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中国大陆区域6级以上地震舒曼谐振异常特征分析 被引量:4

Extraction and Analysis of Schumann Resonance Anomaly Features based on Earthquakes Larger than M6.0 in China's Mainland
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摘要 基于云南地区永胜台观测的地磁南北和东西分量,详细介绍了舒曼谐振参数的提取方法以及应用于震例的分析方法。采用4个洛仑兹函数叠加形式的最小二乘拟合得到谐振频率、幅度和半宽,并得到各谐振参数的小时均值。在此基础上,利用各谐振参数前15天滑动中值作为背景值,采用四分位阈值分析地震期间出现的正负异常现象。通过对2010年舒曼谐振仪器架设以来中国大陆8次6级以上地震舒曼谐振的正负异常的分析,初步得到了舒曼谐振的异常特征。地震期间谐振幅度增强是最常见的特征,并偶尔伴随谐振频率和半宽的变化。谐振幅度增强现象并不只局限于高阶谐振。地震期间发现多个谐振参数在同一天出现异常变化。本文得到的舒曼谐振异常特征与报导过的多次震例研究结果一致。本文采用的方法可作为分析地震舒曼谐振异常的一种有效方法。 Based on geomagnetic north-south (NS) and east-west (EW) components observed at Yongsheng observatory in Yunnan,southwestern China,this paper presents in detail the extraction method of Schumann Resonance (SR) parameters and analysis method for earthquake anomaly.We obtain peak frequency,relatvie peak amplitude and half-width by least square fitting of spectra by the sum of four Lorentz functions,and then get hourly average of SR parameters.We use 15 days running median of SR parameter as a background value and analyze positive and negative anomalies with a quartile threshold during 15 days before and 2 days after the earthquakes.Through statistical analysis of positive and negative anomalies for eight earthquakes larger than M6.0 occurred in China's Mainland since September of 2010,we obtain initial SR anomaly features.Increased amplitude before earthquakes is the most common characteristic,and occasionally with peak frequency and half-width changes.Amplitude enhancement phenomenon does not occurr only at higher order resonance.It was found that anomalies of two or more SR parameters took place in the same day during the period of earthquakes.SR anomaly features in this paper are consistent with several published case study results,and the method presented in this paper can be an effective way to analyze SR anomaly before earthquakes.
出处 《地震》 CSCD 北大核心 2015年第1期123-130,共8页 Earthquake
基金 中国地震局地震预测研究所基本科研业务费重点项目(2013IES010105 2014IES010203) 部门任务专项(2013IES0306)联合资助
关键词 舒曼谐振 谐振参数 异常特征 中国大陆 Schumann Resonance (SR) SR parameter Anomaly feature
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