Abstract
A noise-reduction method with sliding windows in the frequency-space (f-x) domain, called the local f-x Cadzow noise-reduction method, is presented in this paper. This method is based on the assumption that the signal in each window is linearly predictable in the spatial direction while the random noise is not. For each Toeplitz matrix constructed by constant frequency slice, a singular value decomposition (SVD) is applied to separate signal from noise. To avoid edge artifacts caused by zero percent overlap between windows and to remove more noise, an appropriate overlap is adopted. Besides flat and dipping events, this method can enhance curved and conflicting events. However, it is not suitable for seismic data that contains big spikes or null traces. It is also compared with the SVD, f-x deconvolution, and Cadzow method without windows. The comparison results show that the local Cadzow method performs well in removing random noise and preserving signal. In addition, a real data example proves that it is a potential noise-reduction technique for seismic data obtained in areas of complex formations.
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Bekara M and van der Baan M. Local singular value decomposition for signal enhancement of seismic data. Geophysics. 2007. 72(2): V59–V65
Bekara M and van der Baan M. Random and coherent noise attenuation by empirical mode decomposition. Geophysics. 2009. 74(5): V89–V98
Blu T, Dragotti P L, Vetterli M, et al. Sparse sampling of signal innovations: Theory, algorithms and performance bounds. IEEE Signal Processing Magazine. 2008. 25: 31–40
Cadzow J A. Signal enhancement: A composite property mapping algorithm. IEEE Transaction On Acoustics, Speech, and Signal Processing. 1988. 36(1): 49–62
Canales L L. Random noise reduction. 54th Annual International Meeting, SEG, Expanded Abstracts. 1984. 525–527
Lu W K and Mou Y G. An improved SVD filter. Oil Geophysical Prospecting. 1996. 31(5): 736–741 (in Chinese)
Lu W K. Adaptive noise attenuation of seismic images based on singular value decomposition and texture direction detection. Journal of Geophysics and Engineering. 2006. 3(1): 28–34
Naghizadeh M and Sacchi M D. Multistep autoregressive reconstruction of seismic records. Geophysics. 2007. 72(6): V111–V118
Shen H Y and Li Q C. SVD (singular value decomposition) seismic wave field noise elimination in frequency domain. Oil Geophysical Prospecting. 2010. 45(2): 185–189 (in Chine se)
Stephenson D S. Linear prediction and maximum entropy methods in NMR spectroscopy. Progress in NMR spectroscopy. 1988. 20: 516–626
Trickett S. F-x eigenimage noise suppression. 72nd Annual International Meeting, SEG, Expanded Abstracts. 2002. 2166–2169
Trickett S. F-xy eigenimage noise suppression. Geophysics. 2003. 68(2): 751–759
Trickett S. F-xy Cadzow noise suppression. 78th Annual International Meeting, SEG, Expanded Abstracts. 2008. 2586–2590
Tyapkin Y K, Marmalyevskyy N Y, Gornyak Z V, et al. Source-generated noise suppression using the singular value decomposition. Petroleum Science. 2005. 2(2): 57–65
Ulrych T J, Freire S and Siston P. Eigenimages processing of seismic section. 58th Annual International Meeting, SEG, Expanded Abstracts. 1988. 1261–1265
Yuan S Y and Wang S X. Noise attenuation without spatial assumptions about seismic coherent events. 80th Annual International Meeting, SEG, Expanded Abstracts. 2010. 3524–3528
Zwartjes P M and Sacchi M D. Fourier reconstruction of nonuniformly sampled, aliased seismic data. Geophysics. 2007. 72(1): V21–V23
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Yuan, S., Wang, S. A local f-x Cadzow method for noise reduction of seismic data obtained in complex formations. Pet. Sci. 8, 269–277 (2011). https://doi.org/10.1007/s12182-011-0144-y
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DOI: https://doi.org/10.1007/s12182-011-0144-y