Modeling China's deep temperature field with ML-based heat flow and layered heat production data

Accurate modeling of the deep crustal temperature field is critical for geothermal resource assessment and understanding lithospheric thermal evolution. Traditional approaches relying on interpolation-based terrestrial heat flow (THF) map and average radiogenic heat production often fail to capture the geological heterogeneity of the crust, particularly in data-sparse regions. In this study, we present a refined framework that integrates a machine learning-predicted THF map with vertically stratified radiogenic heat production data, applied within a one-dimensional steady-state heat conduction model. This approach allows us to compute deep temperature profiles across mainland China at multiple depths (3-10 km) with improved resolution and geological consistency. The results align well with borehole observations and reveal extensive high-temperature zones exceeding 150 °C in regions such as the Tibet-Sanjiang Orogen, North China Craton, and Yangtze Craton. Moreover, based on modeled temperatures at 3 km and 10 km, we derived a geothermal gradient map and identified eight favorable geothermal belts characterized by high vertical gradients and deep temperatures suitable for enhanced geothermal system (EGS) deployment. This study offers a scalable framework for geothermal prospecting in data-sparse regions and provides strategic insights for future deep energy exploration.