Mitigating the Rockfall and Rockburst Risk in South African Gold and Platinum Mines through Advanced Knowledge of the Rock Mass - RASIM 2022

The South African Mining, Extraction, Research, Development and Innovation (SAMERDI) strategy was launched in 2019 with the goal of modernizing the mining industry. It comprises three major research initiatives: improvement in the efficiency of extraction and health and safety in current mining operations, development of fully mechanized systems to mine narrow tabular ore bodies in hard rock, and development of extraction systems to break rock without using explosives. The crosscutting Advanced Orebody Knowledge (AOK) program seeks to develop and implement drilling and geophysical technologies that can characterize the rock mass ahead of mining and identify potentially hazardous geological features. This knowledge will be integrated with information derived from seismic monitoring. Mining methods, layouts and rock support systems will be adjusted accordingly to mitigate the risk of rockbursts. Boreholes are routinely drilled ahead of mine workings using diamond-impregnated bits. The drill core is logged to identify lithology and geological structures (especially dykes and faults that may be seismogenic), sample the ore body for grade control, and detect areas that may contain water and/or flammable gas. Methods and technology to improve the efficiency of drilling and maximize the information obtained from the drilling process, borehole core, and the borehole itself are being investigated. A range of geophysical technologies are being developed to map the rock mass from the skin of the excavation to a distance of several tens of meters. Light Detection and Ranging (LiDAR) is used to map the geometry of the excavation, including prominent joint, vein and fracture sets and the presence and performance of rock support elements and systems. Ground Penetrating Radar (GPR) is used to penetrate deeper into the rock mass and image joints, fractures bedding planes that influence the stability of the hanging and side walls. Image processing techniques known as ‘seismic attributes’ are being applied to the radargrams to improve interpretation. Electrical Resistivity Tomography (ERT) and various active and passive seismic techniques (e.g. Tunnel Seismic Profiling (TSP), ambient noise tomography) achieve even greater penetration into the rock mass. Finally, machine learning is used to improve the processing and interpretation of seismic and radar data.