1887
Volume 73, Issue 4
  • E-ISSN: 1365-2478

Abstract

Abstract

Thin interbeds are important geological structures in seismic exploration, and the analysis of their thickness variations is a key step in seismic interpretation. As a typical signal‐processing technology, the time–frequency analysis method maps one‐dimensional signals to the two‐dimensional time–frequency domain, which can capture the changes of the instantaneous frequency. On this basis, the time–frequency analysis method can be used to analyse the thickness changes of thin interbeds. To analyse the thickness changes more accurately, the adopted time–frequency analysis method needs to have high time–frequency resolution and robustness. This paper proposes a novel method named the multi‐synchrosqueezing polynomial chirplet transform. First, the second‐order instantaneous frequency estimation operator is obtained through the corrected polynomial chirplet transform. Then, through squeezing and rearranging, the fuzzy time–frequency energy is gradually concentrated on the corresponding second‐order multiple instantaneous frequency estimation operator to obtain the multi‐synchrosqueezing polynomial chirplet transform. Simulated signals are used to demonstrate that multi‐synchrosqueezing polynomial chirplet transform has robustness while improving time–frequency resolution. Simultaneously, simulated and real seismic signals are used to verify that the proposed method can analyse the thickness variation of thin interbeds.

© 2025 European Association of Geoscientists & Engineers. © 2025 European Association of Geoscientists & Engineers.
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2025-04-17
2026-02-15

