Collaborative Research: Frameworks: Seismic COmputational Platform for Empowering Discovery (SCOPED)

合作研究：框架：增强发现能力的地震计算平台（SCOPED）

• 批准号: 2103494
• 负责人: Yinzhi Wang
• 金额: $ 64.68万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2103494&HistoricalAwards=false
• 关键词: Collaborative; Research; Frameworks; Seismic; COmputational

Seismology is the most powerful tool for investigating the interior structure of Earth—from its surface down to the inner core—and its wide range of processes, including earthquakes, volcanic activity, glacial processes, oceanic and environmental processes, and human-caused processes such as nuclear explosions or hydraulic fracturing in oil and gas exploration. Seismology cannot achieve its greatest potential without harnessing state-of-the-art computing capabilities for the dual purpose of scientific modeling and analysis of rapidly increasing data sets. The SCOPED (Seismic COmputational Platform for Empowering Discovery) project establishes a computing platform that delivers data, computation, and service to the seismological community in a way that promotes education, innovation, and discovery, and enables efficient solutions to outstanding scientific problems in geophysics. By focusing on openly available data, openly available software, and virtual training, SCOPED opens seismological research to a broad range of users. Four research components emphasize openly available software for the purpose of characterizing Earth's subsurface structure and the wide range of natural and man-made events that are recorded by seismometers every day. Training of seismologists is a central focus of the project. SCOPED training workshops (seismoHackweeks) are open to the community. Emphasis on virtual research and training diversifies strategies to engage minority groups entering computational geosciences. The project trains a new generation of seismologists to harness the latest capabilities for processing and modeling large data sets. The SCOPED project establishes cyberinfrastructure that provides fast access to large seismic archives from a suite of containerized open-source computational tools for big data analysis, machine learning, and high-performance simulations. The implementation focuses on four interconnected, compute- and data-intensive research components: seismic imaging of Earth’s interior, waveform modeling of earthquakes and Earth structure, monitoring of Earth structure using ambient noise, and precision monitoring of earthquakes and faults. Each research component is enabled by open-source codes that meet, or aspire to meet, best practices for software development. The project contains several transformative components. First, it offers compute performance for both model- and data-driven seismological problems. Hundreds of terabytes of waveform data are directly accessible both to modelers—for data assimilation problems—and to data scientists for processing, analysis, and exploration. Second, it establishes a direct collaborative link among four teams of seismologists at four institutions and a team of computational scientists at Texas Advanced Computing Center. This unity reflects the necessity of both groups to achieve research-ready codes that can exploit high-performance computing (HPC) and Cloud systems. Third, it establishes a gateway with ready-to-run (or adapt) container images and data as a service for the seismological community. Fourth, it develops computational tools that promote the democratization of HPC/Cloud with cutting-edge data processing and modeling software through their scalability from laptops to HPC or Cloud systems and through their portability with containerization. Finally, although the development of cyberinfrastructure is the main priority, ancillary scientific results from advanced techniques are expected to offer insights into fundamental seismological problems. The project has the potential for discoveries across fields (seismology, Earth science, computer science, data science, material science), as well as societal relevance in the realms of seismic hazard assessment, environmental science, cryosphere, earthquake early warning, energy systems, and geophysical detection of nuclear proliferation.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.

地震学是研究地球内部结构（从表面到内核）及其广泛过程的最强大工具，包括地震、火山活动、冰川过程、海洋和环境过程以及人为过程，例如正如石油和天然气勘探中的核爆炸或水力压裂一样，如果不利用最先进的计算能力对快速增长的数据集进行科学建模和分析，地震学就无法发挥其最大潜力。 （Seismic COmputational Platform for Empowering Discovery）项目建立了一个计算平台，以促进教育、创新和发现的方式向地震界提供数据、计算和服务，并通过聚焦有效解决地球物理学中的突出科学问题。基于公开可用的数据、公开可用的软件和虚拟培训，SCOPED 向广泛的用户开放地震学研究，四个研究部分强调公开可用的软件，以描述地球地下结构和广泛的自然特征。地震仪每天记录的人造事件是该项目的核心重点，该项目向社区开放，强调虚拟研究和培训，以吸引少数群体进入计算领域。该项目培训新一代地震学家利用最新的能力来处理和建模大型数据集，该项目建立了网络基础设施，可以快速访问大型地震档案。用于大数据分析、机器学习和高性能模拟的容器化开源计算工具套件，其实施重点关注四个相互关联的计算和数据密集型研究组件：地球内部的地震成像、地震和地球的波形建模。结构、利用环境噪声监测地球结构以及精确监测地震和断层。每个研究组件均由满足或渴望满足软件开发最佳实践的开源代码支持。 ，它提供计算建模者可以直接访问数百 TB 的波形数据（用于数据同化问题），数据科学家也可以进行处理、分析和探索。四个机构的四个地震学家团队和德克萨斯州高级计算中心的一个计算科学家团队之间的这种团结反映了两个小组实现可利用高性能计算（HPC）和云系统的研究就绪代码的必要性。它为地震界建立了一个具有可立即运行（或适应）的容器图像和数据即服务的网关。第四，它开发了计算工具，通过尖端的数据处理和建模软件来促进 HPC/云的民主化。最后，尽管网络基础设施的发展是主要优先事项，但先进技术的辅助科学成果预计将为基本地震学问题提供见解。跨领域（地震学、地球科学、计算机科学、数据科学、材料科学）发现的潜力，以及地震灾害评估、环境科学、冰冻圈、地震早期预警、能源系统和地球物理探测领域的社会相关性该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。