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Seismic noise attenuation using an improved variational mode decomposition method

YATONG ZHOU,  YUE CHI
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School of Electronic and Information Engineering, Hebei University of Technology, Xiping Road No. 5340, Beichen District, Tianjin 300401, P.R. China,
JSE 2020, 29(1), 29–47;
Submitted: 5 September 2018 | Accepted: 31 October 2019 | Published: 1 February 2020
© 2020 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/ )
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Abstract

Zhou, Y.T. and Chi, Y., 2020. Seismic noise attenuation using an improved variational mode decomposition method. Journal of Seismic Exploration, 29: 29-47. Seismic noise suppression is an important step in the seismic imaging community. We propose a dip-separated denoising method to attenuate spatially incoherent random noise. The variational mode decomposition (VMD) method is used to decompose the seismic data into different dip bands. It has a solid theoretical foundation of mathematics and high calculation efficiency. Besides, compared with the recursive mode decomposition algorithms, e.g., the EMD and EEMD methods, it has advantages in solving the mode mixing problem and more powerful anti-noise performance. The VMD method can adaptively decompose a seismic signal into several intrinsic mode functions (IMF). Decomposing the seismic data into oscillating IMF components is equivalent of decomposing the seismic data into different dipping components. To automatically define the optimal number of most oscillating components, we design the Kurtosis method. To eliminate the errors caused by end effect, we use a waveform matching extension algorithm to improve the VMD. The singular spectrum analysis (SSA) method is used to approximate the low-rank components in each separated dip band. In this paper, a simulated seismic dataset and a real seismic dataset are analyzed by the proposed algorithm. The results indicate that the proposed algorithm is robust to noise and has high de-noising precision.

Keywords
random noise suppression
variational mode decomposition
singular spectrum analysis
intrinsic mode functions
References
  1. Bai, M. and Wu, J., 2017. Efficient deblending using median filtering without correctnormal moveout - with comparison on migrated images. J. Seismic Explor., 26: 455-
  2. Bai, M. and Wu, J., 2018. Seismic deconvolution using iteartive transform-domain sparseinversion. J. Seismic Explor., 27: 103-116.
  3. Bai, M., Wu, J., Xie, J. and Zhang, D., 2018a. Least-squares reverse time migration ofblended data with low-rank constraint along structural direction. J. Seismic Explor.,27: 29-48.
  4. Bai, M., Wu, J. and Zhang, H., 2019. Iterative deblending of simultaneous-source datausing smoothed singular spectrum analysis. J. Appl. Geophys., 161: 261-269.
  5. Bai, M., Wu, J., Zu, S. and Chen, W., 2018b. A structural rank reduction operator forremoving artifacts in least-squares reverse time migration. Comput. Geosci., 117:9-20.
  6. Bekara, M. and van der Baan, M., 2007. Local singular value decomposition for signalenhancement of seismic data. Geophysics, 72(2): V59-V65.
  7. Canales, L., 1984. Random noise reduction. Expanded Abstr., 54th Ann. Internat. SEGMtg., Atlanta: 525-527.
  8. Candés, E.J., Demanet, L., Donoho, D.L. and Ying, L., 2006. Fast discrete curvelettransforms. SIAM, Multiscale Model. Simulat., 5: 861-899.
  9. Chen, W., Chen, Y. and Cheng, Z., 2017a. Seismic time-frequency analysis using animproved empirical mode decomposition algorithm. J. Seismic Explor., 26: 367-380.
  10. Chen, W., Chen, Y. and Liu, W., 2016a. Ground roll attenuation using improvedcomplete ensemble empirical mode decomposition. J. Seismic Explor., 25: 485-495.
  11. Chen, W. and Song, H., 2018. Automatic noise attenuation based on clustering andempirical wavelet transform. J. Appl. Geophys., 159: 649-665.
  12. Chen, W., Xie, J., Zu, S., Gan, S. and Chen, Y., 2017b. Multiple reflections noiseattenuation using adaptive randomized-order empirical mode decomposition. IEEEGeosci. Remote Sens. Lett., 14: 18-22.
  13. Chen, W., Yuan, J., Chen, Y. and Gan, S., 2017c. Preparing the initial model for iterativedeblending by median filtering. J. Seismic Explor., 26: 25-47.
  14. Chen, W., Zhang, D. and Chen, Y., 2017d. Random noise reduction using a hybridmethod based on ensemble empirical mode decomposition. J. Seismic Explor., 26:227-249.
  15. Chen, Y., 2017. Fast dictionary learning for noise attenuation of multidimensionalseismic data. Geophys. J. Internat., 209: 21-31.
  16. Chen, Y., 2018a,. Automatic velocity analysis using high-resolution hyperbolic Radontransform. Geophysics, 83(4): A53-A57.
