Setting the Stage for Multi-Spectral Acoustic Backscatter Research
Notice bibliographique
Résumé
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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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,002 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».