Curvelet denoising

TitleCurvelet denoising
Publication TypeConference
Year of Publication2008
AuthorsVishal Kumar
Conference NameSINBAD 2008
KeywordsPresentation, SINBAD, SLIM
Abstract

The separation of signal and noise is an important issue in seismic data processing. By noise we refer to the incoherent noise which is present in the data. In our case, we showed curvelets concentrate seismic signal energy in few significant coefficients unlike noise energy that is spread all over the coefficients. The sparsity of seismic data in the curvelet domain makes curvelets an ideal choice for separating the noise from the seismic data. In our approach the denoising problem is framed as curvelet-regularized inversion problem. After initial processing, we applied the algorithm to the poststack data and compared our results with conventional wavelet denoising.

URLhttps://www.slim.eos.ubc.ca/Publications/Private/Conferences/SINBAD/2008/kumar2008SINBADcd/kumar2008SINBADcd.pdf
Citation Keykumar2008SINBADcd