| 项目编号: | 1551462
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| 项目名称: | Collaborative Research: Mining Seismic Wavefields |
| 作者: | Gregory Beroza
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| 承担单位: | Stanford University
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| 批准年: | 2016
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| 开始日期: | 2016-05-01
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| 结束日期: | 2018-04-30
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| 资助金额: | 286562
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| 资助来源: | US-NSF
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| 项目类别: | Standard Grant
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| 国家: | US
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| 语种: | 英语
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| 特色学科分类: | Geosciences - Earth Sciences
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| 英文关键词: | dense seismic array
; seismic waveform datum
; aftershock seismicity
; cyberinfrastructure
; earthquake
; seismic station
; mining seismic wavefield
; undetected seismic event
; seismic source
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| 英文摘要: | 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. |
| 资源类型: | 项目
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| 标识符: | http://119.78.100.158/handle/2HF3EXSE/92428
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| Appears in Collections: | 全球变化的国际研究计划
科学计划与规划
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| Recommended Citation: | |
Gregory Beroza. Collaborative Research: Mining Seismic Wavefields. 2016-01-01.
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