  17. Chen, Y., 2018b. Fast waveform detection for microseismic imaging using unsupervisedmachine learning. Geophys. J. Internat.,, 215: 1185-1199.
  18. Chen, Y. and Fomel, S., 2015. Random noise attenuation using local signal-and-noiseorthogonalization. Geophysics, 80(6): WD1-WD9.
  19. Chen, Y. and Fomel, S., 2018. EMD-seislet transform. Geophysics, 83(1): A27-A32.
  20. Chen, Y., Fomel, S. and Hu, J., 2014. Iterative deblending of simultaneous-sourceseismic data using seislet-domain shaping regularization. Geophysics, 79(5):V179-V189.
  21. 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.
  22. Chen, Y. and Ma, J., 2014. Random noise attenuation by f-x empirical modedecomposition predictive filtering. Geophysics, 79(3): V81-V91.
  23. Chen, Y., Ma, J. and Fomel, S., 2016b. Double-sparsity dictionary for seismic noiseattenuation. Geophysics, 81(2): V17-V30.
  24. Chen, Y., Zhang, D., Jin, Z., Chen, X., Zu, S., Huang, W. and Gan, S., 2016c.
  25. Simultaneous denoising and reconstruction of 5D seismic data via dampedrank-reduction method. Geophys. J. Internat.,, 206: 1695-1717.
  26. Chen, Y., Zu, S., Wang, Y. and Chen, X., 2019. Deblending of simultaneous-source datausing a structure-oriented space-varying median filter. Geophys. J. Internat., 216:1214-1232.
  27. Dragomiretskiy, K. and Zosso, D., 2014. Variational mode decomposition. IEEETransact. Sign. Process., 62: 531-544.
  28. Fomel, S. and Liu, Y., 2010. Seislet transform and seislet frame. Geophysics, 75(3):V25-V38.
  29. Gan, S., Wang, S., Chen, Y. and Chen, X., 2016a. Simultaneous-source separation usingiterative seislet-frame thresholding. IEEE Geosci. Remote Sens. Lett., 13: 197-201.
  30. Gan, S., Wang, S., Chen, Y., Chen, X., Huang, W. and Chen, H., 2016b. Compressivesensing for seismic data reconstruction via fast projection onto convex sets based onseislet transform. J. Appl. Geophys., 130: 194-208.
  31. Gan, S., Wang, S., Chen, Y., Chen, X. and Xiang, K., 2016c. Separation of simultaneoussources using a structural-oriented median filter in the flattened dimension. Comput.Geosci., 86: 46-54.
  32. Gan, S., Wang, S., Chen, Y., Qu, S. and Zu, S., 2016d. Velocity analysis of simultaneous-source data using high-resolution semblance-coping with the strong noise. Geophys.J. Internat., 204: 768-779.
  33. Gan, S., Wang, S., Chen, Y., Zhang, Y. and Jin, Z., 2015. Dealiased seismic datainterpolation using seislet transform with low-frequency constraint. IEEE Geosci.Remote Sens. Lett., 12: 2150-2154.
  34. Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.-C., Tung,
  35. C.C. and Liu, H.H., 1998. The empirical mode decomposition and the Hilbertspectrum for nonlinear and non-stationary time series analysis. Proc. Roy. Soc.London, Series A, 454: 903-995.
  36. Huang, W., Wang, R., Chen, X. and Chen, Y., 2017a. Double least squares projectionsmethod for signal estimation. IEEE Transact. Geosci. Remote Sens., 55: 4111-4129.
  37. Huang, W., Wang, R., Yuan, Y., Gan, S. and Chen, Y., 2017b. Signal extraction usingrandomized-order multichannel singular spectrum analysis. Geophysics, 82(2): V59-V74.
  38. Huang, W., Wang, R., Zhang, D., Zhou, Y., Yang, W. and Chen, Y., 2017c.
  39. Mathematical morphological filtering for linear noise attenuation of seismic data.Geophysics, 82(6): V369-V384.
  40. Huang, W., Wang, R., Zu, S. and Chen, Y., 2017d. Low-frequency noise attenuation inseismic and microseismic data using mathematical morphological filtering. Geophys.J. Internat., 211: 1318-1340.
  41. Huang, W., Wang, R. and Chen, Y., 2018. Regularized non-stationary morphologicalreconstruction algorithm for weak signal detection in micro-seismic monitoring.
  42. Methodology. Geophys. J. Internat., 213: 1189-1211.
  43. Li, H., R. Wang, S. Cao, Y. Chen, and W. Huang, 2016a. A method for low-frequencynoise suppression based on mathematical morphology in microseismic monitoring.Geophysics, 81(3): V159-V 167.
