Study of denoising method for nonhyperbolic prestack seismic reflection data
|
摘要
Removing random noise in seismic data is a key step in seismic data processing. A failed denoising may introduce many artifacts, and lead to the failure of final processing results. Seislet transform is a wavelet-like transform that analyzes seismic data following variable slopes of seismic events. The local slope is the key of seismic data. An earlier work used traditional normal moveout(NMO) equation to construct velocity-dependent(VD) seislet transform, which only adapt to hyperbolic condition. In this work, we use shifted hyperbola NMO equation to obtain more accurate slopes in nonhyperbolic situation. Self-adaptive threshold method was used to remove random noise while preserving useful signal. The synthetic and field data tests demonstrate that this method is more suitable for noise attenuation.
Removing random noise in seismic data is a key step in seismic data processing. A failed denoising may introduce many artifacts, and lead to the failure of final processing results. Seislet transform is a wavelet-like transform that analyzes seismic data following variable slopes of seismic events. The local slope is the key of seismic data. An earlier work used traditional normal moveout(NMO) equation to construct velocity-dependent(VD) seislet transform, which only adapt to hyperbolic condition. In this work, we use shifted hyperbola NMO equation to obtain more accurate slopes in nonhyperbolic situation. Self-adaptive threshold method was used to remove random noise while preserving useful signal. The synthetic and field data tests demonstrate that this method is more suitable for noise attenuation.
Removing random noise in seismic data is a key step in seismic data processing. A failed denoising may introduce many artifacts, and lead to the failure of final processing results. Seislet transform is a wavelet-like transform that analyzes seismic data following variable slopes of seismic events. The local slope is the key of seismic data. An earlier work used traditional normal moveout(NMO) equation to construct velocity-dependent(VD) seislet transform, which only adapt to hyperbolic condition. In this work, we use shifted hyperbola NMO equation to obtain more accurate slopes in nonhyperbolic situation. Self-adaptive threshold method was used to remove random noise while preserving useful signal. The synthetic and field data tests demonstrate that this method is more suitable for noise attenuation.
引文
Claerbout J F. 2008. Basic Earth imaging: stanford exploration project, http://sepwww.stanford.edu/sep/prof/.
Donoho D L, Johnstone J M. 1994. Ideal spatial adaptation by wavelet shrinkage. Biometrika, 81(3): 425-455.
Fomel S. 2006. Towards the seislet transform. SEG Technical Program Expanded Abstracts. Society of Exploration Geophysicists, 2847-2851.
Fomel S, Grechka V. 2001. Nonhyperbolic reflection moveout of P waves. An overview and comparison of reasons: Technical Report CWP-372. Golden:Colorado School of Mines.
Fomel S, Liu Y. 2010. Seislet transform and seislet frame. Geophysics, 75(3): V25-V38.
Gou F Y, Liu C, Liu Y, et al. 2014. Complex seismic wavefield interpolation based on the Bregman iteration method in the sparse transform domain. Applied Geophysics, 11(3): 277-288.
Hansen P C, O'Leary D P. 1993. The use of the L-curve in the regularization of discrete ill-posed problem. SIAM Journal on Scientific Computing, 14(6): 1487-1503.
Lu W K, Liu J. 2007. Random noise suppression based on discrete cosine transform//SEG Technical Program Expanded Abstracts, 2668-2672. https://doi.org/10.1190/1.2793021
Liu Y, Fomel S, Liu C. 2015. Signal and noise separation in prestack seismic data using velocity-dependent seislet transform. Geophysics, 80(6): WD117-WD128.
Liu Y, Fomel S, Liu C, et al. 2009. High-order seislet transform and its application of random noise attenuation. Chinese Journal of Geophysics. 52(8): 2142-2151. (in Chinese)
Montefusco L B, Papi S. 2003. A parameter selection method for wavelet shrinkage denoising. BIT Numerical Mathematics, 43(3): 611-626.
Neelamani R, Baumstein A I, Gillard D G, et al. 2008. Coherent and random noise attenuation using the curvelet transform. The Leading Edge, 27(2): 240-248.
Ristau P J, Moon W M. 2001. Adaptive filtering of random noise in 2-D geophysical data. Geophysics, 66(1): 342-349.
Yang P, Fomel S. 2015.Seislet-based morphological component analysis using scale-dependent exponential shrinkage. Journal of Applied Geophysics, 118: 66-74.
Donoho D L, Johnstone J M. 1994. Ideal spatial adaptation by wavelet shrinkage. Biometrika, 81(3): 425-455.
Fomel S. 2006. Towards the seislet transform. SEG Technical Program Expanded Abstracts. Society of Exploration Geophysicists, 2847-2851.
Fomel S, Grechka V. 2001. Nonhyperbolic reflection moveout of P waves. An overview and comparison of reasons: Technical Report CWP-372. Golden:Colorado School of Mines.
Fomel S, Liu Y. 2010. Seislet transform and seislet frame. Geophysics, 75(3): V25-V38.
Gou F Y, Liu C, Liu Y, et al. 2014. Complex seismic wavefield interpolation based on the Bregman iteration method in the sparse transform domain. Applied Geophysics, 11(3): 277-288.
Hansen P C, O'Leary D P. 1993. The use of the L-curve in the regularization of discrete ill-posed problem. SIAM Journal on Scientific Computing, 14(6): 1487-1503.
Lu W K, Liu J. 2007. Random noise suppression based on discrete cosine transform//SEG Technical Program Expanded Abstracts, 2668-2672. https://doi.org/10.1190/1.2793021
Liu Y, Fomel S, Liu C. 2015. Signal and noise separation in prestack seismic data using velocity-dependent seislet transform. Geophysics, 80(6): WD117-WD128.
Liu Y, Fomel S, Liu C, et al. 2009. High-order seislet transform and its application of random noise attenuation. Chinese Journal of Geophysics. 52(8): 2142-2151. (in Chinese)
Montefusco L B, Papi S. 2003. A parameter selection method for wavelet shrinkage denoising. BIT Numerical Mathematics, 43(3): 611-626.
Neelamani R, Baumstein A I, Gillard D G, et al. 2008. Coherent and random noise attenuation using the curvelet transform. The Leading Edge, 27(2): 240-248.
Ristau P J, Moon W M. 2001. Adaptive filtering of random noise in 2-D geophysical data. Geophysics, 66(1): 342-349.
Yang P, Fomel S. 2015.Seislet-based morphological component analysis using scale-dependent exponential shrinkage. Journal of Applied Geophysics, 118: 66-74.