Abstract
Seismic wavelet extraction always plays a central role in high-resolution seismic processing. Conventional methods assume that seismic data are stationary when a constant wavelet is considered, which ignores the time-varying characteristics of seismic wavelets. In reality, seismic data are nonstationary because of attenuation, scattering, and other physical processes during propagation, which means that the frequency spectrum of seismic signal changes from shallow to deep formations. We have developed a time-varying wavelet extraction method by using a highly energy-concentrated time-frequency representation technique. Time-varying wavelets are generated according to the local frequency spectrum at every instant. In addition, because the estimations of parameters for wavelet extraction are fully data-driven, the results of the proposed method are more accurate and suitable for the nonstationary nature of actual seismic data. Synthetic tests indicate the reliability and robustness of the proposed method, even under noise contamination. By applying the time-varying wavelet extracted using the proposed method to seismic inversion on a field data example, we obtain the deconvolution result with improved resolution and a better fit to the well-log reflectivity compared to that by using conventional wavelet extraction methods.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Allen J., 1977. Short term spectral analysis, synthesis, and modification by discrete fourier transform. IEEE Trans. Acoust. Speech Signal Process., 25, 235–238
Dai Y.S., Wang R.R., Li C., Zhang P. and Tan Y.C., 2016. A time-varying wavelet extraction using local similarity. Geophysics, 81, V55–V68
Daubechies I., Lu J.F. and Wu H.T., 2011. Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool. Appl. Comput. Harmon. Anal., 30, 243–261
Feng X., Liu C., Yang B.J., Cui F.J. and Li Q.X., 2002. The extractive method of seismic wavelet in different time window and the application in synthetic seismogram. Progr. Geophys., 17, 71–77 (in Chinese)
Hu W., Liu J., Bear L. and Marcinkovich C., 2011. A robust and accurate seismic attenuation tomography algorithm. SEG Technical Program Expanded Abstracts 2011, 2727–2731
Li C. and Liu X., 2015. A new method for interval q-factor inversion from seismic reflection data. Geophysics, 80, R361–R373
Li J., Wang S., Yang D., Tang G. and Chen Y., 2018. Subsurface attenuation estimation using a novel hybrid method based on fwe function and power spectrum. Explor. Geophys., 49, 220–230
Lines L.R. and Treitel S., 1985. Wavelets, well logs and Wiener filters. First Break, 3 DOI: https://doi.org/10.3997/1365-2397.1985014
Liu W., Cao S., Wang Z., Jiang K., Zhang Q. and Chen Y., 2018. A novel approach for seismic time-frequency analysis based on high-order synchrosqueezing transform. IEEE Geosci. Remote Sens. Lett., 15, 1159–1163
Liu W. and Chen W., 2019. Recent advancements in empirical wavelet transform and its applications. IEEE Access, 7, 103770–103780
Liu W. and Duan Z., 2020. Seismic signal denoising using f-x variational mode decomposition. IEEE Geosci. Remote Sens. Lett., 17, 1313–1317
Liu W., Chen W. and Zhang Z., 2020. A novel approach for rolling bearing fault diagnosis based on high-order synchrosqueezing transform and detrended uctuation analysis. IEEE Access, 8, 12533–12541
Margrave G.F., Lamoureux M.P. and Henley D.C., 2011. Gabor deconvolution: Estimating reflectivity by nonstationary deconvolution of seismic data. Geophysics, 76, W15–W30
Misra S. and Chopra S., 2011. Mixed-phase wavelet estimationa case study. CSEG Recorder, 36, 33–35
Sacchi M.D. and Ulrych T.J., 2000. Nonminimum-phase wavelet estimation using higher order statistics. The Leading Edge, 19, 80–83
Sinha S., Routh P.S., Anno P.D. and Castagna J.P., 2005. Spectral decomposition of seismic data with continuous-wavelet transform. Geophysics, 70, P19–P25
Tao Y., Cao S., Ma Y. and Ma M., 2020. Second-order adaptive synchrosqueezing s transform and its application in seismic ground roll attenuation. IEEE Geosci. Remote Sens. Lett., 8, 1308–1312
van der Baan M., 2008. Time-varying wavelet estimation and deconvolution by kurtosis maximization. Geophysics, 73, V11–V18
van der Baan M., 2012. Bandwidth enhancement: Inverse q filtering or time-varying Wiener deconvolution? Geophysics, 77, V133–V142
Wang Y., 2015. Frequencies of the Ricker wavelet. Geophysics 80, A31–A37
Zhang R. and Fomel S., 2017. Time-variant wavelet extraction with a local-attribute-based time-frequency decomposition for seismic inversion. Interpretation, 5, SC9–SC16
Acknowledgements
The research was supported by the Development Program of China (Grant No. SQ2017YFGX030021). The authors gratefully acknowledge this financial support.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Jiang, Y., Cao, S., Chen, S. et al. A data-driven method for time-varying wavelet extraction based on the local frequency spectrum. Stud Geophys Geod 65, 70–85 (2021). https://doi.org/10.1007/s11200-020-1251-2
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11200-020-1251-2