Bayesian ambient noise inversion for geoacoustic uncertainty estimation.
Bibliographic record
Abstract
The noise produced by wind-driven breaking waves in shallow water provides a method for probing the seabed, and drifting vertical arrays have been deployed for remote sensing of geoacoustic parameters by estimating the frequency- and angle-dependent reflection coefficients. In addition, techniques such as spectral factorization allow obtaining the impulse response of the multilayered seabed environment. This impulse response carries information of the sediment acoustic properties that can be extracted and passed as prior information to a Bayesian framework for the estimation of geoacoustic parameters and its corresponding uncertainties, which ultimately determine the resolution of the method. The Bayesian formulation estimates a joint posterior probability density function, from which marginal density functions, moments, and covariances between geoacoustic parameters of interest can be quantified. In this work, Bayesian inversion based on Markov-chain Monte Carlo sampling is applied to simulated ambient noise data for the estimation of layer thicknesses, compressional sound speed, density, and sediment attenuation, and the resolution of the method is explored as a function of qualities of the data such as array design and wind speed. The approach is applied to experimental data collected near Sicily.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 0.001 |
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".