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Seismic random noise attenuation using multi-scale sparse dictionary learning

JINWEI FANG1 ,  LIANG ZHANG2* ,  HUI ZHOU3 ,  SHENGDONG LIU1 ,  BO WANG1 ,  WENJIE CHEN4
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1 State Key Laboratory of Deep Geomechanics & Underground Engineering, School of Resource and Earth Science, School of Mechanics and Civil Engineering, China University of Mining and Technology, Xuzhou, Jiangsu 221116, P.R. China,
2 Institute of Geosciences and Info-Physics, Central South University, Changsha, Hunan, 410083, P.R. China,
3 State Key Laboratory of Petroleum Resources and Prospecting, CNPC Key Lab of Geophysical Exploration, China University of Petroleum, Changping 102249, Beijing, P.R. China,
4 College of Information Technology, Guangxi Police College, Nanning, Guangxi 530028, P.R. China,
JSE 2022, 31(2), 177–202;
Submitted: 27 August 2021 | Accepted: 1 February 2022 | Published: 1 April 2022
© 2022 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

Fang, J.W., Zhang, L., Zhou, H., Liu, Z.D., Wang, B. and Chen, W.J., 2022. Seismic random noise attenuation using multi-scale sparse dictionary learning. Journal of Seismic Exploration, 31: 177-202. Seismic data contain random noise, which affects data processing and interpretation and possibly limits the use of seismic data in parameter building and attribute prediction. To effectively remove such noise, we develop a denoising workflow based on multi-scale sparse dictionary learning. The multi-scale sparse dictionary learning method decreases the complexity of data by two approaches. One is seismic data-sparse representation from the data domain to the wavelet domain by wavelet bases. The other is denoising by sparse dictionary learning in a certain frequency band without the noise effect from other frequency bands. Meanwhile, the wavelet transform can also reduce the dimension of the data, which ensures the computational efficiency of the proposed method. After analyzing the effects of sparse dictionary learning parameters on seismic data denoising, we test the proposed method on two synthetic and two field datasets. We learn from the examples that our approach can effectively recover signals from simulated and real noisy data, as well as time-variant noisy data. Compared with the sparse dictionary learning, K-singular value decomposition dictionary learning, and double-sparsity dictionary (DSD), our method can obtain the best-denoised result with less effective signals leaking in noise sections.

