Journal of South China University of Technology (Natural Science Edition) ›› 2020, Vol. 48 ›› Issue (3): 76-82.doi: 10.12141/j.issn.1000-565X.190179

• Architecture & Civil Engineering • Previous Articles     Next Articles

Time-Frequency Analysis of Blasting Seismic Signal Based on CEEMDAN

SUN Miao1 WU Li1 YUAN Qing2 ZHOU Yuchun1 MA Chenyang1 WANG Yufeng1   

  1. 1. 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; 2. Postdoctoral Research Station, CCCC Second Harbour Engineering Co. , Ltd. , Wuhan 430040, Hubei, China
  • Received:2019-04-15 Revised:2019-11-04 Online:2020-03-25 Published:2020-03-01
  • Contact: 吴立(1963-),男,教授,博士生导师,主要从事工程爆破和地下建筑工程理论技术研究。 E-mail:lwu@cug.edu.cn
  • About author:孙苗(1993-),女,博士生,主要从事水下钻孔爆破理论研究。E-mail:2357152544@qq.com
  • Supported by:
    Supported by the National Natrual Science Foundation of China (41672260)

Abstract: 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 timefrequency 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.

Key words: empirical mode decomposition, complete ensemble empirical mode decomposition with adaptive noise, mode mixing, permutation entropy