Research on the Time lag between Groundwater level variation and Water Burst in Fissure Media Mine Tunnel Excavation by FEFLOW

Mining activities in fissure media pose significant challenges due to the complex and unpredictable behavior of groundwater flow, which can lead to hazardous water bursts. These bursts threaten operational safety, infrastructure integrity, and environmental sustainability. This study investigates the time lag between groundwater level variations and water bursts during tunnel excavation, emphasizing its role as an early-warning mechanism. Using the Maoping lead-zinc mining area as a case study, a conceptual model was developed to simulate groundwater flow dynamics, incorporating geological and hydrogeological data. The analysis focuses on hydraulic head, pressure distribution, water flux, velocity, and time lag, particularly within fractures 1 to 3, which represent varying hydraulic conditions. The results highlight the importance of monitoring wells in critical zones, where sudden hydraulic head reductions act as precursors to water inrush events. A negative time lag of -9, -8, and − 10 steps was observed, corresponding to different monitoring locations, establishing it as a reliable indicator of transient hydraulic behavior. High-risk zones, such as Fracture 2, exhibit dominant groundwater transport roles, characterized by high velocities and significant fluxes. These findings emphasize the need for prioritizing such fractures for real-time monitoring and proactive mitigation strategies. By integrating time lag analysis with advanced numerical simulations, this study provides a robust framework for enhancing safety in underground mining operations. The proposed early-warning system enables timely evacuation of workers, adjustment of excavation activities, and reinforcement of critical zones, mitigating risks and reducing economic losses. Furthermore, this study highlights the importance of integrating hydrogeological monitoring with predictive modeling to address groundwater flow challenges in fractured rock systems. The findings contribute to improved risk management strategies and pave the way for refining predictive tools through field applications.