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 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".