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

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.