Sparse adaptive template matching and filtering for 2D seismic images with dual-tree wavelets and proximal methods

Mai Quyen Pham 1, 2 Caroline Chaux 3 Laurent Duval 4, 2 Jean-Christophe Pesquet 1
1 LIGM - Laboratoire d'Informatique Gaspard-Monge
2 IFPEN - IFP Energies nouvelles
3 I2M - Institut de Mathématiques de Marseille
4 Direction Technologie, Informatique et Mathématiques appliquées
Abstract : This paper proposes a novel approach for echo-like multiple removal in two-dimensional seismic images. It is based on constrained adaptive filtering associated with geometric wavelets. Approximate templates of multiple reflections are assumed to be available and they are matched to multiple reflections throughout estimated finite impulse response filters. The problem is formulated under a constrained convex optimization form where the data of interest and filters are estimated jointly. Proximal approaches are used to perform the minimization of the derived criterion. The effectiveness of the proposed approach is demonstrated with various noise levels on realistic simulated data and on field seismic data.
Keywords : Convex optimization Parallel algorithms Wavelets Adaptive filters Geophysics Sparsity
Type de document :
Communication dans un congrès
ICIP - International Conference on Image Processing 2015, Sep 2015, Québec City, Canada. IEEE International Conference on Image Processing, pp.2339-2343, 2015, 〈10.1109/ICIP.2015.7351220〉
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Mai Quyen Pham, Caroline Chaux, Laurent Duval, Jean-Christophe Pesquet. Sparse adaptive template matching and filtering for 2D seismic images with dual-tree wavelets and proximal methods. ICIP - International Conference on Image Processing 2015, Sep 2015, Québec City, Canada. IEEE International Conference on Image Processing, pp.2339-2343, 2015, 〈10.1109/ICIP.2015.7351220〉. 〈hal-01278494〉

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