AccScience Publishing / JSE / Article
Submit to JSE
Cite this article
47
Views
Journal Browser
Volume | Year
Issue
Search
Forthcoming Issue
Current Issue
View All
News and Announcements
View All
ARTICLE

Fully automatic random noise attenuation using empirical wavelet transform

WEI CHEN1,2 ,  HUI SONG1,2 ,  XIAOYU CHUAI3
Show Less
1 Key Laboratory of Exploration Technology for Oil and Gas Resources of the Ministry of Education, Yangtze University, Daxue Road 111, Caidian District, Wuhan 430100, P.R. China.,
2 Hubei Cooperative Innovation Center of Unconventional Oil and Gas, Daxue Road 111, Caidian District, Wuhan 430100, P.R. China.,
3 Hebei Coal Research Institute, Xingtai 054000, P.R. China.,
JSE 2019, 28(2), 147–162;
Submitted: 3 April 2018 | Accepted: 4 December 2018 | Published: 1 April 2019
© 2019 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/ )
Download PDF
Cite
XML
Abstract

Chen, W., Song, H. and Chuai, X.Y., 2019. Fully automatic random noise attenuation using empirical wavelet transform. Journal of Seismic Exploration, 28: 147-162. Strong noise in seismic data seriously affects many steps in seismic data processing and imaging. While most traditional methods depend on carefully tuned input parameters by human, we are proposing an automatic noise attenuation algorithm to facilitate a fast preprocessing of massive prestack seismic data. In the proposed algorithm, the non-stationary seismic data is first adaptively decomposed into empirical components via empirical wavelet transform (EWT) according to the frequency contents in the data. Then, the first component is selected to represent the useful signals. This process can be implemented in a fully automatic way. We compare the decompositions from EWT and the empirical mode decomposition (EMD) and find that the EWT has a stronger capability in separating the useful signals and the random noise. We also test the proposed algorithm in both multi-channel synthetic and field data examples. The results demonstrate that the new adaptive method can obtain better denoising performance than the state-of-the-art methods.

Keywords
random noise suppression
empirical wavelet transform
seismic signal processing
automatic processing
intrinsic mode function
References
  1. Abma, R. and Claerbout, J.,1995. Lateral prediction for noise attenuation by t—x and f—xtechniques. Geophysics, 60: 1887-1896.
  2. Bai, M. and Wu, J., 2017,. Efficient deblending using median filtering without correctnormal moveout - with comparison on migrated images. J. Seismic Explor., 26:455-479.
  3. Bai, M. and Wu, J., 2018. Seismic deconvolution using iteartive transform-domain sparseinversion. J. Seismic Explor., 27: 103-116.
  4. Bai, M., Wu, J., Xie, J. and Zhang, D., 2018. Least-squares reverse time migration ofblended data with low-rank constraint along structural direction. J. Seismic Explor.,27: 29-48.
  5. Bekara, M. and van der Baan, M., 2007. Local singular value decomposition for signalenhancement of seismic data. Geophysics, 72: V59-V65.
  6. Canales, L., 1984. Random noise reduction. Expanded Abstr, 54th Ann. Internat. SEGMtg., Atlanta: 525-527.
  7. Candés, E.J., Demanet, L., Donoho, D.L. and Ying, L., 2006. Fast discrete curvelettransforms. SIAM Multisc. Model. Simulat., 5: 861-899.
  8. Chen, Y., 2016. Dip-separated structural filtering using seislet thresholding and adaptiveempirical mode decomposition based dip filter. Geophys. J. Internat., 206: 457-469.
  9. Chen, Y., 2017. Fast dictionary learning for noise attenuation of multidimensionalseismic data. Geophys. J. Internat., 209: 21-31.
  10. Chen, Y., 2018a. Automatic microseismic event picking via unsupervised machinelearning. Geophys. J. Internat., 212: 88-102.
  11. Chen, Y., 2018b. Automatic velocity analysis using high-resolution hyperbolic Radontransform. Geophysis, 83(4): A53-A57.
