Geological Society of America

T271 Science and Technology Advancement in Responsible Mining of Critical Minerals from Primary and Secondary Resources II

Wednesday, 25 September 2024
1:30 PM - 5:30 PM
209B

RARE EARTH ELEMENTS – MULTIPHYSICS AI-AIDED AUTONOMOUS PROSPECTING (REE -MAP)

Paper number: T271-2
Program: Technical Sessions
Time: 1:35 PM – 1:50 PM

Authors

Yuxin Wu Presenting Author
Lawrence Berkeley National Laboratory, Berkeley, CA
Emil Rofors
Lawrence Berkeley National Laboratory, Berkeley, CA
Robin Thibaut
Lawrence Berkeley National Laboratory, Berkeley, CA
Jiannan Wang
Lawrence Berkeley National Laboratory, Berkeley, CA
Michael L. Whittaker
Lawrence Berkeley National Laboratory, Berkeley, CA
John Wu
Lawrence Berkeley National Laboratory, Berkeley, CA
Karthika Balan
Lawrence Berkeley National Laboratory, Berkeley, CA
Chunwei Chou
Lawrence Berkeley National Laboratory, Berkeley, CA
Jun Woo Chung
Rochester Institute of Technology, Rochester, NY
Baptiste Dafﬂon
Lawrence Berkeley National Laboratory, Berkeley, CA
Nicola Falco
Lawrence Berkeley National Laboratory, Berkeley, CA
Jim Panaro
Robindale Energy, Latrobe, PA
Arun Persaud
Lawrence Berkeley National Laboratory, Berkeley, CA
Brian Quiter
Lawrence Berkeley National Laboratory, Berkeley, CA

Abstract

Field scale mapping of Rare Earth Elements and Critical Minerals (REE-CM) in unconventional and secondary sources presents significant challenges due to low and variable concentrations and the unique characteristics of each source material. Identifying "hot zones," or areas with high REE-CM concentrations, is crucial for assessing the economic feasibility of these sources. This study proposes an AI-aided multi-physics approach for the rapid and autonomous field scale characterization of REE-CM hot zones in mine tailings, focusing on coal. The multi-physics approach integrates advanced geophysical, radiological, and optical technologies deployed on aerial and surface platforms, combined with core and lab analytical methods. This provides a cross-scale capability from whole tailing REE-CM hot zone identification to detailed mineralogical characterization. AI capabilities are key to integrating multi-physics datasets for identifying hot zones and optimizing sensing technology deployment.

Leveraging the expertise at Berkeley Lab across multiple disciplines, we have developed an efficient, autonomous, and economically viable method for REE-CM extraction from secondary sources. Field tests were carried out at coal refuse and ash sites in Pennsylvania were carried out to explore the correlation between multi-physical signals and REE concentrations, with a goal of demonstrating the feasibility of such an approach. Successful implementation will enhance the economic viability of REE-CM recovery, guiding the prioritization of extraction efforts.

Geological Society of America Abstracts with Programs. Vol. 56, No. 5

doi: 10.1130/abs/2024AM-404345

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