Keywords
Source term estimation; Variational Bayes inference; Gamma dose rate measurement; CVX optimization toolbox; Covariance matrix model
Location
Session B1: Data Assimilation Techniques for Uncertainty Reduction
Start Date
13-7-2016 2:50 PM
End Date
13-7-2016 3:10 PM
Abstract
Detection of release of an atmospheric pollutant is a problem of interest in environmental sciences. We are concerned with estimation of unknown source term of the release. Formally, the problem is formulated as a linear model where measurements are explained using source-receptor- sensitivity matrix obtained from atmospheric transport model and source term vector which has to be estimated. Specifically, we estimate the release of radioactivity from measurements of gamma dose rate (GDR). The problem of isolation of activity of individual nuclides is poorly conditioned. We propose a probabilistic model with the prior information on intervals of nuclide ratios. The model parameters are estimated using the Variational Bayes method. The proposed algorithm is tested on simulated scenario with 16 nuclides and compared with state-of-the-art optimization approaches.
Included in
Civil Engineering Commons, Data Storage Systems Commons, Environmental Engineering Commons, Hydraulic Engineering Commons, Other Civil and Environmental Engineering Commons
Bayesian Estimation of Source Term of Atmospheric Radiation Release with Interval Prior
Session B1: Data Assimilation Techniques for Uncertainty Reduction
Detection of release of an atmospheric pollutant is a problem of interest in environmental sciences. We are concerned with estimation of unknown source term of the release. Formally, the problem is formulated as a linear model where measurements are explained using source-receptor- sensitivity matrix obtained from atmospheric transport model and source term vector which has to be estimated. Specifically, we estimate the release of radioactivity from measurements of gamma dose rate (GDR). The problem of isolation of activity of individual nuclides is poorly conditioned. We propose a probabilistic model with the prior information on intervals of nuclide ratios. The model parameters are estimated using the Variational Bayes method. The proposed algorithm is tested on simulated scenario with 16 nuclides and compared with state-of-the-art optimization approaches.
