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An Evaluation of AI Models’ Performance for Three Geothermal Sites

Author

Listed:
  • Ebubekir Demir

    (Mining Engineering Department, Colorado School of Mines, Golden, CO 80401, USA)

  • Mahmut Cavur

    (Mining Engineering Department, Colorado School of Mines, Golden, CO 80401, USA
    Management Information System Department, Kadir Has University, Istanbul 34083, Türkiye)

  • Yu-Ting Yu

    (Mining Engineering Department, Colorado School of Mines, Golden, CO 80401, USA)

  • H. Sebnem Duzgun

    (Mining Engineering Department, Colorado School of Mines, Golden, CO 80401, USA)

Abstract

Current artificial intelligence (AI) applications in geothermal exploration are tailored to specific geothermal sites, limiting their transferability and broader applicability. This study aims to develop a globally applicable and transferable geothermal AI model to empower the exploration of geothermal resources. This study presents a methodology for adopting geothermal AI that utilizes known indicators of geothermal areas, including mineral markers, land surface temperature (LST), and faults. The proposed methodology involves a comparative analysis of three distinct geothermal sites—Brady, Desert Peak, and Coso. The research plan includes self-testing to understand the unique characteristics of each site, followed by dependent and independent tests to assess cross-compatibility and model transferability. The results indicate that Desert Peak and Coso geothermal sites are cross-compatible due to their similar geothermal characteristics, allowing the AI model to be transferable between these sites. However, Brady is found to be incompatible with both Desert Peak and Coso. The geothermal AI model developed in this study demonstrates the potential for transferability and applicability to other geothermal sites with similar characteristics, enhancing the efficiency and effectiveness of geothermal resource exploration. This advancement in geothermal AI modeling can significantly contribute to the global expansion of geothermal energy, supporting sustainable energy goals.

Suggested Citation

  • Ebubekir Demir & Mahmut Cavur & Yu-Ting Yu & H. Sebnem Duzgun, 2024. "An Evaluation of AI Models’ Performance for Three Geothermal Sites," Energies, MDPI, vol. 17(13), pages 1-29, July.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:13:p:3255-:d:1427567
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    References listed on IDEAS

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    1. Moraga, J. & Duzgun, H.S. & Cavur, M. & Soydan, H., 2022. "The Geothermal Artificial Intelligence for geothermal exploration," Renewable Energy, Elsevier, vol. 192(C), pages 134-149.
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    Cited by:

    1. Shan, Kun & Cong, Lianghan & Yu, Ziwang & Ye, Xiaoqi, 2026. "Artificial intelligence empowering geothermal energy development: A full-lifecycle review from exploration to operation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 226(PE).

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