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

GSTDTAP

项目编号	1849930
	EAGER: Developing a temporally adaptive decomposition framework for analyzing long-term echosounder time series
	Wu-Jung Lee (Principal Investigator)
主持机构	University of Washington
项目开始年	2019
	2019
项目结束日期	2020-12-31
资助机构	US-NSF
项目类别	Standard Grant
项目经费	281608(USD)
国家	美国
语种	英语
英文摘要	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.
文献类型	项目
条目标识符	http://119.78.100.173/C666/handle/2XK7JSWQ/213141
专题	环境与发展全球科技态势
推荐引用方式 GB/T 7714	Wu-Jung Lee .EAGER: Developing a temporally adaptive decomposition framework for analyzing long-term echosounder time series.2019.