KAUST DepartmentPhysical Sciences and Engineering (PSE) Division
Earth Science and Engineering Program
Permanent link to this recordhttp://hdl.handle.net/10754/593694
MetadataShow full item record
AbstractLinearized 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.
PublisherSociety of Exploration Geophysicists
Conference/Event nameSEG Technical Program Expanded Abstracts 2013