Artificial Intelligence–Driven Assessment of Geothermal Potential Based on Radioactive Heat Production: A Case Study from Western Türkiye

In recent years, the exploration and utilization of geothermal energy have received growing attention as a sustainable alternative to conventional energy sources. Data-driven reliable identification of geothermal reservoirs, particularly in crystalline basement terrains, is crucial for reducing exploration uncertainties and costs. In these geological settings, magnetic susceptibility and radioactive heat production, along with seismic wave characteristics, play a vital role in evaluating geothermal energy potential. Building on this foundation, our study integrates in situ and laboratory measurements, collected using advanced sensors from spatially diverse locations, with statistical and unsupervised artificial intelligence (AI) clustering models. This integrated framework improves the effectiveness and reliability of identifying clusters of potential geothermal sites. We applied this approach to the migmatitic gneisses within the Simav Basin in western Türkiye. Among the selected statistical models, Density-Based Spatial Clustering of Applications with Noise, and among the AI-based models, the Autoencoder-based Deep Clustering identified the most promising and narrowly defined subregions with high geothermal production potential. The potential geothermal sites predicted by the AI models align closely with those identified by statistical models and show strong agreement with independent datasets, including existing drilling locations, thermal springs, and the distribution of major earthquake epicenters in the region.