Temporal analysis and early warning of rock instability in hard rock mines based on CNN-LSTM and CUSUM

Abstract

Underground mining in hard-rock mines is conducted under complex geological conditions, where rock masses are prone to sudden failure. To address this challenge, this study proposes an integrated early-warning framework coupling microseismic (MS) monitoring with a Convolutional Neural Network – Long Short-Term Memory (CNN-LSTM) predictor and a Cumulative Sum (CUSUM) change-point detector. The CNN-LSTM forecasts key MS parameters (seismic energy and source radius) from real-time monitoring data. A new indicator, Q—defined as the ratio of seismic energy to the cube of source radius—is introduced to quantify energy release density during rock fracturing. CUSUM tracks the evolving Q series to identify significant deviations and issue early warnings. The framework is validated using MS data from the Dongtangzi lead – zinc mine in the tectonically active Qinling Orogenic Belt, northwestern China. Results show that, compared with conventional approaches, prediction performance is markedly improved: relative to a standalone LSTM, RMSE and MAE decrease by ∼30% – 56%, while R² increases by ∼20% – 58%. The framework remains robust under complex monitoring conditions and provides warning signals up to one week in advance. Moreover, the post-blasting attenuation of Q provides quantitative insights into stress-release mechanisms and surrounding rock behavior. These findings highlight the framework’s practical applicability and theoretical significance for mining hazard prediction.

Keywords

rock instability / microseismic monitoring / convolutional neural network-long short-term memory (CNNLSTM) / cumulative sum (CUSUM) / early warning

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