@article{eage:/content/journals/10.1111/1365-2478.12732, author = "Mokhtari, Ahmadreza and Gholami, Ali and Siahkoohi, Hamid Reza", title = "Fast hyperbolic deconvolutive Radon transform using generalized Fourier slice theorem", journal= "Geophysical Prospecting", year = "2019", volume = "67", number = "2", pages = "408-422", doi = "https://doi.org/10.1111/1365-2478.12732", url = "https://www.earthdoc.org/content/journals/10.1111/1365-2478.12732", publisher = "European Association of Geoscientists & Engineers", issn = "1365-2478", type = "Journal Article", keywords = "Velocity‐stack inversion", keywords = "Generalized Fourier slice theorem", keywords = "Deconvolutive Radon transform", keywords = "Sparse inversion", abstract = "ABSTRACT The hyperbolic Radon transform has a long history of applications in seismic data processing because of its ability to focus/sparsify the data in the transform domain. Recently, deconvolutive Radon transform has also been proposed with an improved time resolution which provides improved processing results. The basis functions of the (deconvolutive) Radon transform, however, are time‐variant, making the classical Fourier based algorithms ineffective to carry out the required computations. A direct implementation of the associated summations in the time–space domain is also computationally expensive, thus limiting the application of the transform on large data sets. In this paper, we present a new method for fast computation of the hyperbolic (deconvolutive) Radon transform. The method is based on the recently proposed generalized Fourier slice theorem which establishes an analytic expression between the Fourier transforms associated with the data and Radon plane. This allows very fast computations of the forward and inverse transforms simply using fast Fourier transform and interpolation procedures. These canonical transforms are used within an efficient iterative method for sparse solution of (deconvolutive) Radon transform. Numerical examples from synthetic and field seismic data confirm high performance of the proposed fast algorithm for filling in the large gaps in seismic data, separating primaries from multiple reflections, and performing high‐quality stretch‐free stacking.", }