Cosmic-ray muons for imaging: Monte Carlo simulation studies into assaying nuclear waste packages, deep geological repositories, and carbon reservoirs

Cosmic-ray muons are a powerful, highly versatile and increasingly widely used tool for imaging and assay. Three studies into the use of these muons for applications in the energy sector are here presented. Each study has been conducted with Monte Carlo computational simulations and explores many aspects of the problem. The first is assay of stored nuclear waste; a method based on machine learning to identify materials inside waste drums is developed and applied. The second study focuses on verifying the design information of long-term nuclear waste repositories. Tests are made into the detectability of unknown voids inside a waste repository using muon radiography, and an imaging algorithm is implemented. Finally, the problem of continuous monitoring of an undersea storage site for carbon dioxide is considered, making use of data from a real, active storage site to test muon radiography’s efficacy to detect and monitor movement of the stored carbon dioxide.