基于CEEMD和排列熵的矿山微震信号降噪研究

Study on noise reduction of mine microseismic signals based on CEEMD and permutation entropy

针对矿山微震信号采集过程中通常会混入高频噪声的问题,提出了基于互补集合经验模态分解(CEEMD)和排列熵的降噪方法对微震信号进行降噪处理。使用CEEMD将信号分解成从高频到低频的数个本征模态函数;计算各模态分量的排列熵值定量表征其随机性程度;通过对含噪声信息较多的IMF分量进行小波阈值降噪,并通过信号重构得到降噪后的微震信号。仿真信号和真实微震信号降噪效果表明,基于CEEMD和排列熵的降噪效果优于CEEMD和EMD降噪法,该方法可以有效去除微震信号中的高频噪声,为微震信号的进一步分析提供技术支持。

In view of the problem that high frequency noise usually mixes in the process of microseismic signal acquisition in mines.The noise reduction method based on the complementary ensemble empirical modal decomposition(CEEMD)and permutation entropy is proposed for noise reduction of microseismic signals.The signal is decomposed into several eigenmode function from high to low frequencies using CEEMD;the ranking entropy value of each mode function component is calculated to quantitatively characterize its degree of randomness;wavelet threshold noise reduction of the IMF component containing more noise information is conducted and the noise-reduced microseismic signal is obtained by signal reconstruction.The noise reduction effect of simulated and real microseismic signals shows that the noise reduction effect based on CEEMD and permutation entropy is better than that of CEEMD and EMD noise reduction method,which can effectively remove the high frequency noise in microseismic signals and provide technical support for further analysis of microseismic signals.