摘要
爆破监测信号多为含噪信号,噪声会使经验模态分解(EMD)的结果产生严重的模态混淆,使用改进算法EEMD对模态混淆有一定的抑制作用但效果并不明显。为此本研究将使用自适应补充集合经验模态分解(CEEMDAN)来处理含噪信号。比较EMD、EEMD、CEEMDAN对仿真信号的分解结果,计算EMD、EEMD、CEEMDAN得到的IMF的排列熵值,对EMD、EEMD、CEEMDAN的分解结果进行Hilbert变换,并比较三者时频谱的分辨率。最后将CEEMDAN用于水下钻孔爆破地震波时频分析中,结果表明:CEEMDAN不仅对模态混淆具有一定的抑制作用,且其分解结果经过Hilbert变换得到的时频谱在时域和频域上都具有较高的分辨率。
Blasting monitoring signals are mostly noisy signals,and nosie can cause serious mode confusion in empirical mode decomposition(EMD)results.The improved EEMD algorithm can restrain mode confusion to some extent,but the effect is not obvious.Therefore,the complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)was used to process noisy signals.Firstly,the decomposition results of simulation signals with EMD,EEMD and CEEMDAN were compared,and the permutation entropy values of IMF obtained by EMD,EEMD and CEEMDAN were calculated.Then the decomposition results of EMD,EEMD and CEEMDAN were Hilbert-transformed,and their spectral resolutions were compared.Finally,CEEMDAN was applied to the time-frequency analysis of underwater borehole blasting seismic waves.The results show that CEEMDAN can suppress the mode confusion,and its time-frequency spectrum has high resolution in both time and frequency domain.
作者
孙苗
吴立
袁青
周玉纯
马晨阳
汪煜烽
SUN Miao;WU Li;YUAN Qing;ZHOU Yuchun;MA Chenyang;WANG Yufeng(Engineering Research Center of Rock-Soil Drilling&Excavation and Protection of the Ministry of Education∥Faculty of Engineering,China University of Geosciences,Wuhan 430074,Hubei,China;Postdoctoral Research Station,CCCC Second Harbour Engineering Co.,Ltd.,Wuhan 430040,Hubei,China)
出处
《华南理工大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2020年第3期76-82,共7页
Journal of South China University of Technology(Natural Science Edition)
基金
国家自然科学基金资助项目(41672260)。
关键词
经验模态分解
自适应补充集合经验模态分解
模态混淆
排列熵
empirical mode decomposition
complete ensemble empirical mode decomposition with adaptive noise
mode mixing
permutation entropy
