An adaptive Bayesian inference algorithm to estimate the parameters of a hazardous atmospheric release

Abstract : In the eventuality of an accidental or intentional atmospheric release, the reconstruction of the source term using measurements from a set of sensors is an important and challenging inverse problem. A rapid and accurate estimation of the source allows faster and more efficient action for first-response teams, in addition to providing better damage assessment. This paper presents a Bayesian probabilistic approach to estimate the location and the temporal emission profile of a pointwise source. The release rate is evaluated analytically by using a Gaussian assumption on its prior distribution, and is enhanced with a positivity constraint to improve the estimation. The source location is obtained by the means of an advanced iterative Monte-Carlo technique called Adaptive Multiple Importance Sampling (AMIS), which uses a recycling process at each iteration to accelerate its convergence. The proposed methodology is tested using synthetic and real concentration data in the framework of the Fusion Field Trials 2007 (FFT-07) experiment. The quality of the obtained results is comparable to those coming from the Markov Chain Monte Carlo (MCMC) algorithm, a popular Bayesian method used for source estimation. Moreover, the adaptive processing of the AMIS provides a better sampling efficiency by reusing all the generated samples.
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Contributeur : François Septier <>
Soumis le : lundi 7 décembre 2015 - 12:12:38
Dernière modification le : jeudi 11 janvier 2018 - 06:27:22

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Harizo Rajaona, François Septier, Patrick Armand, Yves Delignon, Christophe Olry, et al.. An adaptive Bayesian inference algorithm to estimate the parameters of a hazardous atmospheric release. Atmospheric Environment, Elsevier, 2015, 122, pp.748-762. 〈10.1016/j.atmosenv.2015.10.026〉. 〈hal-01238921〉

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