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Synchrosqueezing transform and its applications in seismic data analysis

WEI LIU1,2 ,  SIYUAN CAO1,2 ,  YANG LIU1,2 ,  YANGKANG CHEN3
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1 State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum (Beijing), 18 Fuxue Road, Changping, Beijing 102249, P.R. China.,
2 CNPC Key Laboratory of Geophysical Exploration, China University of Petroleum (Beijing), 18 Fuxue Road, Changping, Beijing 102249, P.R. China.,
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.,
JSE 2016, 25(1), 27–44;
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/ )
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Abstract

Liu, W., Cao, S., Liu, Y. and Chen, Y., 2016. Synchrosqueezing transform and its applications in seismic data analysis. Journal of Seismic Exploration, 25: 27-44. Time-frequency representation has been widely used in seismic data analysis because it can reveal a lot of information hidden in the seismic amplitude profiles. The short-time Fourier transform and wavelet transform are two popular methods to decompose a signal from time domain to time-frequency domain. However, the applications of both approaches are limited due to the trade-offs between time and frequency resolutions. The synchrosqueezing transform (SST) is a wavelet-based time-frequency reassignment method, which can produce an improved time-frequency resolution. In this paper, we extend the application of SST to hydrocarbon detection, ground roll suppression and random noise attenuation. In hydrocarbon detection, the SST shows high resolution in both time and frequency dimensions than continuous wavelet transform (CWT), which facilitates a better delineation of the location of low-frequency anomalies more clearly. In ground roll suppression, the SST performs better than the commonly used high-pass filtering and f-k filtering which damages the seismic reflections more or less. In random noise attenuation, the SST can be significantly more effective in both the removal of random noise and the preservation of useful reflection events compared with f-x deconvolution.

