DEVELOPMENT OF PREDICTIVE TOOLS TO IDENTIFY CRITICAL MINERAL ENRICHMENT IN HIGH-ALUMINA UNDERCLAYS OF THE APPALACHIAN AND ILLINOIS BASINS, USA

The USGS Earth Mapping Resources Initiative (Earth MRI) works to identify mineralized areas or deposits across the United States that may host critical minerals. Aluminum-rich clays associated with coal horizons have the potential to be low-grade, large-volume, critical mineral resources. Past studies show that alumina is routinely 20-40% in these clay layers, and preliminary geochemical data indicate that some clay layers host anomalously high (>300 ppm) rare earth element (REE) concentrations. However, understanding of the distribution of metals in varying lithologies and stratigraphic positions is limited by data density and a lack of modern geochemical data. Ongoing work through Earth MRI includes geochemical analyses, mapping of known deposits, and determination of the stratigraphic and lithologic intervals with the greatest promise of hosting these elevated critical mineral resources.

As part of a multi-year study, a stratigraphic and spatially representative set of underclay samples were collected from eight states in the Appalachian and Illinois basins, geochemically analyzed by the USGS, and then processed with Orange, an open-source machine learning and statistical analysis software. Using Orange, Principal Component Analysis (PCA) and cross-plots were created to determine the elements that correlate with higher REE values. The trends indicate that in these samples higher REE totals (>375 ppm) have strong positive correlations with several elements, including Al, P, Th, and Sr, and weakly positive correlations to several others. Higher total REEs were inversely correlated with Mg.

These initial results were then compared with handheld X-Ray Fluorescence (hhXRF) measurements and used to inform the sampling strategy for a second round of sample collection. As a final step, results from each round of sampling will be compared to determine if the screening exercise resulted in higher overall REE totals using the major elemental associations. If successful, this workflow can enable quick-look or field-level selection of samples using the hhXRF and development of a semi-quantitative predictive tool to help locate potential REE deposits.