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
Show Less
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: 29 September 2016 | Accepted: 29 March 2017 | Published: 1 June 2017
© 2017 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.Geophysics, 68: 2000-2009. doi: 10.1190/1.1635053
  2. Chen, J., Dai, Y.S., Zhang, Y.N., Wei, Y.Q. and Ding, J.J., 2013. Summary of the evaluationapproaches for seismic wavelet pick-up based on higher order statistics. Oil Geophys.
  3. Prosp., 48: 497-503. doi:10.13810/j.cnki.issn. 1000-7210.2013.03.024.
  4. Dai, Y.S., Zhang, M.M., Zhang, Y.N., Ding, J.J. and Wang, R.R., 2015. Time-variantmixed-phase seismic wavelet estimation based on spectral modeling in the time-frequencydomain. Oil Geophys. Prosp., 50: 830-838.doi: 10. 13810/j.cnki.issn. 1000-7210.2015.05.004.292 ZHANG, DAI, WANG & TAN
  5. Economou, N. and Vafidis, A., 2010. Spectral balancing GPR data using time-variant bandwidthin the t-f domain. Geophysics, 75: J19-J27. doi:10.1190/1.3374464.
  6. Han, J. and van der Baan, M., 2013. Empirical mode decomposition for seismic time-frequencyanalysis. Geophysics, 78: 09-019. doi:10.1190/geo2012-0199. 1.
  7. Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q.A., Yen, N.C., Tung, C.C.and Liu, H.H., 1998. The empirical mode decomposition and the Hilbert spectrum fornonlinear and non-stationary time series analysis. Proc. Roy. Soc. Mathemat., Phys.
  8. Engineer. Sci., 454: 903-995. doi:10.1098/rspa. 1998.0193.
  9. Li, G.F., Liu, Y., Zheng, H. and Huang, W., 2015. Absorption decomposition andcompensation via a two-step schem. Geophysics, 80: V145-V155.doi: 10. 1190/ge02015-0038.1.
  10. Longbottom, J., Walden, A.T. and White, R.E., 1988. Principles and application of maximum
  11. Kurtosis phase estimation. Geophys. Prosp., 36: 115-138.doi: 10.1111/j. 1365-2478. 1988.tb02155.x.
  12. Margrave, G.F., Lamoureux, M.P. and Henley, D.C., 2011. Gabor deconvolution: Estimatingreflectivity by nonstationary deconvolution of seismic data. Geophysics, 76: W15-W30.doi: 10.1190/1.3560167.
  13. Matsuoka, T. and Ulrych, T.J., 1984. Phase estimation using the bispectrum: Proc. IEEE, 72:1403-1411. doi:10.1109/PROC. 1984. 13027.
  14. Oliveira, S.A.M. and Lupinacci, W.M., 2013. Li-norm inversion method for deconvolution inattenuating media. Geophys. Prosp., 61: 771-777. doi:10.1111/1365-2478. 12002.
  15. Porsani, M.J., Ursin, B. and Silva, M.G., 2013. Dynamic estimation of reflectivity byminimum-delay seismic trace decomposition. Geophysics, 78: V109-V117.doi: 10. 1190/ge02012-0077. 1.
  16. Radad, M., Gholami, A. and Siahkoohi, H.R., 2015. S-transform with maximum energyconcentration: Application to non-stationary seismic deconvolution. J. Appl. Geophys., 118:155-166. doi:10.1016/j.jappgeo.2015.04.010.
  17. Rietsch, E., 1997. Euclid and the art of wavelet estimation, Part I: Basic algorithm for noise-freedata. Geophysics, 62: 1931-1938. doi:10.1190/1.1444293.
  18. Rosa, A.L. and Ulrych, T.J., 1991. Processing via spectral modeling. Geophysics, 56: 1244-1251.doi: 10.1190/1.1443144.
  19. Sajid, M. and Ghosh, D., 2013. A fast and simple method of spectral enhancement. Geophysics,79: V75-V80. doi: 10.1190/ge02013-0179.1.
  20. Sun, X.K., Sun Z.D., Xie, H.W., Liu, L.F., Tao, P. and Wang, Y.G., 2015. A nonstationaryperspective on sparse deconvolution. Oil Geophys. Prosp., 50: 260-266.doi: 10. 13810/j.cnki.issn. 1000-7210.2015.02.009.van der Baan, M., 2008. Time-varying wavelet estimation and deconvolution by Kurtosismaximization. Geophysics, 73: 11-18. doi:10.1190/1.2831936.
  21. Velis, D.R., 2008. Stochastic sparse-spike deconvolution. Geophysics, 73: RI-R9.doi: 10.1190/1.2790584.
  22. Wang, L.L., Gao, J.H., Zhao, W. and Jiang, X., 2012. Enhancing resolution of nonstationaryseismic data by molecular-Gabor transform. Geophysics, 78: V31-V34.doi: 10. 1190/ge02011-0450.1.
  23. White, R.E., 1988. Maximum Kurtosis phase correction. Geophys. J., 95: 371-389.doi: 10.1111/j.1365-246X.1988.tb00475.x.
  24. Yuan, S.Y. and Wang, S.X., 2011. Influence of inaccurate wavelet phase estimation on seismicinversion. Appl. Geophys., 8: 48-59. doi:10.1007/s11770-011-0273-5.
  25. Zhang, G., Li, Y., Rong, J. and Cai, Z., 2011. Compensation for stratigraphic absorption ofseismic attenuation based on the improved generalized S-transform. Extended Abstr., 73rd
  26. EAGE Conf., Vienna. doi:10.3997/2214-4609.20149195.
  27. Zhou, H.L., Tian, Y.M. and Ye, Y., 2014. Dynamic deconvolution of seismic data based ongeneralized S-transform. J. Appl. Geophys., 108: 1-11. doi:10.1016/j.jappgeo.2014.06.004.
Share
Back to top
Journal of Seismic Exploration, Electronic ISSN: 0963-0651 Print ISSN: 0963-0651, Published by AccScience Publishing