Advances in alternating electromagnetic field data processing for earthquake monitoring in China

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• 作者：GuoZe Zhao (1)
  YaXin Bi (2)
  LiFeng Wang (1)
  Bing Han (1)
  Xiao Wang (3)
  QiBin Xiao (1)
  JunTao Cai (1)
  Yan Zhan (1)
  XiaoBin Chen (1)
  Ji Tang (1)
  JiJun Wang (1)
  
  1. State Key Laboratory for Earthquake Dynamics ; Institute of Geology ; China Earthquake Administration ; Beijing ; 100029 ; China
  2. Computer Science Research Institute ; Faculty of Computing and Engineering ; University of Ulster ; Belfast ; BT37 0QB ; UK
  3. Beijing Qilu Institute of Geology and Mineral Resources ; Beijing ; 102218 ; China
  
• 关键词：seismo ; electromagnetic field ; alternating EM field ; data processing ; wavelet ; stereoscopic observation
• 刊名：Science China Earth Sciences
• 出版年：2015
• 出版时间：February 2015
• 年：2015
• 卷：58
• 期：2
• 页码：172-182
• 全文大小：1,010 KB
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• 刊物主题：Earth Sciences, general;
• 出版者：Springer Berlin Heidelberg
• ISSN：1869-1897

文摘

The alternating electromagnetic (EM) field is one of the most sensitive physical fields related to earthquakes. There have been a number of publications reporting EM anomalies associated with earthquakes. With increasing applications and research of artificial-source extremely low frequency EM and satellite EM technologies in earthquake studies, the amount of observed data from the alternating EM method increases rapidly and exponentially, so it is imperative to develop suitable and effective methods for processing and analyzing the influx of big data. This paper presents research on the self-adaptive filter and wavelet techniques and their applications to analyzing EM data obtained from ground measurements and satellite observations, respectively. Analysis results show that the self-adaptive filter method can identify both natural- and artificial-source EM signals, and enhance the ratio between signal and noise of EM field spectra, apparent resistivity, and others. The wavelet analysis is capable of detecting possible correlation between EM anomalies and seismic events. These techniques are effective in processing and analyzing massive data obtained from EM observations.