JSEJournal of Seismic Exploration0963-0651AccScience PublishingArticleSeismic time-frequency analysis using an improved empirical mode decomposition algorithmhttps://geophysical-press.com/journal/JSE/articles/210CHENWEI,CHENYANGKANG,CHENGZIXIANG20172642017-08-01Chen, W., Chen, Y. and Cheng, Z., 2017. Seismic time-frequency analysis using an improved empirical mode decomposition algorithm. Journal of Seismic Exploration, 26: 367-380. Among the time-frequency analysis approaches, the EMD-based approaches have been proven to show higher spectral-spatial resolution than the traditional approaches. However, the mode mixing problem always exists in these approaches which will affect the subsequent interpretation performance. In this paper, we apply a novel improved complete ensemble empirical mode decomposition (ICEEMD) technique to time-frequency analysis of seismic data. The ICEEMD approach can help decompose a 1D non-stationary signal into intrinsic mode functions with less noise and more physical meaning, and result in a higher frequency resolution in the time-frequency maps. The application of the algorithm to 1D seismic signal can help obtain a more meaningful analysis regarding the non-stationary components. Its application to 2D and 3D seismic data has the potential to enable a better geological and geophysical interpretation. We use a 1D real seismic trace, a 2D seismic section and a 3D seismic cube to show the superior performance of the proposed approach.Empirical Mode Decomposition (EMD), time-frequency analysis, seismic data, Improved Complete Ensemble Empirical Mode Decomposition (ICEEMD), subsurface characterization.

Allen, J.B., 1977. Short term spectral analysis, synthetic and modification by discrete fouriertransform. IEEE Trans. Acoust. Speech Signal Process., 25: 235-238.Cai, H., He, Z. and Huang, D., 2011. Seismic data denoising based on mixed time-frequencymethods. Appl. Geophys., 8: 319-327.Chakraborty, A. and Okaya, D., 1995. Frequency-time decomposition of seismic data usingwavelet-based methods. Geophysics, 60: 1906-1916.Chen, W., Chen, Y. and Liu, W., 2016. Ground roll attenuation using improved complete ensembleempirical mode decomposition. J. Seismic Explor., 25: 485-495.Chen, W., Xie, J., Zu, S., Gan, S. and Chen, Y., 2017a. Multiple reflections noise attenuationusing adaptive randomized-order empirical mode decomposition. IEEE Geosci. Remote Sens.Lett., 14: 18-22.Chen, W., Zhang, D. and Chen, Y., 2017b. Random noise reduction using a hybrid method basedon ensemble empirical mode decomposition. J. Seismic Explor., 26: 25-47.Chen, Y., 2016. Dip-separated structural filtering using seislet thresholding and adaptive empiricalmode decomposition based dip filter. Geophys. J. Internat., 206: 457-469.Chen, Y. and Fomel, S., 2015. Random noise attenuation using local signal-and-noiseorthogonalization. Geophysics, 80: WD1-WD9.Chen, Y. and Jin, Z., 2016. Simultaneously removing noise and increasing resolution of seismic datausing waveform shaping. IEEE Geosci. Remote Sens. Lett., 13: 102-104.Chen, Y., Liu, T., Chen, X., Li, J. and Wang, E., 2014a. Time-frequency analysis of seismic datausing synchrosqueezing wavelet transform. J. Seismic Explor., 23: 303-312.Chen, Y., Zhou, C., Yuan, J. and Jin, Z., 2014b. Application of empirical mode decomposition torandom noise attenuation of seismic data. J. Seismic Explor., 23: 481-495.Cohen, L., 1995. Time-frequency Analysis. Prentice Hall, Inc., New York.Colominas, M.A., Schlotthauer, G. and Torres, M.E., 2014. Improve complete ensemble emd: Asuitable tool for biomedical signal processing. Biomed. Signal Process. Contr., 14: 19-29.Colominas, M.A., Schlotthauer, G., Torres, M.E. and Flandrin, P., 2012. Noise-assisted emdmethods in action. Adv. Adapt. Data Anal., 4: 1250025.Daubechies, I., Lu, J. and Wu, H.