As a rock mass fragments due to stresses in the cave back, individual rock fragments fall onto a pile of caved material and follow a flow path that is influenced by inter alia draw strategy, size distribution of the caved muckpile and undercutting rate and direction. Understanding the flow of broken ore is critical to be able to link the grade and properties of the in situ rock mass, which are characterized using samples from exploration drilling, to the quality of ore reporting to drawpoints during production.
Various models for explaining the gravity flow of fragmented ore have been developed for application to block cave mines. Pierce (2010) developed a model for flow of fragmented rock for use in REBOP (rapid emulator based on particle flow code), a code originally developed by Cundall et al. (2000) that emulates particle flow interactions using observations from other models (physical and numerical). The Cellular Automata model, a stochastic model, uses probability distributions to estimate the movement of blocks within the fragmented ore column (Alfaro & Saavedra, 2004). A 3D version of the model has been adopted for use in the PCBC scheduling package (Villa & Farías, 2016).
Recent developments in marker technology have significantly improved the quality of information available to track the flow of broken ore. Early efforts involved the use of tire markers such as old loader tyres and various steel tube designs (Talu et al., 2010). Markers were assigned unique IDs, placed in exploration shafts or left on undercut or extraction levels (in the case where a lift is planned below an existing cave). The use of Smart Markers, fitted with Radio-frequency identification (RFID) tags, was trialled at the Northparkes block cave mine and the Telfer sub-level cave mine. Scanners located in ore cross cuts, perimeter drives or orepasses, were used to link original Smart Marker locations to the drawpoints from which they were extracted (Whiteman 2010). Real-time monitoring of cave flow was presented by Whiteman et al. (2016), where Cave Tracker markers and detectors were placed in the in situ rock mass, as shown in the next figure. The system was used to monitor the movement of markers in 3D at a time resolution of two days (Whiteman et al., 2016). Real-time monitoring of cave material allows production schedules to be refined daily so that favourable draw conditions are achieved. The technology has direct implications to cave management and the proposed Cave-to-Mill research.
