Signal separation in convolutive mixtures : contributions to blind separation of sparse sources and adaptive subtraction of seismic multiples

Abstract : The recovery of correlated signals from their linear combinations is a challenging task and has many applications in signal processing. We focus on two problems that are the blind separation of sparse sources and the adaptive subtraction of multiple events in seismic processing. A special focus is put on convolutive mixtures: for both problems, finite impulse response filters can indeed be estimated for the recovery of the desired signals.For instantaneous and convolutive mixing models, we address the necessary and sufficient conditions for the exact extraction and separation of sparse sources by using the L0 pseudo-norm as a contrast function. Equivalences between sparse component analysis and disjoint component analysis are investigated.For adaptive multiple subtraction, we discuss the limits of methods based on independent component analysis and we highlight equivalence with Lp-norm-based methods. We investigate how other regularization parameters may have more influence on the estimation of the desired primaries. Finally, we propose to improve the robustness of adaptive subtraction by estimating the extracting convolutive filters directly in the curvelet domain. Computation and memory costs are limited by using the uniform discrete curvelet transform.
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Yves-Marie Batany. Signal separation in convolutive mixtures : contributions to blind separation of sparse sources and adaptive subtraction of seismic multiples. Earth Sciences. PSL Research University; Universidade estadual de Campinas (Brésil), 2016. English. ⟨NNT : 2016PSLEM093⟩. ⟨tel-01788820⟩

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