Results 251 to 260 of about 70,196 (284)
Some of the next articles are maybe not open access.
Bayesian Gaussian Mixture Linear Inversion in Geophysical Inverse Problems
Proceedings, 2015zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Dario Grana +2 more
openaire +2 more sources
Bayesian anisotropic Dix inversion
SEG Technical Program Expanded Abstracts 2013, 2013We present a Bayesian method for anisotropic Dix inversion and illustrate it with a synthetic 1D example. The method consists of two sequential constrained Dix type inversions, in which the first step solves for interval NMO velocity using the Bayesian Dix inversion developed by Buland et al. (2011).
Eirik Dischler +2 more
openaire +1 more source
GEOPHYSICS, 2006
A new, fast inversion approach for time-lapse seismic data is developed where the uncertainty of the inversion results is an integral part of the solution. The inversion method estimates changes in the elastic material properties of a reservoir because of production of hydrocarbons, including uncertainty bounds on these estimates.
Arild Buland, Youness El Ouair
openaire +1 more source
A new, fast inversion approach for time-lapse seismic data is developed where the uncertainty of the inversion results is an integral part of the solution. The inversion method estimates changes in the elastic material properties of a reservoir because of production of hydrocarbons, including uncertainty bounds on these estimates.
Arild Buland, Youness El Ouair
openaire +1 more source
Sparsity-promoting Bayesian inversion
Inverse Problems, 2012A computational Bayesian inversion model is demonstrated. It is discretization invariant, describes prior information using function spaces with a wavelet basis and promotes reconstructions that are sparse in the wavelet transform domain. The method makes use of the Besov space prior with p = 1, q = 1 and s = 1, which is related to the total variation ...
Niinimäki Kati +3 more
openaire +1 more source
2015
This chapter provides a general introduction, at the high level, to the backward propagation of uncertainty/information in the solution of inverse problems, and specifically a Bayesian probabilistic perspective on such inverse problems. Under the umbrella of inverse problems, we consider parameter estimation and regression.
openaire +1 more source
This chapter provides a general introduction, at the high level, to the backward propagation of uncertainty/information in the solution of inverse problems, and specifically a Bayesian probabilistic perspective on such inverse problems. Under the umbrella of inverse problems, we consider parameter estimation and regression.
openaire +1 more source
Bayesian linearized AVO inversion
GEOPHYSICS, 2003A new linearized AVO inversion technique is developed in a Bayesian framework. The objective is to obtain posterior distributions for P‐wave velocity, S‐wave velocity, and density. Distributions for other elastic parameters can also be assessed—for example, acoustic impedance, shear impedance, and P‐wave to S‐wave velocity ratio.
Arild Buland, Henning Omre
openaire +1 more source
2011
The posterior distribution in a nonparametric inverse problem is shown to contract to the true parameter at a rate that depends on the smoothness of the parameter, and the smoothness and scale of the prior. Correct combinations of these characteristics lead to the minimax rate.
Knapik, B.T. +2 more
openaire +1 more source
The posterior distribution in a nonparametric inverse problem is shown to contract to the true parameter at a rate that depends on the smoothness of the parameter, and the smoothness and scale of the prior. Correct combinations of these characteristics lead to the minimax rate.
Knapik, B.T. +2 more
openaire +1 more source
Bayesian inference for inverse problems – statistical inversion
e & i Elektrotechnik und Informationstechnik, 2007Unlike deterministic inversion methods, statistical approaches are capable of taking into account inherent measurement and model uncertainties into the inverse problem solution in a very simple and natural way. Statistical inversion theory reformulates inverse problems as problems of Bayesian statistical inference. In this framework, the solution to an
openaire +1 more source