Seismic image restoration using nonlinear least squares shape optimization

Abstract : In this article we present a new method for seismic image restoration. When observed, a seismic image is the result of an initial deposit system that has been transformed by a set of successive geological deformations (flexures, fault slip, etc) that occurred over a large period of time. The goal of seismic restoration consists in inverting the deformations to provide a resulting image that depicts the geological deposit system as it was in a previous state. Providing a tool that quickly generates restored images helps the geophysicists to recognize geological features that may be too strongly altered in the observed image. The proposed approach is based on a minimization process that expresses geological deformations in terms of geometrical constraints. We use a quickly converging Gauss-Newton approach to solve the system. We provide results to illustrate the seismic image restoration process on real data and present how the restored version can be used in a geological interpretation framework.
Type de document :
Article dans une revue
Procedia Computer Science, Elsevier, 2013, 18, pp.732-741. <10.1016/j.procs.2013.05.237>
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Contributeur : Dimitri Komatitsch <>
Soumis le : vendredi 15 novembre 2013 - 23:08:12
Dernière modification le : mardi 10 mai 2016 - 11:06:06

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Mathieu Gilardet, Sébastien Guillon, Bruno Jobard, Dimitri Komatitsch. Seismic image restoration using nonlinear least squares shape optimization. Procedia Computer Science, Elsevier, 2013, 18, pp.732-741. <10.1016/j.procs.2013.05.237>. <hal-00905097>

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