Noise attenuation using adaptive wavelet threshold based on CEEMD in f-x domain

Ji, M., Zhao, X.Y., Zhu, W., You, Y.C., Zhang, J.F., Shang, M., Chuai, X., Xue, Y., Lian, C.H. and Chen, W., 2023. Noise attenuation using adaptive wavelet threshold based on CEEMD inf-x domain. Journal of Seismic Exploration, 32: 131-153. Noise attenuation plays an important role in seismic signal processing. Complementary Ensemble Empirical Mode Decomposition (CEEMD) is a classic algorithm for signal decomposition and is usually used for denoising. This algorithm is used to attenuate random noise by removing some high-frequency intrinsic mode functions (IMFs), apparently resulting in insufficient noise attenuation and loss of effective signal. Wavelet threshold denoising can be used to attenuate the useless part and enhance the useful part of the signal by selecting the appropriate threshold. Wavelet threshold denoising is often combined with CEEMD in time domain to achieve relatively good effects, but some of signal between seismic traces are fragmented. This paper proposes improved adaptive wavelet threshold denoising based on CEEMD in f-x domain. The new threshold function we proposed is constructed on the basis of the traditional soft and hard threshold functions, which overcomes the constant deviation and avoids the phase step phenomenon. The processing results for simulated and field data show that the proposed method has better attenuation effect on random noise than traditional methods.
- Chakraborty, A. and Okaya, D., 1995. Frequency time decomposition of seismic data
- using wavelet based methods. Geophysics, 60(6): 1906-1916.
- Chen, Y. and Ma, J., 2014. Random noise attenuation by f-x empirical mode
- decomposition predictive filtering. Geophysics, 79(3): V81-V91, 2014.
- Chen, Y., Zhang, G., Gan, S. and Zhang, C., 2015. Enhancing seismic reflections
- using empirical mode decomposition in the flattened domain. J. Appl.
- Geophys., 119: 99-105.
- Chen, W., Chen, Y. and Cheng, Z., 2017. Seismic time-frequency analysis using
- an improved empirical mode decomposition algorithm. J. Seismic
- Explor., 26: 367-380.
- Chen, W., Xie, J., Zu, S., Gan, S. and Chen, Y., 2017. Multiple reflection noise
- attenuation using adaptive randomized order empirical mode
- decomposition. IEEE Geosci. Remote Sens. Lett., 14: 18-22.
- Chen, Y. and Fomel, S., 2018. Emd-seislet transform. Geophysics, 83(1): A27-
- A32.
- Colominas, M., Schlotthauer, G. and Torres, M., 2014. Improved complete ensemble
- emd: A suitable tool for biomedical signal processing, Biomed. Signal
- Process. Contr., 14(11): 19-29.
- Fomel, S., 2008. Adaptive multiple subtraction using regularized nonstationary
- regression. Geophysics, 74(1): V25-V33.
- Griffin, D. and Jae, L., 1984. Signal estimation from modified short time Fourier
- transform. IEEE Transact. Acoust., Speech Sign. Process., 32: 236-243.
- Han, J. and Mirko, V., 2015. Microseismic and seismic denoising via ensemble
- empirical mode decomposition and adaptive thresholding, Geophysics,
- 80(6): KS69-KS80.
- Hess, N. and Wickerhauser, M.,1996. Wavelets and time-frequency analysis.
- Proc. IEEE, 84(4): 523-540.
- Huang, N., Shen, Z., Long, S., Wu, M., Shi, H., Zheng, Q., Yen, N., Tung, C.
- and Liu, H., 1998. The empirical mode decomposition and the Hilbert
- spectrum for nonlinear and nonstationary time series analysis. Proc.
- Mathemat. Physic. Engineer. Conf., 454: 903-995.
- Huang, W., Wang, R., Chen, Y., Li, H. and Gan, S., 2016. Damped multichannel
- singular spectrum analysis for 3D random noise attenuation, Geophysics,
- 81(4): V261-V270.
