Accession Number:

AD1004016

Title:

Bayesian Inference for Source Reconstruction: A Real-World Application

Descriptive Note:

Journal Article - Open Access

Corporate Author:

DEFENCE RESEARCH AND DEVELOPMENT SUFFIELD (CANADA) SUFFIELD

Personal Author(s):

• Yee,Eugene
• Hoffman,Ian
• Ungar,Kurt

Report Date:

2014-09-25

Pagination or Media Count:

13.0

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.

Descriptors:

• BAYESIAN INFERENCE
• SAMPLING
• Models
• BAYES THEOREM
• DISPERSIONS

Subject Categories:

• Statistics and Probability

Distribution Statement:

APPROVED FOR PUBLIC RELEASE