2015 GSA Annual Meeting in Baltimore, Maryland, USA (1-4 November 2015)

Paper No. 210-95
Presentation Time: 9:00 AM-6:30 PM

EVALUATING VARIATIONS IN URANIUM, THORIUM, AND POTASSIUM CONTENT IN THE PIERRE SHALE USING NATIONAL URANIUM RESOURCE EVALUATION AERIAL GAMMA-RAY SURVEY DATA


SMITH, Jeremiah1, BURNLEY, Pamela2, HABER, Daniel2 and MALCHOW, Russell3, (1)Geoscience, Univeristy of Nevada, Las Vegas, 4505 S Maryland Parkway, Las Vegas, NV 89154, (2)Geoscience, University of Nevada Las Vegas, 4505 S Maryland Parkway, Las Vegas, NV 89154, (3)National Security Technologies, 4505 S Maryland Parkway, Las Vegas, NV 89154, smithj17@unlv.nevada.edu

Aerial gamma ray surveys are used to detect stray nuclear materials in the environment, such as those released during a nuclear accident. In such a situation it would be very useful to be able to model the natural variation in the gamma ray background before the contamination occurred. Our research group is developing a technique for predicting gamma ray background based on geologic maps. Using aerial gamma ray data from National Uranium Resource Evaluation (NURE) survey, which has flight lines spaced at 1-20 miles, we are able to make coarse maps of the distribution of the uranium (U), thorium (Th), and potassium (K) radioisotopes that are responsible for the terrestrial component of gamma-ray background. In order to make high resolution maps we extrapolate based on geologic formations. Therefore, we are interested in understanding spatial variations in radioelement chemistry within single geologic formations. For this study we are examining the Pierre Shale which is exposed in South Dakota, Colorado and Nebraska. The formation consists of black dusty gray and brownish clay shales, thin layers of bentonite, shaly chalk, thin shaly limes, and thin sandstones deposited in the western interior seaway. By understanding the scale of spatial variation in K, U and Th in this widespread formation we can better understand the length scale over which we can extrapolate when making predictive gamma-ray background models.

DOE/NV/25946--2552