Pure and Applied Geophysics

, Volume 169, Issue 3, pp 499–517 | Cite as

Probability Theory as Logic: Data Assimilation for Multiple Source Reconstruction

  • Eugene YeeEmail author


Probability theory as logic (or Bayesian probability theory) is a rational inferential methodology that provides a natural and logically consistent framework for source reconstruction. This methodology fully utilizes the information provided by a limited number of noisy concentration data obtained from a network of sensors and combines it in a consistent manner with the available prior knowledge (mathematical representation of relevant physical laws), hence providing a rigorous basis for the assimilation of this data into models of atmospheric dispersion for the purpose of contaminant source reconstruction. This paper addresses the application of this framework to the reconstruction of contaminant source distributions consisting of an unknown number of localized sources, using concentration measurements obtained from a sensor array. To this purpose, Bayesian probability theory is used to formulate the full joint posterior probability density function for the parameters of the unknown source distribution. A simulated annealing algorithm, applied in conjunction with a reversible-jump Markov chain Monte Carlo technique, is used to draw random samples of source distribution models from the posterior probability density function. The methodology is validated against a real (full-scale) atmospheric dispersion experiment involving a multiple point source release.


Bayesian inference data assimilation inverse dispersion Markov chain Monte Carlo sensor/model data fusion source reconstruction 



This work has been partially supported by Chemical Biological Radiological-Nuclear and Explosives Research and Technology Initiative (CRTI) program under project number CRTI-07-0196TD.


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Copyright information

© Her Majesty the Queen in Right of Canada 2011

Authors and Affiliations

  1. 1.Defence R&D Canada-SuffieldMedicine HatCanada

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