A new geothermal map of China by machine learning reveals hidden sweet spots

ABSTRACT Geothermal heat flux (GHF) serves as a key parameter in geothermal exploration but is sparsely measured worldwide because of technological challenges and high costs. Traditional interpolation and empirical methods for GHF prediction often provide limited accuracy. Unlike traditional methods, machine-learning methods can integrate multiple geologic factors affecting GHF and capture their nonlinear relationships, leading to more accurate predictions. Here, we develop a gradient boosting regression tree (GBRT) model to predict GHF across China. We screen and choose a comprehensive set of 16 geologic and geophysical features that affect GHF as the input variables of the GBRT model. A global data set comprising 36,325 GHF data points is divided into training and test sets. The training set is used to develop the model, and the test set is used to evaluate its performance. The GBRT model established using the global data set exhibits great performance in predicting GHF in China, achieving an R-squared value of 87% on the test set, which is among the highest reported for GHF prediction. The predictions are validated with geologic features and high-temperature hot spring data, confirming their accuracy. The newly generated GHF map of China reveals previously unidentified high GHF regions (“sweet spots”), providing valuable targets for future geothermal exploration in China.