An Improvement of Velocity Variation with Offset (VVO) Method in Estimating Anisotropic Parameters
Bibliographic record
Abstract
Abstract Seismic anisotropy causes deviation of traveltime reflection from hyperbolic moveout. The deviation can be seen at far offset and its deviation depends on anisotropic parameter and offset. This paper discuss velocity variation with offset (VVO) method as a tool for estimating anisotropic parameters; ε and δ. Anisotropic parameter is one of important aspect in seismic anisotropy analysis. While other methods use non-hyperbolic moveout for estimating anisotropic parameter, VVO method uses hyperbolic assumption for moveout correction and leave reflector unflat at far offset because anisotropy. The method calculates residual traveltime and then changes it into anisotropy velocity to obtain anisotropic parameter using linear inversion method. This paper provides an improvement and limitation of VVO method in estimating anisotropic parameter. Comparison between VVO method and other established method is discussed theoretically in this paper. To test the method, synthetic model is built and the result show promising outcome in predicting ε. Meanwhile accuracy for δ estimation depends on accuracy of moveout velocity. Advantage of VVO method is that ε and δ can be estimated separately using P-wave gather data without well information.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".