Estimation of anisotropy parameters in VTI media
Bibliographic record
Abstract
Anisotropy parameters in a VTI medium can be obtained by anisotropy velocity analysis performed on short-spread or long-spread reflection-seismic data, in combination with check-shot or well-log data. Analysis of three traveltime approximations to the actual reflection traveltime in weak anisotropy media shows that each traveltime approximation has its own requirements for spread length and subsurface anisotropic parameters. The accuracy of the estimated Thomsen's anisotropic parameter δ (or ε) depends not only on the accuracy of the picked NMO velocity (or horizontal velocity) but also on the absolute value of) (ε - δ). The smaller the absolute value of (ε - δ), the higher the accuracy of estimated anisotropy parameter δ or ε. The results of the three traveltime inversions by semblance analysis for synthetic seismic examples demonstrate that nonhyperbolic estimation is better than the modified three-term Taylor series method, and the modified three-term Taylor series method better than hyperbolic estimation. None of these three approaches is suitable for estimating anisotropy parameters when the absolute value of (ε - δ) is large (i.e. |ε - δ| > 0.2 in this case).
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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 | 0.003 |
| 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 | 0.000 | 0.001 |
| 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".