Collaborative Research: Framework: Data: NSCI: HDR: GeoSCIFramework: Scalable Real-Time Streaming Analytics and Machine Learning for Geoscience and Hazards Research

该项目开发了一个实时处理系统，能够处理大量传感器观测结果。该系统的重点是在事件发生时使用机器学习自动检测自然灾害事件。四个组织（UNAVCO、科罗拉多大学、俄勒冈大学和罗格斯大学）合作开发了一个数据框架，用于地球科学和灾害研究的通用实时流分析和机器学习。这项工作将支持对与危险事件（地震、火山爆发、海啸）有关的数据进行快速分析和理解。该项目利用计算机科学家和地球科学家之间的合作，为地球科学和灾害研究开发一个通用实时流分析和机器学习的数据框架。它侧重于将大量数据流聚合和集成到一个支持实时数据流分析的连贯系统中。该框架将提供基于机器学习的工具，用于检测地震和海啸等事件的信号，这些信号可能只有在观察大量观测输入时才能检测到。该架构通过组合一组开源组件来建立一个快速的数据管道，这些组件使大数据应用程序变得可行且更容易开发。该项目的数据来源主要来自目前由UNAVCO和地震学联合研究机构（IRIS）管理的EarthScope网络中的1500多个传感器，以及由罗格斯大学管理的海洋观测站倡议（OOI）电缆阵列数据。机器学习（ML）算法将被研究并应用于海啸和地震用例。最初，该项目计划在多数据环境中采用先进的卷积神经网络方法。该方法仅应用于地震波形，因此该项目将探索将该方法扩展到多数据环境。该方法有望扩展到地震的探测和表征之外，包括其他地球物理信号的开始，如慢滑事件或岩浆入侵，扩大新的科学发现的潜力。该框架应用于Cascadia俯冲带和Yellowstone的用例：这些地点将科学团队的专业知识与EarthScope和OOI仪器最集中的地点相结合。该架构将是可移植和可扩展的，可以在笔记本电脑、本地集群和云上的Docker环境中运行。该项目的组成部分将是使用诸如GitLab和Jupyter notebook等协作在线资源的开发、文档和培训，并利用NSF XSEDE资源使更大的数据集和计算资源更广泛地可用。该奖项由美国国家科学基金会高级网络基础设施办公室颁发，由美国国家科学基金会地球科学理事会的跨领域项目、计算机与信息科学与工程理事会的大数据科学与工程项目以及由美国国家科学基金会地球科学理事会和先进网络基础设施办公室联合赞助的EarthCube项目共同支持。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。

This project develops a real-time processing system capable of handling a large mix of sensor observations. The focus of this system is automation of the detection of natural hazard events using machine learning, as the events are occurring. A four-organization collaboration (UNAVCO, University of Colorado, University of Oregon, and Rutgers University) develops a data framework for generalized real-time streaming analytics and machine learning for geoscience and hazards research. This work will support rapid analysis and understanding of data associated with hazardous events (earthquakes, volcanic eruptions, tsunamis). This project uses a collaboration between computer scientists and geoscientists to develop a data framework for generalized real-time streaming analytics and machine learning for geoscience and hazards research. It focuses on the aggregation and integration of a large number of data streams into a coherent system that supports analysis of the data streams in real-time. The framework will offer machine-learning-based tools designed to detect signals of events, such as earthquakes and tsunamis, that might only be detectable when looking at a broad selection of observational inputs. The architecture sets up a fast data pipeline by combining a group of open source components that make big data applications viable and easier to develop. Data sources for the project draw primarily upon the 1500+ sensors from the EarthScope networks currently managed by UNAVCO and the Incorporated Research Institutions for Seismology (IRIS), as well as the Ocean Observatories Initiative (OOI) cabled array data managed by Rutgers University. Machine learning (ML) algorithms will be researched and applied to the tsunami and earthquake use cases. Initially, the project plans to employ an advanced convolutional neural network method in a multi-data environment. The method has only been applied to seismic waveforms, so the project will explore extending the method to a multi-data environment. The approach is expected to be extensible beyond detection and characterization of earthquakes to include the onset of other geophysical signals such as slow-slip events or magmatic intrusion, expanding the potential for new scientific discoveries. The framework is applied to use cases in the Cascadia subduction zone and Yellowstone: these locations combine the expertise of the science team with locations where EarthScope and OOI have the greatest concentration of instruments. The architecture will be transportable and scalable, running in a Docker environment on laptops, local clusters and the cloud. Integral to the project will be development, documentation and training using collaborative online resources such as GitLab and Jupyter Notebooks, and utilizing NSF XSEDE resources to make larger datasets and computational resources more widely available.This award by the NSF Office of Advanced Cyberinfrastructure is jointly supported by the Cross-Cutting Program within the NSF Directorate for Geosciences, the Big Data Science and Engineering Program within the Directorate for Computer and Information Science and Engineering, and the EarthCube Program jointly sponsored by the NSF Directorate for Geosciences and the Office of Advanced Cyberinfrastructure.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.