Learning from Disturbances, Not Timestamps: A Dynamic Event-Driven Transformer for Rock Burst Forecasting

Rock bursts remain among the most destructive and unpredictable disasters in mining operations, yet existing deep learning methods face significant challenges in engineering practicality, noise robustness, and representing complex inter-event relationships for accurate prediction. To address these limitations, this paper proposes DynamiXFormer, a novel Transformer-based rock burst prediction model. Unlike traditional temporal prediction paradigms, DynamiXFormer establishes a direct mapping from working face advancement to rock burst risk, thereby linking predictions to mining-induced disturbances. The model integrates three innovative modules: an Adaptive Frequency Denoising module that suppresses noise while enhancing salient information from a frequency-domain perspective; a Relative Event Encoding module that constructs inter-event correlation graphs to capture physical attribute correlations and spatio-temporal dependencies; and a Dynamic Sparse Attention mechanism that introduces a strong inductive bias, enabling attention to focus on both local precursory patterns and global critical shifts. Experiments on real-world microseismic monitoring data demonstrate that DynamiXFormer significantly outperforms six baseline models across all prediction horizons and evaluation metrics. In short-term prediction tasks, it achieves a Mean Squared Error as low as 0.000518 and a Recall of up to 97.85%. Ablation studies further validate the individual effectiveness and synergistic effects of the proposed modules. This research provides a new methodology for rock burst early warning, with strong potential to enhance mine safety monitoring and engineering applications.