Computing near-surface velocity models for S-wave static corrections in the τ-p domain
Bibliographic record
Abstract
Near-surface velocity models for static corrections are usually derived from critically-refracted arrivals. In the processing of P-to-S converted-wave data, critically-refracted S-waves are difficult to identify since they are usually overwhelmed by the surface-wave train which propagates at similar velocities. Here we exploit the differences in the moveout of convertedwave events to compute a velocity model for the near-surface and its corresponding static effects. These effects are derived by crosscorrelating receiver gather data transformed to the τ-p domain. The τ-differences between receiver locations is then used in an inversion process to compute a near-surface velocity model. This velocity model can be used for building migration velocity models or to initialize elastic full waveform inversions. Presentation Date: Tuesday, October 16, 2018 Start Time: 8:30:00 AM Location: 213A (Anaheim Convention Center) Presentation Type: Oral
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".