Evaluating Kirchhoff migration using wave-equation generated maximum amplitude traveltimes
Bibliographic record
Abstract
Surface-offset gathers are often preferred to subsurface angle gathers during tomographic velocity updates and velocity model QC. This is because angle gathers have few live traces in the deeper parts of a seismic image even though fold or data redundancy is the highest at late times and deep depths. Wave-equation based surface-offset gathers can be generated, but they are usually seen to be too expensive to be used in common practice. Therefore, we still often rely on conventional ray-based Kirchhoff migration to output surface-offset gathers. Its main limitation is traveltime computation, which is not accurate in complex velocity models. There exist methods of maximum-amplitude traveltime computation based on the wave equation, which produce traveltime maps that can be applied to Kirchhoff migration. Following those ideas, we perform a Kirchhoff migration to output surface offset gathers using traveltime computation by an excitation-amplitude imaging condition. With both synthetic and real data examples, we evaluate the method by comparing with wave-equation Kirchhoff migration. Presentation Date: Wednesday, October 14, 2020 Session Start Time: 1:50 PM Presentation Time: 4:45 PM Location: 362D Presentation Type: Oral
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".