Improving Bayesian radiological profiling of waste drums using Dirichlet priors, Gaussian process priors, and hierarchical modeling.

We present three methodological improvements of our recently proposed approach for Bayesian inference of the radionuclide inventory in radioactive waste drums, from radiological measurements. First we resort to the Dirichlet distribution for the prior distribution of the isotopic vector. The Dirichlet distribution possesses the attractive property that the elements of its vector samples sum up to 1. Second, we demonstrate that such Dirichlet priors can be incorporated within an hierarchical modeling of the prior uncertainty in the isotopic vector, when prior information about isotopic composition is available. Our used Bayesian hierarchical modeling framework makes use of this available information but also acknowledges its uncertainty by letting to a controlled extent the information content of the indirect measurement data (i.e., gamma and neutron counts) shape the actual prior distribution of the isotopic vector. Third, we propose to regularize the Bayesian inversion by using Gaussian process (GP) prior modeling when inferring 1D spatially-distributed mass or, equivalently, activity distributions. As of uncertainty in the efficiencies, we keep using the same stylized drum modeling approach as proposed in our previous work to account for the source distribution uncertainty across the vertical direction of the drum. A series of synthetic tests followed by application to a real waste drum show that combining hierarchical modeling of the prior isotopic composition uncertainty together with GP prior modeling of the vertical Pu profile across the drum works well. We also find that our GP prior can handles both cases with and without spatial correlation. Of course, our GP prior modeling framework only makes sense in the context of spatial inference. Furthermore, the computational times involved by our approach are on the order of a few hours, say about 2, to provide uncertainty estimates for all variables of interest in the considered inverse problem. This warrants further investigations to speed up the inference.

2D inversion of in situ gamma-ray spectrometric measurements of 137Cs for site characterization.

Environmental contamination by radioactive materials can be characterized by in situ gamma surface measurements. During such measurements, the field of view of a gamma detector can be tens of meters wide, resulting in a count rate that integrates the signal over a large measurement support volume/area. The contribution of a specific point to the signal depends on various parameters, such as the height of the detector above the ground surface, the gamma energy and the detector properties, etc. To improve the spatial resolution of the activity concentration, contributions of a radionuclide from nearby areas to the count rate of a single measurement should be disentangled. The experiments described in this paper, deployed 2D inversion of in situ gamma spectrometric measurements using a non-negative least squares-based Tikhonov regularization method. Data were acquired using a portable LaBr3 gamma detector. The detector response as a function of the distance of the radioactive source, required for the inversion process, was simulated using the Monte Carlo N-Particle (MCNP) transport code. The uncertainty on activity concentration was calculated using the Monte Carlo error propagation method. The 2D inversion methodology was first satisfactorily assessed for 133Ba and 137Cs source activity distributions using reference pads. Secondly, this method was applied on a 137Cs contaminated site, making use of above-ground in-situ gamma spectrometry measurements, conducted on a regular grid. The inversion process results were compared with the results from in-situ borehole measurements and laboratory analyses of soil samples. The calculated 137Cs activity concentration levels were compared against the activity concentration value for exemption or clearance of materials which can be applied by default to any amount and any type of solid material. Using the 2D inversion and the Monte Carlo error propagation method, a high spatial resolution classification of the site, in terms of exceeding the exemption limit, could be made. The 137Cs activity concentrations obtained using the inversion process agreed well with the results from the in-situ borehole measurements and those from the soil samples, showing that the 2D inversion is a convenient approach to deconvolute the contribution of radioactive sources from nearby areas within a detector's field of view, and increases the resolution of spatial contamination mapping.

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

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.

Metrology for decommissioning nuclear facilities: Partial outcomes of joint research project within the European Metrology Research Program.

Decommissioning of nuclear facilities incurs high costs regarding the accurate characterisation and correct disposal of the decommissioned materials. Therefore, there is a need for the implementation of new and traceable measurement technologies to select the appropriate release or disposal route of radioactive wastes. This paper addresses some of the innovative outcomes of the project "Metrology for Decommissioning Nuclear Facilities" related to mapping of contamination inside nuclear facilities, waste clearance measurement, Raman distributed temperature sensing for long term repository integrity monitoring and validation of radiochemical procedures.

Thorium-229 quantified in historical Thorium-228 capsules.

Thorium-229 is a valuable, but scarce, radionuclide for nuclear clock applications or targeted alpha therapy. While it is mostly produced by the decay of 233U, 229Th can also be produced by neutron irradiation of 226Ra. At SCK•CEN, capsules containing mainly 228Th (by-product of 226Ra irradiation) were characterized to quantify the present amounts of 229Th, 228Th, 227Ac, 226Ra with high resolution gamma spectroscopy, after a decay period of 40 years in which 228Th has decayed. High purity 229Th was quantified, and after recovery using radiochemical separation processes, it can be used to support ongoing research.

Determination of 137Cs contamination depth distribution in building structures using geostatistical modeling of ISOCS measurements.

Decommissioning of nuclear building structures usually leads to large amounts of low level radioactive waste. Using a reliable method to determine the contamination depth is indispensable prior to the start of decontamination works and also for minimizing the radioactive waste volume and the total workload. The method described in this paper is based on geostatistical modeling of in situ gamma-ray spectroscopy measurements using the multiple photo peak method. The method has been tested on the floor of the waste gas surge tank room within the BR3 (Belgian Reactor 3) decommissioning project and has delivered adequate results.