Automated processing and error detection in multibeam sonar data

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Multibeam sonar data is inherently noisy and prone to errors due to the complexity of the marine environment. Recently, multibeam sonar systems have been implemented on autonomous, or minimally supervised, vessels which either follow preset instructions to map the seafloor or acquire data as the platform transits from one location to another. Autonomous systems provide several benefits to traditional platforms, namely improved safety, as they can map uncharted areas and remove personnel from the vessel. However, the data from these systems do not have the benefits of constant supervision by a sonar operator or data processor; therefore, problems with the data are not immediately identified and corrected and must be compensated for in post-processing. The delay then propagates to an imbalance in the data acquisition to processing time and has slowed the adoption of these data collection platforms. This proposed research program will improve the efficiency of multibeam data processing for autonomous vessels through near-real-time identification of system blunders, environmental artifacts and noise. The result will be a notification and classification system to alert operators of data or system problems, flag noise in the depth measurements, and reduce user interaction with the data. Five objectives make up the research program. 1.Identify and monitor real-time data outputs during acquisition to identify system errors; 2.Automate post-processing of environmental variables to minimize environmental uncertainty; 3.Perform an uncertainty assessment of resulting sonar data; 4.Establish methods for multibeam sonar noise identification and error classification using three-dimensional deep learning; 5.Analyze noise sources and detected errors to improve future autonomous systems; Multibeam sonar datasets for training and testing will be acquired from two vessels: the University of New Brunswick survey launch Heron, working in coastal British Columbia, and the Canadian Coast Guard Ship Amundsen, working throughout the Canadian Arctic. The combination of computational programming, experimental design, and field experience form an immersive HQP training environment for HQP of diverse backgrounds. Program HQP will be introduced to the latest in three-dimensional deep learning algorithms, multibeam sonar data acquisition and processing protocols, and statistical testing in an inclusive training environment within an ocean mapping focused engineering research laboratory. Improving the application of autonomous survey platforms and removing personnel from traditional survey vessels will improve the efficiency of seafloor mapping, limit marine accidents, and reduce the costs of seafloor mapping, especially in remote areas. Only a small percentage of Canadian and international waters are mapped to modern standards, and improving the ability for autonomous vessels to collect this crucial data will provide benefits to Canadians and others around the world.