Block caving is an underground mining method that enables profitable extraction of massive, low-grade orebodies, provided orebody geometry, size and competence (quality) favors caving. The use of the block cave mining method is increasing in popularity as it is the lowest cost underground mining method and it enables large production rates. Despite the trend towards block cave mining, the method still faces several challenges. Improved understanding of how a rockmass responds to caving will lead to safer, more predictable and more productive cave mining operations. Combining large datasets from multiple sources with virtual reality scientific visualization (VRSV) is a viable alternative to understanding complex cave behavior without access to the cave. The block caving mining system is complex due to multiple interrelating factors and it is vital that the block cave mining system is analyzed holistically, rather than optimizing individual factors in isolation. The block cave mining system visualizer (BCMSV) software module was developed within the School of Mining Engineering at the University of New South Wales to harness VRSV for this purpose. The back analysis of Newcrest Mining Limited’s Ridgeway Deeps operation using the BCMSV enabled the phenomenon of pulses in the rate of seismic activity to be identified in the active region of cave propagation. Capturing these regions with seismic space-time sequences (SSTS) led to further analysis into the source mechanisms. Orientation analysis of SSTS damage volumes has enabled active joint sets in critical areas of cave propagation to be correlated to the SSTS, suggesting that the seismicity is related to joint activity. This observation is supported by numerical modelling results and seismic property analysis. The potential exists for the location of SSTS events to be used within the management cycle of a block caving operation to provide an indication of the critically stressed region of the caving column, where cave propagation is likely migrating. Engineering/geology teams can also utilize the contained SSTS orientation information for quickly imaging the rockmass fabric response to stress change to identify the caving mechanisms dominant within that damage volume.

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