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

Xiaohan Zhang, Yuanfu Zhang (), Yuxiu Li, Yunying Huang, Jianlong Zhao, Yuchuan Yi, Junyang Li, Jinchuan Zhang and Dawei Zhang
Additional contact information
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

Energies, 2023, vol. 16, issue 18, 1-24

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 (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/18/6530/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/18/6530/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:16:y:2023:i:18:p:6530-:d:1237229

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().