  12. Chen, Y., 2018c. Non-stationary least-squares complex decomposition for microseismicnoise attenuation. Geophys. J. Internat., 213: 1572-1585.
  13. Chen, Y., Chen, H., Xiang, K. and Chen, X., 2017. Preserving the discontinuities in least-squares reverse time migration of simultaneous-source data. Geophysics, 82(3).doi: 10.1190/GEO2016-0456.1.hen, Y., and Fomel, S., 2015. Random noise attenuation using local signal-and-noiseorthogonalization. Geophysics, 80: WD1—WD9.hen, Y., 2018. EMD-seislet transform. Geophysics, 83(1): A27-A32.hen, Y., Fomel, S. and Hu, J., 2014. Iterative deblending of simultaneous-sourceseismic data using seislet-domain shaping regularization. Geophysics, 79(5):V179-V 189.
  14. Chen, Y., Huang, W., Zhou, Y., Liu, W. and Zhang, D., 2018. Plane-wave orthogonalpolynomial transform for amplitude-preserving noise attenuation. Geophys. J.Internat., 214: 2207-2223.
  15. Chen, Y. and Ma, J., 2014. Random noise attenuation by f-x empirical modedecomposition predictive filtering. Geophysics, 79: V81—V91.QQQ
  16. Chen, Y., Ma, J. and Fomel, S., 2016a. Double-sparsity dictionary for seismic noiseattenuation. Geophysics, 81: V17—V30.
  17. Chen, Y., Zhang, D., Jin, Z., Chen, X., Zu, S., Huang, W. and Gan, S., 2016b.
  18. Simultaneous denoising and reconstruction of 5D seismic data via dampedrank-reduction method. Geophys. J. Internat., 206: 1695-1717.
  19. Donoho, D.L. and Johnstone, LM., 1994. Ideal spatial adaptation by wavelet shrinkage:Biometrika, 81: 425-455.
  20. Fomel, S. and Liu, Y., 2010. Seislet transform and seislet frame. Geophysics, 75:V25-V38.
  21. Gan, S., Chen, Y., Wang, S., Chen, X., Huang, W. and Chen, H., 2016a. Compressivesensing for seismic data reconstruction using a fast projection onto convex setsalgorithm based on the seislet transform. J. Appl. Geophys., 130: 194-208.
  22. Gan, S., Chen, Y., Zu, S., Qu, S. and Zhong, W., 2015a. Structure-oriented singular valuedecomposition for signal enhancement of seismic data. J. Geophys. Engineer., 12:262-272.
  23. Gan, S., Wang, S., Chen, Y., Chen, J., Zhong, W. and Zhang, C., 2016b. Improvedrandom noise attenuation using fx empirical mode decomposition and localsimilarity. Appl. Geophys., 13: 127-134.
  24. Gan, S., Wang, S., Chen, Y. and Chen, X., 2016c. Simultaneous-source separation usingiterative seislet-frame thresholding. IEEE Geosci. Remote Sens. Lett., 13: 197-201.
  25. Gan, S., Wang, S., Chen, Y., Chen, X. and Xiang, K., 2016d. Separation of simultaneoussources using a structural-oriented median filter in the flattened dimension.Comput. Geosci., 86: 46-54.
  26. Gan, S., Wang, S., Chen, Y., Qu, S. and Zu, S., 2016e. Velocity analysis ofsimultaneous-source data using high-resolution semblance-coping with the strongnoise. Geophys. J. Internat., 204: 768-779.
  27. Gan, S., Wang, S., Chen, Y., Zhang, Y. and Jin, Z., 2015b. Dealiased seismic datainterpolation using seislet transform with low-frequency constraint. IEEE Geosci.Remote Sens. Lett., 12: 2150-2154.
  28. Gao, J., Mao, J., Chen, W. and Zheng, Q., 2006. On the denoising method of prestackseismic data in wavelet domain. Chin. J. Geophys., 49: 1155-1163.
  29. Gilles, J., 2013. Empirical wavelet transform. IEEE Transact. Sign. Process., 61:3999-4010.
  30. Li, H., Wang, R., Cao, S., Chen, Y. and W. Huang, W., 2016a. A method forlow-frequency noise suppression based on mathematical morphology inmicroseismic monitoring. Geophysics, 81(3), V159-V 167.
