Regional Damage Warning for Rock Mass via Acoustic Emission and Microseismic Monitoring Data

In the process of deep hard rock mining, dynamic disasters, such as rockbursts and large-scale collapses, pose a serious threat to the production safety and sustainable development of mines. Microseismic monitoring has been widely used in mines as an efficient disaster monitoring tool. However, microseismic monitoring signals exhibit obvious nonlinear and disordered attributes due to the complex rock behavior, mine structure, and excavation disturbance. This poses great challenges for precise monitoring and forewarning of disasters in deep hard rock mines. This study introduced fractal theory and methods to characterize the spatiotemporal energy information of microseismic monitoring signals. Theoretical analysis, numerical simulation, in situ testing, and field monitoring were integrated to establish a comprehensive spatiotemporal energetic fractal characterization model of microseismic monitoring sources. A scale conversion method for the spatial and energy parameters of microseismic events was developed, and the fractal evolution of microseismic monitoring events induced by deep mining activities was systematically investigated. On this basis, a fractal-based grading forewarning system for deep mines was established, providing theoretical and methodological support for accurate disaster prediction in deep hard rock mines.