Collaborative Research: Mining Seismic Wavefields

合作研究：挖掘地震波场

• 批准号: 1551411
• 负责人: John Vidale
• 金额: $ 52.62万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-05-01 至 2019-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1551411&HistoricalAwards=false
• 关键词: Collaborative; Research; Mining; Seismic; Wavefields

A working group of the Southern California Earthquake Center (SCEC) will develop and deploy cyberinfrastructure for mining seismic wavefields through data intensive computing techniques in order to extend similarity search for earthquake detection to massive data sets. Similarity search has been used to understand the mechanics of tectonic tremor, transform our understanding of the depth dependence of faulting, illuminate diffusion within aftershock seismicity, and reveal new insights into induced earthquakes. These results were achieved with modest data volumes ? from ~ 10 seismic stations spanning ~ 10 km ? yet they increased the number of detected earthquakes by a factor of 10 to 100. This geoinformatics project will develop the cyberinfrastructure required to enable high-sensitivity studies of earthquake processes through the discovery of previously undetected seismic events within massive data volumes.This goal of this project is to develop a cyberinfrastructure to mine seismic waveform data. The effort will develop methods and hardware to use coherent signal processing on very large waveform databases to detect, locate and characterize events that cannot be detected by standard network operations (detection of single arrivals, association, location by optimization). The methodology involves the use of a network-based approach for earthquake detection, especially weak and unusual events that in the current method of treating signals individually go unreported. The PIs will work on the large T and the large N problem, where large T is using waveform similarity of multiple events over time to detect earthquakes over long periods of time; and the large N is using waveform similarity of single events over space as recorded on a dense seismic array with up to thousands of stations.The results will greatly increase knowledge of the number of seismic sources of various kinds and potentially identify patterns in earthquake occurrence that could inform hazard and near-term rupture forecasting. Seismicity induced by human activities is an emerging problem that adversely affects energy options for the 21st century, including shale gas development, enhanced geothermal energy, and carbon sequestration. A more complete view of seismicity related to these activities is essential to managing the risks they pose.

南加州地震中心（SCEC）的一个工作组将通过数据密集型计算技术开发和部署用于挖掘地震波场的网络基础设施，以便将地震检测的相似性搜索扩展到大量数据集。相似性搜索已被用来理解构造震颤的机制，改变我们对断层深度依赖性的理解，照亮余震地震活动中的扩散，并揭示诱发地震的新见解。这些结果是用适度的数据量实现的？跨越10公里的10个地震台站然而，它们却使检测到的地震数量增加了10到100倍。这一地理信息学项目将开发必要的网络基础设施，以便通过在大量数据中发现以前未发现的地震事件，对地震过程进行高灵敏度研究，其目标是开发一个网络基础设施，以挖掘地震波形数据。这项工作将开发方法和硬件，以便在非常大的波形数据库上使用相干信号处理来检测、定位和描述标准网络操作（检测单波到达、关联、优化定位）无法检测到的事件。该方法涉及使用基于网络的地震探测方法，特别是在目前单独处理信号的方法中未报告的微弱和异常事件。PI将研究大T和大N问题，其中大T是使用多个事件随时间的波形相似性来检测长时间的地震;而大N是利用空间上单个事件的波形相似性，这些事件记录在一个拥有数千个台站的密集地震台阵上。结果将大大增加对各种震源数量的了解，确定地震发生的模式，为灾害和短期破裂预测提供信息。由人类活动引起的地震是一个新出现的问题，对21世纪的能源选择产生不利影响，包括页岩气开发，增强地热能和碳封存。更全面地了解与这些活动有关的地震活动对于管理它们构成的风险至关重要。