SV-P extraction and imaging for far-offset vertical seismic profile data
Bibliographic record
Abstract
Abstract We have analyzed vertical seismic profile (VSP) data acquired across a Marcellus Shale prospect and found that SV-P reflections could be extracted from far-offset VSP data generated by a vertical-vibrator source using time-variant receiver rotations. Optimal receiver rotation angles were determined by a dynamic steering of geophones to the time-varying approach directions of upgoing SV-P reflections. These SV-P reflections were then imaged using a VSP common-depth-point transformation based on ray tracing. Comparisons of our SV-P image with P-P and P-SV images derived from the same offset VSP data found that for deep targets, SV-P data created an image that extended farther from the receiver well than P-P and P-SV images and that spanned a wider offset range than P-P and P-SV images do. A comparison of our VSP SV-P image with a surface-based P-SV profile that traversed the VSP well demonstrated that SV-P data were equivalent to P-SV data for characterizing geology and that a VSP-derived SV-P image could be used to calibrate surface-recorded SV-P data that were generated by P-wave sources.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".