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

Zhao, Guoze, Bi, Yaxin, Wang, Lifeng, Han, Bin, Wang, Xiao, Xiao, QiBin, Cai, JunTao, Zhan, Yan, Chen, XiaoBin, Tang, Ji and Wang, JiJun (2015) Advances in alternating electromagnetic field data processing for earthquake monitoring in China. Science China Earth Sciences, 58 (2). pp. 172-182. [Journal article]

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URL: http://link.springer.com/article/10.1007%2Fs11430-014-5012-3

DOI: 10.1007/s11430-014-5012-3

Abstract

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.

Item Type:Journal article
Keywords:seismic electromagnetic, alternating EM field, data processing, wavelet, stereoscopic observation
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Artificial Intelligence and Applications
ID Code:31788
Deposited By: Dr Yaxin Bi
Deposited On:24 Jul 2015 08:31
Last Modified:24 Jul 2015 08:31

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