Whiteness and non-gaussianity to blind déconvolution of noisy data : application to seismic data.
type: Dataset
description: This thesis deals with the blind deconvolution of noisy data. We consider the case of seismic data. The inversion of the model need to select higher order statistics according to the distribution of the signals. To solve that, we use the assumptions of whiteness or of nongaussianity. We propose blind déconvolution algorithm in time domain and frequency domain. We measure whiteness by mutual information rate and nongaussianity with the negentropy. Afterwards, we study the sensitivity of the different algorithm with respect to a white Gaussian additive on the data. Theoretically and in practice on real and synthetic data, non-gaussianity appears as the method which provides the better trade off between déconvolution quality and noise amplification.
issued: 2026-05-05T17:35:33.844077
language: fr
modified: 2026-05-05T17:35:33.844081
contact point: Organization
  fn: CCSD
keyword: blancheur
keyword: deconvolution
keyword: donnees-bruitees
keyword: imagerie-sismique
keyword: infoeu-reposemanticsdoctoralthesis
keyword: neguentropie
keyword: non-gaussianite
keyword: sdustugpsciences-of-the-universe-physicsearth-sciencesgeophysics-physicsgeo-ph
keyword: spisignalengineering-sciences-physicssignal-and-image-processing
keyword: statistiques-dordre-superieur
keyword: taux-dinformation-mutuelle
keyword: theses
landing page: https://theses.hal.science/tel-00097161

Whiteness and non-gaussianity to blind déconvolution of noisy data : application to seismic data.
type: Distribution
format: HTML
issued: 2026-05-05T17:35:33.845380
modified: 2026-05-05T17:35:33.829858
access URL: https://theses.hal.science/tel-00097161

test_moissonnage_selune
type: Agent