Date of Award


Document Type


Degree Name

Master of Science


Department of Engineering Physics

First Advisor

David J. Bunker, PhD.


Material identification through hyper-spectral imagery provides a potentially useful data input for background radiation prediction models for gamma spectrum correction in mobile nuclear detection applications. Traditional background correction methods which rely on prior information are often impractical in mobile detection. Prediction models could combine material information with spatial data to develop a suitable substitute to actual background radiation measurements. This research investigates the relationship hyper-spectral properties and natural radioactivity of construction materials. A selection of construction materials are analyzed using three instrumentation methods: 1) gamma-spectroscopy, 2) X-ray fluorescence (XRF), and 3) hyper-spectral imagery. Gamma-spectroscopy focuses on the presence of potassium as well as uranium and thorium series progeny through analysis of (212)Pb, (214)Pb, (214)Bi, and (228)Ac signature peaks. XRF analysis provides the chemical composition of each material. Each materials hyper-spectral characteristics are compared to chemical composition and radioactive properties to determine if any identifying features relate to natural radioactivity.

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