Physics > Geophysics
[Submitted on 5 May 2014 (v1), last revised 22 Sep 2014 (this version, v3)]
Title:A Primal-Dual Proximal Algorithm for Sparse Template-Based Adaptive Filtering: Application to Seismic Multiple Removal
View PDFAbstract: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.
Submission history
From: Pham Mai Quyen [view email][v1] Mon, 5 May 2014 21:22:40 UTC (1,978 KB)
[v2] Tue, 3 Jun 2014 17:59:19 UTC (3,341 KB)
[v3] Mon, 22 Sep 2014 21:08:09 UTC (1,091 KB)
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