One-Step Prediction Random Forest for Induced Seismic Hazard Forecasting: Application to the Luxian area, Southern Sichuan Basin, China

Summary Hydraulic fracturing in unconventional gas development has intensified concerns over induced seismicity, generating significant seismic hazards with potential risk implications for surrounding environments and communities. Accurate prediction and transparent interpretation of such hazards remain open challenges in seismology and engineering practice. This study addresses these challenges by developing a One-Step Prediction Random Forest framework to model the spatiotemporal relationships among hydraulic fracturing well deployment, geological factors, historical seismicity, and the likelihood of future seismic occurrences. A seismic energy labeling scheme based on one-step prediction enables the framework to estimate potential seismic energy release and identify dominant controlling factors through feature attribution. Building on these results, the conventional traffic-light risk management system is conceptually extended to a traffic-light hazard management scheme, which incorporates theoretical insights from risk analysis to improve interpretability and operational relevance. Predictive performance across low-, medium-, and high-hazard scenarios is assessed using confusion matrix analysis, while SHAP-based interpretability confirms that the framework preserves physical consistency by linking geological and operational drivers with seismic energy release. The findings advance methodological innovation in induced seismicity research by combining hazard prediction with a hazard-management framework inspired by risk theory, providing both theoretical insights and practical tools for shale gas development.