Estimating Primaries by Sparse Inversion with Cost-Effective Computation (Communications in Computational Physics, 2020)
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 updatesthe 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 onthe same gradient-descent framework with EPSI, we develop a computationally cost-effective primary estimation approach in which a newly defined parameterization ofprimary-multiple model is adopted and an efficiently defined analytical step-lengthis developed. The developed approach can yield a better primary estimation at lesscomputational cost as compared to EPSI, which is verified by two synthetic datasetsin numerical examples. Moreover, we apply this approach to a shallow-water fielddataset and achieve a desirable performance.