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Energies
Volume 16
Issue 18
10.3390/en16186530
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Article

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

by
Xiaohan Zhang
1,
Yuanfu Zhang
1,*,
Yuxiu Li
1,
Yunying Huang
1,
Jianlong Zhao
1,
Yuchuan Yi
1,
Junyang Li
1,
Jinchuan Zhang
1 and
Dawei Zhang
2
1
School of Energy Resources, China University of Geosciences, Beijing 100083, China
2
Institute of Urban Underground Space and Energy, The Chinese University of Hong Kong (Shenzhen), Shenzhen 518115, China
*
Author to whom correspondence should be addressed.
Energies 2023, 16(18), 6530; https://doi.org/10.3390/en16186530
Submission received: 24 July 2023 / Revised: 30 August 2023 / Accepted: 7 September 2023 / Published: 11 September 2023
(This article belongs to the Section H2: Geothermal)
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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.
Keywords: geothermal anomalies; GIS and remote sensing; hierarchical structure models; spatial analysis geothermal anomalies; GIS and remote sensing; hierarchical structure models; spatial analysis

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MDPI and ACS Style

Zhang, X.; Zhang, Y.; Li, Y.; Huang, Y.; Zhao, J.; Yi, Y.; Li, J.; Zhang, J.; Zhang, D. 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 2023, 16, 6530. https://doi.org/10.3390/en16186530

AMA Style

Zhang X, Zhang Y, Li Y, Huang Y, Zhao J, Yi Y, Li J, Zhang J, Zhang D. 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. 2023; 16(18):6530. https://doi.org/10.3390/en16186530

Chicago/Turabian Style

Zhang, Xiaohan, Yuanfu Zhang, Yuxiu Li, Yunying Huang, Jianlong Zhao, Yuchuan Yi, Junyang Li, Jinchuan Zhang, and 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 16, no. 18: 6530. https://doi.org/10.3390/en16186530

APA Style

Zhang, X., Zhang, Y., Li, Y., Huang, Y., Zhao, J., Yi, Y., Li, J., Zhang, J., & Zhang, D. (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, 16(18), 6530. https://doi.org/10.3390/en16186530

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