Random noise attenuation by f–x spatial projection-based complex empirical mode decomposition predictive filtering
Ma Yan-Yan1,2, Li Guo-Fa1,2, Wang Yao-Jun1,2, Zhou Hui1,2, and Zhang Bao-Jiang3
1. State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, Beijing 102249, China.
2. CNPC Key Laboratory of Geophysical Prospecting, China University of Petroleum, Beijing 102249, China.
3. Petroleum Exploration and Production Research Institute, SINOPEC, Beijing 100083, China.
Abstract The frequency–space (f–x) empirical mode decomposition (EMD) denoising method has two limitations when applied to nonstationary seismic data. First, subtracting the first intrinsic mode function (IMF) results in signal damage and limited denoising. Second, decomposing the real and imaginary parts of complex data may lead to inconsistent decomposition numbers. Thus, we propose a new method named f–x spatial projection-based complex empirical mode decomposition (CEMD) prediction filtering. The proposed approach directly decomposes complex seismic data into a series of complex IMFs (CIMFs) using the spatial projection-based CEMD algorithm and then applies f–x predictive filtering to the stationary CIMFs to improve the signal-to-noise ratio. Synthetic and real data examples were used to demonstrate the performance of the new method in random noise attenuation and seismic signal preservation.
This work is supported financially by the National Natural Science Foundation (No. 41174117) and the Major National Science and Technology Projects (No. 2011ZX05031–001).
Cite this article:
Ma Yan-Yan,Li Guo-Fa,Wang Yao-Jun et al. Random noise attenuation by f–x spatial projection-based complex empirical mode decomposition predictive filtering[J]. APPLIED GEOPHYSICS, 2015, 12(1): 47-54.
[1]
Abma, R., and Claerbout, J., 1995, Lateral prediction for noise attenuation by t−x and f−x techniques: Geophysics, 60(6), 1887-1896.
[2]
Battista, B. M., Knapp, C., McGee, T., and Goebel, V., 2007, Application of the empirical mode decomposition and Hilbert-Huang transform to seismic reflection data: Geophysics, 72(2), 29-37.
[3]
Bekara, M., and van der Baan, M., 2009, Random and coherent noise attenuation by empirical mode decomposition: Geophysics, 74(5), 89-98.
[4]
Canales, L. L., 1984, Random noise reduction: 54th Annual International Meeting, SEG, Expanded Abstracts, 525-527.
[5]
Gulunay, N., 1986, FXDECON and complex Wiener prediction filter: 56th Annual International Meeting, SEG, Expanded Abstracts, 279-281.
[6]
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 for nonlinear and non-stationary time series analysis: Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 454(1971), 903-995.
[7]
Ioup, J. W., and Ioup, G. E., 1998, Noise removal and compression using a wavelet transform: 68th Annual International Meeting, SEG, Expanded Abstracts, 1076-1079.
[8]
Rilling, G., Flandrin, P., Goncalves, P., and Lilly, J. M., 2007, Bivariate Empirical Mode Decomposition: IEEE Signal Processing Letters, 14(12), 936-939.
[9]
Sacchi, M. D., and Kuehl, H., 2001, ARMA formulation of FX prediction error filters and projection filters: Journal of Seismic Exploration, 9(3), 185-197.
[10]
Soubaras, R., 1994, Signal-preserving random noise attenuation by the f-x projection: 64th Annual International Meeting, SEG, Expanded Abstracts, 1576-1579.
[11]
Trickett, S. R., 2003, F-xy eigenimage noise suppression: Geophysics, 68(2), 751-759.
[12]
Wang, D. L., Tong, Z. F., Tang, C., and Zhu, H., 2010, An iterative curvelet thresholding algorithm for seismic random noise attenuation: Applied Geophysics, 7(4), 315-324.
[13]
Yuan, S. Y., and Wang, S. X., 2011, A local f-x Cadzow method for noise reduction of seismic data obtained in complex formations: Petroleum Science, 8(3), 269-277.
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