The robustness of <i>V <sub>P</sub>/V <sub>S</sub> </i> mapping
Bibliographic record
Abstract
With the success of acquisition and processing of multiple component seismic data, people are trying to get more and better information from multicomponent seismic data to characterize the reservoir. Mapping of VP/VS provides important information. Due to the significant difference of frequency spectra of PP and PS seismic volumes, we designed the band pass filter based on the frequency spectrum of PS seismic volume, which has a narrower frequency band and lower dominant frequency, and applied the band pass filter to PP seismic volume. The quality of VP/VS map from PS and filtered PP seismic volumes was significantly improved compared with the quality of VP/VS map from PS and unfiltered PP seismic volumes. Meanwhile, the error from surrounding formations was analyzed because we usually can not get reliable reflection pick from the target formation and have to interpret those coherent events from surrounding formations. The error analysis was based on the interpreted model, and the result was that the effect from surrounding formations was negligible if the velocities of surrounding formations did not change much laterally. The assumption could be satisfied in most cases when we considered the geological background. If the velocities of surrounding formations change significantly, we can limit the area to interpret the pattern of VP/VS to improve the reliability of this method.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".