Generative artificial intelligence and deep ensemble learning for short-term rockburst prediction in underground engineering using microseismic data

Abstract

Rockburst is a dynamic geological hazard characterized by the sudden release of elastic strain energy in underground excavations, posing severe threats to personnel safety. Machine learning (machine learning)-based rockburst prediction using microseismic monitoring faces a critical challenge: class imbalance, where strong rockburst events—the most destructive—are severely underrepresented, leading to poor minority-class predictions. To address this, this study proposes a two-step intelligent framework integrating generative artificial intelligence with deep ensemble learning. First, a conditional tabular generative adversarial network synthesizes high-fidelity samples for minority classes, transforming the imbalanced dataset into a balanced one. Second, a deep random forest is constructed on the augmented dataset to learn the nonlinear mapping between six precursory microseismic parameters—cumulative number of events, cumulative energy, cumulative apparent volume, and their rates of change—and rockburst intensity levels. The whale optimization algorithm enables automatic hyperparameter optimization. Validated on 93 rockburst cases from Jinping II Hydropower Station, the CTGAN-DRF framework increases accuracy from 65% to 90% and Kappa coefficient from 0.53 to 0.87 compared with baseline models. F 1 -scores for slight, moderate, and strong rockburst improve by 0.29, 0.28, and 0.39, respectively. Comparative analysis confirms the superiority of the deep ensemble architecture over shallow alternatives. SHAP-based interpretability analysis reveals that cumulative parameters dominate lower-intensity predictions, while event count rate becomes critical for strong rockburst. The proposed framework provides a reliable and interpretable solution for rockburst prediction under class imbalance, with promising applications in underground engineering safety.