Bayesian inversion of seabed reverberation and scattering data.
Bibliographic record
Abstract
This paper describes a Bayesian approach to the inversion of ocean acoustic reverberation data for scattering and geoacoustic parameters of the seabed. The seabed is modeled as a sediment layer over a semi-infinite basement. Interface scattering occurs at the (rough) upper and lower boundaries of the sediment layer, and volume scattering occurs within the layer (the scattering mechanisms are considered to be independent and are modeled using perturbation theory and the Born approximation). Unknown parameters include geoacoustic properties (sediment-layer thickness and sound speeds, densities, and attenuations for sediments and basement) and scattering properties (roughnesses and scattering strengths for upper and lower sediment boundaries, and volume scattering strength). One dimensional (1-D) and two-dimensional (2-D) marginal probability distributions are computed from the multidimensional posterior probability density (PPD) using Metropolis–Hastings sampling applied in a principal-component parameter space to provide efficient sampling of correlated parameters. Results indicate that reverberation inversion is a strongly nonlinear inverse problem, with highly multimodal marginal distributions and strong interparameter correlations. Addressing this nonlinearity is of key importance to understanding the information content of reverberation data. [Work funded by the 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 | 0.002 | 0.009 |
| 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 | 0.002 |
| 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".