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Automatic wave-equation migration velocity inversion using multiobjective evolutionary algorithms

Vijay Pratap Singh 1 Bertrand Duquet 1 Michel Léger 1 Marc Schoenauer 2 
2 TAO - Machine Learning and Optimisation
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
Abstract : To solve nonlinear seismic velocity inversion problems, we have developed an automatic velocity-estimation technique based on the stochastic method called multiobjective evolutionary algorithms (MOEA). Semblance and differential semblance are used as objective functions. To cope with the high computational cost, we customized MOEA, added domain knowledge (velocity increases with depth, slowly varies along layers, and so forth), which improves the conditioning of the problem and accelerates convergence. This approach is robust because it can cope with large velocity errors. Computational cost of this algorithm is at least two orders of magnitude faster than other stochastic methods and comparable to that of direct gradient methods.
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Contributor : Marc Schoenauer Connect in order to contact the contributor
Submitted on : Thursday, October 16, 2008 - 6:57:17 PM
Last modification on : Sunday, June 26, 2022 - 11:48:48 AM




Vijay Pratap Singh, Bertrand Duquet, Michel Léger, Marc Schoenauer. Automatic wave-equation migration velocity inversion using multiobjective evolutionary algorithms. Geophysics, 2008, 73 (5), pp.VE61. ⟨10.1190/1.2966008⟩. ⟨inria-00331481⟩



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