EAGER: Developing a temporally adaptive decomposition framework for analyzing long-term echosounder time series

科学回声探测仪是一种高频主动声呐系统，广泛用于研究海洋生物，特别是最不具特征的中营养动物，如浮游动物和鱼类。最近，自动回声测深仪的部署激增，导致各种海洋观测平台（包括车辆和系泊处）产生了大量的回声数据。这些数据集具有巨大的潜力，可以在前所未有的空间和时间尺度上促进我们对海洋生态系统的理解。然而，数据的数量和复杂性使高效和有效的分析成为一项主要挑战。本研究旨在建立一种数据驱动的方法，从回声测深数据中自动提取特征，从而解决仪器能力（收集大数据）和解释能力（分析大数据）之间的瓶颈。所开发的方法将加快目前劳动密集型的数据分析过程，并有助于从大型回波数据集中提取天气信息。这项研究的结果将通过开放源代码的回声分析软件包和作为该项目组成部分创建的在线教育材料传播。这项研究直接有助于提高我们更好地了解海洋生态系统的能力，海洋生态系统对全球生物多样性和大量社会人口的经济福祉至关重要。本研究将通过采用最先进的动态非负矩阵分解（NMF）公式，开发一种从高维回波数据中自动发现和跟踪低维时空结构的方法。NMF是一种无监督的机器学习技术，它将复杂的数据分解成一组比原始数据更易于处理和解释的更小的定量描述符的线性组合。该技术在许多基础研究和应用领域取得了巨大成功，包括计算机视觉、神经科学、自然语言处理和互联网零售商推荐系统。在回声分析的背景下，这些低维描述符表征了由回声测深仪成像的水柱中海洋生物的垂直运动和分组活动。然而，传统的NMF并不能很好地解释数据中不断变化的现象。因此，本项目旨在开发一种能够提供时间自适应分解的方法，以适应长期回声测深数据中瞬态特征的复杂性和高时间变异性。研究人员将使用NSF？的海洋天文台计划（OOI），作为方法发展的测试平台。将评估所开发方法的稳定性、稳健性和可解释性，并与传统的回波分析程序进行比较。这项工作将为进一步的机器学习和来自OOI和其他海洋观测系统的大型回波数据集的统计研究奠定基础。该奖项反映了美国国家科学基金会的法定使命，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。

Scientific echosounders are high-frequency active sonar systems used extensively to study life in the ocean, especially the least-characterized mid-trophic animals, such as zooplankton and fish. The recent surge in the deployment of autonomous echosounders has resulted in a deluge of echo data from a variety of ocean observing platforms, including vehicles and moorings. These data sets harbor great potential for advancing our understanding of marine ecosystems at spatial and temporal scales never before possible. However, the volume and complexity of the data make efficient and effective analysis a major challenge. This research is aimed at establishing a data-driven methodology to automatically extract features from echosounder data, thus unblocking this bottleneck between instrumentation capacity (to collect large data) and interpretation capability (to analyze large data). The developed method will expedite the currently labor-intensive data analysis process and help extract synoptic information from large echo data sets. Results of this research will be disseminated through open-source echo analysis software packages and online educational materials created as integrated components of this project. This research contributes directly to improving our ability to better understand the marine ecosystems, which are critical for global biodiversity and the economic well-being of a significant population of society.This research will develop a method for automated discovery and tracking of low-dimensional spatio-temporal structures from high-dimensional echo data by adapting a state-of-the-art dynamic non-negative matrix factorization (NMF) formulation. NMF is an unsupervised machine learning technique that decomposes complex data into a linear combination of a smaller set of quantitative descriptors that are more tractable and interpretable than the original data. This technique has found great success in many basic research and applied fields, including computer vision, neuroscience, natural language processing, and recommender systems for internet retailers. In the context of echo analysis, these low-dimensional descriptors characterize the vertical movements and grouping activities of marine organisms in the water column imaged by the echosounder. However, traditional NMF does not account well for a changing set of phenomena in the data. Therefore, this project aims to develop a method that can provide a temporally adaptive decomposition in order to accommodate the complexity and high temporal variability of transient features in long-term echosounder data. The researchers will use data collected by the two cabled Endurance Array echosounders in NSF?s Ocean Observatories Initiative (OOI) as a test bed for methodology development. The stability, robustness, and interpretability of the developed method will be evaluated and compared with conventional echo analysis routines. This effort will lay the foundation for further machine learning and statistical studies of large echo datasets from the OOI and other ocean observing systems.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.