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References

  1. Allen, J.B. (1977) Short term spectral analysis, synthesis, and modification by discrete Fourier transform. IEEE Transactions on Acoustics, 25, 235–238. https://doi.org/10.1109/TASSP.1977.1162950
     [Google Scholar]
  2. Auger, F. & Flandrin, P. (1995) Improving the readability of time‐frequency and time‐scale representations by the reassignment method. IEEE Transactions on Signal Processing, 43, 1068–1089. Available from: https://doi.org/10.1109/78.382394
     [Google Scholar]
  3. Chen, X., Chen, H., Hu, Y., Xie, Y. & Chen, G. (2023) Statistical synchrosqueezing transform and its application to seismic thin interbed analysis. IEEE Transactions on Geoscience and Remote Sensing, 61, 1–13. Available from: https://doi.org/10.1109/TGRS.2023.3287334
     [Google Scholar]
  4. Daubechies, I., Lu, J. & Wu, H.T. (2011) Synchrosqueezed wavelet transforms: An empirical mode decomposition‐like tool. Applied and Computational Harmonic Analysis, 30, 243–261. Available from: https://doi.org/10.1016/j.acha.2010.08.002
     [Google Scholar]
  5. Gao, J., Chen, W., Li, Y. & Tian, F. (2003) Generalized S transform and seismic response analysis of thin interbedss surrounding regions by Gps. Chinese Journal of Geophysics, 46, 759–768. Available from: https://doi.org/10.1002/cjg2.3395
     [Google Scholar]
  6. Guan, Y., Liang, M. & Necsulescu, D.S. (2019) Velocity synchronous linear chirplet transform. IEEE Transactions on Industrial Electronics, 66, 6270–6280. Available from: https://doi.org/10.1109/TIE.2018.2873520
     [Google Scholar]
  7. Li, L., Cai, H., Han, H., Jiang, Q. & Ji, H. (2020) Adaptive short‐time Fourier transform and synchrosqueezing transform for non‐stationary signal separation. Signal Processing, 166, 107231. Available from: https://doi.org/10.1016/j.sigpro.2019.07.024
     [Google Scholar]
  8. Li, R., Chen, H., Fang, Y., Hu, Y., Chen, X. & Li, J. (2022) Synchrosqueezing polynomial chirplet transform and its application in tight sandstone gas reservoir identification. IEEE Geoscience and Remote Sensing Letters, 19, 1–5. Available from: https://doi.org/10.1109/LGRS.2021.3071318
     [Google Scholar]
  9. Li, Z., Gao, J., Wang, Z., Liu, N. & Yang, Y. (2020) Time‐synchroextracting general chirplet transform for seismic time–frequency analysis. IEEE Transactions on Geoscience and Remote Sensing, 58, 8626–8636. Available from: https://doi.org/10.1109/TGRS.2020.2989403
     [Google Scholar]
  10. Liu, N., Gao, J., Zhang, B., Wang, Q. & Jiang, X. (2019) Self‐adaptive generalized S‐transform and its application in seismic time‐frequency analysis. IEEE Transactions on Geoscience and Remote Sensing, 57, 7849–7859. Available from: https://doi.org/10.1109/TGRS.2019.2916792
     [Google Scholar]
  11. Mann, S. & Haykin, S. (1995) The chirplet transform: Physical considerations. IEEE Transactions on Signal Processing, 43, 2745–2761. Available from: https://doi.org/10.1109/78.482123
     [Google Scholar]
  12. Morlet, J., Arens, G., Fourgeau, E. & Giard, D. (1982) Wave propagation and sampling theory—Part I. Complex signal and scattering in multilayered media. Geophysics, 47, 149–270. Available from: https://doi.org/10.1190/1.1441328
     [Google Scholar]
  13. Oberlin, T., Meignen, S. & Perrier, V. (2015) Second‐order synchrosqueezing transform or invertible reassignment? Towards ideal time‐frequency representations. IEEE Transactions on Signal Processing, 63, 1335–1344. Available from: https://doi.org/10.1109/TSP.2015.2391077
     [Google Scholar]
  14. Pei, S.C. & Huang, S.G. (2018) Adaptive STFT with chirp‐modulated Gaussian window. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)—Proceedings. IEEE. pp. 4354–4358. Available from: https://doi.org/10.1109/ICASSP.2018.8462397
  15. Peng, Z.K., Meng, G., Chu, F.L., Lang, Z.Q., Zhang, W.M. & Yang, Y. (2011) Polynomial chirplet transform with application to instantaneous frequency estimation. IEEE Transactions on Instrumentation and Measurement, 60, 3222–3229. Available from: https://doi.org/10.1109/TIM.2011.2124770
     [Google Scholar]
  16. Pham, D.‐H. & Meignen, S. (2017) High‐order synchrosqueezing transform for multicomponent signals analysis—with an application to gravitational‐wave signal. IEEE Transactions on Signal Processing, 65, 3168–3178. Available from: https://doi.org/10.1109/TSP.2017.2686355
     [Google Scholar]
  17. Song, Y., Hu, Y., Chen, X., Chen, H. & Zhang, P. (2024) Time‐reassigning transform for the time–frequency analysis of seismic data. IEEE Transactions on Geoscience and Remote Sensing, 62, 1–11. Available from: https://doi.org/10.1109/TGRS.2024.3395902
     [Google Scholar]
  18. Stockwell, R.G. (1996) Localization of the complex spectrum: the S transform. IEEE Transactions on Signal Processing, 44, 998–1001. Available from: https://doi.org/10.1109/78.492555
     [Google Scholar]
  19. Thakur, G. & Wu, H.T. (2011) Synchrosqueezing‐based recovery of instantaneous frequency from nonuniform samples. SIAM Journal on Mathematical Analysis, 43, 2078–2095. Available from: https://doi.org/10.1137/100798818
     [Google Scholar]
  20. Tian, Y., Gao, J. & Wang, D. (2022) Synchrosqueezing optimal basic wavelet transform and its application on sedimentary cycle division. IEEE Transactions on Geoscience and Remote Sensing, 60, 1–13. Available from: https://doi.org/10.1109/TGRS.2021.3127268
     [Google Scholar]
  21. Tu, X., Hu, Y., Li, F., Abbas, S. & Liu, Y. (2017) Instantaneous frequency estimation for nonlinear FM signal based on modified polynomial chirplet transform. IEEE Transactions on Instrumentation and Measurement, 66, 2898–2908. Available from: https://doi.org/10.1109/TIM.2017.2730982
     [Google Scholar]
  22. Wang, Q., Gao, J. & Liu, N. (2020) Second‐order synchrosqueezing wave packet transform and its application for characterizing seismic geological structures. IEEE Geoscience and Remote Sensing Letters, 17, 760–764. Available from: https://doi.org/10.1109/LGRS.2019.2935764
     [Google Scholar]
  23. Wang, X., Li, C. & Chen, W. (2021) Seismic thin interbeds analysis based on high‐order synchrosqueezing transform. IEEE Transactions on Geoscience and Remote Sensing, 60, 1–11. Available from: https://doi.org/10.1109/TGRS.2021.3129627
     [Google Scholar]
  24. Yu, G., Wang, Z. & Zhao, P. (2019) Multisynchrosqueezing transform. IEEE Transactions on Industrial Electronics, 66, 5441–5455. Available from: https://doi.org/10.1109/TIE.2018.2868296
     [Google Scholar]
/content/journals/10.1111/1365-2478.70014
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Multi‐synchrosqueezing polynomial chirplet transform and its application to seismic thin interbeds analysis
Geophysical Prospecting 73, 1268 (2025); https://doi.org/10.1111/1365-2478.70014
/content/journals/10.1111/1365-2478.70014
/content/journals/10.1111/1365-2478.70014
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  • Article Type: Research Article
Keyword(s): instantaneous frequency estimation operator; interpretation; multi‐synchrosqueezing polynomial chirplet transform; signal processing; thin interbeds

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