3D high-resolution radon transform based on a strong sparse Lp–1 norm and its applications

Abstract

Abstract Multiple reflections are among the most challenging noises to suppress in seismic data, as they differ from effective waves only in terms of apparent velocity. Also, radon transform, an essential technique for attenuating multiple reflections, has been widely incorporated into various commercial software packages. Thus, this study introduces a 3D radon transform method based on the ${L_{p - 1}}$ norm to enhance sparsity-constraining capability in the transform domain, leveraging high-resolution radon transform techniques. Specifically, an iteratively reweighted least-squares algorithm is employed to obtain the transformed data in the radon domain. Given that the ${L_{p - 1}}$ norm was first applied to the Radon transform in seismic data processing methods, this paper theoretically demonstrates its powerful sparsity-constraining capability. Indeed, the proposed strategy enhances energy concentration in the radon transform domain, better separating primaries from multiples, and ultimately suppressing the multiples. Both model tests and real data indicate that the 3D radon transform constrained by the ${L_{p - 1}}$ norm outperforms existing high-resolution radon transform methods with sparsity constraints regarding energy concentration and effectiveness in multiple reflection attenuation.