With the depletion of shallow deposits, underground mining is reaching greater depths (over 1000 m) all over the world to tackle the mineral supply crisis in the 21st century. In comparison to shallow resource extraction, deep mining is characterized by high in situ geo-stresses, high earth temperature, and high pore/joint water pressure. The high stress concentration caused by the overlying strata, tectonic features, and mining disturbance may lead to severe dynamic disasters such as rockbursts and gas outbursts. High temperature and water pressure will change the properties of the rock mass and result in water inrush hazards.

It is very difficult to predict and prevent mining-induced shock and vibration hazards in deep mines with complex geological environments. In recent years, with the rapid development of computational methods and artificial intelligence, high-reliability and high-speed software/programs/algorithms such as machine learning models, computer vision, deep learning, numerical manifold method, finite difference method, finite element method, discrete element method, fluid-solid coupling, and discontinuous deformation analysis, have been developed and employed to solve large-scale geotechnical engineering problems. However, limited research has been dedicated to advanced computational methods that accurately and reliably predict shock and vibration hazards in deep mines.

The aim of this Special Issue is to utilize advanced computational methods to understand mining-induced shock and vibration hazards, and their prediction and control methods. Original research and review papers are welcome on all relevant topics, especially on theoretical developments, analytical methods, numerical methods, rock testing, site investigation, and case studies.

Potential topics include but are not limited to the following:

• Simulation of dynamic disasters using advanced numerical modelling methods
• Simulation validation by experimental and theoretical work
• Automatic P/S-wave arrival detection and picking
• Optimization algorithms for optimization of sensor placement for mining systems
• Prediction of dynamic hazards by machine learning models
• Big data analytics for microseismic data
• Computer vision and deep learning-based data anomaly detection methods for vibration-based monitoring data
• Data fusion approaches for vibration-based monitoring systems in deep mines
• Monitoring data recovery using deep learning
• Advanced models for analyzing data obtained from microseismic and acoustic emission systems
• Inversion models of the seismic or acoustic emission data