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: 2104537
• 负责人: Abdullah Mueen
• 金额: $ 20万
• 依托单位: University of New Mexico
• 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=2104537&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.

该项目将使加州大学河滨分校、加州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团队能够开发新工具，在现有地震数据库中搜索可能逃避检测的细微地震这些更完整的地震目录将允许更准确的灾害分析和风险降低，所提出的工作的智力价值在于创建新颖的数据表示、定义、算法（以及最终的高度可用的开源代码），这将使地震学界能够使用。极大地扩展他们可以执行的离线和实时分析的类型和规模，该项目的更广泛影响源于创建的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灾害和风险模型来规划和减轻经济和人力损失，例如通过强制实施弹性建筑和基础设施，以及准确评估保险风险。该项目的综合教育和宣传活动已经已经进行了小规模试点，其中包括详细计划，以覆盖 K-12 和大学层面服务不足的社区，并创建适合年级的教学资源，以利用大多数 K-12 学生对戏剧的自然兴趣。地震。人类会注意到大地震，但经常发生的由断层线不断滑动引起的小地震通常不会被注意到，即使是熟练的地震学家也可以利用遥测技术。然而，这些难以察觉的地震可以帮助我们了解引发危险的物理过程。最近，一种称为矩阵剖面的新型数据结构已经出现，它是一种非常有前景的大型数据集中模式发现技术，由计算机科学家和地震学家组成的跨学科团队提出研究技术。使用 Matrix Profile 将可研究的数据集规模扩大 100 倍，以发现 20 倍以上的地震。该项目的智力价值将产生新颖的数据表示、定义、算法（最终，高度）。可用的开源代码），这将使地震学界能够扩展他们可以执行的分析的类型和规模，该项目的更广泛影响不容小觑。地震科学，特别是地震成核、灾害分析和风险降低的物理学，政府和私营企业可以利用这些模型来规划和减轻经济和人员损失，例如通过强制实施弹性建筑和基础设施，和通过准确评估保险风险，该项目的综合教育和推广活动已经进行了小规模试点，其中包括覆盖 K-12 和大学级别服务不足的社区的详细计划。该奖项反映了 NSF 的法定使命，并已得到广泛认可。通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Septor: Seismic Depth Estimation Using Hierarchical Neural Networks

Septor：使用分层神经网络进行地震深度估计

• DOI: 10.1145/3534678.3539166
• 发表时间: 2022
• 期刊: KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
• 作者: Siddiquee, M Ashraf;Souza, Vinicius M.;Baker, Glenn Eli;Mueen, Abdullah
• 通讯作者: Mueen, Abdullah

Combining Filtering and Cross-Correlation Efficiently for Streaming Time Series

有效结合滤波和互相关来处理流时间序列

• DOI: 10.1145/3502738
• 发表时间: 2022
• 期刊: ACM Transactions on Knowledge Discovery from Data
• 影响因子: 3.6
• 作者: Zhong, Sheng;Souza, Vinicius M.;Mueen, Abdullah
• 通讯作者: Mueen, Abdullah

FASER: Seismic Phase Identifier for Automated Monitoring

• DOI: 10.1145/3447548.3467064
• 发表时间: 2021-08
• 期刊: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining
• 作者: F. A. Chowdhury;M. A. Siddiquee;G. Baker;A. Mueen