Estimating Primaries by Sparse Inversion with Cost-Effective Computation

Recently,attenuation of surface-related multiples is implemented by a large-scale sparsity-promoting inversion where the primaries are iteratively estimated without a subtraction process,which is called estimation of primaries by sparse inversion(EPSI).By inverting for surface-free impulse responses,EPSI simultaneously updates the primaries and multiples,both of which contribute to explaining the input data,and therefore promote the global convergence gradually.However,one of the major concerns of EPSI may lie in its high computational cost.In this paper,based on the same gradient-descent framework with EPSI,we develop a computationally cost-effective primary estimation approach in which a newly defined parameterization of primary-multiple model is adopted and an efficiently defined analytical step-length is developed.The developed approach can yield a better primary estimation at less computational cost as compared to EPSI,which is verified by two synthetic datasets in numerical examples.Moreover,we apply this approach to a shallow-water field dataset and achieve a desirable performance.