Inversion of ocean acoustic modal-dispersion and amplitude data for seabed geoacoustic models including attenuation
Bibliographic record
Abstract
The long-range acoustic field due to an initial sound source in a shallow-water waveguide can be expressed as the sum of a number of dispersive, propagating modes. Modal-dispersion data (arrival time as a function of frequency), as extracted with warping time-frequency analysis, have been inverted to estimate seabed sediment sound-speed and density profiles. In this work, measurements of modal amplitudes as a function of frequency are combined with modal-dispersion data in a joint (simultaneous) inversion which also provides sensitivity to frequency-dependent sound attenuation coefficients in the sediment layers. Since the source signature (spectrum) is often unknown in practice, the relative amplitudes between modes can be considered as data, or the source spectrum can itself be included as additional unknowns in the inversion. Here, trans-dimensional Bayesian inversion is applied to modal-dispersion and amplitude data to estimate marginal probability profiles for sediment geoacoustic properties, including attenuation. The signal processing and inversion methods are demonstrated using modal-dispersion and amplitude data extracted from acoustic measurements made by a hydrophone-equipped underwater glider during the 2017 Seabed Characterization Experiment (Work supported by the Office of Naval Research).
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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 | 0.002 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".