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Volume 72 Number 1
  • E-ISSN: 1365-2478

Abstract

Abstract

Ground roll is usually considered as a common linear noise in land seismic data. The existence of the ground roll often masks the effective reflection information of underground media, resulting in the deterioration of seismic data quality. Therefore, ground roll suppression is one of the main tasks in seismic data processing. A large number of previous studies have proved that the time‐frequency signal processing method based on mathematical transformation has shown excellent performance in ground roll attenuation and still has development potential. Meanwhile, a convolutional neural network, as one of the popular deep learning technologies, has also been widely used in the field of seismic signal processing. In this paper, we combine the convolutional neural network with the time‐frequency signal processing method based on mathematical transformation, that is, spatial domain synchrosqueezing wavelet transform, and propose a complete ground roll suppression workflow of shot gathers in spatial wavenumber domain, realizing high‐precision and automatic ground roll removal. Field data examples show that compared with bandpass filtering, FK filtering, time domain synchrosqueezing wavelet transform, spatial domain synchrosqueezing wavelet transform and the convolutional neural network, the spatial domain synchrosqueezing wavelet transform convolutional neural network has achieved satisfactory results in effectively attenuating ground roll and retaining valid information.

© 2024 European Association of Geoscientists & Engineers. © 2022 European Association of Geoscientists & Engineers.
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/content/journals/10.1111/1365-2478.13271
2023-12-18
2025-04-29

