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Open AccessArticle

A Multispectral Bayesian Classification Method for Increased Acoustic Discrimination of Seabed Sediments Using Multi-Frequency Multibeam Backscatter Data

1
Acoustics Group, Faculty of Aerospace Engineering, Delft University of Technology, 2629 HS Delft, The Netherlands
2
Department of Survey Science and Geomatics, Universiti Teknologi MARA Perlis, Arau 02600, Malaysia
3
Department of Applied Geology and Geophysics, Deltares, 3508 AL Utrecht, The Netherlands
*
Author to whom correspondence should be addressed.
Geosciences 2018, 8(12), 455; https://doi.org/10.3390/geosciences8120455
Received: 7 November 2018 / Revised: 27 November 2018 / Accepted: 29 November 2018 / Published: 4 December 2018
(This article belongs to the Special Issue Geological Seafloor Mapping)
Multi-frequency backscatter data collected from multibeam echosounders (MBESs) is increasingly becoming available. The ability to collect data at multiple frequencies at the same time is expected to allow for better discrimination between seabed sediments. We propose an extension of the Bayesian method for seabed classification to multi-frequency backscatter. By combining the information retrieved at single frequencies we produce a multispectral acoustic classification map, which allows us to distinguish more seabed environments. In this study we use three triple-frequency (100, 200, and 400 kHz) backscatter datasets acquired with an R2Sonic 2026 in the Bedford Basin, Canada in 2016 and 2017 and in the Patricia Bay, Canada in 2016. The results are threefold: (1) combining 100 and 400 kHz, in general, reveals the most additional information about the seabed; (2) the use of multiple frequencies allows for a better acoustic discrimination of seabed sediments than single-frequency data; and (3) the optimal frequency selection for acoustic sediment classification depends on the local seabed. However, a quantification of the benefit using multiple frequencies cannot clearly be determined based on the existing ground-truth data. Still, a qualitative comparison and a geological interpretation indicate an improved discrimination between different seabed environments using multi-frequency backscatter. View Full-Text
Keywords: multibeam echosounder; underwater acoustics; seafloor mapping; habitats; marine geophysics; multi-frequency; acoustic scattering; unsupervised classification multibeam echosounder; underwater acoustics; seafloor mapping; habitats; marine geophysics; multi-frequency; acoustic scattering; unsupervised classification
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MDPI and ACS Style

Gaida, T.C.; Tengku Ali, T.A.; Snellen, M.; Amiri-Simkooei, A.; Van Dijk, T.A.G.P.; Simons, D.G. A Multispectral Bayesian Classification Method for Increased Acoustic Discrimination of Seabed Sediments Using Multi-Frequency Multibeam Backscatter Data. Geosciences 2018, 8, 455. https://doi.org/10.3390/geosciences8120455

AMA Style

Gaida TC, Tengku Ali TA, Snellen M, Amiri-Simkooei A, Van Dijk TAGP, Simons DG. A Multispectral Bayesian Classification Method for Increased Acoustic Discrimination of Seabed Sediments Using Multi-Frequency Multibeam Backscatter Data. Geosciences. 2018; 8(12):455. https://doi.org/10.3390/geosciences8120455

Chicago/Turabian Style

Gaida, Timo C.; Tengku Ali, Tengku A.; Snellen, Mirjam; Amiri-Simkooei, Alireza; Van Dijk, Thaiënne A.G.P.; Simons, Dick G. 2018. "A Multispectral Bayesian Classification Method for Increased Acoustic Discrimination of Seabed Sediments Using Multi-Frequency Multibeam Backscatter Data" Geosciences 8, no. 12: 455. https://doi.org/10.3390/geosciences8120455

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