Artilheiro, Fernando, M. F.2023-06-072023-06-07https://unbscholar.lib.unb.ca/handle/1882/32491Multibeam echosounding (MBES) bathymetric surveying is a new hydrographic methodology. Criteria for the most appropriate ways of using MBES systems in hydrography must be established. This report addresses two questions concerning the “cleaning” of MBES bathymetric data: 1. What rules should the hydrographer follow in MBES data cleaning for bathymetric charting, in order to uniformly achieve the most reliable data cleaning results? 2. What features in multibeam acoustic backscatter data can be used to help identify bathymetric anomalies? MBES bathymetric data contains blunders. These errors need to be identified and the corresponding soundings rejected. This is the goal of data cleaning task. Currently, one of the major obstacles of using MBES in hydrography is the data cleaning. Frequently this is because the sequence of data cleaning steps is not always the most logical and uniform. This report summarizes the background information required for the analysis and cleaning of MBES data. Several data cleaning methods are reviewed. For two commercial methods. HDCS (a module from the Hydrographic Information Processing System. Universal Systems Ltd.) and BINSTAT (a module from Neptine. Simrad Norge AS). A logical sequence of steps for their use is proposed; as well suggestions for standardization of processing are made. As there is a correlation between seafloor bathymetric features and side scan targets, this report assesses, for two case studies, how acoustic seabed backscatter information provided by many MBES systems, can be useful as a potential coadjutant of data cleaning. The results obtained show that bathymetric features such as wrecks are highlighted by a decrease of the backscatter strength, and boulders are highlighted by an increase of the backscatter strength. It is concluded that the bottom detection parameter (amplitude or phase) is an important data cleaning factor for making decisions about blunders and real features, whereas the acoustic imagery provides a mean to define areas where either automatic or interactive data cleaning should be used.http://purl.org/coar/access_right/c_16ecsenior report