Estimation of geoacoustic model parameters from modal amplitude information
Bibliographic record
Abstract
This paper analyzes an approach for estimating geoacoustic model parameters from information contained in normal modes of a broadband signal. Propagating modes are resolved by time-warping deconvolved signals from light bulb sound sources deployed at short ranges in shallow water. Amplitudes of the resolved modes contain information about sediment sound attenuation through the modal attenuation coefficient. However, the coefficient also depends on the sediment sound speed and density. A sequential inversion approach was developed that enables effective use of modal amplitudes to estimate sound attenuation in sediments. The inversion is based on a sequential Bayesian approach applied to features of resolved modal data that are highly sensitive to specific geoacoustic model parameters. Travel times of modal frequency components are inverted first for sediment sound speed and sediment layer thickness, and these estimates are used in subsequent stages. The effects of errors in estimates from previous stages are analyzed for the impact on estimates of sound attenuation in the final stage. In particular, it is shown that the sediment density is weakly sensitive and does not have significant impact on the estimation of attenuation.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".