ArcGIS<sup>TM</sup> and Principal Component Analysis of Probe Data to Micro-Map Minerals in Round Top Rare Earth Deposit

Abstract

Rare earth elements (REEs), especially heavy rare earth elements (HREEs), are in demand for their current and emerging applications in advanced technologies. Here we perform computer-driven micro-mapping at the millimeter scale of the minerals that comprise Round Top Mountain, in west Texas, USA. This large rhyolite deposit is enriched in HREEs and such other critical elements as Li, Be, and U. Electron probe microanalysis of 2 × 2 mm areas of thin sections of the rhyolite produced individual maps of 16 elements. These were superimposed to generate a 16-element composition at each pixel. Principal components analysis of elements at each pixel identified the specific mineral at that site. The pixels were then relabeled as the appropriate minerals, thereby producing a single mineral map. The overall mineral composition of the 7 studied samples compared favorably with prior analyses of the Round Top deposit available in the literature. Likewise the range of porosity in the maps was consistent with that of previous direct measurements by water saturation. This new statistical and GIS-based technique provides a robust and unbiased approach to electron microprobe mapping. The study further showed that the high-value yttrofluorite grains exhibited little tendency to cluster with other late-stage trace minerals and that the samples extended the previously documented overall homogeneity of the deposit at field scale to this microscopic scale.

ArcGIS<sup>TM</sup> Proximity and Cluster Analysis of Electron Probe Micro-Maps of Round Top Critical Mineral Deposit

“…In an earlier work [9] we showed that overlaying the X-ray element maps in ArcGIS™ revealed element-mineral correlations to produce mineralogical maps. In this paper we described the use of those detailed maps to study the placement of minor and accessory minerals, relative to one another in order to understand mineral relationships at the micrometer-to-millimeter scale.…”

“…This research is an extension of previous work on the Round Top Mountain deposit in which multivariate statistical analysis (principal component analysis) converted electron microprobe elemental maps [8] into mineral maps [9]. In the current paper, those mineral maps are further analyzed spatially through proximity and cluster analyses using tools in the ArcGIS™ software system.…”

A Fusion of Feature-Oriented Principal Components of Multispectral Data to Map Granite Exposures of Pakistan

“…Analytical techniques for RS are grouped into data-driven and knowledge-based techniques [15]. Several techniques, such as band ratios [16,17], band ratio matrix transformation (BRMT) [18], spectral angle mappers (SAM) [19,20], decorrelation stretching (DS) [17,21], principal components analysis (PCA) [17,22,23], directed principal component analysis (DPCA) [24], feature-oriented principal components selection (FPCS) also known as Crosta technique [25][26][27], spatially weighted PCA [28,29], directed PCA [30,31], and other variants of PCA [32], have been reported for mapping different lithological [33] and environmental [34] features. Recently, Support Vector Machine (SVM), PCA, and independent component analysis (ICA) have been used to map gold-bearing granite-greenstone rocks using AVIRIS-NG hyperspectral and ASTER data [35].…”