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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
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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: 3 December 2018 | Accepted: 20 March 2020 | Published: 1 April 2021
© 2021 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

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 andtime-scale representations by the reassignment method. IEEE Transact. SignalProcess., 43: 1068-1089.
  2. Auger, F., Flandrin, P., Lin, Y., McLaughlin, S., Meignen, S., Oberlin, T. and Wu, H.T.,
  3. Time-frequency reassignment and synchrosqueezing: An overview. IEEESignal Process. Mag., 30(6): 32-41.
  4. Behera, B., Meignen, S. and Oberlin, T., 2018. Theoretical analysis of the second-ordersynchrosqueezing transform. Appl. Computat. Harmon. Analys., 45: 379-404.
  5. Chen, Y., Liu, T., Chen, X., Li, J. and Wang, E., 2014. Time-frequency analysis ofseismic data using synchrosqueezing wavelet transform. J. Seismic Explor., 23:303-312.
  6. Daubechies, I., 1992. Ten lectures on wavelets. SIAM, Philadelphia, PA.
  7. Daubechies, I. and Maes, S., 1996. A nonlinear squeezing of the continuous wavelettransform based on auditory nerve models. Wavel. Medic. Biol., 30: 527-546.
  8. Daubechies, I., Lu, J. and Wu, H.T., 2011. Synchrosqueezed wavelet transforms: Anempirical mode decomposition-like tool. Appl. Comput. Harmon. Anal., 30:243-261.
  9. Gabor, D., 1946. Theory of communication. Part 1: The analysis of information. RadioCommun. Engineer., 93: 429-441.
  10. Gao, J., Chen, W., Li, Y. and Tian, F., 2003. Generalized S transform and seismicresponse analysis of thin interbeds. Chin. J. Geophys., 46: 526-532.
  11. Gao, J., Wan, T., Chen, W. and Mao J., 2006. Three parameter wavelet and itsapplications to seismic data processing. Chin. J. Geophys., 49: 337-347.
  12. Gao, J., Wang, W., Zhu, G., Peng, Y. and Wang, Y., 1996. On the choice of waveletfunctions for seismic data processing. Chin. J. Geophys., 39: 392-400.
  13. Hiawatsch, F. and Boudreaux-Bartels, G., 1992. Linear and quadratic time-frequencysignal representations. IEEE Signal Process. Mag., 9(2): 21-67.
  14. Herrera, R., Han, J. and van der Baan, M., 2014. Applications of the synchrosqueezingtransform in seismic time-frequency analysis. Geophysics, 79(3): V55-V64.
  15. Li, C. and Liang, M., 2012. A generalized synchrosqueezing transform for enhancingsignal time—frequency representation. Signal Process., 9: 2264-2274.
  16. Li, F., Zhou, H., Zhao, T. and Marfurt K.J., 2016. Unconventional reservoircharacterization based on spectrally corrected seismic attenuation estimation. J.Seismic Explor., 25: 447-461.
  17. Lilly, J. and Olhede, S.C., 2010. On the analytic wavelet transform. IEEE Transact.Informat. Theory, 57: 41354156.
  18. Liu, J. and Marfurt, K., 2007. Instantaneous spectral attributes to detect channels.Geophysics, 72(2): P23-P31.
  19. Liu, N. Gao, J. and Wang, Q., 2015. The extraction of instantaneous frequency fromseismic data via synchrosqueezing three parameter wavelet transform.
  20. Expanded Abstr., 85th Ann. Internat. SEG Mtg., New Orleans: 2801-2805.
  21. Liu, N., Gao, J., Zhang, B., Li, F. and Wang, Q., 2018. Time—frequency analysis ofseismic data using a three-parameters S-transform. IEEE Geosci. Remote Sens.Lett., 15: 142-146.
  22. Liu, N., Gao, J., Zhang, Z., Jiang, X. and Lv, Q., 2017. High resolution characterizationof geological structures using synchrosqueezing transform. Interpretation, 5:T75-T85.
