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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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 teacher head, 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".