利用奇异值分解方法实时自动检测P波和S波
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  • 英文篇名:Real-time automatic detectors of P and S waves using singular value decomposition
  • 作者:I.Kurzon ; F.L.Vernon ; A.Rosenberger ; Y.Ben-Zion ; 刘长生 ; 李万金 ; 赵谊
  • 英文作者:I.Kurzon;F.L.Vernon;A.Rosenberger;Y.Ben-Zion;
  • 中文刊名:DZYC
  • 英文刊名:Translated World Seismology
  • 机构:黑龙江省地震局;云南省地震局个旧地震台;
  • 出版日期:2017-07-15
  • 出版单位:世界地震译丛
  • 年:2017
  • 期:v.48;No.292
  • 语种:中文;
  • 页:DZYC201704002
  • 页数:17
  • CN:04
  • ISSN:11-2368/P
  • 分类号:13-29
摘要
利用奇异值分解(SVD)分析实现了一种新的P波和S波震相的自动检测方法。该方法是基于Rosenberger(2010)用于三分量地震波形奇异值分解的实时迭代算法。该算法通过利用奇异值分解并将波形分为P波和S波分量来识别视入射角。我们将该算法应用于滤波后的波形,然后在经过滤波和奇异值分解分开的信道上设置入射角和奇异值检测器,或者应用信噪比(SNR)检测器来拾取P波和S波。安扎地震台网和最近在圣哈辛托断层带地区部署的便携式仪器为测试不同设置的检测算法提供了一个非常密集的地震台网,包括不同震源机制的事件、具有不同场地特性的台站和射线路径偏离奇异值分解算法中使用的逼近。2~30Hz巴特沃思带通滤波器给出了各种事件和台站的最佳性能。我们在许多事件上应用奇异值分解检测,并且从2005年6月M_W5.2地震复杂、强烈的余震序列中得到结果。这个序列经过几位分析人员的彻底复查,确定了主震后第一个小时的294个事件都围绕主震密集分布。我们使用这个数据集来微调自动奇异值分解检测、关联和定位,实现了37%事件的自动识别和定位。所有检测到的事件都落在此密集区内,并且没有虚假的事件。普通的信噪比检测器不会超过11%的成功,并且位置分布更广泛(不完全在复查的群集内)。由奇异值分解检测器检测到震相(P波或S波)的预先知识显著降低了由震相盲信噪比检测器产生的噪声。
        
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