Analysis of geothermal potential in Hangjiahu area based on remote sensing and geographic information system - Xu, Linxia; Wu, Wenyuan; Qian, Junfeng; Huang, Shujia; Xie, Bin; Hu, Tangao; Lang, Xiayi; He, Bowen; Hu, Chenghao (Frontiers in Earth Science, 2023)
Geothermal resources are one of the most valuable renewable energy sources because of their stability, reliability, cleanliness, safety and abundant reserves. Efficient and economical remote sensing and GIS technology has high practical value in geothermal resources exploration. However, different study areas have different geothermal formation mechanisms. In the process of establishing the model, which factors are used to model and how to quantify the factors reasonably is still a problem that needs analysis and study. Taking Hangjiahu area of Zhejiang Province as an example, based on geothermal exploration and remote sensing interpretation data, this paper evaluates the correlation between the existing geothermal hot spots and geothermal related factors such as lithology, fault zone distance, surface water system and its distance, seismic point distance, magmatic rock and volcanic rock distance, surface water, farmland, woodland temperature and so on. The relationship between geothermal potential and distribution characteristics of surface thermal environment, fault activity, surface water system and other factors is explored. AHP analytic hierarchy process and BP neural network are used to establish geothermal potential target evaluation models. Using geothermal exploration model, the potential geothermal areas of Hangjiahu Plain are divided into five grades, and most geothermal drilling sites are distributed in extremely high potential areas and high potential areas. The results show that using remote sensing interpretation data and GIS analysis data, it is feasible to analyze geothermal potential targets based on AHP analytic hierarchy process and BP neural network, and the distribution characteristics of surface thermal environment, fault activity, surface water system and other related factors are also related to geothermal distribution. The prediction results of the model coincide with the existing geothermal drilling sites, which provides a new idea for geothermal exploration.