Bayesian Approach for Multigamma Radionuclide Quantification Applied on Weakly Attenuating Nuclear Waste Drums

Abstract

Gamma spectrometry is a passive nondestructive assay method used to quantify radionuclides present in nuclear objects. Basic methods using empirical calibration with a standard to quantify the activity of nuclear materials by determining the calibration coefficient are ineffective on nonreproducible nuclear objects such as waste packages. Package specifications such as composition or geometry change from one package to another and exhibit large variability of objects. The current standard quantification process uses numerical modeling of the measured scene with few available data such as geometry or composition, in particular density, material, screen, geometric shape, matrix composition, matrix, and source distribution. Some of them are strongly dependent on package data knowledge and operator backgrounds. The French Atomic Energy Commission (CEA) is developing a methodology to quantify nuclear materials in waste packages and waste drums without operator adjustment and internal package configuration knowledge. This method suggests combining a stochastic approach which uses, among others, surrogate models available to simulate the gamma attenuation behavior, a Bayesian approach considering conditional probability densities and prior information of problem inputs, and Markov Chain Monte Carlo (MCMC) algorithms which solve inverse problems, with gamma ray emission radionuclide spectra, and the outside dimensions of the objects of interest. The methodology has been tested to quantify actinide activity with a low bulk density matrix, weakly attenuating compositions, without information on the distribution of the source in terms of actinide masses and materials composing the drums. Activity uncertainties are taken into account.