Geoacoustic inversion of noise coherence in shallow water
Bibliographic record
Abstract
It is known that the geoacoustic properties of a shallow-water sea-bed can be inferred from relatively simple measurements of the ambient noise coherence between a pair of vertically separated hydrophones [D. M. F. Chapman, ‘‘Surface-generated noise in shallow water: A model,’’ Proc. Inst. Acoust. 9, 1–11 (1987)]. The design of an autonomous buoy package for acquiring geoacoustic information by this method is currently being considered by DRDC-Atlantic in support of matched-field localization efforts that are being developed for use with rapidly deployable arrays. Initially, vertical coherence estimates from a simple shallow water noise model were fit to measured coherences by adjusting geoacoustic parameters by a trial and error procedure. A more systematic approach involves combining noise coherence models with nonlinear global optimization methods based on matched-coherence processing concepts to search the space of possible sea-bed parameters more efficiently. In this paper, we report on recent efforts to use a hybrid simplex simulated annealing scheme [M. R. Fallat and S. E. Dosso, ‘‘Geoacoustic inversion via local, global, and hybrid algorithms,’’ J. Acoust. Soc. Am. 105, 3219–3230 (1999)] to match an increasingly realistic suite of candidate geoacoustic parametrizations to some acoustic noise coherent data measured with modified sonobuoys deployed at several shallow water locations on the Scotian Shelf.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".