Multicomponent joint migration velocity analysis in the angle domain for PP-waves and PS-waves
Bibliographic record
Abstract
ABSTRACT Employing the vector processing of multicomponent seismic data, elastic Kirchhoff migration is used to conduct a multicomponent joint migration velocity analysis (MVA) of PP- and PS-waves in the angle domain. In vector-wavefield imaging, the elastic imaging condition has been extended to nonzero time and space shifts. We apply the extended imaging condition to elastic Kirchhoff migration to extract angle-domain common-image gathers (ADCIGs) of PP- and PS-waves. This method, which is derived from 3D wave propagation theory, directly operates on the vector wavefields and automatically resolves the problem of PS-wave polarity reversal in the migration sections. Based on the kinematic characteristics of PP- and PS-waves in the incident-angle domain, the velocity updating functions for P- and S-waves are derived. By combining the ADCIGs flatness criterion and the PP- and PS-wave image-depth consistency principle, the accuracy of P- and S-wave velocities can be assessed. The method was tested on a 2D thin-interbed model and the 2D Marmousi2 model. We found that the proposed method extracts the PP- and PS-wave ADCIGs effectively and produces appropriate P- and S-wave velocity fields. This method can be applied to time-delay gathers and to vertical or inline gathers in the 3D case.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".