  31. Li, H., Wang, R., Cao, S., Chen, Y., Tian, N. and Chen, X., 2016b. Weak signal detectionusing multiscale morphology in microseismic monitoring. J. Appl. Geophys., 133:39-49.
  32. Liu, G. and Chen, X., 2013. Noncausal f-x-y regularized nonstationary predictionfiltering for random noise attenuation on 3D seismic data. J. Appl. Geophys., 93:60-66.
  33. Liu, G., Chen, X., Du, J. and Song, J., 2011. Seismic noise attenuation usingnonstationary polynomial fitting. Appl. Geophys., 8: 18-26.
  34. Liu, W., Cao, S. and Chen, Y., 2016a. Applications of variational mode decomposition inseismic time-frequency analysis. Geophysics, 81(5): V365-V378.
  35. Liu, W., Cao, S. and Chen, Y., 2016b. Seismic time-frequency analysis via empiricalwavelet transform. IEEE Geosci. Remote Sens. Lett., 13: 28-32.
  36. Liu, W., Cao, S., Chen, Y. and Zu, S., 2016c. An effective approach to attenuate randomnoise based on compressive sensing and curvelet transform. J. Geophys. Engineer.,13: 135-145.
  37. Liu, W., Cao, S., Gan, S., Chen, Y., Zu, S. and Jin, Z., 2016d. One-step slope estimationor dealiased seismic data reconstruction via iterative seislet thresholding. IEEEGeosci. Remote Sens. Lett., 13: 1462-1466.
  38. Liu, W., Cao, S., Liu, Y. and Chen, Y., 2016e. Synchrosqueezing transform and itsapplications in seismic data analysis. J. Seismic Explor., 25: 27-44.
  39. Liu, W., Cao, S., Wang, Z., Kong, X. and Chen, Y., 2017. Spectral decomposition forhydrocarbon detection based on YMD and Teager-Kaiser energy. IEEE Geosci.Remote Sens. Lett., 14: 539-543.
  40. Neelamani, R., Baumstein, A., Gillard, D., Hadidi, M. and Soroka, W., 2008. Coherentand random noise attenuation using the curvelet transform. The Leading Edge, 27:240-248.
  41. Qu, S., Zhou, H., Chen, Y., Yu, S., Zhang, H., Yuan, J., Yang, Y. and Qin, M., 2015. Aneffective method for reducing harmonic distortion in correlated vibroseis data. J.Appl. Geophys., 115: 120-128.
  42. Qu, S., Zhou, H., Liu, R., Chen, Y., Zu, S., Yu, S., Yuan, J. and Yang, Y., 2016.
  43. Deblending of simultaneous-source seismic data using fast iterativeshrinkage-thresholding algorithm with firm-thresholding. Acta Geophys., 64:1064-1092.
  44. Siahsar, M.A.N., Abolghasemi, V. and Chen, Y., 2017. Simultaneous denoising andinterpolation of 2D seismic data using data-driven non-negative dictionary learning.Sign. Process., 141: 309-321.
  45. Wang, Y., Zhou, H., Zu, S., Mao, W. and Chen, Y., 2017. Three-operator proximalsplitting scheme for 3D seismic data reconstruction. IEEE Geosci. Remote Sens.Lett., 14: 1830-1834.
  46. Wu, G., Fomel, S. and Chen, Y., 2018. Data-driven time-frequency analysis of seismicdata using non-stationary prony method. Geophys. Prosp., 66: 85-97.
  47. Wu, J. and Bai, M., 2018. Incoherent dictionary learning for reducing crosstalk noise inleast-squares reverse time migration. Comput. Geosci., 114: 11-21.
  48. Wu, J., Wang, R., Chen, Y., Zhang, Y., Gan, S. and Zhou, C., 2016. Multiples attenuationusing shaping regularization with seislet domain sparsity constraint. J. SeismicExplor., 25: 1-9.
