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.

This paper reports on a study to model seabed surfaces using the least-squares support-vector machine algorithm with a sample cross-validation (CV) method. It starts with a brief overview of the sample selection method of the algorithm and gives two important characteristics of the algorithm. It then focuses on the theory of sample CV and the steps of sample selection using this theory. Finally, to verify the validity of the sample CV method in sample selection for the algorithm, the measured multi-beam bathymetric data are selected to calculate and analyse. It is concluded that the sample CV method can reasonably screen out the sounding training samples with a large contribution to the function model, making the constructed function model more reasonable.