基于变分模态分解和小波能量熵的微震信号降噪

Research on microseismic signal denoising based on variational mode decomposition and wavelet energy entropy

微震监测技术被广泛应用于矿业工程、石油天然气开采、安全监测等领域。针对微震监测采集到的微震信号存在随机噪声的问题,本文提出了一种变分模态分解(variational mode decomposition,VMD)和小波能量熵(wavelet energy entropy,WEE)结合改进阈值函数的降噪算法。对原始信号进行VMD分解,将得到的各模态分量(intrinsic mode function,IMF)进行多尺度小波分解,用小波能量熵表征各尺度信号的含噪状态,并以小波能量熵最大子区间的小波系数计算各尺度层的阈值,通过改进阈值函数进行降噪处理后得到新的IMF,重构微震信号。对仿真信号和实测信号进行降噪处理,结果表明,该算法优于经验模态分解(empirical mode decomposition,EMD)、集合经验模态分解(ensemble empirical mode decomposition,EEMD)、VMD结合小波硬阈值和软阈值降噪方法,提高了微震信号的信噪比。

Microseismic monitoring technology is widely used in such fields as mining engineering,petroleum and gas exploitation,and safety monitoring.In order to solve the problem of random noise in microseismic signals collected by microseismic monitoring,a denoising algorithm based on variational mode decomposition(VMD)combined with wavelet energy entropy(WEE)and improved threshold function is proposed.Wavelet transformation is performed on each intrinsic mode function(IMF)component after VMD decomposition of the original microseismic signals.The noise state of each scale signal is characterized by wavelet energy entropy.The threshold of each scale layer is calculated by wavelet coefficients of the maximum subinterval of wavelet energy entropy and then the microseismic signals are reconstructed by the new IMFs denoised through the improved threshold function.The results from numerical simulation signal and real signal show that the proposed algorithm is superior to empirical mode decomposition(EMD),ensemble EMD(EEMD)and VMD combined with wavelet energy entropy hard threshold function and soft threshold function.The signal-to-noise ratio of microseismic signals are improved.