. Seismic sources are fired with time overlap producing seismic records that contain a mixture of sources. These records need to be unmixed before seismic imaging. The unmixing process can be written as an inverse problem where one attempts to solve a linear system of equations to estimate the unmixed seismic data. This article describes a source separation process where we assume that source interferences can be modelled via an erratic noise process. In addition, the ideal unmixed data are assumed to be sparse in the Hyperbolic Radon transform domain. Therefore, the source separation problem is posed as an inverse problem where one seeks to retrieve a sparse model from observations contaminated with erratic (sparse) noise. We present a modification of the fast iterative shrinkage-thresholding algorithm that permits to cope with the simultaneous estimation of sparse Radon coefficients that are required to synthesize the unmixed data. The algorithm is also utilized to estimate the erratic noise caused by source interferences.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.001 | 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".