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

  • Thomas R. Bement
  • Fredric L. Pirkle


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.

Key words

percentiles normal distribution lognormal distribution aerial radiometrics 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Dodd, P. H., 1976, Airborne radiometric reconaissance: the why's and wherefore's,in Summaries and visual presentations: Uranium Geophysical Technical Symposium, U.S. Energy Research and Development Administration and Bendix Field Engineering Corporation, Grand Junction, Colorado, September 14–16, p. 13–20.Google Scholar
  2. Fisz, M., 1963, Probability theory and mathematical statistics: John Wiley and Sons, Inc., New York.Google Scholar
  3. Geometrics, Inc., 1978, Aerial gamma ray and magnetic survey Rock Springs, Rawlins, and Cheyenne quadrangles, Wyoming and the Greeley quadrangle: Open File Report, v. 1, U.S. Department of Energy, GJBX-17(79), Colorado.Google Scholar
  4. Johnson, N. L. and Kotz, S., 1970, Continuous univariate distributions. 1: Houghton Mifflin Co., New York.Google Scholar
  5. Kinderman, A. J. and Ramage, J. G., 1976, Computer generation of normal random variables: Jour. Amer. Stat. Assoc., v. 71, p. 893–896.Google Scholar
  6. Pruvance, D. T., 1980 (Advanced Research Geoscientist. Bendix Field Engineering Corporation), Personal communication.Google Scholar

Copyright information

© Plenum Publishing Corporation 1981

Authors and Affiliations

  • Thomas R. Bement
    • 1
  • Fredric L. Pirkle
    • 2
  1. 1.Los Alamos National LaboratoryLos AlamosUSA
  2. 2.Bendix Field Engineering CorporationGrand JunctionUSA

Personalised recommendations