Quantifying information content of modal dispersion data in geoacoustic inversion
Bibliographic record
Abstract
This paper illustrates the information content of ocean acoustic modal-dispersion data to constrain parameters and uncertainties of seabed geoacoustic models. In particular, time-frequency warping analysis is applied to extract dispersion data consisting of arrival times as a function of frequency for 18 of the first 21 propagating modes from recordings of an impulsive sound source on a vertical hydrophone array. To quantify the information content of these dispersion data to resolve seabed structure, a Bayesian inversion formulation is applied that includes rigorous approaches to model selection and data error modeling. Model selection considers both layered and gradient representations of seabed profiles using trans-dimensional inversion and Bernstein-polynomial basis functions, respectively. In both cases, model parameterizations are determined probabilistically from the data as part of the inversion. The error model assumes a multi-variate Gaussian distribution with unknown variance and covariance for each mode; covariance estimation is formulated in terms of trans-dimensional sampling of zeroth- and first-order autoregressive processes. The applicability of these assumptions/approaches is validated with qualitative (graphical) and quantitative residual analyses. Results are considered as marginal probability profiles for geoacoustic properties, which quantify the resolution of seabed structure versus sub-bottom depth. [Work supported by the Office of Naval Research.]
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".