Bathymetry inversion using constituent Boussinesq equations
Bibliographic record
Abstract
The phenomenon of ocean wave-shoaling, and the associated reduction of ocean wave phase speed with decreased water depth, provides useful information for inferring water depth D (bathymetry) in coastal environments. One strategy for relating D to phase speed C and wave-vector k, of long wave length ocean waves, involves using the 1-dimensional, linear (gravity wave) dispersion relationship C=(g*tanh(kD)/k)/sup 1/2/. In principle, this approach has limitations, because the approach is based on a WKB approximation. Thus, it cannot be applied when D varies appreciably over the wavelength of a shoaling-wave. Also, the approach is restricted to waves that have small wave-height. In the present paper, The authors use a set of marine radar image sequences and apply the linear approximation, via a 3D FFT analysis to the sequences. The authors show that for low to moderate wave heights, the approach does retrieve approximately the correct depth. However, an increase in the RMS wave-height from 1 m to 3.5 m produced a much poorer depth estimate, proving the need for an application of a non-linear wave model to the problem, with an associated new retrieval approach. They outline a new procedure for extracting bathymetry that uses the recently developed constituent Boussinesq (CB) equations. The inversion procedure is accomplished using a standard (Levenberg-Marquardt-like), 1-dimensional, cost function minimization procedure.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".