本文以福建省85个测震台站2012年全年噪声资料中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，以85个台站的PDF 图为基础，将噪声异常分成缺数异常、低噪处异常、高噪处异常、中噪处异常等4类。依据4类异常的特征分别找出每一类异常的遴选方法，再将这4种挑选方法相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，遴选效果很好，可用于对台站噪声实时监测。

This dissertation used the noise data in vertical recording during 2012 as the research object． We divided the noise records to 5min short segment，calculated the power spectral of each segment，and drew PDF diagram by using probability distribution functions． We then determined the station of the high or low noise reference line using the method of the grid probability． Then on the basis of the anomalies of the station in the PDF，the abnormal noise were divided into four categories: dropped packets abnormal，low noise abnormal，high noise abnormal，median noise abnormal． Four selection methods were found by the station of the high or low noise reference line，and real-time monitoring system of seismic noise was formed by combining four selection methods． Noise records of 85 seismic stations in Fujian Province in July 2013 was selected to verify． The results show that noise-recognition of most station anomalies can reach 99% ． As the average error rate is less than 1%，the effect of selection is very good． Therefore，the method can be applied to station noise in real-time monitoring．

国家科技支撑计划(2009BAK55B00)、福建省地震局科技攻着项目(G201401)联合资助