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Sparse dictionary learning for seismic noise attenuation using a fast orthogonal matching pursuit algorithm

YATONG ZHOU1 ,  SHUHUA LI1 ,  JIANYONG XIE2 ,  DONG ZHANG2 ,  YANGKANG CHEN3
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1 School of Electronic and Information Engineering, Hebei University of Technology, Xiping Road 5340, Beichen District, Tianjin 300401, P.R. China. zyt_htu@126.com,
2 State Key Laboratory of Petroleum Resources and Prospecting University of Petroleum-Beijing, Fuxue Road 18, Beijing 102249, P.R. China. xjyshl@sina.com; zhangdongconan@163.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. ykchen@utexas.edu,
JSE 2017, 26(5), 433–454;
Submitted: 9 November 2016 | Accepted: 26 July 2017 | Published: 1 October 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/ )
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Abstract

Zhou, Y., Li, S., Xie, J., Zhang, D. and Chen, Y., 2017. Sparse dictionary learning for seismic noise attenuation using a fast orthogonal matching pursuit algorithm. Journal of Seismic Exploration, 26: 433-454. Attenuation of random noise is a long-standing problem in seismic data processing. One of the most widely used approaches is based on sparse transforms. In the geophysics community, most of the currently used sparse transforms have fixed bases, which we call analytical transforms. In this paper, we seek a different type of sparse transform, with variant bases, to attenuate random noise. We call this type of transform dictionary learning-based (DLB) sparse transforms, because it can adaptively train a sparse dictionary from the observed data to adapt to different seismic data. To increase the efficiency of sparse dictionary learning, we propose to apply a fast orthogonal matching pursuit (OMP) algorithm for sparse coding. We use both synthetic and field data examples to show the superior performance of the dictionary learning-based transform over fixed-basis transforms, and much improved efficiency in sparse coding associated with the fast OMP algorithm, which is one of the two steps in the DLB transform.

Keywords
sparse dictionary learning
denoising
fast OMP algorithm
References
  1. Aharon, M., Elad, M. and Bruckstein, A.M., 2006. The K-SVD: An algorithm for designing ofever-complete dictionaries for sparse representation. IEEE Transact. Sign. Process., 54:4311-4322.
  2. Bekara, M. and van der Baan, M., 2007. Local singular value decomposition for signal enhancementof seismic data. Geophysics, 72: V59-V65.
  3. Canales, L., 1984. Random noise reduction. Expanded Abstr., 54th Ann. Internat. SEG Mtg.,Atlanta: 525-527.
  4. Chen, Y., 2015a. Deblending using a space-varying median filter. Explor. Geophys., 46: 332-341.
  5. Chen, Y., 2015b. Iterative deblending with multiple constraints based on shaping regularization.IEEE Geosci. Remote Sens. Lett., 12: 2247-2251.
  6. Chen, Y., 2016. Dip-separated structural filtering using seislet thresholding and adaptive empiricalmode decomposition based dip filter. Geophys. J. Internat., 206: 457-469.452 ZHOU, LI, XIE, ZHANG & CHEN
  7. Chen, Y., 2017. Fast dictionary learning for noise attenuation of multidimensional seismic data.Geophys. J. Internat., 209: 21-31.
  8. Chen, Y. and Fomel, S., 2015a. EMD-seislet transform. Expanded Abstr., 85th Ann. Internat. SEGMtg., New Orleans: 4775-4778.
  9. Chen, Y. and Fomel, S., 2015b. Random noise attenuation using local signal-and-noiseorthogonalization. Geophysics, 80: WD1-WD9.
  10. Chen, Y., Fomel, S. and Hu, J., 2014a. Iterative deblending of simultaneous-source seismic datausing seislet-domain shaping regularization. Geophysics, 79: V179-V189.
  11. Chen, Y. and Jin, Z., 2015. Simultaneously removing noise and increasing resolution of seismic datausing waveform shaping. IEEE Geosci. Remote Sens. Lett., 13: 102-104.
  12. Chen, Y., Jin, Z., Gan, S., Yang, W., Xiang, K., Bai, M. and Huang, W., 2015. Iterativedeblending using shaping regularization with a combined PNMO-MF-FK coherency filter.J. Appl. Geophys., 122: 18-27.
  13. Chen, Y. and Ma, J., 2014. Random noise attenuation by f-x empirical mode decompositionpredictive filtering. Geophysics, 79: V81-V91.
  14. Chen, Y., Ma, J. and Fomel, S., 2016a. Double-sparsity dictionary for seismic noise attenuation.Geophysics, 81: V17-V30.
  15. Chen, Y., Zhang, D., Huang, W. and Chen, W., 2016b. An open-source matlab code package forimproved rank-reduction 3D seismic data denoising and reconstruction. Comput. Geosci.,95: 59-66.
