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Title: Statistical Techniques Applied to Aerial Radiometric Surveys (STAARS): cluster analysis. National Uranium Resource Evaluation

Technical Report ·
OSTI ID:6634352

One objective of the aerial radiometric surveys flown as part of the US Department of Energy's National Uranium Resource Evaluation (NURE) program was to ascertain the regional distribution of near-surface radioelement abundances. Some method for identifying groups of observations with similar radioelement values was therefore required. It is shown in this report that cluster analysis can identify such groups even when no a priori knowledge of the geology of an area exists. A method of convergent k-means cluster analysis coupled with a hierarchical cluster analysis is used to classify 6991 observations (three radiometric variables at each observation location) from the Precambrian rocks of the Copper Mountain, Wyoming, area. Another method, one that combines a principal components analysis with a convergent k-means analysis, is applied to the same data. These two methods are compared with a convergent k-means analysis that utilizes available geologic knowledge. All three methods identify four clusters. Three of the clusters represent background values for the Precambrian rocks of the area, and one represents outliers (anomalously high /sup 214/Bi). A segmentation of the data corresponding to geologic reality as discovered by other methods has been achieved based solely on analysis of aerial radiometric data. The techniques employed are composites of classical clustering methods designed to handle the special problems presented by large data sets. 20 figures, 7 tables.

Research Organization:
Bendix Field Engineering Corp., Grand Junction, CO (USA); Los Alamos National Lab., NM (USA); Texas Tech Univ., Lubbock (USA)
DOE Contract Number:
AC13-76GJ01664
OSTI ID:
6634352
Report Number(s):
GJBX-202(82); ON: DE83004397
Resource Relation:
Other Information: Portions of document are illegible
Country of Publication:
United States
Language:
English