Fast simultaneous seismic source separation using Stolt migration and demigration operators
Bibliographic record
Abstract
ABSTRACT Stolt migration is a Fourier-domain imaging operator that assumes a constant-velocity media. We have developed a multivelocity version of the Stolt migration and demigration operators to derive a transform that can decompose seismic data into a sparse collection of coefficients in the image space. This Stolt-based transform is similar to the apex-shifted hyperbolic Radon transform (ASHRT). However, the Stolt-based transform is considerably faster than the classical ASHRT because it uses two fast operators (forward and inverse fast Fourier transforms) to estimate the data coefficients in the image space. We used this Stolt-based transform as a tool for simultaneous seismic source separation by removing erratic interference noise in common receiver gathers. Estimating the coefficients of interference-free seismic data using the Stolt-based transform was posed as an inverse problem. The solution of this inverse problem was found by minimizing a cost function that included a sparsity-promoting regularization term. In addition, the cost function incorporated a robust misfit function that was not sensitive to erratic interferences. Our tests on synthetic and field data examples determined that this new transform can efficiently remove interference noise and achieve fast simultaneous seismic source separation.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".