Ground-roll separation of seismic data based on morphological component analysis in two-dimensional domain
Xu Xiao-Hong1, Qu Guang-Zhong1, Zhang Yang1, Bi Yun-Yun1, and Wang Jin-Ju2
1. School of Computer and Information, Hefei University of Technology, Hefei Anhui 230009, China.
2. School of Mathematics, Hefei University of Technology, Hefei Anhui 230009, China.
Abstract：
Ground roll is an interference wave that severely degrades the signal-to-noise ratio of seismic data and affects its subsequent processing and interpretation. In this study, according to differences in morphological characteristics between ground roll and reflected waves, we use morphological component analysis based on two-dimensional dictionaries to separate ground roll and reflected waves. Because ground roll is characterized by low-frequency, low-velocity, and dispersion, we select two-dimensional undecimated discrete wavelet transform as a sparse representation dictionary of ground roll. Because of a strong local correlation of the reflected wave, we select two-dimensional local discrete cosine transform as the sparse representation dictionary of reflected waves. A sparse representation model of seismic data is constructed based on a two-dimensional joint dictionary then a block coordinate relaxation algorithm is used to solve the model and decompose seismic record into reflected wave part and ground roll part.The good effects for the synthetic seismic data and application of real seismic data indicate that when using the model, strong-energy ground roll is considerably suppressed and the waveform of the reflected wave is effectively protected.
Xu Xiao-Hong,Qu Guang-Zhong,Zhang Yang et al. Ground-roll separation of seismic data based on morphological component analysis in two-dimensional domain[J]. APPLIED GEOPHYSICS, 2016, 13(1): 116-126.
[1]
Askari, R., and Siahkoohi, H. R., 2008, Ground roll attenuation using the S and x-f-k transforms:Geophysical Prospecting,56(1), 105-114.
[2]
Candes, E., Demanet, L., Donoho, D., et al., 2006, Fast discrete curvelet transforms: Multiscale Modeling & Simulation, 5(3), 861-899.
[3]
Chen, W. C., Wang C., and Gao, J. H., 2013, Sparsity optimized separation of Ground-roll noise based on morphological diversity of seismic waveform components: Chinese J. Geophys., 56(8), 2771-2782.
[4]
Chen, Y., Jiao, S., Ma, J., Chen, H., Zhou, Y., and Gan, S., 2015, Ground-Roll Noise Attenuation Using a Simple and Effective Approach Based on Local Band-Limited Orthogonalization:Geoscience and Remote Sensing Letters, IEEE,12(11), 2316-2320.
[5]
Deighan, A. J., and Watts, D. R., 1997, Ground-roll suppression using the wavelet transform:Geophysics,62(6), 1896-1903.
[6]
Donoho, D. L., 2006, Compressed sensing: IEEE Transactions on Information Theory,52(4), 1289-1306.
[7]
Elad, M., Starck, J. L., Querre, P., et al., 2005, Simultaneous cartoon and texture image inpainting using morphological component analysis : Applied and Computational Harmonic Analysis, 19(3), 340-358.
[8]
Gui, L. H., 2010, Application of basis pursuit to ground rolls separation and suppresion:Master Thesis, Southwest Jiaotong University, ChenDu.
[9]
Karsli, H., and Bayrak, Y., 2004, Using the Wiener-Levinson algorithm to suppress ground-roll: Journal of Applied Geophysics, 55(3), 187-197.
[10]
Li, H. S.,Wu, G. C., and Yin X. Y., 2013, Amplitude-preserved ground-roll suppression method based on morphological component analysis: OGP, 48(3), 351-358.
[11]
Ma, J., Plonka, G., and Chauris, H., 2010, A new sparse representation of seismic data using adaptive easy-path wavelet transform:Geoscience and Remote Sensing Letters, IEEE,7(3), 540-544.
[12]
Rubinstein, R., Bruckstein, A. M., and Elad, M., 2010, Dictionaries for sparse representation modeling:Proceedings of the IEEE,98(6), 1045-1057.
[13]
Starck, J. L., Elad, M., and Donoho, D., 2004, Redundant multiscale transforms and their application for morphological component separation: Advances in Imaging and Electron Physics, 132, 287-348.
[14]
Starck, J. L., Elad, M., and Donoho, D. L., 2005, Image decomposition via the combination of sparse representations and a variational approach: IEEE Transactions on Image Processing, 14(10), 1570-1582.
[15]
Starck, J. L., Fadili, J., and Murtagh, F., 2007, The undecimated wavelet decomposition and its reconstruction: IEEE Transactions on Image Processing, 16(2), 297-309.
[16]
Trad, D., Ulrych, T., and Sacchi, M., 2003, Latest views of the sparse Radon transform: Geophysics, 68(1), 386-399.
[17]
Tang, G., Ma, J. W., and Yang, H. Z., 2012, Seismic data denoising based on learning-type overcomplete dictionaries: Applied Geophysics, 9(1), 27-32.
[18]
Wang, W., Gao, J., Chen, W., et al., 2012, Data adaptive ground-roll attenuation via sparsity promotion: Journal of Applied Geophysics, 83, 19-28.
[19]
Wang, Y., and Wu, R. S., 2000, Seismic data compression by an adaptive local cosine/sine transform and its effects on migration: Geophysical Prospecting, 48(6), 1009-1031.
[20]
Zhang, H., Chen, X. H., and Wu, X. M., 2013, Seismic data reconstruction based on CS and Fourier theory:Applied Geophysics,10(2), 170-180.
[21]
Zhang, H., Pan, D. M., and Zhang, X. Y., 2007, Application of 2-D wavelet transfomation in eliminating ground roll interference: Geophsical Prospecting for Petroleum, 46(2), 147-150.
[22]
Zheng, J., J., Yin, X. Y., Zhang, G. Z., Wu, G. H., and Zhang, Z. S., 2010, The surface wave suppression using the second generation curvelet transform: Applied Geophysics, 7(4), 325-335.
2016, Vol. 13 
