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地震噪声异常实时监测
林彬华1,2) 金星1,2) 廖诗荣1) 李军1) 黄玲珠1) 陈慧芳1)
1)福建省地震局,福州市华鸿路7号 350003;2)福州大学,福州市闽侯县大学城学园路2号 350002
摘要:
本文采用福建省85个测震台站2012年全年噪声资料的垂直向记录作为研究对象,将噪声记录以每5min为单位进行分段,求出每小段的功率谱,应用概率分布函数方法绘出台站的PDF图,之后利用网格概率法确定出台站的高低噪声参照线。另外,根据85个台站的PDF图异常,将噪声异常分成四类:缺数异常、低噪处异常、高噪处异常、中噪处异常。依据四类异常的特征分别研究出四类异常的挑选方法,再将这四种挑选方法结合形成地震噪声实时监测系统。选取福建省85个测震台站2013年7月份的噪声记录进行验证,结果表明:85个台站应用地震噪声实时监测系统识别出来的异常正确率都达到90%以上,挑选效果很好,并可应用于台站噪声实时监测。
关键词:  地震噪声  PSD  PDF  功率谱  异常  数据质量
DOI:
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基金项目:
Research on Real-time Monitoring of Abnormal Seismic Noise
Lin Binhua1,2),Jin Xing1,2),Liao Shirong1),Li Jun1),Huang Linzhu1),and Chen Huifang1)
1) Earthquake Administration of Fujian Province,Fuzhou 350003,China;2) Fuzhou University,Fuzhou 350116,China
Abstract:
The noise data in vertical component records of 85 seismic stations in Fujian Province during 2012 is used as the research object in this paper. The noise data is divided into fiveminute segments to calculate the power spectra. The high reference line and low reference line of station are then identified by drawing a probability density function graph (PDF) using the power spectral probability density function. Moreover,according to the anomalies of PDF graphs in 85 seismic stations,the abnormal noise is divided into four categories: dropped packet, low noise, high noise, and median noise anomalies. Afterwards,four selection methods are found by the high or low noise reference line of the stations,and the system of real-time monitoring of seismic noise is formed by combining the four selection methods. Noise records of 85 seismic stations in Fujian Province in July 2013 are selected for verification,and the results show that the anomalous noise-recognition system could reach a 90% success rate at most stations and the effect of selection are very good. Therefore,it could be applied to the seismic noise real-time monitoring in stations.
Key words:  Seismic noise  Power spectral density  Probability density function  Power spectrum  Abnormity  Data quality