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.

第三个将为COD提供对位置的地震波形数据的处理，并表征了各种各样的地震事件，并且可以在地球上识别可能会为ZARD和接近末期破裂的预测中识别出的模式。对于具有数据s的重要程序，当技术开发的动力开发到大量波形数据集的证明概念以及地震学界的更广泛使用时，这是无法处理的。连续和/或密集的数据与高性能计算和可扩展算法结合使用，可以用基于工作的地震检测方法，从而极大地改善了弱和不寻常的事件，这些事件可用于使用传统的地震来源。利用歧视的区别，uire的源头或模板的知识。 t的t也无法访问，即基于相邻陈述的波形相似性的密集网络的功能在网络上检测到的重复信号，并改善后电流输出，以使Seismologalst Andiz e false检测到空间密集的记录。该奖项反映了NSF'SFFly的使命，并使用基金会的知识分子优点和更广泛的影响评估标准进行了评估。