Bibliographic record
Abstract
Multi-angle swath bathymetry (MASB) sonars are typically used for bathymetry applications but their unique characteristics make them useful for generating clear side-scan images of the bottom, free from artifacts produced by wakes, surface bounce multi-path signals and water column targets. MASB sonars use a small array of long vertically stacked array elements to estimate the angle of arrival for backscatter. Unlike interferometric sonars which can estimate only one angle of arrival, MASB sonars can estimate the arrival angle of multiple same-time targets and therefore the potential exists for separating bottom backscatter from unwanted backscatter. Side-scan sonar is used to obtain high resolution images of the bottom but because of the wide beam it also picks up backscatter from wakes, water column targets, and surface bounce multi-path. This unwanted backscatter obscures the bottom image sometimes making it necessary to resurvey the area if these artifacts are present. Wakes are particularly bothersome in high traffic water ways and busy harbors where it may be necessary to get clear images of the bottom for security or search and recovery applications. This paper shows how MASB processing techniques coupled with beam steering can be used to generate artifact free images of the bottom. The paper begins by briefly outlining MASB sonar principles and then presents the methodology for generating clear bottom images. Element and composite beam patterns are presented for an actual MASB sonar system and these patterns are discussed in the context of target discrimination and artifact removal. Examples are presented of actual side-scan images contaminated by wakes, water column targets, and surface bounce multi-path signals. The data for these images is processed using the techniques described and new side-scan images are presented free of artifacts. A byproduct of the process is that side-scan images of wakes and water column targets can be produced alone without the bottom. This type of image is useful for situations where the water column or wake targets are of primary interest. Finally, conclusions are drawn with regard to the application of these techniques for obtaining unobscured bottom images or images of water column targets or wakes alone for security, and search and survey applications in high traffic areas.
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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) | 0.003 | 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".