Data Processing Method of Multibeam Bathymetry Based on Sparse Weighted LS-SVM Machine Algorithm

In this paper, on the basis of the sparse weighted least-squares support vector machine (LS-SVM) algorithm, the sparse weighted LS-SVM surface is established and the corresponding steps are given. Then, multibeam bathymetric anomalies can be detected using the sparse weighted LS-SVM surface. The construction of the sparse weighted LS-SVM surface requires four aspects: selection of kernel function, parameters calculation of sparse weighted LS-SVM, selection of support vectors, calculation of weight coefficients. Finally, to verify the validity of sparse weighted LS-SVM surface in multibeam bathymetric anomalies detection, the measured multibeam bathymetric data are selected to calculate and analyze. The conclusion of the experiment is introduced, that is, the sparse weighted LS-SVM surface has a better performance compared to the traditional polynomial surface function, and the sparse weighted LS-SVM surface is more capable of reflecting the overall change of seabed topography. Compared with the combined uncertainty and bathymetry estimator surface, the proposed method utilizes the raw soundings rather than the nodes, so the sparse weighted LS-SVM surface can better reflect the detailed information of seabed topography.