  16. Chen, Y., Zhang, D., Jin, Z., Chen, X., Zu, S., Huang, W. and Gan, S., 2016c. Simultaneousdenoising and reconstruction of 5D seismic data via damped rank-reduction method.Geophys. J. Internat., 206: 1695-1717.
  17. 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.
  18. Duijndam, A.J., Schonewille, M.A. and Hindriks, C.O.H., 1999. Reconstruction of band-limitedsignals, irregularly sampled along one spatial direction. Geophysics, 64: 524-538.
  19. Fomel, S. and Liu, Y., 2010. Seislet transform and seislet frame. Geophysics, 75: V25-V38.
  20. Gan, S., Chen, Y., Zu, S., Qu, S. and Zhong, W., 2015a. Structure-oriented singular valuedecomposition for random noise attenuation of seismic data: J. Geophys. Engineer., 12: 262-
  21. Gan, S., Wang, S., Chen, Y., Zhang, Y. and Jin, Z., 2015b. Dealiased seismic data interpolationusing seislet transform with low-frequency constraint. IEEE Geosci. Remote Sens. Lett., 12:2150-2154.
  22. Gan, S., Chen, Y., Wang, S., Chen, X., Huang, W. and Chen, H., 2016a. Compressive sensingfor seismic data reconstruction using a fast projection onto convex sets algorithm based onthe seislet transform. J. Appl. Geophys., 130: 194-208.
  23. Gan, S., Wang, S., Chen, Y., Chen, J., Zhong, W. and Zhang, C., 2016b. Improved random noiseattenuation using f-x empirical mode decomposition and local similarity. Appl. Geophys. ,13: 127-134.
  24. Gan, S., Wang, S., Chen, Y. and Chen, X., 2016c. Simultaneous-source separation using iterativeseislet-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 simultaneous sourcesusing 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 of simultaneous-sourcedata using high-resolution semblance-coping with the strong noise. Geophys. J. Internat.,204: 768-779.
  27. Hennenfent, G. and Herrmann, F., 2006. Seismic denoising with nonunformly sampled curvelets.Comput. Sci. Engineer., 8: 16-25.
  28. Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.-C., Tung, 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, 454:903-995.SPARSE DICTIONARY LEARNING 453
  29. Huang, W., Wang, R., Chen, Y., Li, H. and Gan, S., 2016a. Damped multichannel singularspectrum analysis for 3D random noise attenuation. Geophysics, 81: V261-V270.
  30. Huang, W., Wang, R., Chen, Y., Yuan, Y. and Zhou, Y., 2016b. Randomized-order multi-channelsingular spectrum analysis for simultaneously attenuating random and coherent noise.
  31. Expanded Abstr., 86th Ann. Internat. SEG Mtg., Dallas: 4777-4781.
  32. Huang, W., Wang, R., Yuan, Y., Gan, S. and Chen, Y., 2017. Signal extraction usingrandomized-order multichannel singular spectrum analysis. Geophysics, 82(2): V59-V74.
  33. Huang, W., Wang, R., Zhang, M., Chen, Y. and Yu, J., 2015. Random noise attenuation for 3Dseismic data by modified multichannel singular spectrum analysis. Extended Abstr., 77th
  34. EAGE Conf., Madrid. DOI.10.3997/2214- 4609.201412830.
  35. Huang, W., Wang, R., Zhou, Y., Chen, Y. and Yang, R., 2016c. Improved principal componentanalysis for 3D seismic data simultaneous reconstruction and denoising. Expanded Abstr. ,86th Ann. Internat. SEG Mtg., Dallas.
  36. Kaplan, S.T., Sacchi, M.D. and Ulrych, T.J., 2009. Sparse coding for data-driven coherent andincoherent noise attenuation. Expanded Abstr., 79th Ann. Internat. SEG Mtg., Houston:3327-3331.
  37. Kong, D. and Peng, Z., 2015. Seismic random noise attenuation using shearlet and total generalizedvariation. J. Geophys. Engineer., 12: 1024-1035.
  38. Kong, D., Peng, Z., He, Y. and Fan, H., 2016. Seismic random noise attenuation using directionaltotal variation in the shearlet domain. J. Seismic Explor., 25: 321-338.
  39. 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.
  40. Liu, G., Fomel, S., Jin, L. and Chen, X., 2009a. Stacking seismic data using local correlation.Geophysics, 74: V43-V48.
  41. Liu, Y., Liu, C. and Wang, D., 2009b. A 1D time-varying median filter for seismic random,spike-like noise elimination. Geophysics, 74: V17-V24.
  42. Liu, C., Wang, D., Hu, B. and Wang, T., 2016a. Seismic deconvolution with shearlet sparsityconstrained inversion. J. Seismic Explor., 25: 433-445.
  43. Liu, W., Cao, S. and Chen, Y., 2016b. Applications of variational mode decomposition in seismictime-frequency analysis. Geophysics, 81: V365-V378.
