Collaborative Research: Mining Seismic Wavefields

该奖项将为继续开发处理大量地震波形数据的方法提供资金。这将导致各种定位和特征地震事件的数量大幅增加，并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“采矿地震波场”NSF地理信息学赠款已经在处理数据量方面取得了重大进展，这些数据量在项目开始时是不可能处理的。这一奖项将再资助一年的工作，并将保持这种技术开发的势头，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，供地震界更广泛地使用。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，大大改进对使用传统方法难以或不可能检测到的微弱和异常事件的检测。大量地震学观测证实，近震震源产生类似的信号。利用这种相似性的辨别能力已经导致了许多基本的发现；然而，大多数基于相似性的检测方法需要源波形或模板的先验知识。基于成对或多次匹配对具有未知签名的信号的盲/不知情搜索已经取得了一些成功，但该方法的幼稚实现受到随时间的计算的二次缩放的影响，使得感兴趣的问题即使对于最有能力的计算机也是不可访问的。类似地，对于密集网络，连续波形数据的可用性激励基于相邻站处的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伸缩相似性搜索。作为空间稀疏记录研究的一部分，要解决的技术挑战是为通过网络检测的重复信号开发改进的保持相似性的压缩，并改进搜索输出的后处理，这将分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台站之间的相似性，这将允许在四个维度的未混叠弹性波场中进行相似性搜索。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.