Bayesian inference of 1D activity profiles from segmented gamma scanning of a heterogeneous radioactive waste drum. (September 2021)

• Record Type: Journal Article
• Authors: Laloy, Eric; Rogiers, Bart; Bielen, An; Boden, Sven
• Is Part Of: Applied radiation and isotopes. Volume 175(2021)
• Publication Date: 2021-09
• Subjects: Segmented gamma scanning (SGS) -- Markov chain Monte Carlo (MCMC) -- Source distribution uncertainty -- Uncertainty quantification -- Spatially-distributed inference; Radiology -- Periodicals; Radiation -- Industrial applications -- Periodicals; Nuclear chemistry -- Periodicals; Internet resource; Periodical; 660.298
• DOI: 10.1016/j.apradiso.2021.109803
• Languages: English
• ISSNs: 0969-8043

Abstract

Abstract: We present a Bayesian approach to probabilistically infer vertical activity profiles within a radioactive waste drum from segmented gamma scanning (SGS) measurements. Our approach resorts to Markov chain Monte Carlo (MCMC) sampling using the state-of-the-art Hamiltonian Monte Carlo (HMC) technique and accounts for two important sources of uncertainty: the measurement uncertainty and the uncertainty in the source distribution within the drum. In addition, our efficiency model simulates the contributions of all considered segments to each count measurement. Our approach is first demonstrated with a synthetic example, after which it is used to resolve the vertical activity distribution of 5 nuclides in a real waste package. Highlights: A novel Bayesian approach to interpret segmented gamma scanning measurements. Probabilistic inference of vertical activity profiles within the drum. The approach accounts for both measurement and source distribution uncertainty. The approach considers the contribution of all segments to each count measurement.