Seismic Data Reconstruction Based on Curvelet-transform and Joint Method
The collection of seismic data in field surveys is often affected by uncontrollable factors such as terrain,resulting in irregular and missing seismic data.In order to not compromise the sub-sequent seismic data processing,it is necessary to reconstruct the missing seismic data.However,traditional reconstruction algorithms often have slow convergence rates.To address this issue,a new joint operator is proposed,which combines the fast threshold iterative method with the linear Breg-man method.This combination further improves the efficiency of the algorithm on the basis of the ac-celeration of fast threshold iteration method,resulting in a fast and high-precision reconstruction method.During the reconstruction process,the curvelet domain is used as the sparse transform do-main,and the hard thresholding function and exponential threshold model are employed.Theoretical data simulations and practical data verification demonstrate that compared to traditional seismic data reconstruction methods,this method achieves significant improvements in reconstruction quality and efficiency.Additionally,this method exhibits certain noise robustness.By leveraging the efficient convergence of the fast threshold iterative method,the reconstruction speed is further accelerated.
seismic data reconstructionsparse inversionfast threshold iteration methodLinear Bregman methodcurvelet transform
万方数据
维普
