Azimuthal amplitude variation with offset analysis of physical modeling data acquired over an azimuthally anisotropic medium
Bibliographic record
Abstract
ABSTRACT We evaluated a quantitative amplitude analysis of 3D physical model reflection data acquired over an experimental phenolic layer that modeled a fractured medium with one set of vertical fractures. The phenolic layer was overlain by two isotropic layers, the uppermost being water, and the data acquisition was designed to avoid the interference of the primary and ghost events. The elastic stiffness coefficients and hence the anisotropy of the phenolic layer were known in advance from a previous traveltime analysis. The reflection amplitudes from the top of the phenolic layer required corrections to make them suitable for an amplitude study. In addition to the usual amplitude corrections applied to seismic field data, a directivity correction specific to the physical model transducers was applied. The corrected amplitudes along different azimuths showed a clear azimuthal variation caused by the phenolic layer and agreed with amplitudes predicted theoretically. An amplitude variation with angle and azimuth inversion was performed for horizontal transverse isotropy (HTI) parameters of the phenolic layer. We determined from the inversion results that from the azimuthally varying P-wave reflectivity response, it was possible to estimate HTI parameters that compared favorably to those obtained previously by a traveltime analysis. This result made it possible to compute the S-wave splitting parameter γ (historically determined from S-wave data and directly related to fracture density) from a quantitative analysis of the PP data.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".