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

协作研究：框架：数据：NSCI：HDR：GeoSCIFramework：用于地球科学和灾害研究的可扩展实时流分析和机器学习

• 批准号: 1835661
• 负责人: Diego Melgar
• 金额: $ 40.55万
• 依托单位: University of Oregon Eugene
• 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 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1835661&HistoricalAwards=false
• 关键词: Collaborative; Research; Framework; Data; NSCI

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 and Division of Earth Sciences 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.

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。该系统的重点是随着事件的发生，使用机器学习对自然危害事件的检测自动化。 四组织合作（UNAVCO，科罗拉多大学，俄勒冈大学和罗格斯大学）开发了一个数据框架，用于用于地球科学和危害研究的广义实时流媒体分析和机器学习。 这项工作将支持对与危险事件（地震，火山喷发，海啸）相关的数据的快速分析和理解。 该项目使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，用于用于地球科学和危害研究的广义实时流媒体分析和机器学习。 它着重于将大量数据流的聚合和集成到一个相干系统中，该系统支持实时分析数据流。该框架将提供基于机器学习的工具，该工具旨在检测事件的信号，例如地震和海啸，只有在查看广泛的观察输入选择时才能检测到。 该体系结构通过组合一组开源组件来设置快速数据管道，这些开源组件使大数据应用程序可行且更易于开发。该项目的数据源主要借鉴了来自UNAVCO目前管理的Earthscope网络的1500多个传感器，以及由Rutgers University管理的INSISMOLOGY（IRIS）以及海洋观测机构倡议（OOI）充电阵列数据。 机器学习（ML）算法将进行研究并应用于海啸和地震用例。 最初，该项目计划在多DATA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方法扩展到多数据环境。 预计该方法将是可扩展的，无法检测到地震的检测和表征，包括其他地球物理信号的发作，例如慢滑行事件或岩浆入侵，从而扩大了新科学发现的潜力。 该框架适用于卡斯卡迪亚俯冲带和黄石的用例：这些位置将科学团队的专业知识与Earthscope和OOI的仪器浓度最高的位置相结合。 该体系结构将是可运输和可扩展的，在笔记本电脑，本地簇和云的码头环境中运行。 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 Cyber​​infrastructure is jointly supported by the Cross-Cutting Program and Division of Earth Sciences within the NSF Directorate for Geosciences, the Big Data Science and Engineering Program在计算机和信息科学和工程局以及NSF地球科学局和高级网络基础设施办公室共同赞助的EarthCube计划中，该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评估来通过评估来获得支持的。

项目成果

Real‐Time Fault Tracking and Ground Motion Prediction for Large Earthquakes With HR‐GNSS and Deep Learning

利用 HR-GNSS 和深度学习对大地震进行实时故障跟踪和地面运动预测

• DOI: 10.1029/2023jb027255
• 发表时间: 2023
• 期刊: Journal of Geophysical Research: Solid Earth
• 作者: Lin, Jiun‐Ting;Melgar, Diego;Sahakian, Valerie J.;Thomas, Amanda M.;Searcy, Jacob
• 通讯作者: Searcy, Jacob

Deep Coseismic Slip in the Cascadia Megathrust Can Be Consistent With Coastal Subsidence

卡斯卡迪亚巨型逆冲断层中的深层同震滑动可能与海岸沉降一致

• DOI: 10.1029/2021gl097404
• 发表时间: 2022
• 期刊: Geophysical Research Letters
• 影响因子: 5.2
• 作者: Melgar, Diego;Sahakian, Valerie J.;Thomas, Amanda M.
• 通讯作者: Thomas, Amanda M.

Generation and Validation of Broadband Synthetic P Waves in Semistochastic Models of Large Earthquakes

• DOI: 10.1785/0120200049
• 发表时间: 2020-08
• 期刊: Bulletin of the Seismological Society of America
• 影响因子: 3
• 作者: D. Goldberg;D. Melgar