A novel method for remote depth estimation of buried radioactive contamination

Existing remote radioactive contamination depth estimation methods for buried radioactive wastes are either limited to less than 2 cm or are based on empirical models that require foreknowledge of the maximum penetrable depth of the contamination. These severely limits their usefulness in some real life subsurface contamination scenarios. Therefore, this work presents a novel remote depth estimation method that is based on an approximate three dimensional linear attenuation model that exploits the benefits of using multiple measurements obtained from the surface of the material in which the contamination is buried using a radiation detector. Simulation results showed that the proposed method is able to detect the depth of caesium-137 and cobalt-60 contamination buried up to 40 cm in both sand and concrete. Furthermore, results from experiments show that the method is able to detect the depth of caesium-137 contamination buried up to 12 cm in sand. The lower maximum depth recorded in the experiment is due to limitations in the detector and the low activity of the caesium-137 source used. Nevertheless, both results demonstrate the superior capability of the proposed method compared to existing methods

Integration of ground-penetrating radar and gamma-ray detectors for non-intrusive localisation of buried radioactive sources

This thesis reports on the integration of ground-penetrating radar (GPR) and gamma ray detectors to improve the non-intrusive localisation of radioactive wastes buried in porous materials such as soil and concrete. The research was undertaken in two phases. In the first phase, a new non-intrusive technique for retrieving the depth of a buried radioactive source from two-dimensional raster radiation images was developed. The images were obtained by moving a gamma-ray detector in discrete steps on the surface of the material volume in which the source is buried and measuring the gamma spectrum at each step. The depth of the source was then estimated by fitting the intensity values from the measured spectra to an approximate three-dimensional gamma-ray attenuation model. This procedure was first optimised using Monte Carlo simulations and then validated using experiments. The results showed that this method is able to estimate the depth of a 658 kBq caesium-137 point source buried up to 18 cm in each of sand, soil and gravel. However, the use of only gamma-ray data to estimate the depth of the sources requires foreknowledge of the density of the embedding material. This is usually III IV difficult without having recourse to intrusive density estimation methods or historical density values. Therefore, the second phase of the research employed integrated GPR and gamma ray detection to solve this density requirement problem. Firstly, four density models were investigated using a suite of materials and the best model was then used to develop the integration method. Results from numerical simulations showed that the developed integration method can simultaneously retrieve the soil density and the depth and radius of disk-shaped radioactive objects buried up to 20 cm in soil of varying conditions with a elative error of less than 10%. Therefore, the integration method eliminates the need for prior knowledge of the density of the embedding material. This work represents the first time data from these two systems i.e., GPR and gamma-ray detector, will be integrated for the detection and localisation of radioactive sources. Furthermore, the results from the developed methods confirm that an integrated GPR and gamma-ray detector system is a viable tool for non-intrusive localisation of buried radioactive sources. This will enable improved characterisation of buried radioactive wastes encountered during the decommissioning of nuclear sites and facilities

Radiation Sensing: Design and Deployment of Sensors and Detectors

Radiation detection is important in many fields, and it poses significant challenges for instrument designers. Radiation detection instruments, particularly for nuclear decommissioning and security applications, are required to operate in unknown environments and should detect and characterise radiation fields in real time. This book covers both theory and practice, and it solicits recent advances in radiation detection, with a particular focus on radiation detection instrument design, real-time data processing, radiation simulation and experimental work, robot design, control systems, task planning and radiation shielding

A Model For Remote Depth Estimation Of Buried Radioactive Wastes Using CdZnTe Detector

This paper presents the results of an attenuation model for remote depth estimation of buried radioactive wastes using a Cadmium Zinc Telluride (CZT) detector. Previous research using an organic liquid scintillator detector system showed that the model is able to estimate the depth of a 329 kBq Cs-137 radioactive source buried up to 12 cm in sand with an average count rate of 100 cps. The results presented in this paper showed that the use of the CZT detector extended the maximum detectable depth of the same radioactive source to 18 cm in sand with a significantly lower average count rate of 14 cps. Furthermore, the model also successfully estimated the depth of a 9 kBq Co-60 source buried up to 3 cm in sand. This confirms that this remote depth estimation method can be used with other radionuclides and wastes with very low activity. Finally, the paper proposed a performance parameter for evaluating radiation detection systems that implement this remote depth estimation method