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References

  1. Akan, A. & Cura, O.Z. (2021) Time‐frequency signal processing: Today and future. Digital Signal Processing, 119, 103216. https://doi.org/10.1016/j.dsp.2021.103216
     [Google Scholar]
  2. Askari, R. & Siahkoohi, H.R. (2007) Ground roll attenuation using the S and x‐f‐k transforms. Geophysical Prospecting, 56(1), 105–114. https://doi.org/10.1111/j.1365‐2478.2007.00659.x
     [Google Scholar]
  3. Candès, E.J. & Donoho, D.L. (1999) Ridgelets: a key to higher‐dimensional intermittency. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 357(1760), 2495–2509. https://doi.org/10.1098/rsta.1999.0444.375
     [Google Scholar]
  4. Candès, E.J. & Donoho, D.L. (2004) New tight frames of curvelets and optimal representations of objects with piecewise C2 singularities. Communications on Pure and Applied Mathematics, 57(2), 219–266. https://doi.org/10.1002/cpa.10116
     [Google Scholar]
  5. Chen, W., Chen, Y. & Liu, W. (2016) Ground roll attenuation using improved complete ensemble empirical mode decomposition. Journal of Seismic Exploration, 25, 485–495.
     [Google Scholar]
  6. Chen, Y., Zhang, M., Bai, M. & Chen, W. (2019) Improving the signal‐to‐noise ratio of seismological datasets by unsupervised machine learning. Seismological Research Letters, 90, 1552–1564. https://doi.org/10.1785/0220190028
     [Google Scholar]
  7. Chen, Y. & Fomel, S. (2015) Random noise attenuation using local signal‐and‐noise orthogonalization. Geophysics, 80, WD1–WD9. https://doi.org/10.1190/geo2014‐0227.1
     [Google Scholar]
  8. Chen, Y., Jiao, S., Ma, J., Chen, H., Zhou, Y. & Gan, S. (2015) Ground‐roll noise attenuation using a simple and effective approach based on local band‐limited orthogonalization. IEEE Geoscience and Remote Sensing Letters, 12(11), 2316–2320. https://doi.org/10.1109/LGRS.2015.2475280
     [Google Scholar]
  9. Cunha, A., Pochet, A., Lopes, H. & Gattass, M. (2020) Seismic fault detection in real data using transfer learning from a convolutional neural network pre‐trained with synthetic seismic data. Computers & Geosciences, 135, 104344. https://doi.org/10.1016/j.cageo.2019.104344
     [Google Scholar]
  10. Daubechies, I., Lu, J. & Wu, H.‐T. (2011) Synchrosqueezed wavelet transforms: an empirical mode decomposition‐like tool. Applied and Computational Harmonic Analysis, 30(2), 243–261. https://doi.org/10.1016/j.acha.2010.08.002
     [Google Scholar]
  11. Daubechies, I. & Maes, S. (1996) A nonlinear squeezing of the continuous wavelet transform based on auditory nerve models. In: Aldroubi, A. & Unser, M. (Eds.) Wavelets in medicine and biology. New York: Routledge, pp. 527–544. https://doi.org/10.1371/journal.pone.0062234
     [Google Scholar]
  12. Deighan, A.J. & Watts, D.R. (1997) Ground‐roll suppression using the wavelet transform. Geophysics, 62(6), 1896–1903. https://doi.org/10.1190/1.1444290.
     [Google Scholar]
  13. Di, H. & Alregib, G. (2018) Seismic fault detection from Post‐stack amplitude by convolutional neural networks. In 80th EAGE Conference and Exhibition 2018, EAGE, Copenhagen, Denmark, pp. 1–5. https://doi.org/10.3997/2214‐4609.201800733
     [Google Scholar]
  14. Di, H., Shafiq, A., Wang, Z. & Alregib, G. (2019) Improving seismic fault detection by super‐attribute‐based classification. Geophysics, 7(3), SE251–SE267. https://doi.org/10.1190/INT‐2018‐0188.1
     [Google Scholar]
  15. Fomel, S. (2007) Local seismic attributes. Geophysics, 72, A29–A33. https://doi.org/10.1190/1.2437573
     [Google Scholar]
  16. Guo, Y., Peng, S., Du, W. & Li, D. (2021) Fault and horizon automatic interpretation by CNN: a case study of coalfield. Journal of Geophysics and Engineering, 17(6), 1016–1025. https://doi.org/10.1093/jge/gxaa060
     [Google Scholar]
  17. Halliday, D., Bilsby, P., West, L., Kargh, Ed & Quigley, J. (2015) Scattered ground‐roll attenuation using model‐driven interferometry. Geophysical Prospecting, 63(1), 116–132. https://doi.org/10.1111/1365‐2478.12165
     [Google Scholar]
  18. Hijazi, S., Kumar, R. & Rowen, C. (2015) Using convolutional neural networks for image recognition. San Jose, CA: Cadence Design Systems Inc.
     [Google Scholar]
  19. Hu, Y., Wang, L., Cheng, F., Lou, Y., Shen, C. & Mi, B. (2016) Ground‐roll noise extraction and suppression using high‐resolution linear Radon transform. Journal of Applied Geophysics, 128, 8–17. https://doi.org/10.1016/j.jappgeo.2016.03.007
     [Google Scholar]
  20. Jiao, S., Chen, Y., Bai, M., Yang, W., Wang, E. & Gan, S. (2015) Ground roll attenuation using non‐stationary matching filtering. Journal of Geophysics and Engineering, 12, 922–933. https://doi.org/10.1088/1742‐2132/12/6/922
     [Google Scholar]
  21. Kaur, H., Fomel, S. & Pham, N. (2020) Seismic ground‐roll noise attenuation using deep learning. Geophysical Prospecting, 68(7), 2064–2077. https://doi.org/10.1111/1365‐2478.12985
     [Google Scholar]
  22. Liu, W., Cao, S., Liu, Y. & Chen, Y. (2016) Synchrosqueezing transform and its applications in seismic data analysis. Journal of Seismic Exploration, 25(1), 27–44.
     [Google Scholar]
  23. Liu, X. (1999) Ground roll suppression using the Karhunen‐Loeve transform. Geophysics, 64, 564–566, https://doi.org/10.1190/1.1444562
