Ship-of-Opportunity Noise Inversions for Geoacoustic Profiles of a Layered Mud-Sand Seabed
Bibliographic record
Abstract
This paper considers the use of broadband noise from a ship-of-opportunity in statistical inference for estimating geoacoustic parameters of a layered mud-sand seabed model via trans-dimensional (trans-D) Bayesian matched-field inversion, with applications to data collected with a bottom-moored horizontal array in the 2017 Seabed Characterization Experiment conducted on the New England Shelf. The trans-D approach applied here samples probabilistically over possible model parameterizations (different numbers of seabed layer interfaces), and provides quantitative uncertainty estimates of seabed geoacoustic profiles. Inversions are carried out for acoustic data sets collected both when the ship-of-opportunity (a container ship) was oriented with its bow and with its stern towards the array. A third inversion involved combining data from a series of segments along the ship track. Inversion results image an upper sediment layer 3-7 m thick with low-sound speed (close to the water sound speed) over higher speed sediment, with indication of a transition layer above the interface. Sediment parameter estimates from the inversions are in good agreement with direct measurements from sediment cores and other geophysical data collected in the experiment area.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".