Bayesian Inference for Source Reconstruction – A Real-World Application

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Authors
  1. Yee, E.
  2. Hoffman, I.
  3. Ungar, K.
Corporate Authors
Defence Research and Development Canada, Suffield Research Centre, Ralston AB (CAN);Health Canada, Ottawa Ont (CAN)
Abstract
This paper applies a Bayesian probabilistic inferential methodology for the reconstruction of the location and emission rate from an actual contaminant source (emission from the Chalk River Laboratories medical isotope production facility) using a small number of activity concentration measurements of a noble gas (Xenon-133) obtained from three stations that form part of the International Monitoring System radionuclide network. The sampling of the resulting posterior distribution of the source parameters is undertaken using a very efficient Markov chain Monte Carlo technique that utilizes a multiple-try differential evolution adaptive Metropolis algorithm with an archive of past states. It is shown that the principal difficulty in the reconstruction lay in the correct specification of the model errors (both scale and structure) for use in the Bayesian inferential methodology. In this context, two different measurement models for incorporation of the model error of the predicted concentrations are considered. The performance of both of these measurement models with respect to their accuracy and precision in the recovery of the source parameters is compared and contrasted.
Keywords
inverse source modeling;Markov chain Monte Carlo;atmospheric dispersion modeling;Bayesian inference;noble gases;monitoring;data fusion
Report Number
DRDC-RDDC-2014-P50 — External Literature
Date of publication
25 Sep 2014
Number of Pages
13
DSTKIM No
CA039558
CANDIS No
800484
Format(s):
Electronic Document(PDF)

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