Enhancing crustal reflection data through curvelet denoising

TitleEnhancing crustal reflection data through curvelet denoising
Publication TypeJournal Article
Year of Publication2011
AuthorsVishal Kumar, Jounada Oueity, Ron Clowes, Felix J. Herrmann
JournalTechnophysics
Volume508
Number1-4
Page106-116
Month07
KeywordsProcessing, SLIM
Abstract

Suppression of incoherent noise, which is present in the seismic signal and may often lead to ambiguous interpretation, is a key step in processing associated with crustal reflection data. In this paper, we make use of the parsimonious representation of seismic data in the curvelet domain to perform the noise attenuation while preserving the coherent energy and its amplitude information. Curvelets are a recently developed mathematical transform that has as one of its properties minimal overlap between seismic signal and noise in the transform domain, thereby facilitating signal-noise separation. The problem is cast as an inverse problem and the results are obtained by updating the solution at each iteration. We demonstrate the effectiveness of this procedure at removing noise on both synthetic shot gathers and a synthetic stacked seismic section. We then apply curvelet denoising to deep crustal seismic reflection data where the signal-to-noise ratio is low. The reflection data were recorded along Lithoprobe's SNORCLE Line 1 across Paleoproterozoic-Archean domains in Canada's Northwest Territories. After initial processing, we apply the iterative curvelet denoising to both pre-stack shot gathers and post-stack data. Ground roll, random noise and much of the anomalous vertical energy is removed from the pre-stack shot gathers, to the extent that crustal reflections, including those from the Moho, are clearly seen on individual gathers. Denoised stacked data show a series of dipping reflections in the lower crust that extend into the Moho. The Moho itself is relatively flat and characterized by a sharp, narrow band of reflections. Comparing the results for the stacked data with those from F-X deconvolution, curvelet denoising outperforms the latter by attenuating incoherent noise with minimal harm to the signal. Because curvelet denoising retains amplitude information, it provides opportunities for further studies of seismic sections through attribute analyses. Curvelet denoising provides an important new tool in the processing toolbox for crustal seismic reflection data.

URLhttp://www.sciencedirect.com/science/article/pii/S0040195110003227
DOI10.1016/j.tecto.2010.07.01
Citation Keykumar2010TNPecr