Bayesian AVO inversion of fluid and anisotropy parameters in VTI media using IADR-Gibbs algorithm
Bibliographic record
Abstract
Fluid identification and anisotropic parameters characterization are crucial for shale reservoir exploration and development. However, the anisotropic reflection coefficient equation, based on the transverse isotropy with a vertical axis of symmetry (VTI) medium assumption, involves numerous parameters to be inverted. This complexity reduces its stability and impacts the accuracy of seismic amplitude variation with offset (AVO) inversion results. In this study, a novel anisotropic equation that includes the fluid term and Thomsen anisotropic parameters is rewritten, which reduces the equation's dimensionality and increases its stability. Additionally, the traditional Markov Chain Monte Carlo (MCMC) inversion algorithm exhibits a high rejection rate for random samples and relies on known parameter distributions such as the Gaussian distribution, limiting the algorithm's convergence and sample randomness. To address these limitations and evaluate the uncertainty of AVO inversion, the IADR-Gibbs algorithm is proposed, which incorporates the Independent Adaptive Delayed Rejection (IADR) algorithm with the Gibbs sampling algorithm. Grounded in Bayesian theory, the new algorithm introduces support points to construct a proposal distribution of non-parametric distribution and reselects the rejected samples according to the Delayed Rejection (DR) strategy. Rejected samples are then added to the support points to update the proposal distribution function adaptively. The equation rewriting method and the IADR-Gibbs algorithm improve the accuracy and robustness of AVO inversion. The effectiveness and applicability of the proposed method are validated through synthetic gather tests and practical data applications.
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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 | 0.000 |
| 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".