-T., 2011. Synchrosqueezed wavelet transforms: An empiricalmode decomposition-like tool. Appl. Computat. Harmon. Analys., 30: 243-261.Daubechies, I. and Maes, S., 1996. A nonlinear squeezing of the continuous wavelet transformbased on auditory nerve models wavelets in medicine and biology. CRC Press, Boca Raton:527-546.Fomel, S., 2010. Predictive painting of 3-D seismic volumes. Geophysics, 75: A25-A30.Fomel, S., 2013. Seismic data decomposition into spectral components using regularizednonstationary autoregression. Geophysics, 78: 069-076.Gan, S., Wang, S., Chen, Y., Chen, J., Zhong, W. and Zhang, C., 2016. Improved random noiseattenuation using fx empirical mode decomposition and local similarity. Appl. Geophys. 13:127-134.TIME-FREQUENCY ANALYSIS VIA ICEEMD 379Han, J. and van der Baan, M., 2013. Empirical mode decomposition for seismic time-frequencyanalysis. Geophysics, 78: 09-019.Han, J. and van der Baan, M., 2015. Microseismic and seismic denoising via ensemble empiricalmode decomposition and adaptive thresholding. Geophysics, 80: KS69-KS80.Herrer, R.H., Han, J. and van der Baan, M., 2014. Applications of the synchrosqueezing transformin seismic time-frequency analysis. Geophysics, 79: V55-V64.Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., 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. London Series A, 454:903-995.Huang, W., Wang, R., Chen, Y., Li, H. and Gan, S., 2016. Damped multichannel singularspectrum analysis for 3D random noise attenuation. Geophysics, 81(4): V261-V270Jeffrey, C., and William, J., 1999. On the existence of discrete Wigner distributions. IEEE SignalProc. Lett., 6: 304-306.Kong, D., Peng, Z., He, Y. and Fan, H., 2016. Seismic random noise attenuation using directionaltotal variation in the shearlet domain. J. Seismic Explor., 25: 321-338.Kopecky, M., 2010. Ensemble empirical mode decomposition: Image data analysis with white-noisereflection. Acta Polytechn., 50: 49-56.Lin, H., Li, Y., Ma, H., Yang, B. and Dai, J., 2015. Matching-pursuit-based spatial-tracetime-frequency peak filtering for seismic random noise attenuation. IEEE Geosci. RemoteSens. Lett., 12: 394-398.Liu, C., Wang, D., Hu, B. and Wang, T., 2016a. Seismic deconvolution with shearlet sparsityconstrained inversion. J. Seismic Explor., 25: 433-445.Liu, G., Fomel, S. and Chen, X., 2011. Time-frequency analysis of seismic data using localattributes. Geophysics, 76: P23-P34.Lin a380 CHEN, CHEN & CHENGWu, X., Uden, R. and Chapman, M., 2016b. Shale anisotropic elastic modeling and seismicreflections. J. Seismic Explor., 25: 419-432.Wu, Z. and Huang, N.E., 2009. Ensemble empirical mode decomposition: A noise-assisted dataanalysis method. Advanc. Adapt. Data Analys., 1: 1-41.Xie, Q., Chen, Y., Zhang, G., Gan, S. and Wang, E., 2015. Seismic data analysis usingsynchrosqueezeing wavelet transform - a case study applied to boonsville field. ExtendedAbstr., 77th EAGE Conf., Madrid.doi: 10.3997/2214-4609.201412752.Zhang, Q., Chen, Y., Guan, H. and Wen, J., 2016. Well-log constrained inversion for lithologycharacterization: a case study at the jz25-1 oil field, China. J. Seismic Explor., 25: 121-129.Zhang, X., Han, L., Wang, Y. and Shan, G., 2010. Seismic spectral decomposition fast matchingpursuit algorithm and its application. Geophys. Prosp. Petrol., 49: 1-6.Zhong, W., Chen, Y., Gan, S. and Yuan, J., 2016. Li norm regularization for 3D seismic datainterpolation. J. Seismic Explor., 25: 257-268.