| 地震噪声异常实时监测 |
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| 引用本文： | 林彬华,金星,廖诗荣,李军,黄玲珠,陈慧芳.地震噪声异常实时监测[J].中国地震研究,2016,30(2):224-232. |
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| 作者姓名： | 林彬华 金星 廖诗荣 李军 黄玲珠 陈慧芳 |
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| 作者单位： | 福建省地震局，福州市华鸿路7号 350003；福州大学，福州市闽侯县大学城学园路2号 350002;福建省地震局，福州市华鸿路7号 350003；福州大学，福州市闽侯县大学城学园路2号 350002;福建省地震局，福州市华鸿路7号 350003;福建省地震局，福州市华鸿路7号 350003;福建省地震局，福州市华鸿路7号 350003;福建省地震局，福州市华鸿路7号 350003 |
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| 基金项目： | This project was sponsored by the National Key Technology R＆D Program of China ( 2009BAK55B00 )，and the Earthquake Industry Research Project (201508012)． |
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| 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。
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| 关 键 词： | 地震噪声 PSD PDF 功率谱 异常 数据质量 |
| 收稿时间： | 2013/12/30 0:00:00 |
Research on Real-time Monitoring of Abnormal Seismic Noise |
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| Lin Binhu,Jin Xing,Liao Shirong,Li Jun,Huang Linzhu and Chen Huifang.Research on Real-time Monitoring of Abnormal Seismic Noise[J].Earthquake Research in China,2016,30(2):224-232. |
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| Authors: | Lin Binhu Jin Xing Liao Shirong Li Jun Huang Linzhu and Chen Huifang |
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| 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 July2013 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. |
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| Keywords: | Seismic noise Power spectral density Probability density function Power spectrum Abnormity Data quality |
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