First arrival enhancement by statics preserving filtering using surface-consistent constraints
Bibliographic record
Abstract
A workflow for first arrival enhancement by imposing surfaceconsistent constraints on a statics-preserving filter is presented. Similar methods initialize the workflow assuming zero initial static. We explore the advantages of building an initial time shift model by maximizing cross-correlations in the commonoffset domain. Then, time shifts are inverted using gradient descent and decomposed into surface-consistent components. This allows us to combine multi-shot information and help discard anomalous time-shift values. Our method was tested against other different approaches, which differ in the type of initial model (with or without), the technique of time shift estimation (cross-correlation maximization, sparsity maximization and time-shift inversion) and the inclusion of multi-shot information (surface-consistent constraint). A proof of concept is presented using a simple synthetic model, and then applied to a complex synthetic model (Marmousi). We conclude that by inverting time shifts with a surface consistent constraint, we get a denoising workflow resistant to low signalto-noise ratios and large values of statics in the input data.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".