Bayesian inversion of seabed reverberation and scattering data via parallel tempering
Bibliographic record
Abstract
This paper describes Bayesian inversion of reverberation data for seabed scattering and geoacoustic parameters using the method of parallel tempering. The seabed is modeled as a sediment layer over a semi-infinite basement, with interface scattering occurring at the rough upper and lower boundaries of the sediment and volume scattering within the layer. The scattering mechanisms are considered to be independent and are modeled using perturbation theory and the Born approximation. Unknown parameters include seabed geoacoustic properties (sediment thickness and sound speeds, densities, and attenuations for the sediment and basement) and scattering properties (roughnesses and scattering strengths for upper and lower layer boundaries and volume scattering strength for the sediments). The reverberation inversion problem is found to be strongly nonlinear with a highly multi-modal posterior probability density (PPD). Standard Markov-chain Monte Carlo (MCMC) methods, such as Metropolis–Hastings sampling, are ineffective at sampling the complicated parameter space. However, parallel tempering, which runs multiple MCMC chains at a series of increasing temperatures with probabilistic transitions between chains, effectively samples the multi-modal PPD. Methods to increase efficiency using multiple parallel chains at each temperature and exploiting improved mixing with temperature are considered.
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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 | 0.006 |
| 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) | 0.001 | 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".