GSA Connects 2023 Meeting in Pittsburgh, Pennsylvania

Paper No. 255-4
Presentation Time: 2:20 PM

A MULTI-SCALE, GEO-DATA SCIENCE METHOD FOR ASSESSING UNCONVENTIONAL CRITICAL MINERAL RESOURCES


CREASON, C. Gabriel1, JUSTMAN, Devin2, YESENCHAK, Rachel3, MONTROSS, Scott4, WINGO, Patrick5, THOMAS, R. Burt6 and ROSE, Kelly6, (1)US Department of Energy, National Energy Technology Laboratory, 1450 Queen Ave SW, Albany, OR 97321; Oregon State University, College of Earth, Ocean, and Atmospheric Sciences, Corvallis, OR 97330, (2)NETL Support Contractor, National Energy Technology Laboratory, 1450 Queen Ave SW, Albany, OR 97321, (3)Department of Geology & Geography, West Virginia University, Morgantown, WV 26506, (4)Department of Energy, National Energy Technology Laboratory, 1450 Queen Ave SW, Albany, OR 97321, (5)Leidos Research Support Team, National Energy Technology Laboratory, 1450 Queen Ave SW, Albany, OR 97321, (6)US Department of Energy, National Energy Technology Laboratory, 1450 Queen Ave SW, Albany, OR 97321

Critical minerals (CM) supply raw materials that constitute many of our essential infrastructure, defense, technology, and electrification needs. Currently, production and refinement of these materials from conventional sources is limited to few regions globally, which makes supply of these resources particularly vulnerable to disruption. To help overcome these risks and meet growing demand, attention has focused on identifying and developing resource potential of unconventional geologic CM sources, such as rare-earth elements in sedimentary systems. However, the unconventional nature of these types of sources means they are often poorly characterized and/or under-explored with respect to conventional counterparts.

We present a regional case study for an Unconventional Rare-earth and Critical minerals (URC) assessment method for predicting and identifying REE resource potential and occurrence in unconventional systems in the Central Appalachian Basin (CAB). The method utilizes a geologic and geospatial data-driven approach, informed and guided by knowledge of REE enrichment processes, to systematically predict and identify areas of higher enrichment. Results from the test case indicate locations with potential for different types of coal-REE deposits, demonstrating its utility for reducing the area of exploration and identifying sites for more detailed investigation. Building upon the regional scale assessment capability, ongoing science-based enhancements to the method will allow for finer-scale (e.g., mine-scale) predictions required to support technical and economic assessments.