Common-reflection-point （CRP） gather is a bridge that connects seismic data and petro- physical parameters. Pre-stack attributes extraction and pre-stack inversion, both of them are impor- tant means of reservoir pr...Common-reflection-point （CRP） gather is a bridge that connects seismic data and petro- physical parameters. Pre-stack attributes extraction and pre-stack inversion, both of them are impor- tant means of reservoir prediction. Quality of CRP gather usually has great impact on the accuracy of seismic exploration. Therefore, pre-stack CRP gathers noise suppression technology becomes a major research direction. Based on the vector decomposition principle, here we propose a method to suppress noise. This method estimates optimal unit vectors by searching in various directions and then sup- presses noise through vector angle smoothing and restriction. Model tests indicate that the proposed method can separate effective signal from noise very well and suppress random noise effectively in single wavenumber case. Application of our method to real data shows that the method can recover effective signal with good amplitude preserved from pre-stack noisy seismic data even in the case of low signal to noise ratio （SNR）.展开更多
基金supported by the National Science and Technology Major Project of China(No.2011ZX05024-001-01)
文摘Common-reflection-point （CRP） gather is a bridge that connects seismic data and petro- physical parameters. Pre-stack attributes extraction and pre-stack inversion, both of them are impor- tant means of reservoir prediction. Quality of CRP gather usually has great impact on the accuracy of seismic exploration. Therefore, pre-stack CRP gathers noise suppression technology becomes a major research direction. Based on the vector decomposition principle, here we propose a method to suppress noise. This method estimates optimal unit vectors by searching in various directions and then sup- presses noise through vector angle smoothing and restriction. Model tests indicate that the proposed method can separate effective signal from noise very well and suppress random noise effectively in single wavenumber case. Application of our method to real data shows that the method can recover effective signal with good amplitude preserved from pre-stack noisy seismic data even in the case of low signal to noise ratio （SNR）.
