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

该项目开发了一个能够处理大量传感器观测数据的实时处理系统。该系统的重点是使用机器学习自动检测正在发生的自然灾害事件。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、火山喷发、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来开发一个数据框架，用于地学和灾害研究的通用实时流分析和机器学习。它侧重于将大量数据流聚合并集成到支持数据流实时分析的连贯系统中。该框架将提供基于机器学习的工具，旨在检测地震和海啸等事件的信号，这些信号可能只有在查看广泛的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快速数据管道，使大数据应用程序可行且更易于开发。该项目的数据来源主要来自目前由联安办事处和联合地震研究所(IRIS)管理的EarthScope网络的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的用例。最初，该项目计划在多数据环境中使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。预计该方法将扩展到地震的探测和表征之外，以包括慢滑事件或岩浆侵入等其他地球物理信号的开始，从而扩大新的科学发现的潜力。该框架适用于卡斯卡迪亚俯冲带和黄石公园的用例：这些地点将科学团队的专业知识与EarthScope和OOI仪器最集中的地点结合在一起。该架构将是可移植和可扩展的，运行在笔记本电脑、本地集群和云上的Docker环境中。该项目的组成部分是利用GitLab和Jupyter笔记本等协作在线资源进行开发、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获得。该奖项由NSF高级网络基础设施办公室联合支持，由NSF地球科学局内的横切计划、计算机和信息科学与工程局内的大数据科学和工程计划以及由NSF地球科学局和高级网络基础设施办公室联合赞助的地球立方体计划共同支持。该奖项反映了NSF的法定使命，通过使用基金会的智力优势和更广泛的审查标准进行评估，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.