     [Google Scholar]
  24. Liu, Y. & Fomel, S. (2013) Seismic data analysis using local time‐frequency decomposition. Geophysical Prospecting, 61(3), 516–525. https://doi.org/10.1111/j.1365‐2478.2012.01062.x
     [Google Scholar]
  25. Liu, Z., Chen, Y. & Ma, J. (2018) Ground roll attenuation by synchrosqueezed curvelet transform. Journal of Applied Geophysics, 151, 246–262. https://doi.org/10.1016/j.jappgeo.2018.02.016
     [Google Scholar]
  26. Melo, P.E.M., Porsani, M.J. & Silva, M.G. (2009) Ground‐roll attenuation using a 2D time‐derivative filter. Geophysical Prospecting, 57, 343–353. https://doi.org/10.1111/j.1365‐2478.2008.00740.x
     [Google Scholar]
  27. Pan, X., Cap, S., Zu, S., Xu, Y. & Sun, X. (2020) Synchroqueezed wavelet transform based groundroll suppression. Journal of Applied Geophysics, 179, 10433. https://doi.org/10.1016/j.jappgeo.2012.04.004
     [Google Scholar]
  28. Prasad, S. & Mahalanabis, A.K. (1980) Adaptive filter structures for deconvolution of seismic signal. IEEE Transaction on Geoscience and Remote Sensing, 18, 267–273. https://doi.org/10.1109/TGRS.1980.4307502
     [Google Scholar]
  29. Saad, O.M. & Chen, Y. (2020) Deep denoising autoencoder for seismic random noise attenuation. Geophysics, 85, V367–V376. https://doi.org/10.1190/geo2019‐0468.1
     [Google Scholar]
  30. Saad, O.M. & Chen, Y. (2021) A fully unsupervised and highly generalized deep learning approach for random noise suppression. Geophysical Prospecting, 69, 709–726. https://doi.org/10.1111/1365‐2478.13062
     [Google Scholar]
  31. Saatilar, R. & Canitez, N. (1988) A method of ground‐roll elimination. Geophysics, 53, 894–902. https://doi.org/10.1190/1.1442526
     [Google Scholar]
  32. Tan, Y., He, C., Wang, Y. & Zhao, Z. (2013) Ground roll attenuation using a time‐frequency dependent polarization filter based on the S transform. Applied Geophysics, 10(3), 279–294. https://doi.org/10.1007/s11770‐013‐0383‐3
     [Google Scholar]
  33. Tao, Y., Cao, S., Ma, Y. & Ma, M. (2020) Second‐order adaptive synchrosqueezing s transform and its application in seismic ground roll attenuation. IEEE Geoscience and Remote Sensing Letters, 17(8), 1308–1312. https://doi.org/10.1109/LGRS.2019.2946368
     [Google Scholar]
  34. Thakur, G., Brevdo, E., Fučkar, N.S. & Wu, H.‐T. (2013) The Synchrosqueezing algorithm for time‐varying spectral analysis: robustness properties and new paleoclimate applications. Signal Processing, 93(5), 1079–1094. https://doi.org/10.1016/j.sigpro.2012.11.029
     [Google Scholar]
  35. Tiapkina, O., Landrø, M., Tyapkin, Y. & Link, B. (2012) Single‐station SVD‐based polarization filtering of ground roll: perfection and investigation of limitations and pitfalls. Geophysics, 77(2), V41–V59. https://doi.org/10.1190/GEO2011‐0040.1
     [Google Scholar]
  36. Treitel, S., Shanks, J. & Frasier, C. (1967) Some aspects of fan filtering. Geophysics, 32, 789–800. https://doi.org/10.1190/1.1439889
     [Google Scholar]
  37. Wang, W., Gao, J., Chen, W. & Xu, J. (2012) Data adaptive ground‐roll attenuation via sparsity promotion. Journal of Applied Geophysics, 83, 19–28. https://doi.org/10.1016/j.jappgeo.2012.04.004
     [Google Scholar]
  38. Welford, J.K. & Zhang, R. (2004) Ground‐roll suppression from deep crustal seismic reflection data using a wavelet‐based approach: a case study from western Canada. Geophysics, 69(4), 877–884. https://doi.org/10.1190/1.1778231
     [Google Scholar]
  39. Yu, S., Ma, J. and Wang, W. (2019) Deep learning for denoising. Geophysics, 84(6), V333‐V350. https://doi.org/10.1190/GEO2018‐0668.1
     [Google Scholar]
  40. Yuan, Y., Si, X. & Zheng, Y. (2020) Ground‐roll attenuation using generative adversarial networks. Geophysics, 85(4), WA255–WA267. https://doi.org/10.1190/GEO2019‐0414.1
     [Google Scholar]
  41. Zhang, C. & van der Baan, M. (2022) Ground‐roll attenuation using a dual‐filter‐bank convolutional neural network. IEEE Transactions on Geoscience and Remote Sensing, 60, 1–11. https://doi.org/10.1109/TGRS.2021.3110303
     [Google Scholar]
  42. Zhang, C. & van der Baan, M. (2021) Complete and representative training of neural networks: A generalization study using double noise injection and natural images. Geophysics, 86(3), V197–V206. https://doi.org/10.1190/GEO2020‐0193.1
     [Google Scholar]
  43. Zhao, Y., Li, Y. & Yang, B. (2020) Low‐frequency desert noise intelligent suppression in seismic data based on multiscale geometric analysis convolutional neural network. IEEE Transactions on Geoscience and Remote Sensing, 58(1), 650–665. https://doi.org/10.1109/TGRS.2019.2938836
     [Google Scholar]
/content/journals/10.1111/1365-2478.13271
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Ground roll intelligent suppression based on spatial domain synchrosqueezing wavelet transform convolutional neural network
Geophysical Prospecting 72, 247 (2023); https://doi.org/10.1111/1365-2478.13271
/content/journals/10.1111/1365-2478.13271
/content/journals/10.1111/1365-2478.13271
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  • Article Type: Research Article
Keyword(s): Data processing; Signal processing

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