Seismic trace noise removal by smoothed SureShrink

Vargas, R.N. and Veiga, A.C.P., 2020. Seismic trace noise removal by smoothed SureShrink. Journal of Seismic Exploration, 29: 363-370. Seismic traces are usually corrupted by Additive White Gaussian Noise (AWGN). AWGN hinders the evaluation of seismic attributes and can lead to distortions during seismic interpretation. Therefore, the development of methods that can effectively remove the noise and extract the signal from the seismic trace is critical. Here we propose a new seismic trace noise removal method called SureShrinkWin, which evaluates the estimates obtained by the SureShrink method when SureShrink is applied in signal windows. To validate the efficacy of the SureShrinkWin method, we performed a Monte Carlo Simulation that considered sixteen seismic traces that were obtained from the astsa R package.
- Condat, L., 2013. A direct algorithm for 1D total variation denoising. IEEE Sign.Process. Lett., 20: 1054-1057.
- Damati, A., Daoud, 0. and Hamarsheh, Q., 2016. Enhancing the odd peaks detection in
- OFDM systems using wavelet transforms. Internat. J. Communic., Netw. Syst.Sci, 9: 295-303.
- Donoho, D.L. and Johnstone, I.M., 1995. Adapting to unknown smoothness via waveletshrinkage. J. Am. Statist. Assoc., 90: 432, 1200-1224.
- Gomez, J.L. and Velis, D.R., 2016. 'A simple method inspired by empirical modedecomposition for denoising seismic data. Geophysics, 81(6): V403-V413.
- Han, J. and van der Baan, M., 2015. Microseismic and seismic denoising via ensembleempirical mode decomposition and adaptive thresholding. Geophysics, 80(6):KS69-KS80.
- Han, G. and Xu, Z., 2016. Electrocardiogram signal denoising based on a new improvedwavelet thresholding. Rev. Sci. Instrum., 87(8): 084303.
- Liu, Y., Dang, B., Li, Y., Lin, H. and Ma, H., 2016. Applications of Savitzky-Golayfilter for seismic random noise reduction. Acta Geophys. 64: 101-124.
- Meyer, Y., 1993. Wavelets: Algorithms and Applications. Soc. Industr. Appl.Mathemat, Philadelphia, PA.
- Mohanalin, J., Prabavathy, S., Torrents-Barrena, J., Puig, D. and Beena, M., 2016.
- A novel wavelet seismic denoising method using type ii fuzzy. Appl. SoftComput., 48: 7-S521.
- Mooney, CL. 1997. Monte Carlo Simulation. Sage Publications, Vol. 116. Thousandaks, CA.
- Mousavi, S.M. and Langston, C.A., 2016. Hybrid seismic denoising using higher-orderstatistics and improved wavelet block thresholding. Bull. Seismol. Soc. Am.,106: 1380-1393.
- Ning, X. and Selesnick, I.W., 2013. ECG enhancement and QRS detection based onsparse derivatives. Biomed. Sign. Process. Contr., 8: 713-723.
- Percival, D.B. and Walden, A.T., 2006. Wavelet Methods for Time Series Analysis.Cambridge University Press, Cambridge.
- Perrone, D. and Favaro, P., 2016. A clearer picture of total variation blinddeconvolution. IEEE Transact. Patt. Analys. Mach. Intell., 38: 1041-1055.
- Stein, C.M., 1981. Estimation of the mean of a multivariate normal distribution. Ann.Statist., 9: 1135-1151.
- Vargas, R.N. and Veiga, A.C.P., 2017. Seismic trace noise reduction by wavelets anddouble threshold estimation. IET Signal Process., 1069-1075.