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

Timo Gaida,Alireza Amiri-Simkooei,Tengku Tengku Ali,Thaiënne van Dijk,Mirjam Snellen,Dick Simons

doi:10.3390/geosciences8120455

Timo Gaida, Alireza Amiri-Simkooei + Show 4 more

Open Access

https://doi.org/10.3390/geosciences8120455

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Abstract

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.

Highlights

Multibeam echosounders (MBESs) have become the most valuable tool for seafloor mapping providing high-resolution bathymetry and acoustic backscatter datasets [1]
The Bayesian acoustic sediment classification (ASC) method is introduced for the classification of multispectral multibeam echosounders (MBESs) backscatter
The method accounts for the natural variability of the backscatter strength by assuming the measured backscatter per beam and frequency to result in a number of discrete seafloor types that each corresponds to a Gaussian distribution

Summary

Introduction

Multibeam echosounders (MBESs) have become the most valuable tool for seafloor mapping providing high-resolution bathymetry and acoustic backscatter datasets [1]. Various classification methods, employing MBES bathymetry, backscatter, and their second order moments, have been developed to characterize seabeds or riverbeds in the last two decades [2]. They aim at maximizing the performance in discriminating between different seabed environments or sediment types. Acoustic backscatter strength is the most common feature used in seabed classification [2]. The backscatter strength is dependent on the composition of the seabed, angle of incidence, and acoustic frequency [3]. Volume heterogeneity, bulk density, as well as Geosciences 2018, 8, 455; doi:10.3390/geosciences8120455 www.mdpi.com/journal/geosciences

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