Estimating percentiles in aerial radiometric data using normal and lognormal distributional assumptions

Abstract

Implicitly or explicitly, percentile estimation is an important aspect of the analysis of aerial radiometric survey data. Standard deviation maps are produced for quadrangles which are surveyed as part of the United States Department of Energy's National Uranium Resource Evaluation. These maps show where variables differ from their mean values by more than one, two, or three standard deviations. Data may or may not be log-transformed prior to analysis. These maps have specific percentile interpretations only when proper distributional assumptions are met. Monte Carlo results are presented in this paper which show the consequences of estimating percentiles by (1)assuming normality when the data are really from a lognormal distribution, and (2)assuming lognormality when the data are really from a normal distribution.