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To decrease the time consumption and the labor intensity in the absolute datum transfer of traditional seafloor control network measurement, a new method, namely sailing-circle positioning method, is put forward in this paper. First, the traditional intersection positioning model is improved by considering the equivalent sound velocity profile error as an unknown parameter in the adjustment model. Second, the effect of geometric dilution of precision (GDOP) on positioning accuracy is analyzed. By seeking for the minimum of GDOP, it is concluded that the absolute datum transfer can achieve the highest accuracy in the condition of sailing along a circle relative to other sailing paths. Moreover, the optimal radius of the circle for the accurate datum transfer is also given out. Besides, the correlation between the accuracy of datum transfer and the sound velocity error in this method is analyzed. Finally, the new method was tested and verified by the experiments in Songhua lake with the water depth of 60 m and in South China sea with the water depth of 2000 m, respectively. These experiment results show that the new method can improve the accuracy and efficiency of traditional datum transfer method significantly. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]