Efficient Monte Carlo sampling of inverse problems using a neural network-based forward—applied to GPR crosshole traveltime inversion
نویسندگان
چکیده
منابع مشابه
Monte Carlo sampling of solutions to inverse problems
Probabilistic formulation of inverse problems leads to the definition of a probability distribution in the model space. This probability distribution combines a priori information with new information obtained by measuring some observable parameters (data). As, in the general case, the theory linking data with model parameters is nonlinear, the a posteriori probability in the model space may no...
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The weILposedr7ess of the problems is not always guaranteed in inverse problems, unlike the forward problems. Dnts, a number of methods for giving wellposedrjess hm?e been studied in mathematical fields. In the ,field qf neural! networks, the network inversion method. for solving inverse problems was proposed; it is useflll but does not dissolute the ill-posedness of inverse problems. To overco...
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Monte Carlo methods have become important in analysis of nonlinear inverse problems where no analytical expression for the forward relation between data and model parameters is available, and where linearization is unsuccessful. In such cases a direct mathematical treatment is impossible, but the forward relation materializes itself as an algorithm allowing data to be calculated for any given m...
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We present an application of the SIPPI Matlab toolbox, to obtain a sample from the a posteriori probability density function for the classical tomographic inversion problem. We consider a number of different forward models, linear and non-linear, such as ray based forward models that rely on the high frequency approximation of the wave-equation and ’fat’ ray based forward models relying on fini...
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ژورنال
عنوان ژورنال: Geophysical Journal International
سال: 2017
ISSN: 0956-540X,1365-246X
DOI: 10.1093/gji/ggx380