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.
Abstract
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.
关键词
VD-seislet transform /
denoising /
self-adaptive threshold method /
H-curve
Key words
VD-seislet transform /
denoising /
self-adaptive threshold method /
H-curve
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基金
Supported by Project of National Natural Science Foundation of China (No.41004041)
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