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Random noise reduction using a hybrid method based on ensemble empirical mode decomposition
WEI CHEN1 ,
DONG ZHANG2 ,
YANGKANG CHEN3*
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1
Institute of Geophysics and Petroleum Resources, Yangtze University, Daxue Road 111, Caidian District, Wuhan 430100, P.R. China. cwjycd@163.com,
2
State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, Fuxue Road 18, Beijing 102200, P.R. China. zhangdongconan@sina.com,
3
Bureau of Economic Geology, John A. and Katherine G. Jackson School of Geosciences, The University of Texas at Austin, University Station, Box X, Austin, TX 78713 8924, U.S.A. chenyk1990@gmail.com,
JSE 2017, 26(3), 227–249;
Submitted: 25 May 2016 | Accepted: 25 March 2017 | Published: 1 June 2017
© 2017 by the Authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
Abstract
Chen, W., Zhang, D. and Chen, Y., 2017. Random noise reduction using a hybrid method based on ensemble empirical mode decomposition. Journal of Seismic Exploration, 26: 227-249. We have proposed a novel hybrid random noise reduction method based on ensemble empirical mode decomposition (EEMD) and wavelet threshold filtering. Firstly, the frequency band ranges of effective signal and noise in the initial seismic data set are studied by Fourier spectrum analysis method. Secondly, we make use of EEMD method to obtain the intrinsic mode functions (IMFs) of each original noisy trace. Wavelet threshold filtering is then applied to the high frequency IMFs of each trace to acquire the new denoised high frequency IMFs. Finally, by stacking the filtered high frequency IMFs with the low frequency IMFs and the trend item, we can obtain the ultimately denoised seismic data set. The proposed approach is confirmed via two synthetic data examples and one field data example. The results demonstrate that the proposed approach can achieve much cleaner denoising performance without harming most useful signals.
Keywords
ensemble empirical mode decomposition (EEMD)
wavelet threshold filtering
seismic data
random noise reduction
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