Anisotropy estimation for a simulated fractured medium using AVAZ inversion: A physical modeling study
Bibliographic record
Abstract
We present a linear prestack amplitude inversion of PP data, collected through physical modeling, for the Thomsen anisotropy parameters (ε, δ, and γ) of a simulated fractured medium. 3D physically-modeled PP data were acquired along several azimuths over a phenolic layer using the Physical Seismic Modeling Facility at the University of Calgary. The PP amplitudes picked from the reflection off the top of the fractured layer for several azimuths were used as input for the inversion. A linearized PP reflection coefficient approximation for an HTI (horizontal transverse isotropy) medium was used to facilitate the least-squares AVAZ inversion. Some constraints on the vertical velocities and density were also incorporated in the inversion process. The results for all three anisotropy parameters from AVAZ inversion compared very favourably to those obtained previously by a traveltime inversion. This result makes it possible to compute the shear-wave splitting parameter, γ, (historically determined from shear-wave data) 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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".