Keywords
synchrosqueezing transform
hydrocarbon detection
ground roll suppression
random noise attenuation
continuous wavelet transform
References
  1. Allen, J.B., 1977. Short term spectral analysis, synthetic and modification by discrete fourier
  2. transform. IEEE Trans. Acoust. Speech Sign. Process., 25: 235-238.
  3. Askari, R. and Siahkoohi, H.R., 2008. Ground roll attenuation using the S and x-f-k transform.
  4. Geophys. Prosp., 56: 105-114.
  5. Beresford-Smith, G. and Rango, R., 1988. Dispersive noise removal in t-x space: Application to
  6. Arctic data. Geophysics, 53: 346-358.
  7. Canales, L., 1984. Random noise reduction. Expanded Abstr., 54th Ann. Internat. SEG Mtg.,
  8. Atlanta: 525-527.
  9. Chakraborty, A. and Okaya. D., 1995. Frequency-time decomposition of seismic data using
  10. wavelet-based method. Geophysics, 60: 1906-1916.
  11. 44 LIU, CAO, LIU & CHEN
  12. Chen, Y., Liu, T., Chen, X. and Wang, E., 2014. Time-frequency analysis of seismic data using
  13. synchrosqueezing wavelet transform. Expanded Abstr., 84th Ann. Internat. SEG Mtg.,
  14. Denver: 1589-1592.
  15. Chen, Y. and Ma, J., 2014. Random noise attenuation by f-x empirical mode decomposition predictive
  16. filtering. Geophysics, 79: V81-V91.
  17. Chen, Y. and Fomel, S., 2015. Random noise attenuation using local signal-and-noise
  18. orthogonalization. Geophysics, 80: WD1-WD9.
  19. Chen, Y., Li, S.X., Zhang, G. and Gan, S., 2015. Delineating karstification using synchrosqueezing
  20. wavelet transform. Expanded Abstr., 85th Ann. Internat. SEG Mtg., New Orleans: in press.
  21. Cohen, L., 1966. Generalized phase-space distribution functions. J. Math. Phys., 7: 781-786.
  22. Cohen, L., 1995. Time-frequency Analysis. Prentice-Hall, New York.
  23. Daubechies, I. and Maes, S., 1996. A nonlinear squeezing of the continuous wavelet transform based
  24. on auditory nerve models wavelets in medicine and biology. In: Aldroubi, A. and Unser, M.
  25. (Eds.), Wavelets in Medicine and Biology. CRC Press, Boca Raton: 527-546.
  26. Daubechies, I., Lu, J. and Wu, H.T., 2011. Synchrosqueezed wavelet transform: An empirical mode
  27. decomposition-like tool. Appl. Comput. Harmon. Anal., 30: 243-261.
  28. Gabor, D., 1946. Theory of communication. J. Inst. Electr. Engin., 93: 429-497.
  29. Han, J. and van der Baan, M., 2013. Empirical mode decomposition for seismic time-frequency
  30. analysis. Geophysics, 78: 9-19.
  31. Herrera, R.H., Han, J. and van der Baan. M., 2014. Applications of the synchrosqueezing transform
  32. in seismic time-frequency analysis. Geophysics, 79: V55-V64.
  33. Herrera, R.H., Tary, J.B., van der Baan, M. and Eaton, D.W., 2015. Body wave separation in the
  34. time-frequency domain. IEEE Geosci. Remote Sens., 12: 364-368.
  35. Jeffrey, C. and William, J., 1999. On the existence of discrete Wigner distributions. IEEE Signal
  36. Proc. Lett., 6: 304-306.
  37. Li, C. and M. Liang., 2012. A generalized synchrosqueezing transform for enhancing signal
  38. time-frequency representation. Signal Process., 92: 2264-2274.
  39. Li, C. and Liang, M., 2012. Time-frequency signal analysis for gearbox fault diagnosis using a
  40. generalized synchrosqueezing transform. Mech. Syst. Signal Process., 26: 205-217.
  41. Mallat, S.G. and Zhang, Z.F., 1993. Matching pursuits with time frequency dictionaries. IEEE Trans.
  42. Signal Process., 41: 3397-3415.
  43. Meignen, S., Oberlin, T. and McLaughlin, S., 2012. A new algorithm for multicomponent signals
  44. analysis based on synchrosqueezing: With an application to signal sampling and denoising.
  45. IEEE Trans. Signal Process., 61: 5787-5798.
  46. Portniaguine, O. and Castagna, J., 2004. Inverse spectral decomposition. Expanded Abstr., 74th Ann.
  47. Internat. SEG Mtg., Denver: 1786-1789.
  48. Stockwell, R.G., Mansinha, L. and Lowe, R.P., 1996. Localization of the complex spectrum: The
  49. S-transform. IEEE Trans. Signal Process., 44: 998-1001.
  50. Thakur, G.E., Brevdo, N.S., Fuckar, N.S. and Wu, H.T., 2013. The synchrosqueezing algorithm
  51. for time-varying spectral analysis: Robustness properties and paleoclimate applications. Signal
  52. Process., 93: 1079-1094.
  53. Wang, P., Gao, J. and Wang, Z., 2014. Time-frequency analysis of seismic data using
  54. synchrosqueezing transform. IEEE Geosci. Remote Sens., 11: 2042-2044.
  55. Wang, Y., 2007. Seismic time-frequency spectral decomposition by matching pursuit. Geophysics,
  56. 72: V13-V20.
  57. Wu, X.Y. and Liu, T.Y., 2010. Seismic spectral decomposition and analysis based on Wigner-Ville
  58. distribution for sandstone reservoir characterization in West Sichuan depression. Geophysics,
  59. 7: 126-134.
  60. Yuan, S.Y. and Wang, S.X., 2011. A local f-x Cadzow method for noise reduction of seismic data
  61. obtained in complex formations. Petrol. Sci., 8: 269-277.
  62. Yuan, S.Y. and Wang, S.X., 2013. Spectral sparse Bayesian learning reflectivity inversion. Geophys.
  63. Prosp., 61: 735-746.
  64. Zhang, X., Han, L., Wang, Y. and Shan, G., 2010. Seismic spectral decomposition fast matching
  65. pursuit algorithm and its application. Geophys. Prosp. Petrol., 49: 1-6.
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Journal of Seismic Exploration, Electronic ISSN: 0963-0651 Print ISSN: 0963-0651, Published by AccScience Publishing
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