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

Time-frequency analysis of seismic data for the characterization of geologic structures via synchro-extracting transform

ZHEN LI1,2 ,  JINGHUAI GAO1,2 ,  ZHIGUO WANG2,3 ,  NAIHAO LIU1,2 ,  FENGYUAN SUN1,2
Show Less
1 School of Information and Communications Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, P.R. China.,
2 National Engineering Laboratory for Offshore Oil Exploration, Xi’an, Shaanxi 710049, P.R. China.,
3 School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, P.R. China.,
JSE 2021, 30(2), 101–120;
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

Li, Z., Gao, J.H., Wang, Z.G., Liu, N.H. and Sun, F.Y., 2020. Time-frequency analysis of seismic data for the characterization of geologic structures via synchroextracting transform. Journal of Seismic Exploration, 30: 101-120. Time-frequency (TF) analysis based method is one of the most powerful tools in seismic data processing and interpretation. To delineate the subsurface geological structures clearly, the energy concentration of the seismic TF representation (TFR) is as high as possible. The traditional TF methods, such as the short-time Fourier transform (STFT) and wavelet transform (WT), have been widely applied for seismic TF analysis. However, they suffer from the diffused TFR because of the Heisenberg uncertainty principle. To achieve a higher energy-concentrated TF result, the synchrosqueezing transform (SST) was proposed. The SST method has been successfully used for seismic processing. In this paper, we introduce a novel approach for seismic spectrum analysis based on the synchroextracting transform (SET), which is an extension of the Fourier-based SST (FSST). The SET extracts the TF information only located at the instantaneous frequency (IF) trajectory of the analyzed signal and removes the most smeared TF energy, which leads to a highly sharpened TFR. We also put forward a theorem to prove that the SET can get the exact IF of a linear chirp signal. All the results of the synthetic signal and real seismic data demonstrate the validity and effectiveness of the proposed method.

