Cite this article
1
Download
5
Views
Journal Browser
Volume | Year
Issue
Search
News and Announcements
View All
ARTICLE

Application of multi-synchrosqueezed generalized S-transform in seismic time-frequency analysis

QIAN WANG1 XUEHUA YANG1 BO TANG1 NAIHAO LIU2,3 JINGHUAI GAO2,3
Show Less
1 School of Mathematics and Statistics, Hubei University of Arts and Science, 296 Longzhong Road, Xiang Yang 410000, P.R. China,
2 School of Electronic and Information Engineering, Xi’an Jiaotong University, 28 Xianning West Road, Xi’an 710049, P.R. China,
3 National Engineering Laboratory for Offshore Oil Exploration, Xi’an Jiaotong University, 28 Xianning West Road, Xi’an 710049, P.R. China,
JSE 2023, 32(1), 39–49;
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

Wang, Q., Yang, X.H., Tang, B., Liu, N.H. and Gao, J.H., 2023. Application of multi- synchrosqueezed genaralized S-transform in seismic time-frequency analysis. Journal of Seismic Exploration, 32: 39-49 Time-frequency (TF) analysis is an important tool in seismic data processing that describes the frequency response of subsurface rocks and reservoirs. In this paper, we propose a new TF method to characterize the time-varying feature of seismic signals, the proposed method is based on a generalized S-transform and employs a multi- synchrosqueezing algorithm. The technique provides a highly energy-concentrated TF representation using a novel local estimation of instantaneous frequency. Synthetic and field data examples show that the proposed method has a superior performance in depicting strong time-varying signals and can be used to identify subtle stratigraphy with high resolution.

Keywords
time-frequency analysis
multi-synchrosqueezing
generalized S-transform
reservoir characterization
References
  1. Auger, F. and Flandrin, P., 1995. Improving the readability of time-frequency and time-
  2. scale representations by the reassignment method. IEEE Trans. Signal Process.,
  3. 43: 1068-1089.
  4. Chakraborty, A. and Okaya, D., 1995. Frequency-time decomposition on seismic data
  5. using wavelet-based methods. Geophysics, 60: 1906-1916.
  6. Daubechies, I., Lu, J. and Wu, H., 2011. Synchrosqueezed wavelet transform: An
  7. empirical mode decomposition-like tool. Appl. Comput. Harmon. Anal., 30: 243-
  8. Ebrom, D., 2004. The low-frequency gas shadow on seismic section. The Leading Edge,
  9. 23: 772.
  10. Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N-C., Tung,
  11. C.C. and Liu, H.H., 1998. The empirical mode decomposition and the Hilbert
  12. spectrum for nonlinear and nonstationary time series analysis. Proc. R. Soc. Lond.
  13. A, 454: 903-995.
  14. Huang, Z., Zhang, J., Zhao, T. and Sun, Y., 2016. Synchrosqueezing S-transform and its
  15. application in seismic spectral decomposition. IEEE Transact. Geosci, Remote
  16. Sensing., 54: 817-825.
  17. Jeffrey, C. and William, J., 1999. On the existence of discrete Wigner distributions.
  18. IEEE Signal Process. Lett., 6: 304-306.
  19. Li, C. and Liang M., 2012. A generalized synchrosqueezing transform for enhancing
  20. signal time-frequency representation. Signal Process., 92: 2264-2274.
  21. Liu, N., Gao, J., Zhang, Z., Jiang, X. and Lv, Q., 2017. High-resolution characterization
  22. of geologic structures using the synchrosqueezing transform, Interpretation, 5:
  23. T75-T85.
  24. Oberlin, T., Meignen, S. and Perrier, V., 2014. The Fourier-based synchrosqueezing
  25. transform. ICASSP, Italy: 315-319.
  26. Reine, C., van der Baan, M. and Clark, R., 2009. The robustness of seismic attenuation
  27. measurements using fixed- and variable-window time-frequency transform.
  28. Geophysics, 74: 123-135.
  29. Sinha, S., Routh, P.S., Anno, P.D. and Castagna, J.P., 2005. Spectral decomposition of
  30. seismic data with continuous wavelet transform. Geophysics, 70: 19-25.
  31. Stockwell, R., Mansinha, L. and Lowe, R., 1996. Localization of complex spectrum: The
  32. S-transform. IEEE Transact. Signal Process., 44: 998-1001.
  33. Wang, Q., Gao, J., Liu, N. and Jiang, X., 2018. High-resolution Seismic Analysis Using
  34. the Synchrosqueezing Generalized S-Transform. IEEE Geosci. Remote Sens.
  35. Lett., 15: 374-378.
  36. Wang, C., Gao, J., 2018. High-dimension waveform inversion with cooperative
  37. coevolutionary differential evolution algorithm. IEEE Geosci. Remote Sens.
  38. Lett., 19: 297-301.
  39. Wang, P., Gao, J. and Wang, Z., 2014. Time-frequency analysis of seismic data using
  40. synchrosqueezing transform. IEEE Geosci. Remote Sens. Lett., 11: 2042-2044.
  41. Yu, G., Wang, Z. and Zhao, P., 2019. Multisynchrosqueezing transform. IEEE Transact.
  42. Ind. Electron, 66: 5441-5455.
Share
Back to top
Journal of Seismic Exploration, Electronic ISSN: 0963-0651 Print ISSN: 0963-0651, Published by AccScience Publishing