Accurate interpolation with high-resolution time-variant Radon transforms

Abstract

Abstract It is well known that a sparse hyperbolic Radon transform (RT) can be used to extend the aperture of aperture limited data, filter noise, and fill gaps. In the same manner, an elliptical RT can achieve similar results when applied to slant stack sections. A problem with these transformations is that they have a time-variant kernel that results in slow implementation. By defining a model space in terms of an irregularly sampled velocity space to minimize the number of unknowns during the inversion and using sparse matrices, however, the computation time can be kept low enough for practical application. We implement hyperbolic and elliptical time domain RTs by using inversion via weighted conjugate gradient methods with a sparseness constraint. The hyperbolic RT performs accurate interpolation in common-midpoint (CMP) gathers, while the elliptical RT attenuates sampling artifacts in slant stack sections obtained from CMP gathers with poor sampling and gaps.