Development of an inversion method for the improved determination of the spatial distribution of radioactive materials using UAVs

  • Sevda Goren

Student thesis: Master's ThesisMaster of Science by Research (MScR)


The global population has been forecast to reach around 8 billion people by 2020, according to the World Bank. This rise will further increase the total global annual energy and resource demands. This population rise is tensioned against a recognised need to urgently decarbonise energy and transport systems to curb further greenhouse gas emissions and mitigate the magnitude of anthropogenic climate change. Although nuclear energy is one of the cleanest energy sources with respect to CO2 emissions, it still carrier stigma, related to severe nuclear hazards such as the INES level 7 accidents at the Chernobyl Nuclear Power Plant (CNPP) and the Fukushima Daichii Nuclear Power Plant (FNPP) since large amounts of radiological contamination were released into the environment and its impact was felt globally. In retrospect, the response to the CNPP and the FNPP events would have been much quicker and better informed if radiation mapping technologies were more advanced and available; especially unmanned aerial vehicles (UAVs). Therefore, a novel UAV system and an inversion method has been developed to analyse radiometric data for determining the location of radioactive contamination caused by nuclear power plant releases with high spatial resolution. The radiometric datasets were acquired in the Chernobyl Exclusion Zone of Ukraine because of distribution of Cesium-137. The data were measured in total counts per second for airborne and ground surveys. Three-dimensional geographical data were used to define the location of each radiometric surveys. The processing workflow was designed by testing Algebraic Reconstruction method (ART), Karcmarz algorithms, and parameters, to find the optimal processing solution. The processing steps included generating a 3D elevation model derived from geographical data, before numerically accounting for the predicted on-the ground radiation field by back-working (inverting) for geometrical dilution (inverse square law) and attenuation by air. Data processing was coded in MATLAB and executed using the University of Bristol supercomputer. The final radiation maps derived from a novel hyperspectral gamma imaging (HSGI) technique by using ART method achieved high spatial resolution. Combining the results of the processed radiometric data obtained by UAVs and in-situ from the same areas allows for comparison and confirmation of the high spatial distribution of the contaminant sources on the ground.
Date of Award6 Dec 2019
Original languageEnglish
Awarding Institution
  • University of Bristol
SupervisorThomas Bligh Scott (Supervisor)

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