基于自组织神经网络的声学底质分类研究

Abstract

研究利用多波束测深系统获取的反向散射强度数据,应用自组织(Self Organizing Map,简称SOM)神经网络分类方法实现了对海底泥、砂、砾石和基岩等底质类型的快速、有效识别。通过实验示例,将SOM神经网络的分类结果与传统海底地质取样获取的真实底质类型进行分析比较,表明该方法是可行和有效的。 Multibeam sonar systems can provide hydrographic quality depth data as well as high-resolution seafloor sonar images. Using the seafloor-backscattered data from each beam and with automatic classification, seabed sediments distribution maps can be obtained directly. In this paper, the self-organizing map (SOM) neural network is used in acoustic seafloor classification from multibeam sonar data. This method can rapidly identify all kinds of seafloor types such as mud, sand, gravel and rock in the experimental surveying areas. Compared with the traditional geologic grab method, the experiment indicates that the SOM method is feasible and valid.