Collaborative Research: Mining Seismic Wavefields

合作研究：挖掘地震波场

• 批准号: 1818611
• 负责人: Zhigang Peng
• 金额: $ 6.35万
• 依托单位: Georgia Tech Research Corporation
• 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 2020-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1818611&HistoricalAwards=false
• 关键词: Collaborative; Research; Mining; Seismic; Wavefields

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，这将导致定位和表征的各种地震事件的数量大幅增加，并有可能识别地震发生的模式，从而为灾害和附近地区提供信息。最初的“采矿地震波场”国家科学基金会地理信息学资助在处理项目开始时无法处理的数据量方面取得了重大进展，该资助将资助该项目额外一年的工作。这种技术发展的势头是为了完成对大量波形网络数据集的概念验证项目的分析，并部署基础设施以供地震界更广泛使用。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，从而大大提高对微弱和异常事件的检测，而使用传统方法很难或不可能检测到这些事件，大量地震观测证实，邻近地震源会产生类似的地震。利用这种相似性的判别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对源波形或模板进行盲目/无信息的搜索，以基于成对或未知的特征。多重匹配已经取得了一些成功，但是这种方法的简单实现同样会受到计算随时间的二次缩放的影响，因此即使对于最强大的计算机，也无法解决感兴趣的问题。对于密集网络，连续波形数据的可用性激发基于相邻台站波形相似性的替代检测方案，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，要解决的技术挑战是开发改进的相似性。 -保留通过网络检测到的重复信号的压缩，并改进搜索输出的后处理，这将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，这将扩展最近开发的波场匹配技术的相似性。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。

项目成果

A Multi-Channel Approach for Automatic Microseismic Event Association using RANSAC-based Arrival Time Event Clustering (RATEC)

• DOI: 10.1016/j.eqrea.2021.100008
• 发表时间: 2017-02
• 期刊: Earthquake Research Advances
• 作者: Lijun Zhu;L. Chuang;J. McClellan;E. Liu;Zhigang Peng

Abundant aftershock sequence of the 2015 Mw7.5 Hindu Kush intermediate-depth earthquake

• DOI: 10.1093/gji/ggy016
• 发表时间: 2018-05
• 期刊: Geophysical Journal International
• 影响因子: 2.8
• 作者: Chenyu Li;Zhigang Peng;D. Yao;Hao Guo;Z. Zhan;Haijiang Zhang

Long‐Period Long‐Duration Events Detected by the IRIS Community Wavefield Demonstration Experiment in Oklahoma: Tremor or Train Signals?

俄克拉荷马州 IRIS 社区波场演示实验检测到的长周期长持续时间事件：震颤还是火车信号？

• DOI: 10.1785/02201080081
• 发表时间: 2018
• 期刊: Seismological Research Letters
• 影响因子: 3.3
• 作者: Li, Chenyu;Li, Zefeng;Peng, Zhigang;Zhang, Chengyuan;Nakata, Nori;Sickbert, Tim
• 通讯作者: Sickbert, Tim