Bayesian Activity Estimation and Uncertainty Quantification of Spent Nuclear Fuel Using Passive Gamma Emission Tomography

In this paper, we address the problem of activity estimation in passive gamma emission tomography (PGET) of spent nuclear fuel. Two different noise models are considered and compared, namely, the isotropic Gaussian and the Poisson noise models. The problem is formulated within a Bayesian framework a...

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Bibliographic Details
Published in:Journal of imaging 2021-10, Vol.7 (10), p.212
Main Authors: Eldaly, Ahmed Karam, Fang, Ming, Di Fulvio, Angela, McLaughlin, Stephen, Davies, Mike E., Altmann, Yoann, Wiaux, Yves
Format: Article
Language:English
Subjects:
Algorithms
Automation
Bayesian analysis
Bayesian inference
Chain scission
Computer simulation
Estimates
Gamma emission
imaging
Industrial plant emissions
Inverse problems
Investigations
Markov chain Monte Carlo
Markov chains
Mathematical models
Methods
Noise
Nuclear fuels
Nuclear power plants
Nuclear weapons
Optimization
Parameters
Sensors
Sparsity
Spent nuclear fuels
Tomography
Uncertainty
uncertainty quantification
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Summary:In this paper, we address the problem of activity estimation in passive gamma emission tomography (PGET) of spent nuclear fuel. Two different noise models are considered and compared, namely, the isotropic Gaussian and the Poisson noise models. The problem is formulated within a Bayesian framework as a linear inverse problem and prior distributions are assigned to the unknown model parameters. In particular, a Bernoulli-truncated Gaussian prior model is considered to promote sparse pin configurations. A Markov chain Monte Carlo (MCMC) method, based on a split and augmented Gibbs sampler, is then used to sample the posterior distribution of the unknown parameters. The proposed algorithm is first validated by simulations conducted using synthetic data, generated using the nominal models. We then consider more realistic data simulated using a bespoke simulator, whose forward model is non-linear and not available analytically. In that case, the linear models used are mis-specified and we analyse their robustness for activity estimation. The results demonstrate superior performance of the proposed approach in estimating the pin activities in different assembly patterns, in addition to being able to quantify their uncertainty measures, in comparison with existing methods.
ISSN:2313-433X
2313-433X
DOI:10.3390/jimaging7100212