Seabed Mixed Sediment Classification with Multi-beam Echo Sounder Backscatter Data in Jiaozhou Bay

Abstract

The multi-beam echo sounder system can not only obtain high-precision seabed bathymetry data, but also obtain high-resolution seabed backscatter strength data. A number of studies have applied acoustic remote sensing method to classify seabed sediment type with multi-beam backscatter strength data, and obtained better classification results than the traditional sediment sampling method. However, these studies mainly focus on the single type sediment classification or seabed mixed sediment classification using single beam data, not multi-beam echo sounder data. Based on backscatter strength data by a high-frequency (300 kHz) Simrad EM3000 multi-beam echo sounder and seabed sediment sampling data of Jiaozhou Bay in Qingdao, China, we establish the relation model between seabed backscatter strength and sediment type characteristics after data processing and corrections. The purpose of data processing is to diminish or weaken the influence of local bottom slope and near nadir reflection on backscatter strength data. Processed backscatter strength data through corrections and compensations can better characterize the features of the seabed sediments. Applying the back propagation neural network method based on genetic algorithm, we present a fast and accurate seabed classification method in this paper which could identify not just a single type of sediment-like rock, sand but also mixed types of sediment like sand gravel, clayey silt and sand-silt-clay.