  49. Xie, J., Di, B., Schmitt, D., Wei, J. and Chen, Y., 2018. Estimation of 6 and cl3 oforganic-rich shale from laser ultrasonic technique (LUT) measurement. Geophysics,83(4): C137-C152.
  50. Xue, Y., Chang, F., Zhang, D. and Chen, Y., 2016a. Simultaneous sources separation viaan iterative rank-increasing method. IEEE Geosci. Remote Sens. Lett., 13:1915-1919.
  51. Xue, Y., Man, M., Zu, S., Chang, F. and Chen, Y., 2017. Amplitude-preserving iterativedeblending of simultaneous source seismic data using high-order radon transform.J. Appl. Geophys., 139: 79-90.
  52. Xue, Y., Yang, J., Ma, J. and Chen, Y., 2016b. Amplitude-preserving nonlinear adaptivemultiple attenuation using the high-order sparse radon transform. J. Geophys.Engineer., 13: 207-219.
  53. Yang, W., Wang, R., Wu, J., Chen, Y., Gan, S. and Zhong, W., 2015. An efficient andeffective common reflection surface stacking approach using local similarity andplane-wave flattening. J. Appl. Geophys., 117: 67-72.
  54. Yang, Y., Li, D., Tong, T., Zhang, D., Zhou, Y. and Chen, Y., 2018. Denoisingcontrolled-source electromagnetic data using least-squares inversion. Geophysics,83(4): E229-E244.
  55. Zhang, D., Chen, Y., Huang, W. and S. Gan, S., 2016. Multi-step damped multichannelsingular spectrum analysis for simultaneous reconstruction and denoising of 3Dseismic data. J. Geophys. Engineer., 13: 704-720.
  56. Zhang, R. and Ulrych, T., 2003. Physical wavelet frame denoising. Geophysics, 68:225-231.
  57. Zhao, Q., Du, Q., Gong, X. and Chen, Y., 2018. Signal-preserving erratic noiseattenuation via iterative robust sparsity-promoting filter. IEEE Transact. Geosci.Remote Sens., 56: 1558-1564.
  58. Zhong, W., Chen, Y., Gan, S. and Yuan, J., 2016. Li. norm regularization for 3D seismicdata interpolation. J. Seismic Explor., 25: 257-268.
  59. Zhou, Y., Li, S., Zhang, D. and Chen, Y., 2018. Seismic noise attenuation using an onlinesubspace tracking algorithm. Geophys. J. Internat., 212: 1072-1097.
  60. Zhou, Y., Shi, C., Chen, H., Xie, J., Wu, G. and Chen, Y., 2017. Spike-like blendingnoise attenuation using structural low-rank decomposition. IEEE Geosci. RemoteSens. Lett., 14: 1633-1637.
  61. Zu, S., Zhou, H., Chen, H., Zheng, H. and Chen, Y., 2017a. Two field trials fordeblending of simultaneous source surveys: why we failed and why we succeeded? J.Appl. Geophys., 143: 182-194.
  62. Zu, S., Zhou, H., Chen, Y., Qu, S., Zou, X., Chen, H. and Liu, R., 2016a. A periodicallyvarying code for improving deblending of simultaneous sources in marineacquisition. Geophysics, 81: V213-V225.
  63. Zu, S., Zhou, H., Chen, Y., Pan, X., Gan, S. and Zhang, D., 2016b. Interpolating big gapsusing inversion with slope constraint. IEEE Geosci. Remote Sens. Lett., 13:1369-1373.
  64. Zu, S., Zhou, H., Li, Q., Chen, H., Zhang, Q., Mao, W. and Chen, Y., 2017b.
  65. Shot-domain deblending using least-squares inversion. Geophysics, 82(4):V241-V256.
  66. Zu, S., Zhou, H., Mao, W., Zhang, D., Li, C., Pan, X. and Chen, Y., 2017c. Iterativedeblending of simultaneous-source data using a coherency-pass shaping operator.Geophys. J. Internat., 211: 541-557.
Share
Back to top
Journal of Seismic Exploration, Electronic ISSN: 0963-0651 Print ISSN: 0963-0651, Published by AccScience Publishing
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here
Accept