Iteratively adaptive regularization in inverse modeling with Bayesian outlook - application on geophysical data

An algorithm of model-based nonlinear inversion scheme with applications on geophysical examples is proposed. In the framework of classical least squares optimization, the algorithm uses adaptively controlled smoothness constraint in an iterative minimization scheme through an appropriate selection...

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Bibliographic Details
Published inInverse problems in science and engineering Vol. 13; no. 6; pp. 655 - 670
Main Author Roy, Indrajit G.
Format Journal Article
LanguageEnglish
Published 01.12.2005
Subjects
86-08
Adaptive regularization
AMS Subject Classifications: 86A22
Bayes' paradigm
Nonlinear inversion
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Summary:An algorithm of model-based nonlinear inversion scheme with applications on geophysical examples is proposed. In the framework of classical least squares optimization, the algorithm uses adaptively controlled smoothness constraint in an iterative minimization scheme through an appropriate selection of regularization parameter. A new formula in computing regularization parameter, which is a variant of Engl's formalism of a posteriori regularization is proposed. A regularized Gauss-Newton type optimization scheme along with conjugate gradient algorithm is used for nonlinear error minimization. The applicability of the algorithm is demonstrated through numerical experiments inverting synthetically generated vertical electrical sounding, first arrival travel time refraction seismic data and travel time data of Rayleigh wave generated due to acoustic emission source over homogeneous and isotropic medium.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
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ISSN:1741-5977
1741-5985
DOI:10.1080/17415970500171076