Geoacoustic inversion from ambient noise data using a trans-dimensional Bayesian approach
Bibliographic record
Abstract
Geoacoustic inversion of seabed parameters from ambient noise in shallow water is a promising technique, with potential advantages over active survey methods such as low environmental impact, easier deployment procedures, and less restrictive hardware requirements. Bayesian inversion provides a framework to estimate the posterior probability density (PPD) of geoacoustic parameters. Parameter and uncertainty estimates can be obtained from PPD moments, such as the maximum a posteriori model, means, correlations, and marginal distributions. A fundamental step in the inversion is the selection of a model parametrization (i.e., number of seabed layers) consistent with the data information content. Recent developments in Bayesian inversion of seismic and active-source acoustic data have considered a trans-dimensional approach to model selection, where the number of model parameters is treated as unknown. Different models are sampled according to their support by the data, accounting for parametrization uncertainty in the geoacoustic parameter uncertainty estimates. This work applies a trans-dimensional reversible-jump Markov chain Monte Carlo algorithm to ambient noise reflection coefficient data. The approach is demonstrated with data collected on the Malta Plateau using a vertical line array. [Work supported by ONR.]
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".