Bayesian inference for source term estimation – Application to the International Monitoring System radionuclide network


  1. Yee, E.
  2. Hoffman, I.
  3. Ungar, K.
  4. Malo, A.
  5. Ek, N.
  6. Bourgouin, P.
Corporate Authors
Defence Research and Development Canada, Suffield Research Centre, Ralston AB (CAN)
In recent years, there has been an enormous quantity of data obtained from the International Monitoring System radionuclide network for the verification of the Comprehensive Nuclear-Test-Ban Treaty. The complexity of the instruments deployed here, of the radionuclide sources, and of the myriad of scientific questions related to treaty verification lead invariably to complex inference problems (associated with source term estimation) that require the application of sophisticated statistical tools. In this report, we demonstrate that a rigorous and general framework for addressing these problems is through Bayesian probability theory, allowing the rational inference of the posterior probability distribution of the source parameters of interest given any prior information and available activity concentration measurements. The methodology is demonstrated by application to two different problems: namely, the emission rate profile reconstruction of a radioxenon release from the Fukushima Daiichi nuclear power plant and source reconstruction (location and emission rate) of a radioxenon release from the Chalk River Laboratories (CRL) medical isotope production facility. The sampling of the resulting posterior distribution of the source parameters is undertaken using two different Markov chain Monte Carlo techniques: namely, nested sampling and multiple-try differential evolution adaptive Metropolis sampling with a past archive. For the Fukushima nuclear power plant release, it is dem

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atmospheric dispersion;Bayesian inference;International Monitoring System;radionuclide network;Markov chain Monte Carlo;model and measurement errors;source reconstruction
Report Number
DRDC-RDDC-2014-R71 — Scientific Report
Date of publication
01 Oct 2014
Number of Pages
Electronic Document(PDF)

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