Abstract: This study provides new seismo-electromagnetic data processing methods to extract the anomalous signals by combining the principal component analysis(PCA)and local correlation tracking(LCT)methods. The PCA method can separate signals of different frequencies by projecting them to different axes according to their energy. So it can solve the problems of identifying the relatively weak signals in strong interference background to a certain extent. The LCT method is more suitable for non-stationary signal processing compared with classical cross-correlation method. This method is based on the good spatial correlation between the magnetic field components of different ELF stations to pick up the correlation coefficient, so as to achieve the purpose of weak anomalies signals identification. As a case study of the M4.6 Jinggu earthquake in Yunnan, China, we investigated the electromagnetic data observed by ELF stations near the epicenter. First, we applied the PCA method to the magnetic-filed data and got the temporal variation of percentage of each principal component. The results indicate that the contribution of the second principal components, which may relate with the earthquake, increased significantly about a week before the earthquake. Then, we applied the LCT method to the magnetic-filed data processing as well, and the results of both north-south and east-west magnetic field components showed that local correlation coefficient saw anomalies about a week before the earthquake, which had a good corresponding relationship with the former results by PCA method. Both means for the same earthquake case got a consistent conclusion, which not only enhanced the reliability of the results, but also confirmed the effectiveness of two methods applied in the earthquake-related electromagnetic anomalies extraction. We also discussed the possible relationship between these anomalies and the earthquake. Although there are no direct evidence and supporting theories in terms of the relationship between abnormal electromagnetic signals and earthquakes at present, the studies in this paper may strengthen the understanding of seismoelectromagnetic phenomena and promote further research.

Key words: earthquake, electromagnetic anomalies, principal component analysis, local correlation tracking, signal processing

摘要： 将主成分分析法和局部互相关追踪法应用于地震电磁数据的处理分析，尝试从较强干扰背景中提取相对较弱的电磁异常信息。主成分分析方法能够将不同频段的原始信号由强到弱投影到不同的轴上，使不同的信号分离开来，从而在一定程度上解决在较强干扰背景中识别相对较弱信号的难题；局部互相关追踪法相对于经典互相关方法更适用于非平稳信号的处理，该方法基于不同极低频电磁观测台站对应磁场分量之间的空间互相关性对相关系数异常进行拾取，从而达到弱信号识别的目的。以云南省景谷县2015年11月14日4.6级地震为例，分别运用主成分分析法和局部互相关追踪法对震中附近的极低频观测台观测的电磁资料进行处理分析。首先，将主成分分析方法应用于台站磁场分量数据的处理，得到各主成分及其所占能量比随时间的变化，结果表明震前1周左右与地震相关的第2主成分所占能量比显著增加；其次，利用局部互相关追踪法对该震例进行处理分析并与主成分分析法处理的结果进行对比，结果显示SN及EW向磁场分量的局部互相关结果于地震前1周左右均出现相关系数异常，与主成分分析方法的处理结果基本一致，并探讨了这些异常可能与地震活动性之间存在的关系。虽然目前电磁异常现象的产生与地震之间的确切关系尚缺乏直接的证据与理论支持，但2种方法对电磁异常信号的有效提取将有助于加深对地震电磁现象的认知、理解与进一步研究。

关键词: 地震, 电磁异常, 主成分分析法, 局部互相关追踪法, 信号处理