T255 Regional Characterization of Critical Mineral Potential

Wednesday, 18 October 2023
1:30 PM - 5:30 PM
324

APPLYING PROBABILISTIC METHODS TO ESTIMATE IN SITU RARE EARTH METAL RESOURCES IN ILLINOIS BASIN COAL SEAMS

Paper number: T255-6
Program: Technical Sessions
Time: 2:35 PM – 2:50 PM

Authors

Abstract

Estimation of in situ rare earth element (REE) resources in Illinois basin coal seams by probabilistic methods suggests significant, previously unaccounted for, volumes of REE in-place. These probabilistic volumetric methods are applied to the problem of estimating in situ rare earth element resources in the Illinois Basin as part of the Illinois Basin Carbon Ore and Critical Minerals Project. Estimates have been made for major coal seams (Survant, Colchester, Springfield, Herrin, and Danville coals) at the county, state, and basin scale. These estimates are based on legacy thickness maps, extent maps, and newly-collected geochemical data. A key enabler for this study is the translation of geologic expertise on the geochemical and physical parameters of coals, including their roof and floor rocks, into probability distributions that accurately describe those features. Initial results suggest over 100 million short tons of REEs in-place, before accessibility adjustments of resources are evaluated. In particular, the Colchester coal is found to be significantly higher in REE than the other major coal seams. The majority of those resources are distributed in the roof (overburden) of the coal seam. Sensitivity analysis suggests that the coal itself may be of limited significance in terms of total REE. Comparison of resource volumes from sums of county estimates vs. basin-wide direct estimate methods clearly show the impact of degrees of freedom on uncertainty ranges. These results demonstrate that in situ REE resources in coal seams may be larger than previously thought. This may assist in the first-principles prediction of coal waste pile REE content and volumes. Careful translation of geological knowledge into probability distributions enables believable and reasonable quantitative modeling regarding problems with elevated uncertainties and outcomes.

Geological Society of America Abstracts with Programs. Vol. 55, No. 6

doi: 10.1130/abs/2023AM-390746

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