Converted-wave seismic exploration: Methods
Bibliographic record
Abstract
Abstract Multicomponent seismic recording (measurement with vertical- and horizontal-component geophones and possibly a hydrophone or microphone) captures the seismic wavefield more completely than conventional single-element techniques. In the last several years, multicomponent surveying has developed rapidly, allowing creation of converted-wave or P-S images. These make use of downgoing P-waves that convert on reflection at their deepest point of penetration to upcoming S-waves. Survey design for acquiring P-S data is similar to that for P-waves, but must take into account subsurface VP/VS values and the asymmetric P-S ray path. P-S surveys use conventional sources, but require several times more recording channels per receiving location. Some special processes for P-S analysis include anisotropic rotations, S-wave receiver statics, asymmetric and anisotropic binning, nonhyperbolic velocity analysis and NMO correction, P-S to P-P time transformation, P-S dip moveout, prestack migration with two velocities and wavefields, and stacking velocity and reflectivity inversion for S-wave velocities. Current P-S sections are approaching (and in some cases exceeding) the quality of conventional P-P seismic data. Interpretation of P-S sections uses full elastic ray tracing, synthetic seismograms, correlation with P-wave sections, and depth migration. Development of the P-S method has taken about 20 years, but has now become commercially viable.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.010 |
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".