289 Estimating Natural Resources Using Geoscience Data

Methods to estimate the availability, location, and quantity of natural resources (e.g., geothermal, petroleum, critical minerals) vary by resource type, scale, and data availability. This session addresses modeling that targets any of these aspects.

Wednesday, 22 October 2025
1:30 PM - 5:30 PM
HBGCC, 302C

Recursive Modeling Connects Geochemistry and Genomics in Critical Minerals Characterization

Paper number: 289-5
Program: Technical Sessions
Time: 2:55 PM – 3:15 PM

Authors

Abstract

Appalachian abandoned coal mine drainage (AMD) has been identified as an unconventional domestic feedstock for critical minerals (CM) and rare earth elements (REE) in the United States. Various methodologies are employed by researchers to concentrate and extract CM and REE materials at the fluids or solid phase, including chemical and biological approaches. The wide range of concentrations of CM and REE in AMD and remediated precipitants necessitates expedient identification of optimal sites for extraction. Comprehensive spatiotemporal surveys of AMD sites to determine CM, REE, and biological composition are cost prohibitive to complete at scale. To address this, a machine learning (ML) model was developed using existing literature that includes CM, REE, and the usually reported AMD parameters: aluminum, manganese, iron, sulfate, and pH. Several models developed consistently report REE values with >90% accuracy. This model was applied to predict REE content from under characterized sites from the AMD reporting website Datashed.org. Functionality of the model was confirmed through physical back validation of 40 sites, including solids and fluid geochemical analysis. Following geochemical analysis, characterization of microbial communities via DNA extraction and 16S rRNA amplicon microbial community sequencing was performed on a subsection of sediments from the back validation phase. Sequencing revealed taxonomic trends that allow for future modeling avenues in REE recovery and targeted biological mining approaches. Computational prospecting of geochemistry begets microbiological prospecting in AMD, setting the stage for possible characterization and biological mining modeling pipelines across global feedstocks.

Geological Society of America Abstracts with Programs. Vol. 57, No. 6

doi: 10.1130/abs/2025AM-412467

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