AccScience Publishing / JSE / Article
Submit to JSE
Cite this article
2
Download
7
Views
Journal Browser
Volume | Year
Issue
Search
Forthcoming Issue
Current Issue
View All
News and Announcements
View All
ARTICLE

Demodulated synchrosqueezing S-transform and its application and economic value in seismic data analysis

JUN ZHOU1 ,  LIEJUN LI2 ,  QINGNAN HE1 ,  WEI LIU3 ,  HONGZHI MA1
Show Less
1 Nanchang Institute of Science and Technology, Gezaoshan Road 998, Honggutan District, Nanchang 330108, P.R. China.,
2 Nanchang Normal College of Applied Technology, Mingyueshan Road 1599 Honggutan District, Nanchang 330108, P.R. China.,
3 College of Mechanical and Electrical Engineering, Beijing University of Chemical Technology, North Third Ring Ring 15, Chaoyang District, Beijing 100029, P.R. China.,
JSE 2023, 32(3), 229–242;
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/ )
Download PDF
Cite
XML
Abstract

Zhou, J., Li, L.J., He, Q.G., Liu, W. and Ma, H.Z., 2023. Demodulated synchrosqueezing S-transform and its application and economic value in seismic data analysis. Journal of Seismic Exploration, 32: 229-242. The synchrosqueezing transforms based on short-time Fourier transform (FSST) and wavelet transform (SSWT) have been widely used in the analysis of non-stationary signals. In the light of the superiority of S-transform (ST) over short-time Fourier transform (STFT) and wavelet transform (WT) in time-frequency representation (TFR), we propose a novel time-frequency analysis method, termed as demodulate synchrosqueezing S-transform (DSSST), which achieves a highly energy-concentrated TFR by making full use of two operations including demodulation technique and ST-based synchrosqueezing transform (SSST). The formulas for the DSSST and its inverse transform are derived. Synthetic example shows that the DSSST has higher time-frequency resolution compared with the standard ST and SSST. Then we apply the DSSST to perform the spectral decomposition on a real field data including gas-filled sand. The results demonstrate that the DSSST can be utilized to well characterize the spectral anomalies related to hydrocarbon reservoir, which renders that this new approach is promising for seismic data analysis.

Keywords
time-frequency representation
S-transform
synchrosqueezing transform
demodulate synchrosqueezing S-transform
hydrocarbon detection
References
  1. Allen, J.B., 1977. Short term spectral analysis, synthetic and modification by discrete
  2. Fourier transform. IEEE Transact. Acoust., Speech, Sign. Process., 25: 235-238.
  3. Auger, F. and Flandrin, P., 1995. Improving the readability of time-frequency and
  4. time-scale representations by the reassignment method. IEEE Trans. Signal Process.,
  5. 43(5): 1068-1089.
  6. Castagna, J.P., Sun, S. and Siegfried, R.W., 2003. Instantaneous spectral analysis:
  7. detection of low-frequency shadows associated with hydrocarbons. The Leading
  8. Edge, 22: 120-127.
  9. Chen, Y., Liu, T., Chen, X., Li, J. and Wang, E., 2014. Time-frequency analysis of
  10. seismic data using synchrosqueezing wavelet transform. J. Seismic Explor., 23:
  11. 303-312.
  12. Daubechies, I., Lu, J. and Wu, H.T., 2011. Synchrosqueezed wavelet transform: An
  13. empirical mode decomposition-like tool. Appl. Comput. Harmon. Anal., 30:
  14. 243-261.
  15. Herrera, R.H., Han, J. and van der Baan, M., 2014. Applications of the synchrosqueezing
  16. transform in seismic time-frequency analysis. Geophysics, 79(3): V55-V64.
  17. Huang, Z., Zhang, J., Zhao, T. and Sun, Y., 2016. Synchrosqueezing S-transform and its
  18. application in seismic spectral decomposition. IEEE Transact. Geosci. Remote
  19. Sens., 54: 817-825.
  20. Liu, N., Gao, J., Zhang, B., Wang, Q. and Jiang X., 2019. Self-adaptive generalized
  21. S-transform and its application in seismic time-frequency analysis. IEEE Transact..
  22. Geosci. Remote Sens., 57: 7849-7859.
  23. Liu, W., Cao, S., Liu, Y. and Chen, Y., 2016. Synchrosqueezing transform and its
  24. applications in seismic data analysis. J. Seismic Explor., 25: 27-44.
  25. Liu, Y. and Fomel, S., 2013. Seismic data analysis using local time-frequency
  26. decomposition. Geophys. Prosp., 61: 516-525.
  27. Sinha, S., Routh, P., Anno, P. and Castagna, J., 2005. Spectral decomposition of seismic
  28. data with continuous-wavelet transform. Geophysics, 70(6): 19-25.
  29. Stockwell, R.G., Mansinha, L. and Lowe, R.P., 1996. Localization of the complex
  30. spectrum: the S transform. IEEE T. Signal Proces., 44(4): 998-1001.
  31. Thakur, G., Brevdo, E., Fuckar, N.S. and Wu, H.T., 2013. The synchrosqueezing
  32. algorithm for time-varying spectral analysis: Robustness properties and new
  33. paleoclimate applications. Signal Process., 93: 1079-1094.
  34. Thakur, G. and Wu, H.T., 2011. Synchrosqueezing-based recovery of instantaneous
  35. frequency from nonuniform samples. SIAM J. Math. Anal., 43: 2078-2095.
  36. Wang, Q., Gao, J., Liu, N. and Jiang, X., 2018. High-resolution seismic time-frequency
  37. analysis using the synchrosqueezing generalized S-transform. IEEE Geosci. Remote
  38. Sens. Lett., 15: 374-378.
  39. Yang, H., 2015. Synchrosqueezed wave packet transforms and diffeomorphism based
  40. spectral analysis for 1D general mode decompositions. Appl. Comput. Harmon. A.,
  41. 39: 33-66.
Share
Back to top
Journal of Seismic Exploration, Electronic ISSN: 0963-0651 Print ISSN: 0963-0651, Published by AccScience Publishing
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here
Accept