A new method to identify earthquake swarms applied to seismicity near the San Jacinto Fault, California
A new method to identify earthquake swarms applied to seismicity near the San Jacinto Fault, California
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一种识别地震群的新方法应用于加利福尼亚州圣哈辛托断层附近的地震活动
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发表时间：
2016
期刊：
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通讯作者：
P. Shearer
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作者：
Qiong Zhang;P. Shearer
了解地震在空间和时间上的聚集性是重要的，但也是具有挑战性的，因为地震模式的复杂性和地震目录的大而多样的性质。蜂群是特别令人感兴趣的，因为它们很可能是地壳物理变化的结果，例如缓慢滑动或流体流动。由余震序列产生的震群和集群都可以跨越广泛的空间和时间尺度。在这里，我们测试和实现了一种新的方法来识别不同大小的地震活动簇，并将它们与随机发生的背景地震活动区分开来。我们的方法在空间和时间上搜索最近的相邻地震，并在更大的空间/时间窗口中将相邻地震的数量与背景事件进行比较。将我们的方法应用于加利福尼亚州的圣哈辛托断裂带，我们总共发现了类似的震群。这些群的大小从0.14到7.23公里，持续时间从15min到22d。最引人注目的空间格局是SJFZ南北两端的震群比例大于其中央段，这可能与两端更多的正断层事件有关。为了探索可能的驱动机制，我们用线性迁移模型和扩散迁移模型研究了至少包含20个事件的群体中事件的空间迁移。我们的结果表明，SJFZ震群可以更好地用流体流动来解释，因为它们估计的线性迁移速度远远小于典型蠕变事件的线性迁移速度，同时发现了较大的最佳拟合水力扩散系数。
Understanding earthquake clustering in space and time is important but also challenging because of complexities in earthquake patterns and the large and diverse nature of earthquake catalogues. Swarms are of particular interest because they likely result from physical changes in the crust, such as slow slip or fluid flow. Both swarms and clusters resulting from aftershock sequences can span a wide range of spatial and temporal scales. Here we test and implement a new method to identify seismicity clusters of varying sizes and discriminate them from randomly occurring background seismicity. Our method searches for the closest neighbouring earthquakes in space and time and compares the number of neighbours to the background events in larger space/time windows. Applying our method to California's San Jacinto Fault Zone (SJFZ), we find a total of 89 swarm-like groups. These groups range in size from 0.14 to 7.23 km and last from 15 min to 22 d. The most striking spatial pattern is the larger fraction of swarms at the northern and southern ends of the SJFZ than its central segment, which may be related to more normal-faulting events at the two ends. In order to explore possible driving mechanisms, we study the spatial migration of events in swarms containing at least 20 events by fitting with both linear and diffusion migration models. Our results suggest that SJFZ swarms are better explained by fluid flow because their estimated linear migration velocities are far smaller than those of typical creep events while large values of best-fitting hydraulic diffusivity are found.