Bayesian inversion of multi-frequency reflection data with strongly correlated errors for density gradients
Bibliographic record
Abstract
This paper develops a non-linear Bayesian inversion for multi-frequency reflection-loss data with strongly correlated data errors to resolve density and sound-velocity gradients which are often observed in the uppermost sediment layer. Although data errors are usually assumed to be independent in geoacoustic inversion, in reality measured data often show strong error correlations. The inversion developed here is designed to take error correlations into account. A full data covariance matrix is estimated from initial residuals of non-uniformly sampled data. This covariance matrix is then used in the likelihood function of a fast Gibbs sampler to sample the posterior probability density and provide parameter estimates and credibility intervals. Rigorous statistical tests are applied to the resulting data residuals to illustrate the benefits of this error treatment. The approach is applied to reflectivity data collected at a site characterized by low-velocity, water-saturated sediments in the Strait of Sicily. Density and sound-speed gradients are clearly resolved by the reflectivity data and agree with core measurements from the experiment site within the credibility bounds.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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".