McMC-based nonlinear EIVAZ inversion driven by rock physics
Bibliographic record
Abstract
A single set of vertically aligned fractures embedded in a purely isotropic background medium may be considered as a long-wavelength effective transversely isotropic medium with a horizontal symmetry axis (HTI). The estimation of fracture weaknesses is essential for characterizing the anisotropy in HTI media. Using the fractured anisotropic rock-physics models and the wide-azimuth seismic data, elastic impedance inversion variation with incident angle and azimuth, or simply ‘EIVAZ’ for short, can be carried out for the estimation of the normal and tangential fracture weaknesses with the nonlinear Markov chain Monte Carlo (McMC) strategy. Firstly, an inversion method of nonlinear anisotropic elastic impedance (AEI) with the McMC algorithm was proposed, which is used for the inversion of nonlinear AEI information with different angles of incidence and azimuth. Then we extracted the normal and tangential fracture weaknesses directly using the ratio differences of inverted nonlinear AEI data. So we can eliminate the influence of the isotropic background elastic impedance on the anisotropic perturbation elastic impedance and obtain the normal and tangential fracture weaknesses more stably. A test on a 2D over-thrust model shows that the fracture weaknesses are still estimated reasonably with moderate noise. A test on a real data set demonstrates that the estimated results are in good agreement with the results of the well log interpretation, and our McMC-based nonlinear AEI approach appears to be a stable method for predicting fracture weaknesses.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".