Nonintrusive Depth Estimation of Buried Radioactive Wastes using Ground Penetrating Radar and a Gamma Ray Detector

This study reports on the combination of data from a ground penetrating radar (GPR) and a gamma ray detector for nonintrusive depth estimation of buried radioactive sources. The use of the GPR was to enable the estimation of the material density required for the calculation of the depth of the source from the radiation data. Four different models for bulk density estimation were analysed using three materials, namely: sand, gravel and soil. The results showed that the GPR was able to estimate the bulk density of the three materials with an average error of 4.5%. The density estimates were then used together with gamma ray measurements to successfully estimate the depth of a 658 kBq caesium-137 radioactive source buried in each of the three materials investigated. However, a linear correction factor needs to be applied to the depth estimates due to the deviation of the estimated depth from the measured depth as the depth increases. This new application of GPR will further extend the possible fields of application of this ubiquitous geophysical tool

Mapping the spatial distribution and activity of 226Ra at legacy sites through Machine Learning interpretation of gamma-ray spectrometry data

Radium (226Ra) contamination derived from military, industrial, and pharmaceutical products can be found at a number of historical sites across the world posing a risk to human health. The analysis of spectral data derived using gamma-ray spectrometry can offer a powerful tool to rapidly estimate and map the activity, depth, and lateral distribution of 226Ra contamination covering an extensive area. Subsequently, reliable risk assessments can be developed for individual sites in a fraction of the timeframe compared to traditional labour-intensive sampling techniques: for example soil coring. However, local heterogeneity of the natural background, statistical counting uncertainty, and non-linear source response are confounding problems associated with gamma-ray spectral analysis. This is particularly challenging, when attempting to deal with enhanced concentrations of a naturally occurring radionuclide such as 226Ra. As a result, conventional surveys tend to attribute the highest activities to the largest total signal received by a detector (Gross counts): an assumption that tends to neglect higher activities at depth. To overcome these limitations, a methodology was developed making use of Monte Carlo simulations, Principal Component Analysis and Machine Learning based algorithms to derive depth and activity estimates for 226Ra contamination. The approach was applied on spectra taken using two gamma-ray detectors (Lanthanum Bromide and Sodium Iodide), with the aim of identifying an optimised combination of detector and spectral processing routine. It was confirmed that, through a combination of Neural Networks and Lanthanum Bromide, the most accurate depth and activity estimates could be found. The advantage of the method was demonstrated by mapping depth and activity estimates at a case study site in Scotland. There the method identified significantly higher activity (<3Bqg−1) occurring at depth (>0.4m), that conventional gross counting algorithms failed to identify. It was concluded that the method could easily be employed to identify areas of high activity potentially occurring at depth, prior to intrusive investigation using conventional sampling techniques

Bridging the capability gap in environmental gamma-ray spectrometry

Environmental gamma-ray spectroscopy provides a powerful tool that can be used in environmental monitoring given that it offers a compromise between measurement time and accuracy allowing for large areas to be surveyed quickly and relatively inexpensively. Depending on monitoring objectives, spectral information can then be analysed in real-time or post survey to characterise contamination and identify potential anomalies. Smaller volume detectors are of particular worth to environmental surveys as they can be operated in the most demanding environments. However, difficulties are encountered in the selection of an appropriate detector that is robust enough for environmental surveying yet still provides a high quality signal. Furthermore, shortcomings remain with methods employed for robust spectral processing since a number of complexities need to be overcome including: the non-linearity in detector response with source burial depth, large counting uncertainties, accounting for the heterogeneity in the natural background and unreliable methods for detector calibration. This thesis aimed to investigate the application of machine learning algorithms to environmental gamma-ray spectroscopy data to identify changes in spectral shape within large Monte Carlo calibration libraries to estimate source characteristics for unseen field results. Additionally, a 71 × 71 mm lanthanum bromide detector was tested alongside a conventional 71 × 71 mm sodium iodide to assess whether its higher energy efficiency and resolution could make it more reliable in handheld surveys. The research presented in this thesis demonstrates that machine learning algorithms could be successfully applied to noisy spectra to produce valuable source estimates. Of note, were the novel characterisation estimates made on borehole and handheld detector measurements taken from land historically contaminated with 226Ra. Through a novel combination of noise suppression and neural networks the burial depth, activity and source extent of contamination was estimated and mapped. Furthermore, it was demonstrated that Machine Learning techniques could be operated in real-time to identify hazardous 226Ra containing hot particles with much greater confidence than current deterministic approaches such as the gross counting algorithm. It was concluded that remediation of 226Ra contaminated legacy sites could be greatly improved using the methods described in this thesis. Finally, Neural Networks were also applied to estimate the activity distribution of 137Cs, derived from the nuclear industry, in an estuarine environment. Findings demonstrated the method to be theoretically sound, but practically inconclusive, given that much of the contamination at the site was buried beyond the detection limits of the method. It was generally concluded that the noise posed by intrinsic counts in the 71 × 71 mm lanthanum bromide was too substantial to make any significant improvements over a comparable sodium iodide in contamination characterisation using 1 second counts

