Selecting velocity models using Bayesian Information Criterion
Bibliographic record
Abstract
ABSTRACT We present a strategy for selecting the values of model parameters by comparing walkaway vertical seismic profiling data with a multilayered model in the context of Bayesian information criterion. We consider P ‐wave traveltimes and assume elliptical polar velocity dependence. A model with different propagation speeds, depending on the angle of propagation, can be a good approximation for a medium composed of thin layers. While elliptical anisotropy in a one‐layer model yields good results, an efficient tool for multilayer modelling would provide improved inversion results. To obtain the proper set of velocity values for specific parameterizations, we require two steps of optimization. In the first step, we find the signal trajectory; in the second step, we obtain parameter values by minimizing the misfit between the model and the data. By comparing models and data, we choose the best model in the sense of the Bayesian information criterion.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".