Collaborative Research: Mining Seismic Wavefields

该奖项将资助继续开发处理大量地震波形数据的方法。这将导致各种已定位和已定性的地震事件的数量大大增加，并有可能查明地震发生的模式，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地理信息学赠款导致了重大进展，处理了项目开始时不可能处理的数据量。该奖项将为这一努力提供额外一年的资金，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地使用。研究的前提是，连续和/或密集记录的数据，加上高性能计算和可扩展的算法，可以使网络-这是一种基于的地震检测方法，极大地改善了对微弱和异常事件的检测，这些事件使用传统方法很难或不可能检测到。大量的地震学观测证实，近距离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。基于成对或多个匹配的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，但是这种方法的幼稚实现遭受计算随时间的二次缩放，使得即使对于最有能力的计算机也无法访问感兴趣的问题。类似地，对于密集网络，连续波形数据的可用性激发了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，以便能够对地震波场进行可扩展的相似性搜索。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网络上检测到的重复信号开发改进的相似性保持压缩，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。对于空间密集的记录，这将扩展最近开发的波场匹配技术的相似性，在相邻的车站，这将使相似性搜索在四个dimensions.This奖项反映了美国国家科学基金会的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

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.