Setting the Stage for Multi-Spectral Acoustic Backscatter Research
Bibliographic record
Abstract
Acoustic remote sensing of the seabed provides essential information for habitat mapping. The typical products of interest are bathymetry, slope, rugosity and acoustic backscattering strength, with multibeam echosounders (MBES) generally being the tool of choice to acquire these data sets. The combined acoustic response of the seabed and the subsurface can vary with MBES operating frequency. At worst, this can make for difficulties in merging results from different mapping systems or mapping campaigns. At best, however, having observations of the same seafloor at different acoustic wavelengths allows for increased discriminatory power in seabed classification and characterization efforts. The varying response of materials to different wavelengths of electromagnetic energy has been used to great success in the field of satellite remote sensing where the term multi-spectral is used to describe sensors that provide these type of data and also to techniques that take advantage of it. Early research in this field shows promising results from mapping platforms that offer multiple MBES, this typically being done to allow a single platform to provide mapping capabilities over a wide range of depths (e.g. high frequency for shallow water and low frequency for deeper water). With care, the multiple MBES systems on a single platform can be operated simultaneously so as not to interfere with each other and the acquisition of multi-spectral data sets is possible on these platforms. In the past few years, MBES manufacturers have introduced systems with broadband capabilities, allowing users much more choice in terms of selecting the frequency of operation. In some systems, the frequency can be modified on a ping-by-ping basis, allowing potentially for frequency hopping ping configurations that can provide multi-spectral acoustic measurements with a single pass and a single system. Regardless of how the multi-spectral acoustic measurements are acquired, there is a need to provide acoustic processing capabilities that respect the frequency dependence of many of the terms in the sonar equation. For example, transmission loss over the acoustic propagation path, beam apertures and beam patterns can all vary with operating frequency. Not making adequate corrections for these effects can yield misleading results which can detract from the quality of ensuing seafloor characterization efforts. In this talk, we touch on some examples of early multi-spectral work, specifically we explore findings and various acquisition and post-processing hurdles that were discovered, followed by a brief discussion of potential applications. We also introduce how we have made improvements to FMGT, the QPS seabed backscatter processing software, to set the stage for researchers to begin exploring, developing and refining applications for multi-spectral acoustic observations of the seabed. Presenter Bio Jonathan Beaudoin has a Ph.D. (2010) in Geodesy and Geomatics Engineering from the University of New Brunswick and Bachelor's degrees in Geodesy and Geomatics Engineering (2002) and Computer Science (2002), also from UNB. After finishing his Ph.D, he came to CCOM and did research in the field of echosounding uncertainty associated with oceanographic variability, seabed backscatter processing and improving best practices in multibeam echosounder fleet management as the Principal Investigator of the NSF-funded Multibeam Advisory Committee. After nearly four years at CCOM, Jonathan returned to Fredericton, Canada in 2013 to work for QPS where he is Chief Scientist and Product Manager for FMGT, FM Midwater and Qimera.
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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 | 0.029 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.012 | 0.011 |
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".