Review and simulation of passive seismic tomography in block cave mining

Abstract

Seismic tomography methods are progressing in crustal seismology and at the smaller mining scale to recognise highly stressed or fracture-prone areas. Velocity variations measured by seismic tomography represent stress concentrations in the rock mass. Changes in these stress conditions are of interest in mining as they are linked to the instability of the underground openings. Rock fracturing generates seismic waves, which propagate with different velocities through portions of the rock mass that have different moduli. Both known and unknown seismic sources in mining environments generate active and passive tomography data, respectively. Active tomography utilises a known source time and location, while passive seismic tomography uses the mining-induced seismic events, for which the source time and location can only be estimated. Mining-induced seismic events generally have relatively low magnitudes, typically lower than ML = 3. The pattern of stress redistribution varies based on different mining methods at different depths. In this study, development of seismic tomography in the mining industry is traced through a review of background theory and recent applications. Additionally, a block caving simulation is presented, including the imaging of cave development, load distribution, and abutment zones. A simple elastic numerical model is used to model stress distribution surrounding a hypothetical block cave. Velocities are assigned to portions of the model corresponding to the stress level. With this velocity model, synthetic travel times are modelled. The synthetic travel times are then used as input to the tomography code. The velocity distribution, which is then generated through the tomography calculations, is compared to the initial, modelled velocity distribution providing a means for validating the quality of the results of the tomography approach for this application.

A Comprehensive Review on Internet of Things (IoT) and its Implications in the Mining Industry

“…Mine engineers have to review the applicability of these novel methods and educate operating crews accordingly. Several studies introduced and discussed a specific modern technique using recent technologies such as AI and its challenges at a particular stage of mining (Ghasemi et al, 2014;Muduli et al, 2018;Ghaychi Afrouz and Westman, 2018;Chakravorty, 2019;Hyder et al, 2019;McGaughey, 2020). Few studies also specifically introduced IoT for a single task in mining such tailing dam monitoring (Sun et al, 2012), coal mine monitoring system (Hu et al, 2013;Bo et al, 2014;Zhou et al, 2017), positioning systems in coal mines (Liu et al, 2010;Liu and Liu, 2014), ventilation and gas monitoring (Qin et al, 2011;Qian et al, 2016;Gillies et al, 2004;Jo and Khan, 2018), roof support (Singh et al, 2018), maintenance and machinery safety (Atkins et al, 2010;Zhang et al, 2014;McNinch et al, 2019).…”