  44. 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.
  45. Liu, G., Chen, X., Du, J. and Wu, K., 2012. Random noise attenuation using f-xregularized nonstationary autoregression. Geophysics, 77(2): V61-V69.
  46. Liu, W., Cao, S. and Chen, Y., 2016a. Applications of variational mode decomposition inseismic time-frequency analysis. Geophysics, 81(5): V365-V378.
  47. Liu, W., Cao, S. and Chen, Y., 2016b. Seismic time-frequency analysis via empiricalwavelet transform. IEEE Geosci. Remote Sens. Lett., 13: 28-32.
  48. Liu, W., Cao, S., Gan, S., Chen, Y., Zu, S. and Jin, Z., 2016c. One-step slope estimationfor dealiased seismic data reconstruction via iterative seislet thresholding. IEEEGeosci. Remote Sens. Lett., 13: 1462-1466.
  49. Liu, W., Cao, S., Jin, Z., Wang, Z. and Chen, Y., 2018. A novel hydrocarbon detectionapproach via high-resolution frequency-dependent AVO inversion based onvariational mode decomposition. IEEE Transact. Geosci. Remote Sens., 56: 2007-
  50. 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.
  51. Lv, H. and Bai, M., 2018. Learning dictionary in the approximately flattened structuredomain. J. Appl. Geophys., 159: 522-531.
  52. Rubinstein, R., Zibulevsky, M. and Elad, M., 2008. Efficient implementation of the
  53. K-SVD algorithm using batch orthogonal matching pursuit. Techn. Rep.
  54. Siahsar, M.A.N., Abolghasemi, V. and Chen, Y., 2017a. Simultaneous denoising andinterpolation of 2D seismic data using data-driven non-negative dictionary learning.Sign. Process., 141: 309-321.
  55. Siahsar, M.A.N., Gholtashi, S., Kahoo, A.R., Chen, W. and Chen, Y., 2017b. Data-drivenmulti-task sparse dictionary learning for noise attenuation of 3D seismic data.Geophysics, 82(6): V385-V396.
  56. Siahsar, M.A.N., Gholtashi, S., Olyaei, E., Chen, W. and Chen, Y., 2017c. Simultaneousdenoising and interpolation of 3D seismic data via damped data-driven optimalsingular value shrinkage. IEEE Geosci. Remote Sens. Lett., 14: 1086-1090.
  57. Vautard, R., Yiou, P. and Ghil, M., 1992. Singular-spectrum analysis: A toolkit for short,noisy chaotic signals. Physica D: Nonlin. Phenom., 58: 95-126.
  58. Wang, Y., Ma, X., Zhou, H. and Chen, Y., 2018. L1-2 minimization for exact and stableseismic attenuation compensation. Geophys. J. Internat., 213: 1629-1646.
  59. 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.
  60. Wu, J. and Bai, M., 2018a. Attenuating seismic noise via incoherent dictionary learning. J.Geophys. Engineer., 15: 1327.
  61. Wu, J. and Bai, M., 2018b. Fast principal component analysis for stacking seismic data. J.Geophys. Engineer., 15: 295-306.
  62. Wu, J. and Bai, M., 2018c. Incoherent dictionary learning for reducing crosstalk noise inleast-squares reverse time migration. Comput. Geosci., 114: 11-21.
  63. Wu, J. and Bai, M., 2018d. Stacking seismic data based on principal component analysis.J. Seismic Explor., 27: 331-348.
  64. Xue, Y., Chang, F., Zhang, D. and Chen, Y., 2016. Simultaneous sources separation viaan iterative rank-increasing method. IEEE Geosci. Remote Sens. Lett., 13:1915-1919.
  65. Zhang, D., Chen, Y., Huang, W. and Gan, S., 2016. Multi-step damped multichannelsingular spectrum analysis for simultaneous reconstruction and denoising of 3Dseismic data. J. Geophys. Engineer., 13: 704-720.
  66. Zhang, D., Zhou, Y., Chen, H., Chen, W., Zu, S. and Chen, Y., 2017. Hybridrank-sparsity constraint model for simultaneous reconstruction and denoising of 3Dseismic data. Geophysics, 82(5): V351-V367.
  67. Zu, S., Zhou, H., Chen, Y., Qu, S., Zou, X., Chen, H. and Liu, R., 2016. A periodicallyvarying code for improving deblending of simultaneous sources in marineacquisition. Geophysics, 81(3): V213-V225.
  68. 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.
  69. Zu, S., Zhou, H., Li, Q., Chen, H., Zhang, Q., Mao, W. and Chen, Y., 2017b.
  70. Shot-domain deblending using least-squares inversion. Geophysics, 82(4):V241-V256.
  71. 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.
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