Low grazing-angle sea surface reverberation patchiness and statistics observed with a high-frequency multibeam sonar
Bibliographic record
Abstract
Sea-surface reverberation, and in particular backscatter from near-surface bubbles, can significantly limit high-frequency (>10 kHz) active sonar performance. While accepted models for near-surface bubble backscatter exist, these have generally assumed horizontally uniform bubble layers. This study presents measurements using a horizontally-oriented 90 kHz multibeam sonar from a moving ship that show significant spatial variability in low grazing angle sea-surface reverberation. The surface scattering strength and scattering amplitude statistics under sea states 3 to 4 conditions are investigated. The time- and spatially-averaged background reverberation levels were in moderate agreement with well-known bubble layer models. However, the instantaneous backscatter amplitudes exhibited localized patchiness and non-Rayleigh statistical distributions, in agreement with either a K-distribution or a Rayleigh-K mixture model. This variability was attributed to scattering patchiness, bubble-induced extinction and refraction, and near-surface processes such as Langmuir circulation. K-distribution shape parameters from 4 to 15 were estimated from the sonar data, increasing with range as predicted by an effective scatter density model. These low shape factors lead to a larger probability of false alarm than might be predicted using Rayleigh models.
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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) | 0.000 | 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) | 0.001 | 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".