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

• Published in: Science China. Earth sciences Vol. 58; no. 2; pp. 172 - 182
• Main Authors: Zhao, GuoZe, Bi, YaXin, Wang, LiFeng, Han, Bing, Wang, Xiao, Xiao, QiBin, Cai, JunTao, Zhan, Yan, Chen, XiaoBin, Tang, Ji, Wang, JiJun
• Format: Journal Article
• Language: English
• Published: Heidelberg Science China Press 01.02.2015; Springer Nature B.V
• Subjects: Anomalies; China; Data processing; Earth and Environmental Science; Earth Sciences; Earthquakes; Electromagnetic fields; Electromagnetics; EM技术; Environmental monitoring; Grounds; Magnetic fields; Progress; Reporting; Satellite observation; Seismic activity; Seismic phenomena; Spectra; Wavelet analysis; 中国; 交变电磁场; 地震监测; 应用程序; 异常现象; 数据处理; 自适应滤波器; China; China, People's Rep; wavelet; alternating EM field; data processing; seismo-electromagnetic field; stereoscopic observation
• ISSN: 1674-7313; 1869-1897
• DOI: 10.1007/s11430-014-5012-3

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.