Curvelet-based seismic data processing: a multiscale and nonlinear approach

TitleCurvelet-based seismic data processing: a multiscale and nonlinear approach
Publication TypeJournal Article
Year of Publication2008
AuthorsFelix J. Herrmann, Deli Wang, Gilles Hennenfent, Peyman P. Moghaddam
JournalGeophysics
Volume73
Number1
PageA1-A5
Month03
PublisherSEG
KeywordsAcquisition, curvelet transform, Processing, SLIM
Abstract

Mitigating missing data, multiples, and erroneous migration amplitudes are key factors that determine image quality. Curvelets, little ‘‘plane waves,’’ complete with oscillations in one direction and smoothness in the other directions, sparsify a property we leverage explicitly with sparsity promotion. With this principle, we recover seismic data with high fidelity from a small subset (20%) of randomly selected traces. Similarly, sparsity leads to a natural decorrelation and hence to a robust curvelet-domain primary-multiple separation for North Sea data. Finally, sparsity helps to recover migration amplitudes from noisy data. With these examples, we show that exploiting the curvelet’s ability to sparsify wavefrontlike features is powerful, and our results are a clear indication of the broad applicability of this transform to exploration seismology. {\copyright}2008 Society of Exploration Geophysicists

URLhttps://www.slim.eos.ubc.ca/Publications/Public/Journals/Geophysics/2008/herrmann08GEOcbs/herrmann08GEOcbs.pdf
DOI10.1190/1.2799517
Citation Keyherrmann2008GEOPcbs