Abstract
This paper aims to develop a principle for selecting the most informative samples for geological research from extensive collections of rock material. As a tool for this selection, we chose an original method of statistical comparison of X-ray powder diffraction (XRPD) and X-ray fluorescence (XRF) data using factor analysis (FA). A collection of carbonatites and aluminosilicate rocks from the Kontozero Devonian carbonatite paleovolcano complex (198 samples) is presented to test our technique. The factors extracted during FA were successfully mineralogically interpreted according to peak positions on the graphs of factor loadings. For the studied rock collection, this approach allowed us to identify more than 20 rock-forming minerals based only on XRPD data. We also found about ten mineral phases, the lines of which are low-intensity, and/or which overlap with more intense peaks of other minerals in the diffraction patterns. The mineralogical interpretation of the factors of such hidden minerals can be performed through electron probe microanalysis (EPMA) of the samples previously selected using FA. In this study, we report on an algorithm that facilitates the selection of the rock samples exhibiting the greatest contrast in mineral and chemical composition and which contain the entire set of mineral phases occurring in the geological object under study. From the collection of Kontozero rocks we examined, the 30 most representative samples were selected, amounting to about 15% of the initial sample set.
Highlights
Analytical instruments are continually evolving, and their performance increases rapidly
This paper presents the results of an factor analysis (FA)-based investigation of X-ray powder diffraction (XRPD) patterns and complementary X-ray fluorescence (XRF) data on a rock sample collection from the Kontozero carbonatite complex
The number of factors was minimized to the extent necessary to represent all non-random differences in the combined (XRPD + XRF) dataset
Summary
Analytical instruments are continually evolving, and their performance increases rapidly. A line of portable devices for field X-ray fluorescence (XRF) and X-ray powder diffraction (XRPD) analysis has appeared (e.g., [1]). Researchers have the opportunity to work with ever-larger collections of geological samples. Time and labor problems arise when selecting a representative subset of samples that can provide the most comprehensive (mineralogical, geochemical, isotopic, etc.) information for further research. With modern large-scale data processing, various methods of selecting a sample set and reducing its size using statistical tools have proven to be effective, and even irreplaceable [2]. In geology, the standard solution to this problem is based on expert judgment, i.e., on a mostly intuitive approach
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