Automated processing and error detection in multibeam sonar data

由于海洋环境的复杂性，多波束声呐数据本身就存在噪声，容易产生误差。最近，多波束声纳系统已经在自主或最少监督的船只上实施，这些船只要么按照预设的指令绘制海底地图，要么在平台从一个位置转移到另一个位置时获取数据。与传统平台相比，自主系统提供了几个好处，即提高了安全性，因为它们可以绘制未知区域并将人员从船上移走。然而，来自这些系统的数据不具备由声纳操作员或数据处理器持续监督的好处；因此，数据的问题不能立即识别和纠正，必须在后处理中进行补偿。然后，延迟传播到数据采集与处理时间的不平衡，并减缓了这些数据收集平台的采用。该研究计划将通过近实时识别系统错误、环境伪影和噪声，提高自主船舶多波束数据处理的效率。结果将是一个通知和分类系统，提醒操作员数据或系统问题，标记深度测量中的噪音，并减少用户与数据的交互。研究计划由五个目标组成。1.识别和监控采集过程中的实时数据输出，以识别系统错误；2.自动后处理环境变量，以尽量减少环境的不确定性；3.对所得声纳数据进行不确定度评估；4.建立基于三维深度学习的多波束声纳噪声识别与误差分类方法；5.分析噪声源和检测到的错误，以改善未来的自主系统；用于训练和测试的多波束声纳数据集将从两艘船获得：在不列颠哥伦比亚省沿海工作的新不伦瑞克大学调查船Heron和在加拿大北极地区工作的加拿大海岸警卫队船Amundsen。计算编程、实验设计和现场体验相结合，为不同背景的HQP人员形成了沉浸式的HQP训练环境。HQP项目将引入最新的三维深度学习算法、多波束声纳数据采集和处理协议，以及在海洋测绘工程研究实验室的包容性培训环境中进行统计测试。改进自主测量平台的应用，减少传统测量船上的人员，将提高海底测绘的效率，减少海上事故，降低海底测绘的成本，特别是在偏远地区。只有一小部分加拿大和国际水域按照现代标准绘制了地图，提高自主船只收集这些关键数据的能力将为加拿大和世界各地的其他人带来好处。

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.