An inter-comparison of sediment classification methods based on multi-beam echo-sounder backscatter and sediment natural radioactivity data

Abstract

This contribution presents sediment classification results derived from different sources of data collected at the Dordtse Kil river, the Netherlands. The first source is a multi-beam echo-sounder (MBES). The second source is measurements taken with a gamma-ray scintillation detector, i.e., the Multi-Element Detection System for Underwater Sediment Activity (Medusa), towed over the sediments and measuring sediment natural radioactivity. Two analysis methods are employed for sediment classification based on the MBES data. The first is a Bayesian estimation method that uses the average backscatter data per beam and, therefore, is independent of the quality of the MBES calibration. The second is a model-based method that matches the measured backscatter curves to theoretical curves, predicted by a physics-based model. Medusa provides estimates for the concentrations of potassium, uranium, thorium, and cesium, known to be indicative for sediment properties, viz. mean grain size, silt content, and the presence of organic matter. In addition, a hydrophone attached to the Medusa system provides information regarding the sediment roughness. This paper presents an inter-comparison between the sediment classification results using the above-mentioned methods. It is shown that although originating from completely different sources, the MBES and Medusa provide similar information, revealing the same sediment distribution.

Characterizing riverbed sediment using high-frequency acoustics: 2. Scattering signatures of Colorado River bed sediment in Marble and Grand Canyons

“…Video and photographic sampling is more cost effective and does not require time‐consuming laboratory analyses, which allows sampling at greater frequency and coverage [ Rubin et al , ; Van Rein et al , ; Buscombe et al , ]. However, the use of high‐frequency (several hundred kilohertz) acoustic backscatter from swath‐mapping systems to characterize sediment and classify by grain size [ Anderson et al , ; Brown and Blondel , ; Brown et al , ; Snellen et al , ] has the potential to provide near‐complete coverage, which photographic sampling could not practically achieve, at least within the same time and with the same positional accuracy.…”

“…In shallow water, given the lack of robust classification techniques based on the physics of scattering for high‐frequency multibeam systems [ Amiri‐Simkooei et al , ], an alternative phenomenological approach based on statistical analysis of backscatter signals [ Brown and Blondel , ; Snellen et al , ] has become popular. Many such methods proposed to date rely on aggregation of data over scales much larger than the typical scales of sediment patchiness on heterogeneous riverbeds.…”

Remote sensing of river corridors: A review of current trends and future directions

“…The technique has been successfully used to investigate grain‐size packing distribution (Hodge, Brasington, & Richards, ), variations between systems (Storz‐Peretz et al, ), submerged grain size (Smith et al, ), and grain size on large, complex gravel systems using mobile laser scanning (MLS; Wang, Wu, Huang, & Lee, ). Through‐water TLS is ineffective for deeper channels, where instead, MBES data have been used to infer grain size using statistical inference techniques (Eleftherakis, Snellen, Amiri‐Simkooei, Simons, & Siemes, ; Snellen, Eleftherakis, Amiri‐Simkooei, Koomans, & Simons, ). However, the extensive calibration involved and limited spatial applicability restrict the scale of application over which the methods can be used.…”

Characterizing riverbed sediment using high-frequency acoustics: 1. Spectral properties of scattering

“…High frequencies permit short acoustic pulse lengths (order microsecond or less) which, in combination with narrow along‐track beam widths, allow high spatial resolutions (typically centimeters to meters). Since MBES systems are already used to map bathymetry, much attention has been paid to developing techniques that use the acoustic signals measured by the instrument to classify submerged sediment deposits by grain size [ Anderson et al , ; Van Rein et al , ; Snellen et al , ].…”

“…The basic premise behind acoustic sediment classification is that the magnitude of echoes measured by a MBES receive array depend, at least in part, on the composition of bed sediments, that have different backscattering properties due to impedance differences (acoustic hardness) and characteristic surface roughnesses [ Jackson and Richardson , ]. Acoustic sediment classification techniques proposed to date are either statistical [ Amiri‐Simkooei et al , ; Eleftherakis et al , ; Snellen et al , ] or based on physical models [ Jackeman , ; Lyons and Abraham , ; Snellen et al , ]. Statistical models are generally not able to separate the relative contributions of roughness and hardness in the backscatter signal.…”