| [ Information ] [ Publications ] [Signal processing codes] [ Signal & Image Links ] | |
| [ Main blog: A fortunate hive ] [ Blog: Information CLAde ] [ Personal links ] | |
| [ SIVA Conferences ] [ Other conference links ] [ Journal rankings ] | |
| [ Tutorial on 2D wavelets ] [ WITS: Where is the starlet? ] | |
If you cannot find anything more, look for something else (Bridget Fountain) |
|
|
|
|
Update: 2014/05/10
Unveiling meaningful geophysical information from seismic data requires to deal with both random and structured "noises". As their amplitude may be greater than signals of interest (primaries), additional prior information is especially important in performing efficient signal separation. We address here the problem of multiple reflections, caused by wave-field bouncing between layers. Since only approximate models of these phenomena are available, we propose a flexible framework for time-varying adaptive filtering of seismic signals, using sparse representations, based on inaccurate templates. We recast the joint estimation of adaptive filters and primaries in a new convex variational formulation. This approach allows us to incorporate plausible knowledge about noise statistics, data sparsity and slow filter variation in parsimony-promoting wavelet frames. The designed primal-dual algorithm solves a constrained minimization problem that alleviates standard regularization issues in finding hyperparameters. The approach demonstrates significantly good performance in low signal-to-noise ratio conditions, both for simulated and real field seismic data.
@Article{Pham_M_2014_j-ieee-tsp_primal-dual_pastbafasmr,
Title = {A Primal-Dual Proximal Algorithm for Sparse Template-Based Adaptive Filtering: Application to Seismic Multiple Removal},
Author = {M. Q. Pham and L. Duval and C. Chaux and J.-C. Pesquet},
File = {Pham_M_2014_j-ieee-tsp_primal-dual_pastbafasmr-preprint.pdf:Pham_M_2014_j-ieee-tsp_primal-dual_pastbafasmr-preprint.pdf:PDF;Pham_M_2014_j-ieee-tsp_primal-dual_pastbafasmr.pdf:Pham_M_2014_j-ieee-tsp_primal-dual_pastbafasmr.pdf:PDF;Pham_M_2014_j-ieee-tsp_primal-dual_pastbafasmr-preprint.pdf:References\\Pham_M_2014_j-ieee-tsp_primal-dual_pastbafasmr-preprint.pdf:PDF;Pham_M_2014_j-ieee-tsp_primal-dual_pastbafasmr.pdf:References\\Pham_M_2014_j-ieee-tsp_primal-dual_pastbafasmr.pdf:PDF},
Journal = {IEEE Transactions on Siignal Processing},
Month = {Aug.},
Number = {16},
Pages = {4256--4269},
Volume = {62},
Year = {2014},
Abstract = {Unveiling meaningful geophysical information from seismic data requires to deal with both random and structured "noises". As their amplitude may be greater than signals of interest (primaries), additional prior information is especially important in performing efficient signal separation. We address here the problem of multiple reflections, caused by wave-field bouncing between layers. Since only approximate models of these phenomena are available, we propose a flexible framework for time-varying adaptive filtering of seismic signals, using sparse representations, based on inaccurate templates. We recast the joint estimation of adaptive filters and primaries in a new convex variational formulation. This approach allows us to incorporate plausible knowledge about noise statistics, data sparsity and slow filter variation in parsimony-promoting wavelet frames. The designed primal-dual algorithm solves a constrained minimization problem that alleviates standard regularization issues in finding hyperparameters. The approach demonstrates significantly good performance in low signal-to-noise ratio conditions, both for simulated and real field seismic data.},
Doi = {10.1109/TSP.2014.2331614},
}
Convex optimization, Parallel algorithms, Wavelet transforms, Adaptive filters, Geophysical signal processing, Signal restoration, Sparsity, Signal separation