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

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