Sparseness-constrained seismic deconvolution with curvelets

TitleSparseness-constrained seismic deconvolution with curvelets
Publication TypeConference
Year of Publication2005
AuthorsGilles Hennenfent, Felix J. Herrmann, Ramesh Neelamani
Conference NameCSEG Annual Conference Proceedings
KeywordsPresentation, SLIM

Continuity along reflectors in seismic images is used via Curvelet representation to stabilize the convolution operator inversion. The Curvelet transform is a new multiscale transform that provides sparse representations for images that comprise smooth objects separated by piece-wise smooth discontinuities (e.g. seismic images). Our iterative Curvelet-regularized deconvolution algorithm combines conjugate gradient-based inversion with noise regularization performed using non-linear Curvelet coefficient thresholding. The thresholding operation enhances the sparsity of Curvelet representations. We show on a synthetic example that our algorithm provides improved resolution and continuity along reflectors as well as reduced ringing effect compared to the iterative Wiener-based deconvolution approach.


Citation Keyhennenfent2005CSEGscs