In Situ Stress Prediction Using Multiple Seismic Attributes Based on the Random Forest Algorithm

In situ stress characterisation is crucial for the exploration and development of coal resources. Conventional methods based on well‐log data offer high‐accuracy point measurements but are limited to wellbore locations, whereas traditional seismic‐based predictions provide extensive spatial coverage but have lower accuracy and often fail to capture the complex, non‐linear relationships between seismic attributes and the stress field. To bridge this gap, we introduce a data‐driven approach that integrates the strengths of both data types using a random forest (RF) model. In our approach, geomechanical attributes (Young's modulus and Poisson's ratio) and geometric attributes (curvature), derived from pre‐stack inversion, serve as the model's input features. High‐resolution stress values calculated from well logs using the combined spring model serve as the training labels. The RF model, optimised via grid search and cross‐validation, demonstrates high predictive accuracy. We apply the proposed RF‐based method to a field dataset from the Daji area in Shanxi Province, North China, successfully generating a continuous three‐dimensional (3D) in situ stress volume. The resulting stress field exhibits strong spatial consistency with regional tectonic features, validating the model's accuracy and geological applicability. This study demonstrates that a machine learning framework can effectively link seismic data with well‐log‐derived reference stress labels, extending sparse reference stress information into a continuous 3D volume that reliably characterises the inter‐well stress heterogeneity. This provides a practical and effective framework for in situ stress analysis in complex geological settings.