Joint PP‐ and PSV‐wave amplitudes versus offset and azimuth inversion for fracture compliances in horizontal transversely isotropic media
Bibliographic record
Abstract
ABSTRACT We propose a robust approach for the joint inversion of PP‐ and PSV‐wave angle gathers along different azimuths for the elastic properties of the homogeneous isotropic host rock and excess compliances due to the presence of fractures. Motivated by the expression of fluid content indicator in fractured reservoirs and the sensitivity of Lamé impedances to fluid type, we derive PP‐ and PSV‐wave reflection coefficients in terms of Lamé impedances, density, and fracture compliances for an interface separating two horizontal transversely isotropic media. Following a Bayesian framework, we construct an objective function that includes initial models. We employ the iteratively reweighted least‐squares algorithm to solve the inversion problem to estimate unknown parameters (i.e., Lamé impedances, density, and fracture compliances) from PP‐ and PSV‐wave angle gathers along different azimuths. Synthetic tests reveal that the unknown parameters estimated using the joint inversion approach match true values better than those estimated using a PP‐wave amplitude inversion only. A real data test indicates that reasonable results for subsurface fracture detection are obtained from the joint inversion approach.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".