Pushing the limit of earthquake detection with distributed acoustic sensing and template matching: a case study at the Brady geothermal field
Pushing the limit of earthquake detection with distributed acoustic sensing and template matching: a case study at the Brady geothermal field
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DOI：
10.1093/gji/ggy359
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发表时间：
2018-12-01
影响因子：
2.8
通讯作者：
Zhan, Zhongwen
中科院分区：
文献类型：
--
作者：
Li, Zefeng;Zhan, Zhongwen
模板匹配由于其在检测微弱信号方面的优势，在地震及其它地震事件的检测中得到了广泛的应用。最近的研究表明，使用合成的模板匹配大N阵列的应用程序可以潜在地检测地震大大低于噪音水平。在这里，我们应用模板匹配的分布式声学传感（DAS）记录在布雷迪温泉地热田，内华达州的数据。使用5个编目的事件，我们检测到116个事件，发现其中68个远低于噪音水平。我们确认112个事件是真正的地震，通过检查他们的传感器到传感器的互相关部分的模式。这表明DAS和模板匹配的组合具有检测低于噪声水平的微震活动的能力，这对于常规地震阵列和方法是不寻常的。通过更新的目录，我们观察到附近一个地热发电厂关闭期间的地震激增。此外，井下压力记录的快速增加与强烈的地震群相吻合。这些观测结果表明，地震活动频率和井下压力变化之间有很强的相关性。最后，我们研究了可能影响检测性能的几个因素，并比较了不同的空间下采样策略，以便为未来的大N设计和数据处理提供有用的见解。
Template matching has been widely applied in the detection of earthquakes and other seismic events due to its power in detecting weak signals. Recent studies using synthetics have shown that application of template matching to large-N arrays can potentially detect earthquakes substantially below the noise level. Here we apply template matching to the distributed acoustic sensing (DAS) data recorded in the Brady Hot Springs geothermal field, Nevada. Using 5 catalogued events, we detect 116 events and find 68 of them well below the noise level. We confirm 112 events are true earthquakes by examining the patterns of their sensor-to-sensor cross-correlation sections. This demonstrates that the combination of DAS and template matching has capability to detect microseismicity below the noise level, which is unusual for conventional seismic arrays and methods. With the updated catalogue, we observe a surge of earthquakes during the shutdown of a geothermal power plant nearby. In addition, the rapid increases in the downhole pressure record coincide with intense swarms of earthquakes. These observations show a strong correlation between the seismicity frequencies and the downhole pressure changes. Finally, we investigate several factors that may affect the detection performance and compare different strategies for spatial down-sampling, in order to provide helpful insights for future large-N design and data processing.