Elements: Development of cyberinfrastructure to establish a scalable application of self-supervised machine learning for over a decade of NOAA's water column sonar data

要素：开发网络基础设施，以建立可扩展的自监督机器学习应用程序，用于 NOAA 十多年来的水柱声纳数据

• 批准号: 2311843
• 负责人: Carrie Bell
• 金额: $ 59.91万
• 依托单位: University of Colorado at Boulder
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-15 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2311843&HistoricalAwards=false
• 关键词: Elements; Development; cyberinfrastructure; establish; scalable

The health of the ocean is vital to economies throughout the world due to the global importance of ship traffic, commercial and recreational fisheries, tourism, and energy exploration and extraction. One technology that scientists use to understand and predict changes in the ocean is sonar, which allows them to ‘see’ into the ocean to observe its inhabitants and features such as ocean currents, sunken vessels, vents, and ocean bottom. By investigating the water column – the area of the ocean from the surface to the seafloor – using sonar technology, foundational information on the condition of the ocean ecosystems can be learned. Sonars produce vast amounts of data and much of it is interpreted by the trained eye of expert scientists. Unfortunately, modern sonar systems produce more data than scientists can interpret, and fast and accurate ways to extract information are needed. This project’s innovative approach efficiently processes decades of publicly available water column sonar data, which adds up to hundreds of terabytes. This project focuses on economically critical fisheries, and the results show how the patterns of fish schools and small swimming animals called zooplankton change over time and location. The project’s methods are being shared widely so scientists across the world can more easily use water column sonar in their research and interpretation is simplified since the results are directly comparable. The processing of existing data in new ways provides new information about ocean health, and rapid sharing of that information will lead to quicker answers for management decisions. These methods can also be applied to real-time sonar data collected on global fishing vessels and integrated into swarms of scientific ocean robots. When combined with other ocean data, the team can understand why the distribution of essential critters like zooplankton and fish changes, and how climate change can affect global fisheries. The project’s team is also training the next generation of scientists and engineers by using the information learned throughout the project in undergraduate courses for a diversity of students.Water column sonars provide foundational information on the condition of ocean ecosystems and inform marine resource conservation decisions. This project is developing the cyberinfrastructure (CI) required to apply self-supervised machine learning (SSL) to decades (and thus tens of terabytes) of water column sonar data to discover patterns that reflect the spatio-temporal physical and biological structure of aquatic environments. The SSL model is built using multi-frequency echosounder data collected from 1998 to 2022 by the NOAA Northeast Fisheries Science Center. These data are archived at the NOAA National Centers for Environmental Information and accessible as analysis-ready zarr stores on Amazon Web Services. This effort explores different scales of data in different regions of the Northwest Atlantic, evaluates the latency of pattern analysis, and validates the accuracy of the patterns found with domain experts. The project will deliver a CI proof of concept for a new, self-learning, and extensible method to classify acoustic signal patterns from large volumes of data. In combination with climate indicators, it enables advanced understanding of how the distribution of ecosystem essential critters like zooplankton and fish have been changing over time and space, and why.This award by the NSF Office of Advanced Cyberinfrastructure is jointly supported by the NSF Division of Biological Infrastructure (BIO/DBI).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

由于船舶交通，商业和休闲渔业，旅游业以及能源探索和提取的全球重要性，海洋的健康对全世界的经济至关重要。科学家用来理解和预测海洋变化的一种技术是声纳，这使他们可以“看到”海洋，以观察其影响和特征，例如洋流，凹陷的容器，通风孔和海底。通过调查水柱 - 从地面到海底的海洋区域 - 使用声纳技术，可以学习有关海洋生态系统状况的基础信息。声纳会产生大量数据，其中大部分是由训练有素的专家科学家的眼睛来解释的。不幸的是，现代声纳系统产生的数据超过科学家可以解释的数据，并且需要快速，准确的提取信息的方法。该项目的创新方法有效地处理了数十年的公开水柱声纳数据，该项目着重于经济上关键的渔业，结果表明，鱼类学校和小型游泳动物的模式如何随时间和位置随时间和位置变化。该项目的方法被广泛共享，因此全世界的科学家可以更轻松地在其研究和解释中使用水柱声纳，因为结果是直接可比的。以新的方式处理现有数据的新信息有关海洋健康的新信息，并且快速共享该信息将为管理决策提供更快的答案。这些方法也可以应用于在全球捕鱼vissels上收集的实时声纳数据，并将其集成到科学海洋机器人中。当与其他海洋数据结合使用时，团队可以理解为什么浮游动物和鱼类变化等基本小动物的分布以及气候变化如何影响全球渔业。该项目的团队还通过在本科课程中使用整个项目中学到的各种学生的信息来培训下一代科学家和工程师。水专栏声纳提供有关海洋生态系统和信息海洋资源保护决策状况的基础信息。该项目正在开发将自我监督的机器学习（SSL）应用于水柱声纳数据的数十年（因此数十个Terabytes）所需的网络基础设施（CI），以发现反映平均环境的时空物理和生物学结构的模式。 SSL模型是使用NOAA东北渔业科学中心从1998年至2022年收集的多频回声数据构建的。这些数据在NOAA国家环境信息中心存档，可作为Amazon Web服务上的分析Zarr商店访问。这项工作探讨了西北大西洋不同地区的不同数据尺度，评估了模式分析的延迟，并验证了与域专家发现的模式的准确性。该项目将为新的，自学和可扩展的方法提供CI概念验证，以对大量数据分类的声学信号模式进行分类。结合气候指标，它可以深入了解生态系统的分布，例如浮游动物和鱼类，随着时间和空间的变化，以及为什么由NSF先进的网络基础设施办公室授予NSF办公室，共同支持了NSF的NSF基础设施师（BIO/DBI）。利用基金会的知识分子和更广泛的影响审查标准。