Incoherent noise suppression and deconvolution using curvelet-domain sparsity

TitleIncoherent noise suppression and deconvolution using curvelet-domain sparsity
Publication TypeThesis
Year of Publication2009
AuthorsVishal Kumar
UniversityThe University of British Columbia
Thesis Typemasters

Curvelets are a recently introduced transform domain that belongs to a family of multiscale and also multidirectional data expansions. As such, curvelets can be applied to resolution of the issues of complicated seismic wavefronts. We make use of this multiscale, multidirectional and hence sparsifying ability of the curvelet transform to suppress incoherent noise from crustal data where the signal-to-noise ratio is low and to develop an improved deconvolution procedure. Incoherent noise present in seismic reflection data corrupts the quality of the signal and can often lead to misinterpretation. The curvelet domain lends itself particularly well for denoising because coherent seismic energy maps to a relatively small number of significant curvelet coefficents.



Citation Keykumar09THins