Affiliation:
1. University of British Columbia, Seismic Laboratory for Imaging and Modeling, Department of Earth and Ocean Sciences, Vancouver, Canada. .
2. Jilin University, College of Geoexploration Science and Technology, Changchun, China. .
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.
Publisher
Society of Exploration Geophysicists
Subject
Geochemistry and Petrology,Geophysics
Reference29 articles.
1. Aminzadeh, F., J. Brac, and T. Kunz, 1997, 3-D salt and overthrust model. SEG/EAGE 3-D Modeling Series 1: SEG.
2. Focal transformation, an imaging concept for signal restoration and noise removal
3. Candès, E. J. , L. Demanet, D. L. Donoho, and L. Ying, 2006a, Fast discrete curvelet transforms: SIAM Multiscale Modeling and Simulation, 5, 861–899.
4. New multiscale transforms, minimum total variation synthesis: applications to edge-preserving image reconstruction
5. Stable signal recovery from incomplete and inaccurate measurements
Cited by
176 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献