Principal Components Analysis as a Tool for Interpreting Nure Aerial Radiometric Survey Data

Pirkle, Fredric L.; Campbell, Katherine; Wecksung, G.W.

doi:10.1086/628473

Cited by 10 publications

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“…The plane that best fits the three-dimensional normalized radiometric data in a least-squares sense is spanned by the first two eigenvectors ul and u: of the correlation matrix R. The average square of the distance from a data point to the plane is given by X3. For the aerial radiometric data used in the present study, X3 is approximately 5% of the total variance kl + X2 + Xa (Tables 1-3) Pirkle, Campbell, and Wecksung, 1980;Pirkle, 1981). Thus, the position of a data point in three-dimensional space is well approximated by its projection on the plane of the first two principal components.…”

Section: Analysis Methods I: Principal Components Approachmentioning

confidence: 86%

An example of cluster analysis applied to a large geologic data set: Aerial radiometric data from Copper Mountain, Wyoming

Pirkle

Howell

Wecksung

et al. 1984

Mathematical Geology

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One ob/eetive of the aerial radiometric surveys flown as part of the U.S. Department of Energy's National Uranium Resource Evaluation (NURE) program was to ascertain the spatial distribution of near-surface radioelement abundances on a regional scale. Some method for identifying groups of observations with similar ~-ray spectral signatures and radioelement concentration values was therefore required. It is shown in this paper that cluster analysis can identify such groups with or without a priori knowledge of the geology of an area. An approach that combines principal components analysis with convergent k-means cluster analysis is used to classify 6991 observations (eaeh observation comprising three radiometric variables) ]'rom the Precambrian rocks of the Copper Mountain, Wyoming area, This method is compared with a convergent k-means analysis that utitizes available geologic knowledge. Both methods identify four clusters. Three of the clusters represent background values for the Precambrian rocks of the area, and the fourth represents outliers {anoma-lously high 214Bi). A segmentation of the data corresponding to "geologic reality" as inter. preted by other methods has been achieved by perceptive quantitative analysis of aeriaI radiometric data. The techniques employed are composites of classical elustering methods designed to handle the special problems presented by large data sets.

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Section: Analysis Methods I: Principal Components Approachmentioning

confidence: 86%

An example of cluster analysis applied to a large geologic data set: Aerial radiometric data from Copper Mountain, Wyoming

Pirkle

Howell

Wecksung

et al. 1984

Mathematical Geology

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“…They view the principal components as possibly useful but strictly artificial constructs. However, it has been shown that when principal components analysis is applied to aerial•radiometric data it may shed light on the geologic history of an area and may aid in the identification of areas •favorable for uranium deposition (Pirkle• et al, 1980).…”

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confidence: 99%

“…A bimodal second principal. component histogram may be representing various degrees of radioelement mobility while the third principal component histogram may be depicting non-mobilized radioelement distributions (Pirkle et al, 1980;Pirkle in preparation). Depending on the loadings of elements on the principal components other interpretations may be considered for the histograms.…”

Section: Program Operationmentioning

confidence: 99%

“…Duval (1976Duval ( , 1977 applied factor analysis, a technique similar to princi~al components analysis (see section on Principal Components Analysis Versus Factor Analysis), to aerial data and concluded that the technique was useful in mapping geology. Pirkle et al, (1980) used principal components analysis to develop an interpretation of aerial radiometric data suggestive of depositional processes.…”

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Statistical techniques applied to aerial radiometric surveys (STAARS): principal components analysis user's manual. [NURE program]

Koch¹,

Pirkle²,

Schmidt³

1981

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A Principal Components Analysis (PCA) program has been written to aid in the interpretation of multivariate aerial radiometric data collected by the U.S. Department of Energy (DOE) under the National Uranium Resource Evaluation (NURE) •program. The variations exhibited by these da,ta have. been reduced and classified into a number of linear combinations by using the PCA program. The PCA program then generates histograms and outlier maps of the individual variates. Black and white plots can be made •on a Calcomp plotter by the application of follow-up programs. All programs referred to in this guide were written for a DEC-10. From this analysis a geologist may begin to interpret the data structure. Insight into geological processes underlying the data may be obtained.

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Application of the General Linear Model (GLM) and Operations Research (OR) Models to the Optimization of Field Exploration and Development Planning

Wignall

Geoffroy

1987

Statistical Models for Optimizing Mineral Exploration

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Principal Components Analysis as a Tool for Interpreting Nure Aerial Radiometric Survey Data

Cited by 10 publications

References 0 publications

An example of cluster analysis applied to a large geologic data set: Aerial radiometric data from Copper Mountain, Wyoming

An example of cluster analysis applied to a large geologic data set: Aerial radiometric data from Copper Mountain, Wyoming

Statistical techniques applied to aerial radiometric surveys (STAARS): principal components analysis user's manual. [NURE program]

Application of the General Linear Model (GLM) and Operations Research (OR) Models to the Optimization of Field Exploration and Development Planning

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