## 3 Materials and Methods
### ASTER LST Data Product and D-S Evidence Theory Fusion Method  
To minimize solar radiation interference in Landsat 8 daytime data, this study incorporated the ASTER L2 Surface Temperature V003 nighttime product (Table 1), generated using the Temperature/Emissivity Separation (TES) algorithm. This product provides high precision nighttime LST data and eliminates the need for complex inversion procedures [52]. The nighttime LST (\({}^{\circ}\)C) is calculated using the following equation:  
\[\text{T}=0.1\times\text{DN}-273.15 \tag{4}\]  
Here, T represents the nighttime LST in Celsius, DN is the pixel value, and 0.1 is the scaling factor for ASTER LST products. The constant 273.15 is used to convert Kelvin to Celsius. To ensure spatial consistency with Landsat 8 data, the ASTER image was geometrically registered to the Landsat 8 image and resampled to a 30 m resolution, allowing accurate alignment and direct comparison. To overcome the limitations of nighttime data, this study adopts a multi-perspective extraction strategy to identify geothermal anomalies from four distinct perspectives: global, local, elevation, and fault structure buffer zones. These extracted results are used to construct four corresponding evidence layers (A1, A2, A3, and A4), each representing geothermal anomalies with different spatial scales and characteristics. The evidence layers are then integrated using D-S evidence theory to improve the accuracy of geothermal anomaly detection. The detailed methodology is as follows:  
1. Global Threshold Method: After multiple tests and iterative adjustments, the threshold was finally set as the mean temperature plus one standard deviation. This threshold was selected based on comparative analysis with known geothermal hotspots to ensure effective differentiation between anomaly zones and background areas. The extracted results constitute evidence layer A1. 2. Local Block Threshold Method: The study area was divided into 11 rows and 8 columns of sub-regions, with a local threshold in each defined as the mean temperature plus one standard deviation. This method improves the sensitivity to detecting small-scale geothermal anomalies. The extracted results from all sub-regions were integrated to form evidence layer A2. 3. Elevation Partition Threshold Method: To reduce the influence of elevation on surface temperature distribution, the study area was divided into 18 sub-regions at 100-m elevation intervals, and temperature anomalies were extracted from each sub-region. The results were then combined to form evidence layer A3. The cumulative distribution function (CDF) was used to determine the temperature threshold. The temperature distribution function F(\(T_{x}\)) represents the probability that the temperature is less than \(T_{x}\):  
\[F(T_{x})=P\{T\leq T_{x}\} \tag{5}\]  
As \(T_{x}\) increases, the probability of higher temperatures rises accordingly, indicating a greater likelihood of thermal anomalies. The high-temperature threshold \(T_{h}\) is defined according to the proportion of high-temperature anomalies, \(\alpha_{h}\):  
\[P\{T\geq T_{h}\}=\alpha_{h} \tag{6}\]  
Based on the cumulative distribution characteristics of the temperature data, the quantile function \(F\) is used to determine the high-temperature threshold, expressed as  
\[F\bigg{(}\frac{T_{h}-M^{T}}{\sigma^{T}}\bigg{)}=1-\alpha_{h} \tag{7}\]Here, \(T_{h}\) is the high-temperature anomaly threshold, where \(M^{T}\) and \(\sigma^{T}\) represent the mean temperature and standard deviation within each sub-region, respectively. After transformation, the high-temperature anomaly threshold \(T_{h}\) can be expressed as  
\[T_{h}=M^{T}+F^{\prime}(1-\alpha_{h})\sigma^{T} \tag{8}\]  
Here, \(F^{\prime}\) denotes the inverse cumulative distribution function, and \(F^{\prime}(1-\alpha_{h})\) corresponds to the quantile value at a cumulative probability of \(1-\alpha_{h}\). Based on multiple tests and analyses, \(\alpha_{h}=0.2\) was selected, corresponding to a quantile value of 0.842.