ABSTRACT: Multibeam data contains different types of errors that are classified as systematic errors, random errors and gross errors (outliers). Accurate bathymetric base surface production requires efficient cleaning methods to detect and reject the outliers. The manual cleaning method is tedious and time-consuming method. The need for automation of data cleaning is essential to reduce the processing time for multibeam data processing tasks. The newly developed AI-based Machine Learning (ML) method is a promising supervised method for automatic multibeam outlier detection and rejection. In this paper, a Hybrid ML-CUBE method was introduced and evaluated using multibeam datasets collected by Kongsberg EM712 and Reson T50-P multibeam echosounders. It was also found that if the outliers were not successfully detected and rejected, the accuracy of the produced base surfaces are degraded by 0.61 m and 0.58 m for EM712 and T50-P tests, respectively, which exceed the International Hydrographic Organization (IHO) special order. The significance of the proposed Hybrid ML-CUBE method is that it is a rigorous and automatic outlier rejection method for multibeam data cleaning and a rigorous bathymetric surface generation method for random errors reduction.

KEYWORDS: Multibeam Echosounder Data, Bathymetric Data Cleaning, Machine Learning (ML), CUBE Method, Outlier Detection and Rejection, 3D Convolutional Neural Network, IHO Survey Standards, Hybrid ML–CUBE Approach

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