Keywords
time-frequency analysis
seismic data processing
high energy-concentrated
synchroextracting transform
Fourier-based synchrosqueezing transform.
References
  1. Auger, F. and Flandrin, P., 1995. Improving the readability of time-frequency and
  2. time-scale representations by the reassignment method. IEEE Transact. Sign.
  3. Process., 43: 1068-1089.
  4. Auger, F., Flandrin, P., Lin, Y., McLaughlin, S., Meignen, S., Oberlin, T. and Wu, H.T.,
  5. Time-frequency reassignment and synchrosqueezing: An overview. IEEE
  6. Sign. Process. Mag., 30(6): 32-41.
  7. Behera, B., Meignen, S. and Oberlin, T., 2018. Theoretical analysis of the second-order
  8. synchrosqueezing transform. Appl. Computat. Harmon. Analys., 45: 379-404.
  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., 1992. Ten Lectures on Wavelets. SIAM, Philadelphia, PA, U.S.A.
  13. Daubechies, I. and Maes, S., 1996. A nonlinear squeezing of the continuous wavelet
  14. transform based on auditory nerve models. Wavel. Medic. Biol., 30: 527-546.
  15. Daubechies, I., Lu, J. and Wu, H.T., 2011. Synchrosqueezed wavelet transforms: An
  16. empirical mode decomposition-like tool. Appl. Comput. Harmon. Anal., 30:
  17. 243-261.
  18. Gabor, D., 1946. Theory of communication. Part 1: The analysis of information. Radio
  19. Communicat. Engineer., 93: 429-441.
  20. Gao, J., Chen, W., Li Y. and Tian, F., 2003. Generalized S-transform and seismic
  21. response analysis of thin interbeds. Chin. J. Geophys., 46: 526-532.
  22. Gao, J., Wan, T., Chen, W. and Mao, J., 2006. Three parameter wavelet and its
  23. applications to seismic data processing:. Chin. J. Geophys., 49: 337-347.
  24. Gao, J., Wang, W., Zhu, G., Peng, Y. and Wang, Y., 1996. On the choice of wavelet
  25. functions for seismic data processing. Chin. J. Geophys., 39: 392-400.
  26. Hlawatsch, F. and Boudreaux-Bartels, G., 1992. Linear and quadratic time-frequency
  27. signal representations. IEEE Sign. Process. Magaz., 9(2): 21-67.
  28. Herrera, R., Han, J. and van der Baan, M., 2014. Applications of the synchrosqueezing
  29. transform in seismic time-frequency analysis. Geophysics, 79(3): V55-V64.
  30. Li, C. and Liang, M., 2012. A generalized synchrosqueezing transform for enhancing
  31. signal time-frequency representation. Sign. Process., 92: 2264-2274.
  32. Li, F., Zhou, H., Zhao, T. and Marfurt, K.J., 2016. Unconventional reservoir
  33. characterization based on spectrally corrected seismic attenuation estimation.
  34. J. Seismic Explor., 25: 447-461.
  35. Lilly, J. and Olhede, S.C., 2010. On the analytic wavelet transform. IEEE Transact.
  36. Informat. Theory, 57: 4135-4156.
  37. Liu, J. and Marfurt, K.J., 2007. Instantaneous spectral attributes to detect channels.
  38. Geophysics, 72(2): P23-P31.
  39. Liu, N., Gao, J. and Wang, Q., 2015. The extraction of instantaneous frequency from
  40. seismic data via synchrosqueezing three parameter wavelet transform. Expanded
  41. Abstr., 85th Ann. Internat. SEG Mtg., New Orleans: 2801-2805.
  42. Liu, N., Gao, J., Zhang, B., Li, F. and Wang, Q., 2018. Time—frequency analysis of
  43. seismic data using a three parameters S-transform. IEEE Geosci. Remote Sens.
  44. Lett., 15: 142-146.
  45. Liu, N., Gao, J., Zhang, Z., Jiang, X. and Lv, Q., 2017. High resolution characterization
  46. of geological structures using synchrosqueezing transform. Interpretation, 5:
  47. T75-T85.
  48. Liu, W., Cao, S., Liu, Y. and Chen, Y., 2016. Synchrosqueezing transform and its
  49. applications in seismic data analysis. J. Seismic Explor., 25: 27-44.
  50. Lu, W. and Zhang, Q., 2009. Deconvolutive short-time Fourier transform spectrogram.
  51. IEEE Sign. Process. Lett., 16: 576-579.
  52. Lu, W. and Li, F., 2013. Seismic spectral decomposition using deconvolutive short-time
  53. Fourier transform spectrogram. Geophysics, 78(2): V43-V51.
  54. Mallat, S.G. and Zhang, Z., 1993. Matching pursuits with timefrequency dictionaries.
  55. IEEE Transact. Sign. Process., 41: 3397-3415.
  56. Mallat, S.G., 2008. A Wavelet Tour of Signal Processing: The Sparse Way. Academic
  57. Press, Orlando, FL.
  58. Morlet, J., Arens, G., Fourgeau, E. and Glard, D., 1982, Wave propagation and sampling
  59. theory. Part I: Complex signal and scattering in multilayered media. Geophysics,
  60. 47: 203-221.
  61. Oberlin, T., Meignen, S. and Perrier, V., 2014. The Fourier-based synchrosqueezing
  62. transform. IEEE Internat. Conf. Acoust., Speech Sign. Process. (ICASSP)},
  63. 315-319.
  64. Partyka, G. and Gridley, J., 1999. Interpretational applications of spectral decomposition
  65. in reservoir characterization. The Leading Edge, 18: 353-360.
  66. Sejdi¢, E., Djurovic, I. and Jiang, J., 2008. A window width optimized S-transform.
  67. EURASIP, J. Adv. Sign. Process.
  68. Stankovic, L., Djurovic, I., Stankovi¢, S., Simeunovi¢, M., Djukanovi¢, S. and Dakovic,
  69. M., 2014. Instantaneous frequency in time-frequency analysis: Enhanced
  70. concepts and performance of estimation algorithms. Digit. Sign. Process., 35:
  71. 1-13.
  72. Stockwell, R., Mansinha, L. and Lowe, R., 1996. Localization of the complex spectrum:
  73. The S-transform. IEEE Transact. Sign. Process., 44: 998-1001.
  74. Tary, J.B., Herrera, R.H., Han, J. and van der Baan, M., 2014. Spectral estimation-What
  75. is new? What is next?. Rev. Geophys., 52:723-749.
  76. Wang, P., Gao, J. and Wang, Z., 2014. Time-frequency analysis of seismic data using
  77. synchrosqueezing transform. IEEE Geosci. Remote Sens. Lett., 11: 2042-2044.
  78. Wang, Q., Gao, J., Liu, N. and Jiang, X., 2018. High resolution seismic time frequency
  79. analysis using the synchrosqueezing generalized S-transform. IEEE Geosci.
  80. Remote Sens. Lett., 15: 374-378.
  81. Wang, Q. and Gao, J., 2017. Application of synchrosqueezed wave packet transform in
  82. high-resolution seismic time-frequency analysis. J. Seismic Explor., 26:
  83. 587-599.
  84. Wang, X., Zhang, B., Li, F., Qi, J. and Bai, B., 2016. Seismic time-frequency
  85. decomposition by using a hybrid basismatching pursuit technique. Interpretation,
  86. 4(2): T263-T272.
  87. Wang, Y., 2007. Multichannel matching pursuit for seismic trace decomposition.
  88. Geophysics, 75(4): V61-V66.
  89. Wang, Z., Yin, C., Fan, T. and Lei, X., 2012. Seismic geomorphology of a channel
  90. reservoir in Lower Minghuazhen Formation, Laizhouwan subbasin, China.
  91. Geophysics, 77(4): B187-B195.
  92. Yu, G., Yu, M. and Xu, C., 2017. Synchroextracting transform. IEEE Transact. Ind.
  93. Electron., 64: 8042-8054.
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