The conventional total variation(TV)regularization model only considers the first-order derivative information in the horizontal and vertical directions.When dealing with prestack seismic data with curved reflec-tion events,it can severely damage the amplitude information and cause"staircase effects"by suppressing the lateral gradient characteristics of the amplitude.The local dip information of seismic data is often applied to im-prove the amplitude-preserving ability of the TV model.However,the calculation of local dip information itself will be impacted by noise.To address this issue,this paper proposes a high-order TV regularization model to suppress random noise in prestack seismic data in the domain of normal moveout(NMO).This method first transforms the prestack seismic data into the NMO domain,NMO is robust to noise and avoids the calculation of the local dip angle.In the NMO domain,the curved event is flattened,and then high-order TV denoising is per-formed.Finally,the prestack seismic data are restored through inverse NMO.Taking the second-order deriva-tive as an example,a high-order TV regularization inversion denoising objective function is constructed,and a fast optimization method is derived under the split Bregman optimization framework.The processing results of synthetic seismic data and actual seismic data show that this method can not only effectively suppress random noise but also eliminate amplitude distortion caused by curved reflection events and"staircase effects",im-proving the amplitude preservation performance of the TV denoising method.
关键词
高阶TV正则化/动校正(NMO)域/随机噪声/保幅去噪/分裂Bregman优化框架
Key words
high order TV regularization/normal moveout(NMO)domain/random noise suppression/ampli-tude-preserving denoising/split Bregman optimization framework
维普
万方数据