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Predicting Rare Earth Element Potential in Produced and Geothermal Waters of the United States via Emergent Self-Organizing Maps

Author

Listed:
  • Mark A. Engle

    (Department of Earth, Environmental and Resource Sciences, University of Texas at El Paso, 500 West University Ave., El Paso, TX 79930, USA)

  • Charles W. Nye

    (Center for Economic Geology Research, University of Wyoming, Laramie, WY 82071, USA)

  • Ghanashyam Neupane

    (Idaho National Laboratory, Idaho Falls, ID 83415, USA)

  • Scott A. Quillinan

    (Center for Economic Geology Research, University of Wyoming, Laramie, WY 82071, USA)

  • Jonathan Fred McLaughlin

    (Center for Economic Geology Research, University of Wyoming, Laramie, WY 82071, USA)

  • Travis McLing

    (Idaho National Laboratory, Idaho Falls, ID 83415, USA)

  • Josep A. Martín-Fernández

    (Department of Computer Science, Applied Mathematics and Statistics, University of Girona, 17003 Girona, Spain)

Abstract

This work applies emergent self-organizing map (ESOM) techniques, a form of machine learning, in the multidimensional interpretation and prediction of rare earth element (REE) abundance in produced and geothermal waters in the United States. Visualization of the variables in the ESOM trained using the input data shows that each REE, with the exception of Eu, follows the same distribution patterns and that no single parameter appears to control their distribution. Cross-validation, using a random subsample of the starting data and only using major ions, shows that predictions are generally accurate to within an order of magnitude. Using the same approach, an abridged version of the U.S. Geological Survey Produced Waters Database, Version 2.3 (which includes both data from produced and geothermal waters) was mapped to the ESOM and predicted values were generated for samples that contained enough variables to be effectively mapped. Results show that in general, produced and geothermal waters are predicted to be enriched in REEs by an order of magnitude or more relative to seawater, with maximum predicted enrichments in excess of 1000-fold. Cartographic mapping of the resulting predictions indicates that maximum REE concentrations exceed values in seawater across the majority of geologic basins investigated and that REEs are typically spatially co-associated. The factors causing this co-association were not determined from ESOM analysis, but based on the information currently available, REE content in produced and geothermal waters is not directly controlled by lithology, reservoir temperature, or salinity.

Suggested Citation

  • Mark A. Engle & Charles W. Nye & Ghanashyam Neupane & Scott A. Quillinan & Jonathan Fred McLaughlin & Travis McLing & Josep A. Martín-Fernández, 2022. "Predicting Rare Earth Element Potential in Produced and Geothermal Waters of the United States via Emergent Self-Organizing Maps," Energies, MDPI, vol. 15(13), pages 1-21, June.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:13:p:4555-:d:844802
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    References listed on IDEAS

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    1. Hron, K. & Templ, M. & Filzmoser, P., 2010. "Imputation of missing values for compositional data using classical and robust methods," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3095-3107, December.
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