ARTICLE

A quantitative evaluation method based on EMD for determining the accuracy of time-varying seismic wavelet extraction

PENG ZHANG YONGSHOU DAI RONGRONG WANG YONGCHENG TAN
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College of Information and Control Engineering, China University of Petroleum (East China), Qingdao 266580, P.R. China. upczhangpeng@163.com,
JSE 2017, 26(3), 267–292;
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

Zhang, P., Dai, Y., Wang, R. and Tan, Y., 2017. A quantitative evaluation method based on EMD for determining the accuracy of time-varying seismic wavelet extraction. Journal of Seismic Exploration, 26: 267-292. The bandwidth and amplitude of wavelet deconvolution results are the most important indicators of accuracy for time-varying wavelets. To evaluate the accuracies of extracted seismic wavelets based on these indicators, we propose a quantitative evaluation method based on empirical mode decomposition (EMD), which offers the advantages of adaptive decomposition and multi-scale analysis and can highlight local characteristics. First, time-varying seismic wavelets are extracted from a non-stationary seismogram and subjected to deconvolution or reflectivity inversion. Then, to appraise these wavelets, the amplitude spectrum from the deconvolution or inversion results is decomposed into multi-layer intrinsic mode functions (IMF) using EMD. Next, an evaluation parameter is constructed by summing the number of local extremes in all IMFs and normalizing this sum with respect to the number of frequency points in the amplitude spectrum. Larger values of this parameter indicate more accurate extracted wavelets. When applied to both synthetic and field-collected seismic data, the proposed method performs better than conventional methods for evaluating the accuracy of time-varying wavelet extraction.

