Bayesian inversion of reverberation and propagation data for geoacoustic and scattering parameters
Bibliographic record
Abstract
This paper applies nonlinear Bayesian inference theory to quantify the information content of reverberation and short-range propagation data, both individually and in joint inversion, to resolve seabed geoacoustic and scattering properties. The inversion of reverberation data alone is shown to poorly resolve seabed properties because of strong multi-dimensional correlations between parameters. Inversion of propagation data alone is limited by different correlations, but better constrains the geoacoustic parameters. However, propagation data are insensitive to scattering parameters such as Lambert's scattering coefficient. In each case the parameter correlations are inherent in the physics of the forward problem (reverberation and propagation) and cannot be overcome by processing or inversion techniques; rather, the inversion of more informative data is required. This is accomplished here by joint inversion of reverberation and propagation data, weighted according to their respective maximum-likelihood error estimates. Joint inversion of reverberation and propagation data collected on the Malta Plateau (Strait of Sicily) resolves both geoacoustic and scattering properties and achieves smaller uncertainties for all parameters than obtained by the inversion of either data set alone.
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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 | 0.014 |
| 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) | 0.001 | 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".