Collaborative Research: EarthCube Data Capabilities: Volcanology hub for Interdisciplinary Collaboration, Tools and Resources (VICTOR)

合作研究：EarthCube 数据能力：跨学科合作、工具和资源的火山学中心 (VICTOR)

• 批准号: 2126435
• 负责人: Einat Lev
• 金额: $ 32.81万
• 依托单位: Columbia University
• 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 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2126435&HistoricalAwards=false
• 关键词: Collaborative; Research; EarthCube; Data; Capabilities

This pilot project is designed to demonstrate a new and streamlined approach to modeling of volcanic eruptions and analysis of data using advanced cyberinfrastructure. Risks to life and property stem from pyroclastic flows, volcanic ash fallout, and lava flow inundation, so the volcano science community strives to develop computer models to better understand these and similar phenomena. Yet, effective models require a complex interplay between volcano science, physics of flows and computational science. The Volcanology hub for Interdisciplinary Collaboration, Tools and Resources (VICTOR) will build upon lessons learned from previous cyberinfrastructure (VHub). Access will be through a central web portal. All components will be based in the cloud, to allow for demand-based resource management, workflow portability and reproducibility, and to offer access to high-performance computing to a broader scientific community. These components will provide volcano scientists with cyberinfrastructure to accelerate model development and application to reduce the threats from volcano eruptions. An essential component of VICTOR will be exposing and training of the community to latest approaches in computational volcanology through demonstration examples, workshops, tutorials and webinars. Demonstration of science and computational workflows in VICTOR involves implementing new cyberinfrastructure capabilities and libraries of models to: (1) improve model verification, validation, and benchmarking, (2) assess linked and changing hazards, and (3) connect volcanic eruptions with other parts of the magma transport system and with other related earth systems in a robust, streamlined way. These workflows will use data science techniques, including machine learning and numerical inversion to improve parameter estimation and uncertainty quantification. Workflows will be containerized using modern computing tools such as dockers and electronic notebooks, minimizing the time-intensive steps of locating, installing, running and testing models. It will be demonstrated that model inputs and outputs can be standardized using workflows, facilitating studies of linked- and multi-hazard scenarios. Workflows can be saved, re-run, edited and challenged, enhancing reproducibility and reliability of the modeling process. Ultimately, these containerized models, data science tools, and provisioned low-barrier access to computing resources will increase usability by the community and accelerate the science.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.

该试点项目旨在展示一种新的简化方法，利用先进的网络基础设施对火山爆发进行建模和数据分析。生命和财产的风险来自火山碎屑流，火山灰沉降物和熔岩流淹没，因此火山科学界努力开发计算机模型，以更好地理解这些和类似的现象。然而，有效的模型需要火山科学、流动物理学和计算科学之间复杂的相互作用。火山学跨学科合作、工具和资源中心（维克托）将借鉴以前网络基础设施（VHub）的经验教训。访问将通过一个中央门户网站进行。所有组件都将基于云端，以实现基于需求的资源管理、工作流程的可移植性和可复制性，并为更广泛的科学界提供高性能计算。这些组件将为火山科学家提供网络基础设施，以加速模型开发和应用，减少火山爆发的威胁。维克托的一个重要组成部分将是通过演示示例、研讨会、教程和网络研讨会向社区介绍和培训计算火山学的最新方法。 在维克托中演示科学和计算工作流程涉及实施新的网络基础设施能力和模型库，以：（1）改进模型验证，验证和基准测试，（2）评估相关和不断变化的危险，以及（3）将火山爆发与岩浆运输系统的其他部分以及其他相关的地球系统以强大，精简的方式连接起来。这些工作流程将使用数据科学技术，包括机器学习和数值反演，以改善参数估计和不确定性量化。工作流将使用dockers和电子笔记本等现代计算工具进行集装箱化，最大限度地减少定位，安装，运行和测试模型的时间密集型步骤。它将证明，模型的输入和输出可以使用工作流程实现标准化，从而促进对相互关联的和多种危害情景的研究。工作流可以保存、重新运行、编辑和质询，提高建模过程的可重复性和可靠性。最终，这些容器化的模型、数据科学工具和对计算资源的低障碍访问将提高社区的可用性并加速科学发展。该奖项反映了NSF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。