Master of Science (MS)
First Committee Member
Pamela C. Burnley
Second Committee Member
Third Committee Member
Fourth Committee Member
Number of Pages
Aerial gamma ray surveys are an important tool for national security, scientific, and industrial interests in determining locations of both anthropogenic and natural sources of radioactivity. There is a relationship between radioactivity and geology and in the past this relationship has been used to predict geology from an aerial survey. The purpose of this project
is to develop a method to predict the radiologic exposure rate of the geologic materials in an area by creating a model using geologic data, images from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), geochemical data, and pre-existing low spatial resolution aerial surveys from the National Uranium Resource Evaluation (NURE) Survey. Using these data, geospatial areas, referred to as background radiation units, homogenous in terms of K, U, and Th are defined and the gamma ray exposure rate is predicted. The prediction is compared to data collected via detailed aerial survey by our partner National Security Technologies, LLC (NSTec), allowing for the refinement of the technique.
High resolution radiation exposure rate models have been developed for two study areas in Southern Nevada that include the alluvium on the western shore of Lake Mohave, and Government Wash north of Lake Mead; both of these areas are arid with little soil moisture and vegetation. We determined that by using geologic units to define radiation background units of exposed bedrock and ASTER visualizations to subdivide radiation background units of alluvium, regions of homogeneous geochemistry can be defined allowing for the exposure rate to be predicted.
Soil and rock samples have been collected at Government Wash and Lake Mohave as well as a third site near Cameron, Arizona. K, U, and Th concentrations of these samples have been determined using inductively coupled mass spectrometry (ICP-MS) and laboratory counting using radiation detection equipment. In addition, many sample locations also have concentrations determined via in situ radiation measurements with high purity germanium detectors (HPGe) and aerial survey measurements. These various measurement techniques have been compared and found to produce consistent results.
Finally, modeling using Monte Carlo N-Particle Transport Code (MCNP), a particle physics modeling code, has allowed us to derive concentration to exposure rate coefficients. These simulations also have shown that differences in major element chemistry have little impact on the gamma ray emissions of geologic materials.
Geochemistry; Geology; Health Physics; Predictive Modeling; Radiation Detection; Remote Sensing
Geochemistry | Nuclear | Remote Sensing
Haber, Daniel A., "Predictive Modeling of Terrestrial Radiation Exposure from Geologic Materials" (2015). UNLV Theses, Dissertations, Professional Papers, and Capstones. 2537.