Quantifying ocean acoustic propagation uncertainty due to environmental uncertainty from ship-noise inversion
Bibliographic record
Abstract
This paper considers the uncertainty in underwater acoustic propagation predictions that results from uncertainty in oceanic environmental parameters (water column and seabed) as estimated from acoustic inversion methods. The approach is general but is considered here for the inversion of acoustic fields from ships-of-opportunity, which represents a convenient and unobtrusive approach to environmental assessment, but with significant uncertainties for some environmental parameters. In this work, environmental uncertainties are quantified using trans-dimensional (trans-D) Bayesian inversion based on reversible-jump Markov-chain Monte Carlo sampling. Trans-D inversion considers the number of sediment layers in the seabed and/or the number of points defining the water-column sound-speed profile as unknown parameters, sampled probabilistically in the inversion. This approach numerically characterizes the posterior probability density of the environment using a large ensemble of dependent random samples of the model. Predicting acoustic propagation for a much-smaller, randomly-chosen subset of these model samples transforms environmental uncertainty to propagation uncertainty, which can be characterized spatially with standard statistical measures (standard deviation, credibility intervals, etc.). The approach is illustrated using simulations and noise from a large commercial ship recorded on an a horizontal array of hydrophones as part of the 2017 Seabed Characterization Experiment (SBCEX17) at the New England Mud Patch.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.000 | 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".