ARTICLE

Desert seismic exploration low-frequency noise attenuation based on improved co-sparse analysis model

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Department of Information, Jilin University, Changchun 130012, P.R. China,
Department of Geophysics, Jilin University, Changchun 130026, P.R. China,
JSE 2020, 29(6), 505–526;
Submitted: 9 June 2025 | Revised: 9 June 2025 | Accepted: 9 June 2025 | Published: 9 June 2025
© 2025 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

Zhao, Y., Li, Y., Shao, D. and Yang, B.J., 2020. Desert seismic exploration low-frequency noise attenuation based on improved co-sparse analysis model. Journal of Seismic Exploration, 29: 505-525. Desert low-frequency noise is a kind of noise in desert seismic exploration records, with significant low-frequency characteristics. Severe frequency aliasing occurs because the noise is in the same frequency band as the seismic signal. In addition, the interference noise has strong energy over whole time period of seismic records, which makes the signal easily submerged in the noise. These characteristics of desert low-frequency noise challenge traditional denoising methods. Aiming at the noise attenuation of low signal-to-noise ratio (SNR) seismic exploration records in desert areas, first half-quadratic optimization approach is proposed to solve the energy minimization problem instead of common optimization methods in the co-sparse analysis model. And then, shrinkage function is introduced into the model by the additive form of half-quadratic optimization, which makes the model remove the restriction of sparsity promoting function. Finally, according to the characteristics of seismic exploration records, the training data are normalized and then trained. Both the synthetic and real data experiments prove that the improved model can better overcome the frequency aliasing and more thoroughly remove the low-frequency noise compared with the traditional co-sparse analysis model.

Keywords
co-sparse analysis model
half-quadratic optimization
shrinkage function
desert low-frequency noise
noise attenuation
References
  1. Candés, E.J. and Donoho, D.L., 2010. New tight frames of curvelets and optimal
  2. representations of objects with piecewise C2 singularities. Communicat. Pure Appl.
  3. Mathemat., 57: 219-266.
  4. Chen, Y., Ranftl, R. and Pock, T., 2014. Insights into analysis operator learning: from
  5. patch-based sparse models to higher order MRFs. IEEE Transact. Image Process.,
  6. 23:1060-72.
  7. Elad, M. and Aharon, M., 2006. Image denoising via sparse and redundant
  8. representations over learned dictionaries. IEEE Transact. Image Process., 15:
  9. 3736-3745.
  10. Elad, M., Milanfar, P. and Rubinstein, R., 2007. Analysis versus synthesis in signal
  11. priors. Inverse Probl., 23: 947-968.
  12. Emmanuel, J.C. and Donoho, D.L., 2000. Curvelets and reconstruction of images from
  13. noisy radon data. Proc. SPIE - The International Society for Optical Engineering,
  14. Geman, D. and Yang, C., 1994. Nonlinear image recovery with half-quadratic
  15. regularization. IEEE Transact. Image Process., 4: 932.
  16. Geman, D. and Reynolds, G., 2002. Constrained restoration and the recovery of
  17. discontinuities. IEEE Transact. Patt. Analys. Mach. Intellig., 14: 367-383.
  18. Gilboa, G., Sochen, N. and Zeevi, Y.Y., 2004. Image enhancement and denoising by
  19. complex diffusion processes. IEEE Transact. Patt. Analys. Mach. Intellig., 26: 1036.
  20. Harris, PE. and White, R.E., 2010. Improving the performance of fx prediction filtering
  21. at low signal-to-noise ratios. Geophys. Prosp., 45: 269-302.
  22. Hawe, S. , Kleinsteuber, M. and Diepold, K., 2013. Analysis operator learning and its
  23. application to image reconstruction. IEEE Transact. Image Process., 22: 2138-2150.
  24. Hel-Or, Y. and Shaked, D., 2008. A discriminative approach for wavelet denoising.
  25. IEEE Transact. Image Process., 17: 443-457.
  26. Krishnan, D. and Fergus, R., 2009. Dark flash photography. ACM Transact. Graphics, 28:
  27. Li, G., Yue, L. and Yang, B., 2017. Seismic exploration random noise on land: modeling
  28. and application to noise suppression. IEEE Transact. Geosci. Remote Sens., 99:
  29. Liu, D.C. and Nocedal, J., 1989. On the limited memory BFGS method for large scale
  30. optimization. Mathemat. Progr., 45: 503-528.
  31. Ma, J., Plonka, G. and Chauris, H., 2010. A new sparse representation of seismic data
  32. using adaptive easy-path wavelet transform. IEEE Geosci. Remote Sens. Lett., 7:
  33. 540-544.
  34. Pati, Y.C., Rezaiifar, R. and Krishnaprasad, P.S., 2002. Orthogonal matching pursuit:
  35. recursive function approximation with applications to wavelet decomposition.
  36. Conf. Signals, Systems Computing, Pacific Grove, CA.
  37. Rodriguez, LV., Sacchi, M.D. and Gu, Y.J., 2012. Simultaneous recovery of origin time,
  38. hypocentre location and seismic moment tensor using sparse representation theory.
  39. Geophys. J. Internat., 188:1188-1202.
  40. Roth, S. and Black, M.J., 2009. Fields of experts. Internat. J. Comput. Vis., 82: 205.
  41. Schmidt, U. and Roth, S., 2014. Shrinkage Fields for Effective Image Restoration. IEEE
  42. Conf. Computer Vision Pattern Recognition (CVPR). IEEE Computer Society,
  43. Hilton Head Island, SC.
  44. Thode, H.C., 1987. Statistical tools for simulation practitioners. Technometr., 30:
  45. 464-464.
  46. Wang, Y., Yang, J., Yin, W. and Zhang, Y., 2016. A new alternating minimization
  47. algorithm for total variation image reconstruction. SIAM J. Imag. Sci., 1: 248-272.
  48. Yaghoobi, M., Nam, S., Gribonval, R. and Davies, M., 2012. Noise aware analysis
  49. operator learning for approximately cosparse signals. IEEE Internat. Conf. Acoustics,
  50. Speech and Signal Processing ICASSP), Kyoto: 5409-5412.
  51. Yaghoobi, M., Nam, S., Gribonval, R. and Davies, M., 2015. Analysis operator learning
  52. for overcomplete cosparse representations. 19th European Signal Processing IEEE
  53. Conf., Barcelona: 1470-1474.
  54. Zhong, T., Li, Y., Wu, N., Nie, P. and Yang, B., 2015. Statistical analysis of background
  55. noise in seismic prospecting. Geophys. Prosp., 63: 1161-1174.
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Journal of Seismic Exploration, Electronic ISSN: 0963-0651 Print ISSN: 0963-0651, Published by AccScience Publishing