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Real-time reporting of ecosystem metrics from acoustic sensors on gliders

滑翔机上的声学传感器实时报告生态系统指标

基本信息

批准号：
1802918
负责人：
金额：
--
依托单位：
University of East Anglia
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2016
资助国家：
英国
起止时间：
2016 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-1802918
关键词：
Real time reporting ecosystem metrics

项目摘要

The assessment of marine pelagic ecosystems poses a number of methodological challenges for sampling: the requirement for high spatial and temporal resolution, concurrent biological and environmental information, and behavioural responses to sampling equipment. Active acoustic techniques are now routinely used to resolve the high resolution distribution of marine organisms (from zooplankton to fish and larger organisms), typically from large research ships. The ability of Autonomous Underwater Vehicles (AUVs) such as underwater gliders to carry active acoustic sensors pertinent to ecosystem research has only recently been explored. However, due to their high data volume creation, these sensors currently store data locally for retrieval and analysis once the platform is recovered. One of the large appeals of gliders is directing them to regions of interest and receiving data in real-time. The current simple echosounder integrated into gliders generates 256 byte strings per ping (ping rate of 0.25 - 1 Hz). Newer, more complex wideband echosounders an order of magnitude more. These data need processing and constraining into metrics (acoustic area backscattering strength, vertical distribution, aggregation) onboard the glider, which can then be transmitted back. Thus enabling ecosystem descriptors to be transmitted back to shore from the glider/AUV in real-time and realizing these platforms capabilities for ecosystem research relevant to both fisheries management and impact assessments.This PhD project will work with state-of-the-art acoustic instruments to develop on-board processing capabilities to realize this challenge.When acoustic data are displayed in echograms, aggregations and scattering of zooplankton and fish are evident forming diverse spatial patterns such as schools, shoals and diffuse clouds. Echotrace classification techniques enable this complex information to be simplified. This project will use existing acoustic datasets from wideband and narrowband echosounders to develop advanced compression methods that will allow the pertinent information to be transmitted down the low-bandwidth channel and hence influence the mission. Given that onboard processing is limited, our initial approach will be to state-of-the-art machine learning methods such as Deep Neural Networks (currently being used by us in lip-reading) which are trained expensively (offline) but need little run-time computation. Our aim is to develop a compression hierarchy in which the most needed information is sent first, followed by the nuances. The student will work with glider and acoustic instrument manufacturers to implement the developed metrics and processing capabilities into a glider deployment. BAS and UEA deploy gliders in a number of environments (e.g. North Atlantic, Antarctic) and it is envisaged the student will use one of these opportunities to implement their technique.

对海洋中上层生态系统的评估对采样提出了若干方法学挑战：对高空间和时间分辨率的要求、同时提供的生物和环境信息以及对采样设备的行为反应。主动声学技术现在通常用于解决海洋生物(从浮游动物到鱼类和较大生物)的高分辨率分布，通常来自大型科考船。水下滑翔机等自主水下航行器携带与生态系统研究有关的有源声学传感器的能力直到最近才被探索。然而，由于它们创建的数据量很大，这些传感器目前将数据存储在本地，以便在平台恢复后进行检索和分析。滑翔机的一大吸引力是将它们引导到感兴趣的区域，并实时接收数据。目前集成到滑翔机中的简单回声探测仪每ping产生256个字节的字符串(ping速率为0.25-1赫兹)。更新、更复杂的宽带回声探测仪要多一个数量级。这些数据需要处理和约束成滑翔机上的指标(声学面积后向散射强度、垂直分布、聚集)，然后可以传回。因此，能够将生态系统描述符从滑翔机/AUV实时传输回海岸，并实现这些平台的能力，用于与渔业管理和影响评估相关的生态系统研究。该博士项目将与最先进的声学仪器合作，开发船上处理能力，以实现这一挑战。当声学数据以超声图显示时，浮游动物和鱼类的聚集和分散明显形成了不同的空间模式，如学校、浅滩和扩散云。回声跟踪分类技术可以简化这些复杂的信息。该项目将利用宽带和窄带回声探测仪现有的声学数据集，开发先进的压缩方法，使有关信息能够沿低带宽通道传输，从而影响飞行任务。考虑到机载处理有限，我们最初的方法将是最先进的机器学习方法，如深度神经网络(我们目前在唇读中使用)，这些方法经过昂贵的(离线)训练，但需要很少的运行时计算。我们的目标是开发一种压缩层次结构，其中首先发送最需要的信息，然后发送细微差别。学生将与滑翔机和声学仪器制造商合作，将开发的指标和处理能力应用到滑翔机部署中。BAS和UEA在许多环境(如北大西洋、南极)部署滑翔机，预计学生将利用这些机会之一来实施他们的技术。