Evaluation of equivalent-fluid geoacoustic seabed models
Bibliographic record
Abstract
Sound reflection from the seabed is very important in the study of sound propagation in shallow water. As most seabeds support both compressional and shear waves, seabed rigidity affects the reflection loss and phase shift of the bottom reflection. In this case the problem of modelling sound reflection from the seabed becomes more complicated. An approximate method is to replace the solid with a fluid by choosing suitable seabed parameters. This replacement fluid is termed the equivalent-fluid seabed. An equivalent-seabed model is an approximate method to simplify the mathematical analysis and reduce the calculational expense in modelling water-borne shallow water sound propagation, taking seabed shear-wave effects into account. The objective of this work is to develop and evaluate equivalent-fluid seabed models. Special attention is paid to the models of Tindle and Zhang (1992) and Zhang and Tindle (1995). Shear-wave effects on reflection have been studied via prediction of the reflection coefficient. A new effective-seabed model is proposed from the calculation of the effective impedance of the seabed. Comparison of the new model with existing models shows that the new model agrees better with the solid seabed. Furthermore, grazing-angle-dependent parameters of the equivalent-fluid seabed are proposed. It is shown that the use of grazing-angle-dependent parameters gives the best agreement with the values for the real seabed in the prediction of the reflection coefficient.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".