Radiation Sensing: Design and Deployment of Sensors and Detectors

No abstract available

Application of multi-scale assessment and modelling of landfill leachate migration: implications for risk-based contaminated land assessment, landfill remediation, and groundwater protection

There are a large number of unlined and historical landfill sites across Britain, contaminating groundwater and soil resources as well as posing a threat to human health and local communities. There is an essential requirement for robust methodology when carrying out risk-based site investigations prior to risk assessment and remediation of landfill sites. This research has focused upon the methods used during site investigations for two reasons. Firstly, the site investigation is often conducted using field instruments and methods that do not account for the heterogeneous conditions found at landfill sites. Interpreting geophysical conditions between sampled points is a common practise. Given the complex and heterogeneous conditions at landfill sites, such methodology introduces uncertainty into data sets. Secondly, risk estimation models that simulate groundwater flow and contaminant transport require extensive field information. The data used during model construction will significantly impact contaminant transport simulations. Modelling guidelines also need further development, ensuring that sound modelling practises are adhered toduring model construction.To address these concerns, four research objectives were identified: (1) Two new multi-spatial field assessment methods (remote sensing and ground penetrating radar), previously applied in other fields of science, were tested on landfill sites; (2) Kriging was used as a tool to improve landfill-sampling strategies; (3 & 4) Groundwater flow and contaminant transport models were used to evaluate whether different scales of field data and modelling practises influenced modelling assumptions and simulation.The utility of novel field- and airborne-based remote sensing methodologies in identifying the location and intensity of vegetation stress caused by leachate migration and inferring pathways of near surface contamination using patterns of vegetation stress was proven. The results from the kriging investigations demonstrated that additional insight into field conditions could be resolved to identify locations of additional sampling points, and provide information about variability in hydrological data sets. The Ground Penetrating Radar investigations provided three types of valuable near-surface information that could assist in determining landfill risks: buried landfill features, leachate plume locations and local hydrogeological conditions. These combined methods provided detailed synoptic geophysical and contaminant information that would otherwise be difficult to determine. Their application and acceptance as site assessment methods (used under certain landfill conditions) could increase the accuracy of assessing risks posed by landfill leachate.These applications also demonstrated that the most effective site assessments are achieved when integrated with other field data such as soil, vegetation, and groundwater quantity measurements, contaminant concentrations and aerial photographs, providing comprehensive information needed for risk estimation modelling.The modelling analyses found that close attention must be paid to site-specific and model-specific characteristics, as well as modelling practises. These factors influenced model results. By using additional data to infer model parameters, it was evident that the amount of data available will influence the way in which risk will be perceived. The more data that was available during model construction, the higher the risk prediction. This was the case for some seventy- percent of the models.By improving the accuracy of site investigation methodology, and by adhering to robust assessment and modelling practices, a higher level of quality assurance can be achieved in the risk assessment and remediation of contaminating landfill sites. If the improvements and recommendations presented in this research are considered, uncertainties inherent in the site investigation could be reduced, therefore enhancing the accuracy of landfill risk assessment and remedial decisions