The possibilities of compressed sensing based migration

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

Abstract

Linearized waveform inversion or Least-square migration helps reduce migration artifacts caused by limited acquisition aperture, coarse sampling of sources and receivers, and low subsurface illumination. However, leastsquare migration, based on L2-norm minimization of the misfit function, tends to produce a smeared (smoothed) depiction of the true subsurface reflectivity. Assuming that the subsurface reflectivity distribution is a sparse signal, we use a compressed-sensing (Basis Pursuit) algorithm to retrieve this sparse distribution from a small number of linear measurements. We applied a compressed-sensing algorithm to image a synthetic fault model using dense and sparse acquisition geometries. Tests on synthetic data demonstrate the ability of compressed-sensing to produce highly resolved migrated images. We, also, studied the robustness of the Basis Pursuit algorithm in the presence of Gaussian random noise.
Original languageEnglish (US)
Title of host publicationSEG Technical Program Expanded Abstracts 2013
PublisherSociety of Exploration Geophysicists
Pages3900-3904
Number of pages5
ISBN (Print)9781629931883
DOIs
StatePublished - Aug 19 2013

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01

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