Machine learning techniques to repurpose Uranium Ore Concentrate (UOC) industrial records and their application to nuclear forensic investigation

Andrew Jones, A. C. Keatley*, J. Y. Goulermas, T. B. Scott, P. Turner, R. Awbery, M. Stapleton

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)

3 Citations (Scopus)

Abstract

The discipline of nuclear forensics has emerged in response to the illicit trafficking of nuclear materials and aims to determine the provenance of intercepted materials by comparing them to a database of samples of known origin. One of the major challenges of this approach is the availability of reliable inventory data for the various radioactive materials that may be intercepted. Analysing the representative samples is a lengthy process and it can be difficult to obtain data from legacy materials. Therefore previous nuclear forensic studies have often been based on datasets of very limited size. We propose an approach to repurpose pre-existing quality control inventory data from Uranium Ore Concentrates (UOCs) such that it can be exploited for nuclear forensic investigation. Furthermore, it is demonstrated that pattern recognition techniques can be used to successfully utilise this data to reliably infer the country and deposit group of material origin. We have also demonstrated methods for overcoming the issues associated with quality control records; missing data and data represented as less than values.

Original languageEnglish
Pages (from-to)221-227
Number of pages7
JournalApplied Geochemistry
Volume91
Early online date1 Nov 2017
DOIs
Publication statusPublished - 1 Apr 2018

Keywords

  • Classification
  • Deposit
  • Feature selection
  • Missing data
  • Nuclear forensics
  • UOC
  • Uranium Ore Concentrate
  • Visualisation

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