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Seafloor Classification in a Sand Wave Environment on the Dutch Continental Shelf Using Multibeam Echosounder Backscatter Data

1
Acoustics Group, Faculty of Aerospace Engineering, Delft University of Technology, 2629 HS Delft, The Netherlands
2
Department of Geomatics Engineering, Faculty of Civil Engineering and Transportation, University of Isfahan, 81746-73441 Isfahan, Iran
3
Conservation Ecology Group, Groningen Institute for Evolutionary Life Sciences, University of Groningen, P.O. Box 11103, 9700 CC Groningen, The Netherlands
4
NIOZ Royal Netherlands Institute for Sea Research, Department of Estuarine and Delta Systems, and Utrecht University, P.O. Box 140, 4400 AC Yerseke, The Netherlands
5
Department of Applied Geology and Geophysics, Deltares, 3508 AL Utrecht, The Netherlands
*
Author to whom correspondence should be addressed.
Geosciences 2019, 9(3), 142; https://doi.org/10.3390/geosciences9030142
Received: 8 February 2019 / Revised: 11 March 2019 / Accepted: 19 March 2019 / Published: 23 March 2019
(This article belongs to the Special Issue Geological Seafloor Mapping)
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Abstract

High resolution maps of sandy seafloors are valuable to understand seafloor dynamics, plan engineering projects, and create detailed benthic habitat maps. This paper presents multibeam echosounder backscatter classification results of the Brown Bank area of the North Sea. We apply the Bayesian classification method in a megaripple and sand wave area with significant slopes. Prior to the classification, corrections are implemented to account for the slopes. This includes corrections on the backscatter value and its corresponding incident angle. A trade-off in classification resolutions is found. A higher geo-acoustic resolution is obtained at the price of losing spatial resolution, however, the Bayesian classification method remains robust with respect to these trade-off decisions. The classification results are compared to grab sample particle size analysis and classified video footage. In non-distinctive sedimentary environments, the acoustic classes are not attributed to only the mean grain size of the grab samples but to the full spectrum of the grain sizes. Finally, we show the Bayesian classification results can be used to characterize the sedimentary composition of megaripples. Coarser sediments were found in the troughs and on the crests, finer sediments on the stoss slopes and a mixture of sediments on the lee slopes. View Full-Text
Keywords: seafloor mapping; sediment; benthic habitats; multibeam echosounder; acoustic backscatter; Megaripples; sand waves; marine geology seafloor mapping; sediment; benthic habitats; multibeam echosounder; acoustic backscatter; Megaripples; sand waves; marine geology
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Koop, L.; Amiri-Simkooei, A.; J. van der Reijden, K.; O’Flynn, S.; Snellen, M.; G. Simons, D. Seafloor Classification in a Sand Wave Environment on the Dutch Continental Shelf Using Multibeam Echosounder Backscatter Data. Geosciences 2019, 9, 142.

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