Keywords
sparse dictionary learning
wavelet transform
multi-scale denoising
References
  1. Aharon, M., Elad, M. and Bruckstein, A., 2006. K-SVD: An algorithm for designingover-complete dictionaries for sparse representation. IEEE Transact. Signal Process.,54: 4311-4322.
  2. Bao, C., Ji, H. and Shen, Z., 2015. Convergence analysis for iterative data-driven tightframe construction scheme. Appl. Computat. Harmon. Analys., 38: 510-523.
  3. Beckouche, S. and Ma, J., 2014. Simultaneous dictionary learning and denoising forseismic data. Geophysics, 79(3): A27-A31.
  4. Bednar, J.B., 1983. Applications of median filtering to deconvolution, pulse estimation,and statistical editing of seismic data. Geophysics, 48: 1598-1610.
  5. Cai, J.F., Ji, H., Shen, Z. and Ye, G.B., 2014. Data-driven tight frame construction andimage denoising. Appl. Computat. Harmon. Analys., 37: 89-105.
  6. Chen, S.S., Donoho, D.L. and Saunders, M.A., 2001. Atomic decomposition by basispursuit. SIAM review, 43: 129-159.
  7. Chen, Y., 2017. Fast dictionary learning for noise attenuation of multidimensionalseismic data. Geophys. J. Internat., 209: 21-31.
  8. Chen, Y., Ma, J. and Fomel, S., 2016. Double sparsity dictionary for seismic noiseattenuation. Geophysics, 81(2): V103-V116.
  9. Daubechies, I., 1992. Ten lectures on wavelets. CBMS-NSF Regional Conference Seriesin Applied Mathematics, 61. SIAM Publications, Philadelphia.
  10. Daubechies, I, Han, B., Ron, A. and Shen, Z., 2003. Framelets: MRA-basedconstructions of wavelet frames. Appl. Computat. Harmon. Analys., 14: 1-46.
  11. Donoho D.L., 1995. Denoising by soft-thresholding. IEEE Transact. Informat. Theory,41: 613-627.
  12. Fomel, S., Sava, P., Vlad, IL, Liu, Y. and Bashkardin, V., 2013. Madagascar:
  13. Open-source software project for multidimensional data analysis and reproduciblecomputational experiments. J. Open Res. Softw., 1, e8.
  14. Feng, Z., 2021. Seismic random noise attenuation using effective and efficient dictionarylearning. J. Appl. Geophys., 104258.
  15. Hennenfent, G. and Herrmann, F.J., 2006. Seismic denoising with nonuniformly sampledcurvelets. Comput. Sci. Engineer., 8(3): 16-25.
  16. Ioup, J.W. and Ioup, G.E., 1998. Noise removal and compression using a wavelettransform. Expanded Abstr., 68th Ann. Internat. SEG Mtg. New Orleans:1076-1079.
  17. Jawerth, B. and Sweldens, W., 1994. An overview of wavelet based multiresolutionanalyses. SIAM review, 36: 377-412.
  18. Liang, J., Ma, J. and Zhang, X., 2014. Seismic data restoration via data-driven tightframe. Geophysics, 79(3): V65-V74.
  19. Liu, C., Song, C. and Lu, Q., 2017. Random noise de-noising and direct waveeliminating based on SVD method for ground penetrating radar signals. J. Appl.Geophys., 144: 125-133.
  20. Lopes, M.E., 2016. Unknown sparsity in compressed sensing: Denoising and inference.IEEE Transact. Informat. Theory, 62: 5145-5166.
  21. Lv, H. and Bai, M., 2018. Learning dictionary in the approximately flattened structuredomain. J. Appl. Geophys., 159: 522-531.
  22. Mallat, S., 1999. A Wavelet Tour of Signal Processing. Elsevier Science Publishers,Amsterdam.
  23. Margrave, G.F., 1998. Theory of nonstationary linear filtering in the Fourier domain withapplication to time-variant filtering. Geophysics, 63: 244-259.
  24. Miao, X. and Cheadle, S., 1998. Noise attenuation with wavelet transforms. Expanded
  25. Abstr., 68th Ann. Internat. SEG Mtg., New Orleans: 1072-1075.
  26. Naghizadeh, M. and Sacchi, M.D., 2010. Beyond alias hierarchical scale curveletinterpolation of regularly and irregularly sampled seismic data. Geophysics, 75(6):WB189-WB202.
  27. Neelamani, R., Baumstein, A. and Ross, W.S., 2010. Adaptive subtraction usingcomplex-valued curvelet transforms. Geophysics, 75(4): V51-V60.
  28. Ophir, B., Lustig, M. and Elad, M., 2011. Multi-scale dictionary learning using wavelets.
  29. IEEE J. Select. Topics Sign. Process., 5: 1014-1024.
  30. Rubinstein, R., Zibulevsky, M. and Elad, M., 2009. Double sparsity: learning sparsedictionaries for sparse signal approximation. IEEE Transact. Signal Process., 58:1553-1564.
  31. Schnemann, P.H., 1966. A generalized solution of the orthogonal procrustes problem.Psychometrika, 31: 1-10.
  32. Sezer, O.G., Guleryuz, O.G. and Altunbasak, Y., 2015. Approximation and compressionwith sparse orthonormal transforms. IEEE Transact. Image Process., 24: 2328-2343.
  33. Strang, G. and Nguyen, T., 1996. Wavelets and Filter Banks, Wellesley-CambridgePress, New York.
  34. Tropp, J.-A. and Gilbert, A.C., 2007. Signal recovery from random measurements viaorthogonal matching pursuit. IEEE Transact. Informat. Theory, 53: 4655-4666.
  35. Turquais, P., Asgedom, E.G. and Sdéllner, W., 2017. A method of combiningcoherence-constrained sparse coding and dictionary learning for denoising.Geophysics, 82(3): V137-V148.
  36. Wang, D., Saab, R., Yilmaz, O. and Herrmann, F.J., 2008. Bayesian wavefield separationby transform-domain sparsity promotion. Geophysics, 73(5): A33-A38.
  37. Yilmaz, O. and Donherty, S., 2001. Seismic Data Analysis. SEG, Tulsa, OK.
  38. Yu, S., Ma, J. and Osher, S., 2016. Monte Carlo data-driven tight frame for seismic datarecovery. Geophysics, 81(4): V327-V340.
  39. Yu, S., Ma, J., Zhang, X. and Sacchi, M.D., 2015. Interpolation and denoising ofhigh-dimensional seismic data by learning a tight frame. Geophysics, 80(5):V119-V132.
  40. Yuan, Y.O. and Simons, F.J., 2014. Multi-scale adjoint waveform-difference tomographyusing wavelets. Geophysics, 79(3): WA79-WA95.
  41. Zhan, R. and Dong, B., 2016. CT image reconstruction by spatial-Radon domaindata-driven tight frame regularization. SIAM J. Imaging Sci., 9: 1063-1083.
  42. Zhang, R. and Ulrych, T.J., 2003. Physical wavelet frame denoising. Geophysics, 68:225-231.
  43. Zhang, Q., Wang, H., Chen, W. and Huang, G., 2020. A robust method for random noisesuppression based on the Radon transform. J. Appl. Geophys., 104183.
  44. Zhu, L., Liu, E. and Mcclellan, J.H., 2015. Seismic data denoising through multiscaleand sparsity-promoting dictionary learning. Geophysics, 80(6): WD45-WD57.
  45. Zu, S., Zhou, H., Wu, R., Jiang, M. and Chen, Y., 2019. Dictionary learning based on dippatch selection training for random noise attenuation. Geophysics, 84(3):V169-V183.
  46. Zu, S., Zhou, H., Wu, R., Mao, W. and Chen, Y., 2018. Hybrid-sparsity constraineddictionary learning for iterative deblending of extremely noisy simultaneous-sourcedata. IEEE Transact. Geosci. Remote Sens., 57: 2249-2262.
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Journal of Seismic Exploration, Electronic ISSN: 0963-0651 Print ISSN: 0963-0651, Published by AccScience Publishing
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