Block caving is an underground mining method that can provide high production capacities at low operating costs. This enables the profitable extraction of massive, low-grade orebodies and attracts industry towards the method. Block caving operations require significant initial capital investment and predictable cave propagation is crucial to safety and economic success. Lack of access to observe the caving process hinders our ability to learn how the rock mass and geological structures are reacting to mining. Virtual reality scientific visualisation (VRSV) enables the integration and investigation of multiple, large caving data sets and provides a viable alternative to visualise rock mass response without physical access to the cave. Point instrumentation is incapable of providing an overview of cave performance and seismic monitoring, while providing an overview of the rock mass and the response of structures to caving, is only useful when linked to the caving activities. The integration of point instrumentation data with seismic monitoring information, coupled with scientific visualisation of the mining system in a virtual reality environment, provides a powerful means for understanding caving performance.This paper describes the use of a VRSV module at UNSW Australia, designed for investigating the block cave mining system from a case study operation. This module has been used to investigate the location and chronological evolution of seismicity to understand sources of damage resulting in seismic space-time sequences (SSTS) in the caving system. The events within an SSTS represent damage that is spatially and temporally related. An SSTS group represents a collection of microseismic events from a reasonably competent region of the rock mass that has experienced a stress change significant enough to cause sequential damage. SSTS group locations have been seen to be the path of subsequent cave propagation. Thus, SSTS analysis enables insight to further our understanding of rock mass damage in caving geomechanics through analysis of historic data. Such insight has operational potential to learn from recent rock mass activity for optimising the management of undercut advance, draw front advance and cave draw patterns.CITATION:Tibbett, J D, Suorineni, F T and Hebblewhite, B K, 2016. Understanding damage source mechanisms in a caving system using virtual reality scientific visualisation, in Proceedings Seventh International Conference and Exhibition on Mass Mining (MassMin 2016), pp 439-452 (The Australasian Institute of Mining and Metallurgy: Melbourne).