Prestack Vp/Vs scanning and automatic PS to PP time mapping using multicomponent seismic data
Bibliographic record
Abstract
This paper discusses the development and application of a prestack method that scan for the average vertical velocity value, Vp/Vs (γ0), and the stacking velocities of multicomponent seismic (MCS) data using a converted-wave (PS) non-hyperbolic traveltime equation. The procedure entails computing semblance as a function of two variables namely, the PS velocity, Vps, and γ0 with respect to the PS zero-offset time tps0. The results are displayed in 2D plots. The scanning procedure is tested using numerical data sets and real MCS data sets from the Blackfoot Field in southern Alberta. The algorithms work well with either shot gathers or the asymptotic conversion point (ACP) gathers. It is observed that the γ0-log from the scanning procedure, agrees reasonably well with the γ0 values from the well log of Well-09-08 that is located at the ACP gather location. In addition, when this γ0 estimate is used for PS-to-PP time mapping, the time difference between the computed and actual PP at the target level time is found to be 20 ms; being an error of less than 2%. Introduction A number of authors have discussed the benefits offerred by PS-wave exploration; however, certain problems, such as determining γ0, χαν stand between us and the realization of these advantages. Because of this, recovery of γ0 has become a step in multi-component data processing and interpretations. Several workers have used different methods for the recovery of γ0. For example, Gaiser (1996) developed a poststacked cross-correlation method. The method is automatic and is based on correlating PP and PS stacked data sets. Stewart et al. (2003) alluded to a time-isochron method using interpreted PP and PS sections. Like Gaiser’s method, this too is post-stack method that depends on correlating PP and PS events. Thomsen (1999) suggested visually correlating events of the same structural attitude on both PP and PS stacked section; this too, is also a post-stack method. The post-stack methods can work well but may fail with complicated sections, when PP and PS data have very different wavelets, or events of opposite polarity. In view of this, there is need to find an alternate prestack solution to the problems associated with Pand PSwave correlation. In this paper we present a prestack method of estimating γ0 via velocity analysis using a converted-wave, non-hyperbolic traveltime equation. Methodology By combining the PS-wave traveltime equation of Thomsen (1999) and the PS stacking velocity approximation of Tessmer and Behle (1988), we obtain a PS traveltime equation:
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".