Collaborative Research: Mining Seismic Wavefields

合作研究：挖掘地震波场

• 批准号: 1551462
• 负责人: Gregory Beroza
• 金额: $ 28.66万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-05-01 至 2018-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1551462&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的计算技术开发和部署用于采矿地震波场的网络基础设施，以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了解构造震颤的机制，改变了我们对断层深度依赖性，亮起余震地震性内扩散的理解，并揭示了对诱发地震的新见解。这些结果是通过适度的数据量实现的？从〜10个跨越约10公里的地震站点？然而，他们将检测到的地震数量增加了10到100倍。这个地球信息学项目将通过发现以前未发现的大规模数据量中未发现的地震事件来发展地震过程所需的Cyber​​infradstructure。项目是开发网络基础结构来挖掘地震波形数据。这项工作将开发方法和硬件，以在非常大的波形数据库上使用连贯的信号处理来检测，定位和表征无法通过标准网络操作检测到的事件（通过优化检测单个到达，关联，位置，优化）。该方法涉及将基于网络的方法用于地震检测，尤其是在当前对信号单独处理的方法中未报告的弱和异常事件。 PI将在较大的T和大N问题上工作，其中大T正在使用多个事件的波形相似性，以在长时间内检测地震。并且大N正在使用空间上的单个事件的波形相似性，如在密集的地震阵列中所记录的，最多可达数千个站点。结果将大大增加对各种地震来源的数量的了解，并可能识别地震发生的模式。可以告知危害和近期破裂预测。人类活动引起的地震性是一个新兴的问题，它对21世纪的能源选择产生了不利影响，包括页岩气的发展，增强的地热能量和碳固换。与这些活动相关的地震性更完整的观点对于管理它们构成的风险至关重要。