The Distribution of Surface Heat Flow on the Tibetan Plateau Revealed by Data‐Driven Methods

Abstract Surface heat flow (SHF) serves as a vital parameter for assessing the heat transfer from deep Earth to the surface, which can provide crucial insights into internal geodynamic processes. As the “roof of the world,” the Tibetan Plateau and its tectonic evolution are highly important in terms of global climate change and geodynamic study. However, a comprehensive understanding of the SHF distribution across most regions of the Tibetan Plateau is limited due to sparse measurement data. To surmount this limitation, a spatially intelligent approach has been developed: The geographically neural network weighted regression with enhanced interpretability (EI‐GNNWR). This method integrates spatial heterogeneity and nonlinear interactions between geophysical and geological factors to predict the SHF distribution across the Tibetan Plateau. In this study, the EI‐GNNWR model is used to accurately predict SHF across the entire region. After evaluating the effectiveness and interpretability of the EI‐GNNWR model, our results demonstrate that medium to high SHF values are predominantly concentrated in the southern, northeastern, and southeastern sectors of the Tibetan Plateau. These observations suggest that the formation of zones with high SHF values may be strongly influenced by the Moho depth, ridges, topography, and average curvature of satellite gravity gradients. Especially, higher SHF values may indicate more profound geodynamic activities such as collisional orogeny, shear deformation zones, or lithospheric extension. These findings offer novel insights into the spatial patterns of SHF and deepen our understanding of the geothermal formation mechanisms driven by underlying tectonic activities.