Introduction to this special section: Multicomponent seismic
Bibliographic record
Abstract
A seismic source excites a rich variety of elastic waves in the Earth, so it seems reasonable to try to use them all to create a more compelling picture of the subsurface. While P-wave imaging has been enormously successful in this regard, there are conditions when it is less so. But, the demands of energy discovery and recovery require an increasingly comprehensive portrayal of reservoir lithologies, stresses, fractures, and fluids. The multicomponent seismic method is a superset of conventional seismic technology and has the potential to answer to some of these demands. Recording horizontal motion, as well as vertical vibrations and pressures, allows further capturing of the full seismic wavefield, and the additional resultant pictures can provide greater comprehension of subsurface properties, fluids, and their changes. We might liken this to a more complete conversation with “loud” waves (P-waves arriving first with high amplitudes) and “shy” waves (S-waves with lower voices and a more complicated message).
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.048 | 0.042 |
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".