III: Medium: Collaborative Research: Scaling Time Series Analytics to Massive Seismology Datasets

III：媒介：协作研究：将时间序列分析扩展到海量地震数据集

• 批准号: 2104240
• 负责人: Peter Shearer
• 金额: $ 17.77万
• 依托单位: University of California-San Diego Scripps Inst of Oceanography
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2104240&HistoricalAwards=false
• 关键词: III; Medium; Collaborative; Research; Scaling

This project will enable a team of computer scientists and earth scientists at the University of California-Riverside, the University of California-San Diego and the University of New Mexico to develop novel tools to search existing seismographic databases for subtle earthquakes that may have evaded detection. These more complete earthquake catalogues will allow more accurate hazard analysis and risk reduction. The intellectual merit of the proposed work is in creating novel data representations, definitions, algorithms (and ultimately, highly usable open-source code) that will allow the seismological community greatly to expand both the type and the scale of the analytics that they can perform, both offline and in real time. The broader impacts of this project results from the more comprehensive and complete earthquake catalogs created. These have the potential to affect multiple branches of earthquake science. For example, the more accurate hazard and risk models derived from the catalogs can be used by governments and private industry to plan for and mitigate economic and human losses, e.g., by mandating resilient construction and infrastructure, and by accurately assessing insured risk. In addition, the projects comprehensive educational and outreach activities have already been piloted on a small scale and include detailed plans to reach out to underserved communities at the K-12 and college levels, and to create grade-level appropriate teaching resources that exploit the natural interest most K-12 students have in the drama of earthquakes.Humans notice large earthquakes, but the frequently occurring smaller quakes caused by the constant slipping of fault lines typically go unnoticed, even by skilled seismologists with access to telemetry. However, these imperceptible quakes could help us understand the physical processes that trigger hazardous earthquakes, assisting in hazard-reduction efforts. Recently, a novel data structure called the Matrix Profile has emerged as a very promising technique for pattern discovery in large datasets. The PIs, an interdisciplinary team of computer scientists and seismologists, propose to investigate techniques to use Matrix Profile the scale up the size of datasets that can be investigated by 100X magnitude, to find 20X more earthquakes. The intellectual merit of this project will result in novel data representations, definitions, algorithms (and ultimately, highly usable open-source code) that will allow the seismological community to expand both the type and the scale of the analytics that they can perform. The broader impacts of this project are difficult to overstate. Comprehensive and complete earthquake catalogs are foundational to multiple branches of earthquake science, notably the physics of earthquake nucleation, hazard analysis and risk reduction. The hazard and risk models derived from them can be used by governments and private industry to plan for and mitigate economic and human losses, e.g. by mandating resilient construction and infrastructure, and by accurately assessing insured risk. The project’s comprehensive educational and outreach activities have already been piloted on a small scale and include detailed plans to reach out to under-served communities at the K-12 and college levels.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.

这个项目将使加利福尼亚大学河滨分校、加利福尼亚大学圣地亚哥分校和新墨西哥大学的计算机科学家和地球科学家组成的团队能够开发新的工具来搜索现有的地震数据库，以寻找可能未被探测到的细微地震。这些更完整的地震目录将允许更准确的危害分析和降低风险。所提议的工作的智力价值在于创建了新颖的数据表示、定义、算法（以及最终高度可用的开源代码），这将允许地震学社区极大地扩展他们可以执行的分析的类型和规模，无论是离线还是实时。该项目的更广泛的影响源于创建了更全面和完整的地震目录。这些有可能影响地震科学的多个分支。例如，政府和私营企业可以使用从目录中得出的更准确的危害和风险模型来规划和减轻经济和人员损失，例如，通过强制实施具有复原力的建筑和基础设施，以及通过准确评估投保风险。此外，该项目的综合教育和推广活动已经在小范围内进行了试点，其中包括详细的计划，以接触到K-12和大学水平服务不足的社区，并创建适合年级的教学资源，利用大多数K-12学生对地震戏剧的自然兴趣。人类能注意到大地震，但由断层线不断滑动引起的频繁发生的小地震通常不会被注意到，即使是有遥测技术的熟练地震学家也不会注意到。然而，这些难以察觉的地震可以帮助我们了解引发危险地震的物理过程，有助于减少灾害的努力。最近，一种新的数据结构（称为Matrix Profile）作为一种非常有前途的技术出现在大型数据集的模式发现中。pi是一个由计算机科学家和地震学家组成的跨学科团队，他们建议研究使用矩阵剖面的技术，将可以研究100倍震级的数据集的规模扩大，以发现20倍以上的地震。这个项目的智力价值将导致新的数据表示、定义、算法（最终，高度可用的开源代码），这将允许地震学社区扩展他们可以执行的分析的类型和规模。这个项目的广泛影响怎么说都不为过。全面完整的地震目录是地震科学多个分支的基础，特别是地震成核物理、危害分析和风险降低。由此得出的危害和风险模型可被政府和私营企业用于规划和减轻经济和人员损失，例如，通过强制实施具有复原力的建筑和基础设施，以及通过准确评估投保风险。该项目的全面教育和推广活动已经在小规模试点，其中包括向K-12和大学水平服务不足的社区提供服务的详细计划。该奖项反映了美国国家科学基金会的法定使命，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。