Processing seismic data in the presence of residual statics
Bibliographic record
Abstract
Many common processing steps are degraded by static shifts in the data. The effects of static shifts are analogous to noise or missing samples in the data, and therefore can be treated using constraints on sparsity or simplicity. In this paper we show that random static shifts decrease sparsity in the Fourier and Radon transforms, as well as increase the rank of seismic data. We also show that the concepts of sparsity promotion and rank reduction can be used to solve for static shifts as well as to carry out conventional processes in the presence of statics. The first algorithm presented is a modification to the reinsertion step of Projection Onto Convex Sets (POCS) and Tensor Completion (TCOM) that allows for the compensation of residual statics during 5D denoising and interpolation of seismic data. The method allows preserving residual statics during denoising, or correction of residual statics in the case of simultaneous denoising and interpolation. An example is shown for a 5D reconstruction of synthetic data with added noise, missing traces and random static shifts, as well as for a 2D stacked section with missing traces and static shifts. While standard reconstruction struggles in the presence of even small static shifts, reconstruction with simultaneous estimation of statics is able to accurately reconstruct the data. The second algorithm presented is a Statics Preserving Sparse Radon transform (SPSR). This algorithm includes statics in the Radon bases functions, allowing for a sparse representation of statics-contaminated data in the Radon domain.
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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 | 0.000 |
| 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 | 0.000 | 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".