Least-squares reverse time migration with radon preconditioning

Gaurav Dutta, Cyril Agut, Matteo Giboli, Paul Williamson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

We present a least-squares reverse time migration (LSRTM) method using Radon preconditioning to regularize noisy or severely undersampled data. A high resolution local radon transform is used as a change of basis for the reflectivity and sparseness constraints are applied to the inverted reflectivity in the transform domain. This reflects the prior that for each location of the subsurface the number of geological dips is limited. The forward and the adjoint mapping of the reflectivity to the local Radon domain and back are done through 3D Fourier-based discrete Radon transform operators. The sparseness is enforced by applying weights to the Radon domain components which either vary with the amplitudes of the local dips or are thresholded at given quantiles. Numerical tests on synthetic and field data validate the effectiveness of the proposed approach in producing images with improved SNR and reduced aliasing artifacts when compared with standard RTM or LSRTM.
Original languageEnglish (US)
Title of host publicationSEG Technical Program Expanded Abstracts 2016
PublisherSociety of Exploration Geophysicists
Pages4198-4203
Number of pages6
DOIs
StatePublished - Sep 2016

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