Statistical modeling of seismic reflectivities comparing Lévy stable and Gaussian mixture distributions
Bibliographic record
Abstract
The goal of this work is to compare statistical modeling of seismic reflectivities using two heavy‐tailed models: Lévy stable distributions and Gaussian mixture distributions. Distributions of various parameters, such as reflectivities are required inputs for many Monte Carlo simulations in statistical rock physics analyses for reservoir characterizations as well as formulating seismic inverse problem with non‐Gaussian priors. Gaussian mixture models can provide an equally good fit to heavy‐tailed reflectivity data as stable distributions, but with a larger number of fitting parameters. Monte Carlo simulations from stable distributions have a tendency to have more extreme outliers than simulations from Gaussian mixture models. Hence problems related to non‐physical values, infinite moments, and ad‐hoc fixes (truncation, deletion, etc.) tend to occur more often with stable distributions than Gaussian mixture models simulations
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".