Research on the Application of CEEMD and CEEMDAN in Seismic Random Noise Suppression
DOI:
https://doi.org/10.54097/0qtk8q83Keywords:
Empirical Mode Decomposition; Seismic Data Processing; Random Noise Suppression.Abstract
Empirical Mode Decomposition (EMD) is an adaptive method for processing nonlinear and non-stationary data. Complementary Ensemble Empirical Mode Decomposition (CEEMD) and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) are noise-assisted signal processing methods developed from EMD. During seismic data acquisition, various types of noise, including random noise, are inevitably captured, and current methods for dealing with such noise are limited. In this study, these two signal processing methods were applied in simulated experiments and real seismic data to suppress random noise. By analyzing the processing effects and runtime, it was found that CEEMDAN offers better processing performance and speed, making it a valuable tool for practical applications.
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[1] Huang N E, Shen Z, Long S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non- stationary time series analysis [J]. Proceedings of the Royal Society A, 1998, 454(1971 ):903-995.
[2] Luo H M, Song W Q, Xing Y R, et al. Seismic weak signal enhancement processing method based on improved empirical mode decomposition[J]. Progress in Geophysics, 2019, 34(01): 167-173.
[3] Mijovic B, De Vos M, Gligorijevic I, et al. Source separation from single-channel recordings by combining empirical mode decomposition and independent component analysis[J]. IEEE Transaction on Biomedical Engineering.2010, 57(9): 2188–2196.
[4] Wu Z, Huang N E. Ensemble empirical mode decomposition: a noise-assisted data analysis method[J]. Advances in Adaptive Data Analysis, 2009, 1(01): 1-41.
[5] Yeh J R, Shieh J S, Huang N E. Complementary ensemble empirical mode decomposition: A novel noise enhanced data analysis method[J]. Advances in Adaptive Data Analysis, 2010, 2(02): 135-156.
[6] Torres M E, Colominas M A, SCHLOTTHAUER G, et al. A complete ensemble empirical mode decomposition with adaptive noise[C]//2011 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, 2011: 4144-4147.
[7] Hu R Q, Wang Y C, Yin Z H, et al.. Low SNR microseismic first arrival signal detection combined with CEEMDAN and principal component analysis [J]. Oil Geophysical Prospecting, 2019, 54 (01): 45-53+6.
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