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Collaborative Research: Mining Seismic Wavefields

合作研究：挖掘地震波场

基本信息

批准号：
1818579
负责人：
Gregory Beroza
金额：
$ 12.41万
依托单位：
Stanford University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-05-01 至 2019-04-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1818579&HistoricalAwards=false
关键词：
Collaborative Research Mining Seismic Wavefields

项目摘要

This award will fund continued development of methods to process huge volumes of seismic waveform data. This will lead to a great increase in the number of located and characterized seismic events of various kinds and will potentially identify patterns in earthquake occurrence that could inform hazard and near-term rupture forecasting. The initial "Mining Seismic Wavefields" NSF Geoinformatics grant has led to significant progress dealing with data volumes that would have been impossible to process when the project began. This award will fund an additional year of that effort and will maintain this momentum in technique development to complete the analysis of proof-of-concept projects on vast waveform data sets, and to deploy the cyberinfrastructure for wider use by the seismological community.The premise of the research is that continuous and/or densely recorded data coupled with high performance computing and scalable algorithms can enable a network-based approach to earthquake detection that greatly improves the detection of weak and unusual events that would be difficult or impossible to detect using traditional approaches. Numerous seismological observations confirm that proximal earthquake sources generate similar signals. Exploiting the discriminative power of this similarity has led to many fundamental discoveries; however, most similarity-based detection methods require prior knowledge of the source waveform, or template. Blind/uninformed search for signals having unknown signatures based on pair-wise or multiple matches has seen some success, but naïve implementations of this approach suffer from quadratic scaling of computation with time such that problems of interest are inaccessible even for the most capable computers. Similarly, for dense networks, the availability of continuous waveform data motivates alternative detection schemes based on waveform similarity at adjacent stations. This project will further develop efficient data-mining techniques to enable scalable similarity search of seismic wavefields. Technical challenges to be addressed as part of the research for spatially sparse recording are to develop improved similarity-preserving compression for repeating signals detected over a network, and to improve post-processing of search output that will both isolate signals of seismological interest and minimize false detections. For spatially dense recording, this would extend recently developed wavefield matching techniques to similarity across adjacent stations, which would enable similarity search across unaliased elastic wavefields in four dimensions.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.

该奖项将资助继续开发处理大量地震波形数据的方法。这将导致各种类型的地震事件的定位和特征数量大幅增加，并有可能确定地震发生的模式，从而为灾害和近期破裂预测提供信息。最初的“挖掘地震波场”NSF 地理信息学资助在处理数据量方面取得了重大进展，而在项目开始时，这些数据量是不可能处理的。该奖项将为这一努力提供额外一年的资助，并将保持技术开发的势头，以完成对大量波形数据集的概念验证项目的分析，并部署网络基础设施以供地震学界更广泛使用。该研究的前提是，连续和/或密集记录的数据与高性能计算和可扩展算法相结合，可以实现基于网络的地震检测方法，从而大大提高地震检测的准确性。检测使用传统方法很难或不可能检测到的微弱和异常事件。大量地震学观测证实，邻近的地震源会产生类似的信号。利用这种相似性的区分能力已经带来了许多基本发现。然而，大多数基于相似性的检测方法需要先了解源波形或模板。基于成对或多重匹配对具有未知签名的信号进行盲目/不知情的搜索已经取得了一些成功，但这种方法的简单实现会受到计算随时间的二次缩放的影响，使得即使对于最强大的计算机来说，感兴趣的问题也无法解决。同样，对于密集网络，连续波形数据的可用性激发了基于相邻站波形相似性的替代检测方案。该项目将进一步开发高效的数据挖掘技术，以实现地震波场的可扩展相似性搜索。作为空间稀疏记录研究的一部分，需要解决的技术挑战是为通过网络检测到的重复信号开发改进的保留相似性的压缩，并改进搜索输出的后处理，以隔离地震学感兴趣的信号并最大限度地减少错误检测。对于空间密集记录，这将把最近开发的波场匹配技术扩展到相邻站之间的相似性，这将能够在四个维度上对非混叠弹性波场进行相似性搜索。该奖项反映了 NSF 的法定使命，并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。