ABSTRACT: China's geothermal resources have significant potential and broad application prospects, making them an ideal complement to the existing energy system. The reservoir temperature of a geothermal system is a critical parameter influencing well siting and economic appraisal of energy extraction. Developing economical and effective exploration methods for geothermal resources is essential for assessing deep geothermal resources, selecting high-temperature targets, and establishing enhanced geothermal systems (EGS). The electromagnetic method reveals subsurface rock formation characteristics and enables ground temperature estimation through resistivity-temperature relationships. This paper addresses deep geothermal exploration challenges by focusing on reservoir characteristics and key geothermal attributes. It uses wide field electromagnetic method (WFEM) signal processing to identify geothermal system electrical features and employs machine learning (ML) to establish quantitative relationships between rock resistivity and temperature, constructing geothermal field models for accurate ground temperature prediction. By integrating electromagnetic results with geological conditions, the study optimizes deep geothermal resource target identification. A case study in the Fushan Depression's Xixiu area demonstrates the effectiveness of these methods, establishing geoelectric and geothermal field models based on machine learning and logging data to support deep geothermal resource evaluation, development, and utilization.
Zhang et al. (Sun,) studied this question.