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，能够处理大量混合的传感器观测。该系统的重点是使用机器学习自动检测自然灾害事件，因为事件正在发生。 四个组织（UNAVCO、科罗拉多大学、俄勒冈州大学和罗格斯大学）合作开发了一个数据框架，用于地球科学和灾害研究的广义实时流分析和机器学习。 这项工作将有助于快速分析和了解与危险事件（地震、火山爆发、海啸）有关的数据。 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通用实时流分析和机器学习的数据框架。 它专注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，旨在检测地震和海啸等事件的信号，这些信号只有在观察广泛的观测输入时才能检测到。 该架构通过结合一组开源组件建立了一个快速的数据管道，这些组件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合国教科文组织和联合地震研究所管理的EarthScope网络的1500多个传感器，以及罗格斯大学管理的海洋观测站倡议有线阵列数据。 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。 该方法仅适用于地震波形，因此该项目将探索将该方法扩展到多数据环境。 该方法预计将扩展到地震的检测和定性之外，包括其他地球物理信号的开始，如缓慢滑动事件或岩浆侵入，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仪器最集中的地点相结合。 该架构将是可移植和可扩展的，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开发，文档和培训，使用协作在线资源，如GitLab和Githyter Notebooks，并利用NSF XSEDE资源，使更大的数据集和计算资源更广泛地提供。NSF高级网络基础设施办公室的这一奖项得到了NSF地球科学理事会内的交叉项目的共同支持，计算机和信息科学与工程局的大数据科学与工程项目，以及由NSF地球科学理事会和高级网络基础设施办公室联合赞助的EarthCube计划。该奖项反映了NSF的法定使命，并被认为值得支持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。

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.