- Li, H., Wang, R., Cao, S., Chen, Y. and Huang, W., 2016. A method for low-
- frequency noise suppression based on mathematical morphology in
- microseismic monitoring. Geophysics, 81(3): V159-V167.
- Lin, H., Li, Y., Ma, H., Yang, B. and Dai, J., 2015. Matching pursuit based
- spatial trace time frequency peak filtering for seismic random noise
- attenuation. IEEE Geosci. Remote Sens. Lett., 12: 394-398.
- Liu, G., Fomel, S. and Chen, X., 2011. Time frequency analysis of seismic data
- using local attributes. Geophysics, 76(6): 23P.
- Liu, G., Chen, X., Du, J. and Wu, K., 2012. Random noise attenuation using f-x
- regularized nonstationary autoregression, Geophysics, 77(2): V61-V69.
- Liu, G. and Chen, X., 2013. Noncausal f-x-y regularized nonstationary prediction
- filtering for random noise attenuation on 3D seismic data. J. Appl.
- Geophys., 93: 60-66.
- Liu, W., Cao, S. and Chen, Y., 2016. Seismic time frequency analysis via
- empirical wavelet transform. IEEE Geosci. Remote Sens. Lett., 13: 28-32.
- Liu, W. and Chen, W., 2019. Recent advancements in empirical wavelet
- transform and its applications, IEEE Access, 7: 1.
- Med, A., 1998. Time frequency and wavelets in biomedical signal processing. IEEE
- Engineer. Medic. Biol. Mag., 17 (6): 15-97.
- Morlet, J., Arens, G., Fourgeau, E. and Giard, D., 1982. Wave propagation and
- sampling theory part 1i: Sampling theory and complex waves. Geophysics,
- 47: 222-236.
- Ouadfeul, S. and Aliouane, L., 2014. Random seismic noise attenuation data using
- the discrete and the continuous wavelet transforms. Arab. J. Geosci., 7,
- 2531-2537.
- Qu, S., Zhou, H., Liu, R., Chen, Y., Zu, S., Yu, S., Yuan, J. and Yang, Y., 2016.
- Deblending of simultaneous source seismic data using fast iterative
- shrinkage thresholding algorithm with firm thresholding, Acta Geophys.,
- 64:1064-1092.
- Stockwell, R. and Mansinha, L., 1996. Localisation of the complex spectrum:
- The S-transform. IEEE Transact. Sign. Process., 44(4): 998-1001.
- Sun, M., Li, Z., Li, Q. and Wang, W., 2020. A noise attenuation method for
- weak seismic signals based on compressed sensing and ceemd. IEEE
- Access, 8(99): 1.
- Wang, B., Wu, R., Chen, X. and Li, J., 2015. Simultaneous seismic data
- interpolation and denoising with a new adaptive method based on dreamlet
- transform. Geophys. J. Internat., 201: 1180-1192.
- Wu, Z. and Huang, N., 2009. Ensemble empirical mode decomposition: a noise
- assisted data analysis method. Advan. Adapt. Data Analys., 1(1): 1-41.
- Xu, X., Liu, W., Wang, J., Mu, D. and Qian, Z., 2011. Detection of voltage
- flicker caused by intergrated wind power based on adaptive lifting
- wavelet transform, Proced. Engineer., 15: 5105-5110.
- Zhang, L., Bao, P. and Pan, Q., 2001. Threshold analysis in wavelet based
- denoising. Electron. Lett., 37(24): 1485-1486.
- Zhang, R. and Ulrych, T., 2003. Physical wavelet frame denoising, Geophysics,
- 68: 225.
- Zhu, X., Shen, Z., Eckermann, S., Bittner, M., Hirota, I. and Yee, J., 1997.
- Gravity wave characteristics in the middle atmosphere derived from the
- empirical mode decomposition method. J. Geophys. Res. Atmosph.,
- 102(D14): 16.54-16.561.