A Machine Learning Methodology for Predicting Geothermal Heat Flow in the Bohai Bay Basin, China

Geothermal heat flow (GHF), which contains integrated information on geothermal gradient, thermal conductivity, and heat productivity of rocks as well as crustal/mantle heat flow, is a crucial parameter in many studies including the evaluation of geothermal resources in a region and the prediction of ice sheet mass loss. The GHF measurements in the Bohai Bay Basin are insufficient and unevenly distributed, and no model has been developed to predict the GHF distribution. In this paper, using eight geological features as input data and output GHF predictions, four different machine learning algorithms were evaluated and implemented to predict the GHF data in this area: (1) a generalized linear model (GLM), (2) a deep neural network (DNN), (3) a gradient boosted regression tree (GBRT), and (4) a support vector machine (SVM). The DNN model, in which the number of neurons is a multiple of the number of features, tended to perform better than the others. Both the SVM and DNN algorithms demonstrated better accuracy, with average relative prediction errors of 13.3 and 12.7%, respectively. The accuracy improved when using a hybrid SVM and DNN approach, with an average relative prediction error of 12.2%. Based on the above models, we produced three new GHF maps of the Bohai Bay Basin. They showed similar local anomaly features but with different ranges and degrees. The new GHF maps are more detailed and rational than the map produced by kriging interpolation. The predicted GHF had much higher accuracy, which is more consistent with the measured geothermal gradient in the related areas. Finally, we reclassified the basin into four different levels of geothermal exploration target areas based on the new GHF maps.