From 10 M To 11000 M, Automatic Processing Multi-Beam Bathymetric Data Based On Pgo Method

Multi-beam echo sounders (MBESs) are characterized by the high resolution and high density of the sounding data. The processing of MBES bathymetry data is of special interest currently in marine surveying. The Combined Uncertainty and Bathymetry Estimator (CUBE) and surface filtering are the main MBES-processing algorithms for outliers. These algorithms involve five adjustable parameters; however, few studies have looked at parameter optimization. In this paper, a Parameter Group Optimization (PGO) method that determines the optimal parameters of CUBE and surface filtering based on the seafloor topographic characteristics of the survey area is presented. The method includes typical area selection, optimal grid resolution analysis, parameter group testing and batch processing, sounding and grid analysis. Raw MBES datasets from shallow- and deep-water survey areas (between 10 and 11000 m deep) are used to validate the proposed method. The results show that when the optimized parameters are used in the CUBE and filtering algorithm, the outliers are automatically eliminated; the processed bathymetry data is in good agreement with the bathymetry derived by a traditional manual processing method, while the processing efficiency can be improved by more than 8 times.