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

Abstract

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. © 2005 Taylor & Francis.