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

EAGER：开发用于分析长期回声测深仪时间序列的时间自适应分解框架

基本信息

批准号：
1849930
负责人：
Wu-Jung Lee
金额：
$ 28.16万
依托单位：
University of Washington
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-01-01 至 2021-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1849930&HistoricalAwards=false
关键词：
EAGER Developing temporally adaptive decomposition

项目摘要

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.

科学回声测深仪是高频主动声纳系统，广泛用于研究海洋生物，特别是特征最少的中营养动物，例如浮游动物和鱼类。最近，自主回声测深仪的部署激增，导致来自各种海洋观测平台（包括车辆和系泊设施）的大量回声数据。这些数据集具有巨大的潜力，可以在空间和时间尺度上增进我们对海洋生态系统的理解，这是前所未有的。然而，数据的数量和复杂性使得高效且有效的分析成为一项重大挑战。本研究旨在建立一种数据驱动的方法，自动从测深仪数据中提取特征，从而打破仪器能力（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当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源回声分析软件包和作为该项目集成组件创建的在线教育材料进行传播。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，这对于全球生物多样性和大量社会人口的经济福祉至关重要。这项研究将开发一种自动发现和跟踪低维空间的方法。 -通过采用最先进的动态非负矩阵分解（NMF）公式，从高维回波数据中获得时间结构。 NMF 是一种无监督机器学习技术，它将复杂数据分解为一组较小的定量描述符的线性组合，这些描述符比原始数据更容易处理和解释。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的推荐系统。在回波分析的背景下，这些低维描述符描述了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的特征。然而，传统的 NMF 并不能很好地解释数据中一系列不断变化的现象。因此，该项目旨在开发一种可以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化性。研究人员将使用 NSF 海洋观测计划 (OOI) 中的两个有线耐力阵列回声测深仪收集的数据作为方法开发的测试平台。将评估所开发方法的稳定性、鲁棒性和可解释性，并与传统的回波分析例程进行比较。这项工作将为 OOI 和其他海洋观测系统的大型回波数据集的进一步机器学习和统计研究奠定基础。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查进行评估，被认为值得支持标准。