ARTICLE

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
References
  1. Beasley, C.J., Chambers, R.E. and Jiang, Z., 1998. A new look at simultaneous sources. Expanded
  2. Abstr., 68th Ann. Internat. SEG Mtg., New Orleans: 133-135.
  3. Berkhout, A.J., 2008. Changing the mindset in seismic data acquisition. The Leading Edge, 27: 924-
  4. Canales, L., 1984. Random noise reduction. Expanded Abstr., 54th Ann. Internat. SEG Mtg.,Atlanta: 525-527.
  5. Chen, Y., Zhou, C., Yuan, J. and Jin, Z., 2014. Application of empirical mode decomposition torandom noise attenuation of seismic data. J. Seismic Explor., 23: 481-495.
  6. Chen, Y., 2016. Dip-separated structural filtering using seislet thresholding and adaptive empiricalmode decomposition based dip filter. Geophys. J. Internat., 206: 457-469.
  7. Chen, Y., Ma, J. and Fomel, S., 2016. Double-sparsity dictionary for seismic noise attenuation.Geophysics, 81(2): V17-V30.
  8. Chen, Y. and Fomel, S., 2015. Random noise attenuation using local signal-and-noiseorthogonalization. Geophysics, 80(6): WD1-WD9.
  9. Chen, Y. and Jin, Z., 2016. Simultaneously removing noise and increasing resolution of seismic datausing waveform shaping. IEEE Geosci. Remote Sens. Lett., 13: 102-104.
  10. Chen, Y. and Ma, J., 2014. Random noise attenuation by fx empirical-mode decompositionpredictive filtering. Geophysics, 79(3): V81-V91.
  11. Gan, S., Chen, Y., Zu, S., Qu, S. and Zhong, W., 2015. Structure-oriented singular valuedecomposition for random noise attenuation of seismic data. J. Geophys. Engineer. , 12: 262-
  12. Gan, S., Wang, S., Chen, Y., Chen, J., Zhong, W. and Zhang, C., 2016a. Improved random noiseattenuation using fx empirical mode decomposition and local similarity. Appl. Geophys., 13:127-134.
  13. Gan, S., Wang, S., Chen, Y., Chen, X. and Xiang, K., 2016b. Separation of simultaneous sourcesusing a structural-oriented median filter in the flattened dimension. Comput. Geosci., 86:46-54.
  14. Gan, S., Wang, S., Chen, Y., Qu, S. and Zu, S., 2016c. Velocity analysis of simultaneous- sourcedata using high-resolution semblancecoping with the strong noise. Geophys. J. Internat. , 204:768-779.
  15. Han, J. and Mirko, V., 2013. Empirical mode decomposition for seismic time-frequency analysis.Geophysics, 78(2): 09-019.
  16. Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.-C., Tang, C.C.and Liu, H.H., 1998. The empirical mode decomposition and the Hilbert spectrum fornonlinear and non-stationary time series analysis. Proc. Roy. Soc. London. Series A:Mathemat., Phys., Engineer. Sci., 454: 903-995.
  17. Huang, W., Wang, R., Chen, Y., Li, H. and Gan, S., 2016. Damped multichannel singularspectrum analysis for 3D random noise attenuation. Geophysics, 81(4): V261-V270.
  18. Li, H., Wang, R., Cao, S., Chen, Y. and Huang, W., 2016. A method for low-frequency noisesuppression based on mathematical morphology in microseismic monitoring. Geophysics,81(3): V159-V 167.
  19. Liu, G., Fomel, S., Jin, L. and Chen, X., 2009. Stacking seismic data using local correlation.Geophysics, 74: V43-V48.RANDOM NOISE REDUCTION 249
  20. Liu, W., Cao, S., Chen, Y. and Zu, S., 2016a. An effective approach to attenuate random noisebased on compressive sensing and curvelet transform. J. Geophys. Engineer., 13: 135-145.
  21. Liu, W., Cao, S. and Chen, Y., 2016b. Application of variational mode decomposition in randomnoise attenuation and time-frequency analysis of seismic data. Extended Abstr., 78th EAGEConf., Vienna.
  22. Liu, W., Cao, S. and Chen, Y., 2016c. Applications of variational mode decomposition in seismictime-frequency analysis. Geophysics, 81(5): V365-V378.
  23. Liu, Y., 2013. Noise reduction by vector median filtering. Geophysics, 78: V79-V87.
  24. Mao, J. and Gao, J., 2006. On the denoising method of prestack seismic data in the wavelet domain.
  25. Expanded Abstr., 76th Ann. Internat. SEG Mtg., New Orleans: 25(1).
  26. Ulrych, T., Freire, J. and Siston, P., 1988. Eigenimage processing of seismic sections. Expanded
  27. Abstr., 58th Ann. Internat. SEG Mtg., Anaheim: 525-527.
  28. Wang, Y., 1999. Random noise attenuation using forward-backward linear prediction. J. SeismicExplor., 8: 133-142.
  29. Wu, Z. and Huang, N., 2009. Ensemble empirical mode decomposition: A noise-assisted dataanalysis method. Advan. Adapt. Data Anal., 1: 1-41.
  30. Xue, Z., Chen, Y., Fomel, S. and Sun, J., 2016. Seismic imaging of incomplete data andsimultaneous-source data using least-squares reverse time migration with shapingregularization. Geophysics, 81: -S20.
  31. Yang, W., Wang, R., Chen, Y., Wu, J., Qu, S., Yuan, J. and Gan, S., 2015a. Application ofspectral decomposition using regularized non-stationary autoregression to random noiseattenuation. J. Geophys. Engineer., 12: 175-187.
  32. Yang, W., Wang, R., Wu, J., Chen, Y., Gan, S. and Zhong, W., 2015b. An efficient and effectivecommon reflection surface stacking approach using local similarity and plane- waveflattening. J. Appl. Geophys., 117: 67-72.
  33. Yuan, S. and Wang, S., 2013. Edge-preserving noise reduction based on bayesian inversion withdirectional difference constraints. J. Geophys. Engineer., 10(2): 123-136.
  34. Yuan, S., Wang, S. and Li, G., 2012. Random noise reduction using bayesian inversion. J.Geophys. Engineer., 9: 60-68.
  35. Zhang, R. and Ulrych, T., 2003. Physical wavelet frame denoising. Geophysics, 68: 225- 231.
  36. Zu, S., Zhou, H., Chen, Y., Qu, S., Zou, X., Chen H. and Liu, R., 2016. A periodically varyingcode for improving deblending of simultaneous sources in marine acquisition. Geophysics,81(3): V213-V225.
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Journal of Seismic Exploration, Electronic ISSN: 0963-0651 Print ISSN: 0963-0651, Published by AccScience Publishing