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

由于船舶交通、商业和休闲渔业、旅游业以及能源勘探和开采的全球重要性，海洋的健康对世界各地的经济至关重要。科学家用来了解和预测海洋变化的一种技术是声纳，它使他们能够“看到”海洋，观察其居民和特征，如洋流，沉船，喷口和海底。通过使用声纳技术调查水柱-从表面到海底的海洋区域，可以了解海洋生态系统状况的基本信息。声纳产生大量的数据，其中大部分是由训练有素的专家科学家的眼睛解释。不幸的是，现代声纳系统产生的数据比科学家可以解释的要多，需要快速准确的方法来提取信息。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，这些数据加起来高达数百TB。该项目的重点是经济上至关重要的渔业，其结果显示了鱼群和称为浮游动物的小型游泳动物的模式如何随时间和地点而变化。该项目的方法被广泛分享，因此世界各地的科学家可以更容易地在他们的研究中使用水柱声纳，并且由于结果具有直接可比性，解释也得到了简化。以新的方式处理现有数据提供了有关海洋健康的新信息，迅速分享这些信息将有助于更快地作出管理决策。这些方法也可以应用于在全球渔船上收集的实时声纳数据，并集成到科学海洋机器人群中。当与其他海洋数据相结合时，该团队可以理解为什么浮游动物和鱼类等重要生物的分布会发生变化，以及气候变化如何影响全球渔业。该项目的团队还利用在整个项目中为各种学生开设的本科课程中学到的信息来培训下一代科学家和工程师。水柱声纳提供有关海洋生态系统状况的基础信息，并为海洋资源保护决策提供信息。该项目正在开发将自我监督机器学习（SSL）应用于数十年（因此数十TB）水柱声纳数据所需的网络基础设施（CI），以发现反映水生环境时空物理和生物结构的模式。SSL模型是利用NOAA东北渔业科学中心1998年至2022年收集的多频回声测深仪数据建立的。这些数据存档在NOAA国家环境信息中心，并可作为Amazon Web Services上的分析就绪zero-store访问。这项工作探索了西北大西洋不同地区不同规模的数据，评估了模式分析的延迟，并验证了领域专家发现的模式的准确性。该项目将为一种新的、自学习的、可扩展的方法提供CI概念证明，以从大量数据中对声学信号模式进行分类。与气候指标相结合，它使人们能够深入了解浮游动物和鱼类等生态系统重要生物的分布如何随时间和空间而变化，以及为什么。这个由NSF高级网络基础设施办公室颁发的奖项是由NSF生物基础设施部门（BIO/DBI）联合支持的。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.