Quantifying and Modeling the Effects of Internal Waves on Synthetic Aperture Sonar
Bibliographic record
Abstract
Synthetic aperture sonar (SAS) is based on synthetic aperture radar, with a number of key factors increasing the complexity of data collection. One of the assumptions made with respect to SAS image reconstruction is the presence of a constant sound speed. As a nearfield imaging system, SAS is sensitive to the breaking of this assumption. The sound speed in the ocean varies with depth. Variations in sound speed can come in the form of internal waves. Internal waves propagating up the slope of the continental shelf are subject to breaking mechanisms that result in the propagation of boluses shoreward. Internal wave boluses are three dimensional features consisting of colder, higher density water. Since the internal wave boluses are composed of colder seawater, the speed of sound is different than in the surrounding environment. The change in sound speed changes the timing and phase of propagating acoustic rays causing degradation in SAS image quality. Not only do the internal waves violate the constant sound speed assumption made by SAS for image formation, but they also influence the travel of acoustic rays due to a geometric lensing effect. The lensing effect causes large refractive effects near the top of the bolus, resulting in a bright region and shadow region within the image. The goal of this study was to quantify the effects of internal waves on SAS image resolution and subsequently model these effects. The quantification of the effects was performed utilizing point targets within the SAS image. The point spread function of the point targets was estimated and used as a proxy for the image resolution and showed that internal waves can cause resolution loss on the order of two to four times than in the absence of a bolus or sound speed error. A numerical ray tracing model was used to estimate the resolution loss in SAS imagery in the presence of internal waves. An analytical model derived in order to better characterize the impacts of internal waves on SAS resolution. Beamforming was also performed over simulated imagery in the presence and absence of internal waves. The models agreed well with each other and the observed resolution loss in collected SAS data. Based on the success of modeling attempts, it is reasonable to develop a method for full inversion for bolus parameters. Given the agreement of the models with data it may be possible to develop methods to compensate for timing errors caused by the presence of internal waves and return the ideal image resolution. Presenter Bio Nicholas received his B.S. in Physics with a minor in Astronomy and Astrophysics from Villanova University in 2020. His undergraduate studies were supplemented by research in gravitational waves and frustrated magnetism. This research was accompanied by onsite work at the LIGO detector in Livingston Louisiana as well as the TRIUMF facility in Vancouver. Currently, Nicholas is an M.S. Ocean Engineering student with a focus on underwater acoustics and instrumentation. His research interests center around the analysis of internal waves and acoustic propagation and scattering in underwater environments.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".