Acoustic discrimination of relatively homogeneous fine sediments using Bayesian classification on MBES data

Modern seafloor mapping is based on high resolution MBES systems that provide detailed bathymetric and acoustic intensity (backscatter) information. We examine and validate the performance of two unsupervised MBES classification techniques for discriminating acoustic classes of sedimentary units wit...

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Bibliographic Details
Published in:Marine geology 2015-12, Vol.370, p.31-42
Main Authors: Alevizos, Evangelos, Snellen, Mirjam, Simons, Dick G., Siemes, Kerstin, Greinert, Jens
Format: Article
Language:English
Subjects:
Acoustic backscatter
Acoustics
Backscattering
Bayesian analysis
Bayesian classification
Brackish
Classification (sedimentation)
Geoacoustic resolution
Grain size
Macrohabitats
Marine
Sand
Sea beds
Sediment discrimination
Sediments
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Summary:Modern seafloor mapping is based on high resolution MBES systems that provide detailed bathymetric and acoustic intensity (backscatter) information. We examine and validate the performance of two unsupervised MBES classification techniques for discriminating acoustic classes of sedimentary units with small grain size variability. The first technique, based on a principal components analysis (PCA), is commonly used in literature and has been applied for comparison with the more recent approach of Bayesian statistics. By applying these techniques to a MBES dataset from an estuarine area in The Netherlands, we tested their ability to discriminate fine grained sediments (at least 70% silt) holding small percentages of coarser material such as sand, shell hash or shells. We focus on the Bayesian technique as it outputs acoustically significant classes related to backscatter values. This technique utilizes backscatter values averaged over scatter pixels (projected pulse lengths) inside the footprint of each beam. The originality of our application lies in the fact that, the optimal number of classes is derived by utilizing a number of beams simultaneously. It is assumed that the backscatter values per beam vary relatively to the varying seafloor types. By treating the beams separately, across track variation in the seafloor type can also be accounted for. Thereby the classification is guided by outer, more discriminative beams. Additionally we control the optimal number of classes by employing the quantitative criterion of goodness of fit (χ2). The Bayesian acoustic classes show correlation with grain size parameters such as coarse fraction (>500μm) percentage and mean of the grain size (
ISSN:0025-3227
1872-6151
DOI:10.1016/j.margeo.2015.10.007