Keywords
time-varying wavelet extraction
accuracy evaluation
deconvolution
EMD
References
  1. Arild, B. and Henning, O., 2003. Bayesian wavelet estimation from seismic and well data.
  2. Geophysics, 68: 2000-2009. doi: 10.1190/1.1635053
  3. Chen, J., Dai, Y.S., Zhang, Y.N., Wei, Y.Q. and Ding, J.J., 2013. Summary of the evaluation
  4. approaches for seismic wavelet pick-up based on higher order statistics. Oil Geophys.
  5. Prosp., 48: 497-503. doi:10.13810/j.cnki.issn. 1000-7210.2013.03.024.
  6. Dai, Y.S., Zhang, M.M., Zhang, Y.N., Ding, J.J. and Wang, R.R., 2015. Time-variant
  7. mixed-phase seismic wavelet estimation based on spectral modeling in the time-frequency
  8. domain. Oil Geophys. Prosp., 50: 830-838.
  9. doi: 10. 13810/j.cnki.issn. 1000-7210.2015.05.004.
  10. 292 ZHANG, DAI, WANG & TAN
  11. Economou, N. and Vafidis, A., 2010. Spectral balancing GPR data using time-variant bandwidth
  12. in the t-f domain. Geophysics, 75: J19-J27. doi:10.1190/1.3374464.
  13. Han, J. and van der Baan, M., 2013. Empirical mode decomposition for seismic time-frequency
  14. analysis. Geophysics, 78: 09-019. doi:10.1190/geo2012-0199. 1.
  15. Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q.A., Yen, N.C., Tung, C.C.
  16. and Liu, H.H., 1998. The empirical mode decomposition and the Hilbert spectrum for
  17. nonlinear and non-stationary time series analysis. Proc. Roy. Soc. Mathemat., Phys.
  18. Engineer. Sci., 454: 903-995. doi:10.1098/rspa. 1998.0193.
  19. Li, G.F., Liu, Y., Zheng, H. and Huang, W., 2015. Absorption decomposition and
  20. compensation via a two-step schem. Geophysics, 80: V145-V155.
  21. doi: 10. 1190/ge02015-0038.1.
  22. Longbottom, J., Walden, A.T. and White, R.E., 1988. Principles and application of maximum
  23. Kurtosis phase estimation. Geophys. Prosp., 36: 115-138.
  24. doi: 10.1111/j. 1365-2478. 1988.tb02155.x.
  25. Margrave, G.F., Lamoureux, M.P. and Henley, D.C., 2011. Gabor deconvolution: Estimating
  26. reflectivity by nonstationary deconvolution of seismic data. Geophysics, 76: W15-W30.
  27. doi: 10.1190/1.3560167.
  28. Matsuoka, T. and Ulrych, T.J., 1984. Phase estimation using the bispectrum: Proc. IEEE, 72:
  29. 1403-1411. doi:10.1109/PROC. 1984. 13027.
  30. Oliveira, S.A.M. and Lupinacci, W.M., 2013. Li-norm inversion method for deconvolution in
  31. attenuating media. Geophys. Prosp., 61: 771-777. doi:10.1111/1365-2478. 12002.
  32. Porsani, M.J., Ursin, B. and Silva, M.G., 2013. Dynamic estimation of reflectivity by
  33. minimum-delay seismic trace decomposition. Geophysics, 78: V109-V117.
  34. doi: 10. 1190/ge02012-0077. 1.
  35. Radad, M., Gholami, A. and Siahkoohi, H.R., 2015. S-transform with maximum energy
  36. concentration: Application to non-stationary seismic deconvolution. J. Appl. Geophys., 118:
  37. 155-166. doi:10.1016/j.jappgeo.2015.04.010.
  38. Rietsch, E., 1997. Euclid and the art of wavelet estimation, Part I: Basic algorithm for noise-free
  39. data. Geophysics, 62: 1931-1938. doi:10.1190/1.1444293.
  40. Rosa, A.L. and Ulrych, T.J., 1991. Processing via spectral modeling. Geophysics, 56: 1244-1251.
  41. doi: 10.1190/1.1443144.
  42. Sajid, M. and Ghosh, D., 2013. A fast and simple method of spectral enhancement. Geophysics,
  43. 79: V75-V80. doi: 10.1190/ge02013-0179.1.
  44. Sun, X.K., Sun Z.D., Xie, H.W., Liu, L.F., Tao, P. and Wang, Y.G., 2015. A nonstationary
  45. perspective on sparse deconvolution. Oil Geophys. Prosp., 50: 260-266.
  46. doi: 10. 13810/j.cnki.issn. 1000-7210.2015.02.009.
  47. van der Baan, M., 2008. Time-varying wavelet estimation and deconvolution by Kurtosis
  48. maximization. Geophysics, 73: 11-18. doi:10.1190/1.2831936.
  49. Velis, D.R., 2008. Stochastic sparse-spike deconvolution. Geophysics, 73: RI-R9.
  50. doi: 10.1190/1.2790584.
  51. Wang, L.L., Gao, J.H., Zhao, W. and Jiang, X., 2012. Enhancing resolution of nonstationary
  52. seismic data by molecular-Gabor transform. Geophysics, 78: V31-V34.
  53. doi: 10. 1190/ge02011-0450.1.
  54. White, R.E., 1988. Maximum Kurtosis phase correction. Geophys. J., 95: 371-389.
  55. doi: 10.1111/j.1365-246X.1988.tb00475.x.
  56. Yuan, S.Y. and Wang, S.X., 2011. Influence of inaccurate wavelet phase estimation on seismic
  57. inversion. Appl. Geophys., 8: 48-59. doi:10.1007/s11770-011-0273-5.
  58. Zhang, G., Li, Y., Rong, J. and Cai, Z., 2011. Compensation for stratigraphic absorption of
  59. seismic attenuation based on the improved generalized S-transform. Extended Abstr., 73rd
  60. EAGE Conf., Vienna. doi:10.3997/2214-4609.20149195.
  61. Zhou, H.L., Tian, Y.M. and Ye, Y., 2014. Dynamic deconvolution of seismic data based on
  62. generalized S-transform. J. Appl. Geophys., 108: 1-11. doi:10.1016/j.jappgeo.2014.06.004.
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Journal of Seismic Exploration, Electronic ISSN: 0963-0651 Print ISSN: 0963-0651, Published by AccScience Publishing