Bayesian inference of coupled groundwater flow and radiogenic helium-4 production and transport at the catchment scale.

Hydrogeological numerical models are essential for assessing radioactive waste disposal by understanding groundwater flow systems. These models typically rely on hydraulic head data, with other state variables often underutilized in model inversions. In Flanders' Neogene aquifer, where safety studies for Boom Clay are ongoing, existing models face uncertainties due to dependence on hydraulic heads alone.

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.

Estimation of soil radioactivity-depth profiles using Bayesian inversion of borehole gamma spectrometry data.

Inversion of in situ borehole gamma spectrometry data is a faster and relatively less laborious method for calculating the vertical distribution of radioactivity in soil than conventional soil sampling method. However, the efficiency calculation of a detector for such measurements is a challenging task due to spatial and temporal variation of the soil properties and other measurement parameters. In this study, the sensitivity of different soil characteristics and measurement parameters on simulated efficiencies for a 662 keV photon peak were investigated.

2D inversion of in situ gamma-ray spectrometric measurements of Cs 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.

Soil radioactivity-depth profiles from regularized inversion of borehole gamma spectrometry data.

An in situ borehole gamma logging method using a LaBr gamma detector has been developed to characterize aCs contaminated site. The activity-depth distribution of Cs was derived by inversion of the in situ measurement data using two different least squares methods, (i) Least squares optimization (LSO) and (ii) Tikhonov regularization. The regularization parameter (λ) of the Tikhonov regularization method was estimated using three different methods i.

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.

Nested multiresolution hierarchical simulated annealing algorithm for porous media reconstruction.

Microstructure strongly influences flow and transport properties of porous media. Flow and transport simulations within porous media, therefore, requires accurate three-dimensional (3D) models of the pore and solid phase structure. To date, no imaging method can resolve all relevant heterogeneities from the nano- to the centimeter scale within complex heterogeneous materials such as clay, reservoir rocks (e.

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.

Model-based classification of CPT data and automated lithostratigraphic mapping for high-resolution characterization of a heterogeneous sedimentary aquifer.

Cone penetration testing (CPT) is one of the most efficient and versatile methods currently available for geotechnical, lithostratigraphic and hydrogeological site characterization. Currently available methods for soil behaviour type classification (SBT) of CPT data however have severe limitations, often restricting their application to a local scale. For parameterization of regional groundwater flow or geotechnical models, and delineation of regional hydro- or lithostratigraphy, regional SBT classification would be very useful.

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.