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Geothermal Spatial Potential and Distribution Assessment Using a Hierarchical Structure Model Combining GIS, Remote Sensing, and Geophysical Techniques—A Case Study of Dali’s Eryuan Area

Author

Listed:
  • Xiaohan Zhang

    (School of Energy Resources, China University of Geosciences, Beijing 100083, China)

  • Yuanfu Zhang

    (School of Energy Resources, China University of Geosciences, Beijing 100083, China)

  • Yuxiu Li

    (School of Energy Resources, China University of Geosciences, Beijing 100083, China)

  • Yunying Huang

    (School of Energy Resources, China University of Geosciences, Beijing 100083, China)

  • Jianlong Zhao

    (School of Energy Resources, China University of Geosciences, Beijing 100083, China)

  • Yuchuan Yi

    (School of Energy Resources, China University of Geosciences, Beijing 100083, China)

  • Junyang Li

    (School of Energy Resources, China University of Geosciences, Beijing 100083, China)

  • Jinchuan Zhang

    (School of Energy Resources, China University of Geosciences, Beijing 100083, China)

  • Dawei Zhang

    (Institute of Urban Underground Space and Energy, The Chinese University of Hong Kong (Shenzhen), Shenzhen 518115, China)

Abstract

The assessment of geothermal potential has gained prominence among scholars, with a focus on establishing a reliable prediction model to reduce development risks. However, little attention has been given to predicting and evaluating the geothermal potential in Dali’s Eryuan area. This study introduces a novel hierarchical model integrating remote sensing, a Geographic Information System (GIS), and geophysics for the first-ever effective prediction of geothermal potential in Eryuan. The dataset includes lithology, seismic epicenter data, fault distribution, Bouguer gravity anomalies, SRTM-DEM images, and Landsat 8 remote sensing images. These datasets are converted into evidence maps and normalized to generate distinct evidence factor layers. Using the Analytic Hierarchy Process (AHP), a hierarchical model establishes weights for each evidence factor, resulting in a comprehensive prediction map. The results reveal the overall favorable geothermal potential in Eryuan, except the central area. Key hotspots include the Niujie–Sanying–Gromwell Lake and Liantie–Qiaohou, followed by the Youshou, Dengchuan, and Xixiang towns. Validation against known hot springs confirms the model’s accuracy and reliability.

Suggested Citation

  • Xiaohan Zhang & Yuanfu Zhang & Yuxiu Li & Yunying Huang & Jianlong Zhao & Yuchuan Yi & Junyang Li & Jinchuan Zhang & Dawei Zhang, 2023. "Geothermal Spatial Potential and Distribution Assessment Using a Hierarchical Structure Model Combining GIS, Remote Sensing, and Geophysical Techniques—A Case Study of Dali’s Eryuan Area," Energies, MDPI, vol. 16(18), pages 1-24, September.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:18:p:6530-:d:1237229
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    References listed on IDEAS

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    1. Mohamed Abdekareem & Nasir Al-Arifi & Fathy Abdalla & Abbas Mansour & Farouk El-Baz, 2022. "Fusion of Remote Sensing Data Using GIS-Based AHP-Weighted Overlay Techniques for Groundwater Sustainability in Arid Regions," Sustainability, MDPI, vol. 14(13), pages 1-26, June.
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    4. Agust Gudmundsson, 2022. "Transport of Geothermal Fluids along Dikes and Fault Zones," Energies, MDPI, vol. 15(19), pages 1-36, September.
    5. Guowen Wang & Jian Kuang, 2022. "Genetic Analysis of Geothermal Resources in Deep-Seated Fault Area in Tonghe County, Northeast China and Implications of Geothermal Exploration," Sustainability, MDPI, vol. 14(9), pages 1-16, April.
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