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.

该奖项将资助持续开发方法来处理大量地震波形数据。这将导致各种位置和表征的地震事件的数量大大增加，并有可能识别地震发生的模式，这些模式可能导致危害和近期破裂预测。最初的“采矿地震波场” NSF地球信息学赠款导致了与项目开始时无法处理的数据量的重大进展。该奖项将为这一努力提供额外的一年，并将维持技术开发的这一势头，以完成对广泛波形数据集的概念证明项目的分析，并在地震学界部署网络基础架构，以更广泛地使用网络基础，而该研究的前提是，该研究的前提是连续和/或基于良好的进化数据，可以启用高度启动的型号，以启用高度远程的altection that，可以启用高度的Algortequement a aelgobles a Algorith Messement a Algorith Mans，以实现altgobles的范围。弱和不寻常的事件将很难或不可能使用传统方法检测到。许多地震学观察结果证实，近端地震来源会产生类似的信号。利用这种相似性的歧视力量导致了许多基本发现。但是，大多数基于相似性的检测方法需要先验了解源波形或模板。对具有基于配对或多个匹配的未知签名的信号的盲目/不知情搜索已经取得了成功，但是这种方法的幼稚实现遭受了二次计算的缩放，即使对于最有能力的计算机来说，这些问题也无法访问。同样，对于密集的网络，基于相邻站点波形相似性的连续波形数据动机的可用性替代检测方案。该项目将进一步开发有效的数据挖掘技术，以实现对地震波场的可扩展相似性搜索。作为空间稀疏记录的研究的一部分，要解决的技术挑战是为了在网络上重复检测到的重复信号，并改善搜索输出的后处理，这两个都可以隔离地震学兴趣的信号并最大程度地减少虚假检测。对于在空间密集的录制中，这将把最近开发的波场匹配技术扩展到相邻站点的相似性，这将在四个维度上跨越未相信的弹性波场进行相似性搜索。该奖项反映了NSF的法定任务，并通过使用该基金会的智力优点和广泛的影响来评估CRITERIA CRITERIA CRITERIA CRITERIA。