The East China Sea Continental Slope Of The Terrain Classification Based On Multi-beam Data

The Seafloor Terrain Classification (STC) is one method of describing terrain characteristic, and analyzing the complicated extent of the seafloor terrain growth characteristic. It can divide the seafloor characteristic into the different terrain unit. The STC is an important content in the field of the seabed geomorphology, but now it is mainly depended on the descriptive manual classification method, which limits the terrain information reliability. The automatizaton of the STC can greatly improve the precise and intuitionistic representation of the digitization terrain analysis. It also makes the possibility of the quantifiable description to the terrain. This quantifiable and objective description provides the basis for the seabed geomorphology synthesis charting, and offers the important reference value for the submarine geology unit division and the geologic structure recognition. These basis terrain information is important for the marine disaster research and the oil gas resource prospecting. Unfortunately, the relative research activity is very seldom at present.The continent slope in the East China Sea, whose terrain is quite complicated, is an active region involved in many research field. Determining impersonally and accurately the shelf break line and the continental slope toe line in the East China Sea's research, has very profound scientific significance and the application value. In the East China Sea, the multi-beam sidescan data with high resolution and accuracy have been already accumulated, which makes the highly accurate and automatic terrain classification possible.In this study, a research area is chosen in the continent slope and the continent slope nearby in the East China Sea. And the multi-beam bathymetric data with both high resolution and accuracy are used here. The large scale classification of the study area is carried out by means of both the statistical method and the artificial neural network method, respectively. The main research works include:(1) Establishing DEM (Digital Elevation Model) based on the multi-beam data, and extracting the topographic feature factor from DEM;(2) Carrying out the topographic classification in the research area by means of the statistical method based on the topographic feature factor;(3) Realizing the topographic classification in the research area by means of the artificial neural network method based on the topographic feature factor;(4) Implementing the accuracy evaluation and contrast analysis as a result to the classification.The research indicates that the multi-beam data can give the high precision of the STC. The STC using the artificial neural network method is much more accurate than that from the statistical classification method.