Sparse Parabolic Radon Transform with Nonconvex Mixed Regularization for Multiple Attenuation

The existence of multiple reflections brings difficulty to seismic data processing and interpretation in reflection exploration. Parabolic Radon transform is widely used attenuation because it easily implemented, highly robust efficient. However, finite acquisition aperture causes energy diffusion the domain, which leads residuals. In this paper, we propose a sparse parabolic with nonconvex Lq1-Lq2(0<q1,q2<1) mixed regularization (SPRTLq1-Lq2) that constrains sparsity primary overcome improve effect attenuation, respectively. This problem solved approximately by alternating direction method multipliers (ADMM) algorithm, give convergence conditions ADMM algorithm. proposed compared least squares (LSPRT) based on L1 (SPRTL1) for synthetic field data. We demonstrate improves resolution domain data, better results are obtained.