煤岩微弱电磁信号的噪声源识别及去噪方法

Noise auto identification and de-noising method of coal-rock weak electromagnetic signals

煤岩破坏可以产生电磁信号,分析其信号特征对于准确预测煤岩动力灾害有着重要作用,然而当破裂信号较为微弱时,外界干扰因素会对结果分析产生极大影响。为此,在分析实验条件下煤岩受载电磁信号的噪声来源及其各自特征的基础上,提出了循环带阻滤波、基于白噪声统计特征及经验模态分解(EMD)的均值滤波、基于模态分量自相关函数频谱的准周期特征识别及带阻滤波等方法,对不同源头的噪声进行了自动识别及去噪,同时,在信号源未知的情况下提出了评估去噪效果的噪噪比(NNR)方法。结果表明:基于分源去噪方法而得出的型煤电磁信号噪噪比仅为0.136 6,明显优于单一的小波及EMD去噪方法,表明分源去噪方法在煤岩受载微弱电磁信号去噪中有着良好的应用效果。

When coal and rock break,they can produce electromagnetic signals,and the analysis of their characteristics is very important for the accurate prediction of coal rock dynamic disasters. However,when the signal is weak,the interference factors will greatly affect the result analysis. Thus,based on the analysis of noise sources and their respective characteristics of coal rock electromagnetic signals under laboratory conditions,the methods of loop band stop filter,mean filtering method based on white noise statistics and empirical mode decomposition（ EMD）,and periodic feature recognition along with band stop filter based on the frequency characteristics of modal component auto correlation function,etc.,were put forward to auto-identify and de-noise the different noises. In addition,a method of noise to noise ratio（ NNR） was proposed to evaluate the de-noising effect in the case of unknown signal source. The result shows that the NNR of briquette electromagnetic signal by using the de-noising method developed in this study is only 0. 136 6,which is obviously better than the method of EMD or WAVELET. The result quantitatively indicates that the de-noising method has a good effect in the application of coal rock electromagnetic signal de-noising.