McGaughey, WJ, Laflèche, V, Howlett, C, Sydor, JL, Campos, D, Purchase, J & Huynh, S 2017, 'Automated, real-time geohazard assessment in deep underground mines', in J Wesseloo (ed.), Deep Mining 2017: Proceedings of the Eighth International Conference on Deep and High Stress Mining, Australian Centre for Geomechanics, Perth, pp. 521-528, https://doi.org/10.36487/ACG_rep/1704_34_McGaughey
(https://papers.acg.uwa.edu.au/p/1704_34_McGaughey/)
Abstract: We introduce an automated, real-time geohazard assessment system designed specifically for underground mining. The system, which we call Geoscience INTEGRATOR, is based on the quantitative 4D geohazard computational strategy that we previously developed and reported on at the Seventh International Conference on Deep and High Stress Mining in Sudbury, Canada, in September 2014. The computational strategy relies on modelling multiple, independent hazard criteria related to geology, structure, rock mass condition, stress, seismicity, mine geometry, and support on the rock interface where the hazards occur, and building a statistical model that relates past hazard occurrence to the state of those criteria at the time of the hazard (McGaughey 2014). Further research and development has been carried out since then with the objective of developing an operational computational infrastructure that enables practical geohazard assessment using this approach. We have developed a system for automatically assembling and managing the required inputs, managing the geohazard assessment computational processes, and providing meaningful reports to mine operators. This has been accomplished within three recent Canadian research programs managed by the Centre for Excellence in Mining Innovation (CEMI): ‘Smart Underground Monitoring and Integrated Technologies’ (SUMIT), ‘Mining Observatory Data Control Centre’ (MODCC), and ‘Ultra-Deep Mining Network’ (UDMN). The initial system design objective under the SUMIT program was a capability to “provide researchers with user-friendly access to mine datasets and their contextual information to facilitate and optimise research efforts.” The system was further developed in the ensuing projects to provide a powerful 3D visualisation interface and to support, track, and automatically report on 4D dynamic mine models and associated geohazards in a deep operating mine context. It is now being deployed to manage multi-institution, multi-site data from both SUMIT and other major research projects in addition to implementations at operating mine sites to improve rockburst and other types of geotechnical hazard assessment.


Keywords: geohazard, rockburst, seismicity, machine learning, data management