The Research of Reverse-Time Migration for Cross-Hole Seismic
Bibliographic record
Abstract
Cross-hole seismic is the leading technology of development seismology, which is still developing and improving. With the development of down-hole acquisition equipment, cross-hole seismic acquisition technology is getting mature, providing better data for imaging. According to the features of cross-hole survey and cross-hole data, we put forward a reverse-time migration method which is suitable for the wave equation for cross-hole seismic data. We propose finite difference scheme of higher order, and then derive its stability condition in cross-hole seismic. The frequency dispersion problem in cross-hole seismic wave field extrapolation is also discussed. Cross correlation imaging condition is used to realize migration, and Laplace filter is applied to remove low-frequency noise from migration section. Thus finite-difference reverse-time migration method for cross-hole seismic is established. Finally, we build geological models with anomalous ellipsoids, and apply cross-hole seismic wave field simulation and migration to them, thus our method proves its effectiveness. When dealing with real cross-hole seismic data with this method, high-resolution migration sections can be achieved. Key words : Cross-hole seismic; Reverse-time migration; Model test
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".