  44. Liu, W., Cao, S. and Chen, Y., 2016c. Seismic time-frequency analysis via empirical wavelettransform. IEEE Geosci. Remote Sens. Lett., 13: 28-32.
  45. Liu, W., Cao, S., Chen, Y. and Zu, S., 2016d. An effective approach to attenuate random noisebased on compressive sensing and curvelet transform. J. Geophys. Engineer., 13: 135-145.
  46. Liu, W., Cao, S., Liu, Y. and Chen, Y., 2016e. Synchrosqueezing transform and its applicationsin seismic data analysis. J. Seismic Explor., 25: 27-44.
  47. Naghizadeh, M., 2012. Seismic data interpolation and denoising in the frequency-wavenumberdomain. Geophysics, 77: V71-V80.
  48. Neelamani, R., Baumstein, A., Gillard, D., Hadidi, M. and Soroka, W., 2008. Coherent andrandom noise attenuation using the curvelet transform. The Leading Edge, 27: 240-248.
  49. Rubinstein, R., Zibulevsky, M. and Elad, M., 2008. Efficient implementation of the k-svd algorithmusing batch orthogonal matching pursuit. Tech. Rep. Comput. Sci. Dept., Technion IsraelInst. of Technol.
  50. Shahidi, R., Tang, R., Ma, J. and Herrmann, F.J., 2013. Application of randomized samplingschemes to curvelet-based sparsity-promoting seismic data recovery. Geophys. Prosp., 61:973-997.
  51. Wang, D., Liu, C., Liu, Y. and Liu, G., 2008. Application of wavelet transform based on liftingscheme and percentiles soft-threshold to elimination of seismic random noise. Progr.Geophys. (in Chinese), 23: 1124-1130.
  52. Wang, J., Ng, M. and Perz, M., 2010. Seismic data interpolation by greedy local Radon transform.Geophysics, 75: WB225-WB234.
  53. Wu, J., Wang, R., Chen, Y., Zhang, Y., Gan, S. and Zhou, C., 2016. Multiples attenuation usingshaping regularization with seislet domain sparsity constraint. J. Seismic Explor., 25: 1-9.454 ZHOU, LI, XIE, ZHANG & CHEN
  54. Xue, Y., Chang, F., Zhang, D. and Chen, Y., 2016a. Simultaneous sources separation via aniterative rank-increasing method. IEEE Geosci. Remote Sens. Lett., 13: 1915-1919.
  55. Xue, Y., Yang, J., Ma, J. and Chen, Y., 2016b. Amplitude-preserving nonlinear adaptive multipleattenuation using the high-order sparse Radon transform. J. Geophys. Engineer., 13: 207-
  56. 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.
  57. Yang, W., Wang, R., Chen, Y. and Wu, J., 2014. Random noise attenuation using a new spectraldecomposition method. Expanded Abstr., 84th Ann. Internat. SEG Mtg., Denver: 4366-
  58. Yang, W., Wang, R., Chen, Y., Wu, J. Qu, S., Yuan, J. and Gan, S., 2015. Application ofspectral decomposition using regularized non-stationary autoregression to random noiseattenuation. J. Geophys. Engineer., 12: 175-187.
  59. Yu, Z., Ferguson, J., McMechan, G. and Anno, P., 2007. Wavelet-Radon domain dealiasing andinterpolation of seismic data. Geophysics, 72: V41-V49.
  60. Zhang, D., Chen, Y., Huang, W. and Gan, S., 2016a. Multi-step damped multichannel singularspectrum analysis for simultaneous reconstruction and denoising of 3D seismic data. J.Geophys. Engineer., 13: 704-720.
  61. Zhang, D., Chen, Y., Huang, W. and Gan, S., 2016b. Multi-step reconstruction of 3D seismic datavia an improved mass algorithm. Expanded Abstr., CPS/SEG Internat. Geophys. Conf. ,Beijing: 745-749.
  62. Zhong, W., Chen, Y., Gan, S. and Yuan, J., 2016. L,,. norm regularization for 3D seismic datainterpolation. J. Seismic Explor., 25: 257-268.
  63. Zu, S., Zhou, H., Chen, Y., Qu, S., Zou, X., Chen, H. and Liu, R., 2016a. A periodically varyingcode for improving deblending of simultaneous sources in marine acquisition. Geophysics,81: V213-V225.
  64. Zu, S., Zhou, H., Chen, Y., Chen, H., Cao, M. and Xie, C., 2016b. A marine field trial foriterative deblending of simultaneous sources. Expanded Abstr., 86th Ann. Internat. SEGMtg., Dallas: 113-118.
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Journal of Seismic Exploration, Electronic ISSN: 0963-0651 Print ISSN: 0963-0651, Published by AccScience Publishing
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