  23. Liu, W., Cao, S., Liu Y. and Chen, Y., 2016. Synchrosqueezing transform and itsapplications in seismic data analysis. J. Seismic Explor., 25: 27-44.
  24. Lu, W. and Zhang, Q., 2009. Deconvolutive short-time Fourier transform spectrogram.IEEE Signal Process. Lett., 16: 576-579.
  25. Lu, W. and Li, F., 2013. Seismic spectral decomposition using deconvolutive short-time
  26. Fourier transform spectrogram. Geophysics, 78(2): V43-V51.
  27. Mallat, S.G. and Zhang, Z., 1993. Matching pursuits with time-frequency dictionaries.IEEE Transact. Signal Process., 41: 3397-3415.
  28. Mallat, S.G., 2008. A Wavelet Tour of Signal Processing: The Sparse Way. Orlando, FL,USA: Academic.
  29. Morlet, J., Arens G., Fourgeau E. and Glard D., 1982. Wave propagation and samplingtheory. Part I: Complex signal and scattering in multilayered media. Geophysics,47: 203-221.
  30. Oberlin, T., Meignen, S. and Perrier, V., 2014. The Fourier-based synchrosqueezingtransform. IEEE Internat. Conf. Acoust., Speech Signal Process. (ICASSP):315-319.
  31. Partyka, G. and Gridley, J., 1999. Interpretational applications of spectral decompositionin reservoir characterization. The Leading Edge, 18: 353-360.
  32. Sejdi¢c, E., Djurovi¢, I., and Jiang J., 2008. A window width optimized s-transform.EURASIP J. Adv. Signal Process.
  33. Stankovic, L., Djurovi¢, I., Stankovié, S., Simeunovié, M., Djukanovic, S. and Dakovic,
  34. M., 2014. Instantaneous frequency in time-frequency analysis: Enhancedconcepts and performance of estimation algorithms. Digit. Signal Process., 35:1-13.
  35. Stockwell, R., Mansinha, L. and Lowe, R., 1996. Localization of the complex spectrum:
  36. The S transform. IEEE Transact. Signal Process., 44: 998-1001.
  37. Tary, J.B., Herrera, R.H., Han, J. and van der Baan, M., 2014. Spectral estimation -Whatis new? What is next? Rev. Geophys., 52: 723-749.
  38. Wang, P., Gao, J. and Wang, Z., 2014. Time-frequency analysis of seismic data usingsynchrosqueezing transform. IEEE Geosci. Remote Sens. Lett., 11: 2042-2044.
  39. Wang, Q., Gao, J., Liu, N. and Jiang, X., 2018. High resolution seismic time frequencyanalysis using the synchrosqueezing generalized S-transform. IEEE Geosci.Remote Sens. Lett., 15: 374-378.
  40. Wang, Q. and Gao J., 2017. Application of synchrosqueezed wave packet transform inhigh resolution seismic time-frequency analysis. J. Seismic Explor., 26:587-599.
  41. Wang, X., Zhang, B., Li, F., Qi, J. and Bai, B., 2016. Seismic time-frequencydecomposition by using a hybrid basismatching pursuit technique. Interpretation,4(2): T263-T272.
  42. Wang, Y., 2007. Multichannel matching pursuit for seismic trace decomposition.Geophysics, 75(4): V61-V66.
  43. Wang, Z., Yin, C., Fan, T. and Lei, X., 2012. Seismic geomorphology of a channelreservoir in Lowe Minghuazhen Formation, Laizhouwan subbasin, China.Geophysics, 77(4): B187-B195.
  44. Yu G., Yu M., and Xu C., 2017. Synchroextracting Transform. IEEE Trans. Ind.Electron., 64: 8042-8054.
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Journal of Seismic Exploration, Electronic ISSN: 0963-0651 Print ISSN: 0963-0651, Published by AccScience Publishing
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