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Riverbed sediment classification using multi-beam echo-sounder backscatter data.

AliReza Amiri-Simkooei¹, Mirjam Snellen, Dick G Simons

¹Acoustic Remote Sensing Group, Delft Institute of Earth Observation and Space Systems (DEOS), Faculty of Aerospace Engineering, Delft University of Technology, Kluyverweg 1, 2629 HS Delft, The Netherlands. a.amirisimkooei@tudelft.nl

The Journal of the Acoustical Society of America

|October 10, 2009

Summary

This study enhances acoustic sediment classification for shallow waters by improving discrimination power and statistical descriptions. Results show a strong correlation between acoustic classification and sediment grain size.

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Area of Science:

Marine geophysics
Acoustic remote sensing
Sedimentology

Background:

Existing multi-beam echo-sounder backscatter methods struggle in shallow waters due to insufficient scatter pixels.
Gaussian distribution assumptions for backscatter strength are invalid in shallow environments.

Purpose of the Study:

Extend high-frequency acoustic backscatter methodology for shallow water environments.
Enhance sediment classification discrimination power and statistical description.
Correlate acoustic classification with physical sediment properties.

Main Methods:

Applied Bayes decision rule to multi-hypothesis testing using multi-beam echo-sounder backscatter data.
Incorporated high-resolution bathymetry for precise incident angle corrections.
Utilized high-resolution backscatter data averaging to reduce statistical fluctuations.
Analyzed angular evolution of K-distribution shape parameter.

Main Results:

Achieved significant correlation (0.75, disattenuated 0.90) between acoustic classification and sediment mean grain size.
Demonstrated enhanced discrimination power in shallow water environments.
K-distribution analysis indicated a rough riverbed surface.

Conclusions:

The extended methodology effectively classifies sediments in shallow waters.
Acoustic backscatter classification is a reliable proxy for sediment mean grain size.
The riverbed exhibits surface roughness consistent with core analysis.