Mine-scale numerical modelling, seismicity and stresses at Kiirunavaara Mine, Sweden

Abstract

LKAB's Kiirunavaara Mine, located in northern Sweden, has exhibited seismic behaviour since the mining production extended below 700 m depth. Iron ore is mined from the 4.5 km long orebody via sublevel caving at a production rate of 28 m t per annum. The deepest current production level is at approximately 800 m depth, and current mining plans call for mining to about 1,200 m depth. It is thus of critical importance for LKAB to gain a deeper understanding of the stress and rock mass behaviour at the mine. The Kiirunavaara orebody has complex geometry and geology, which is represented using the discontinuum distinct element code 3DEC. As part of a larger series of models investigating the influence of strength and structural geology on rock mass behaviour, the results of multiple continuum models are presented. The goals of these continuum models included: (i) obtain a better understanding of the virgin stress field and redistribution of stresses caused by mining, (ii) further define the extent of mining induced plastic failure; and (iii) increase the understanding of existing failure mechanisms at the mine. The elastic and plastic continuum models accurately produced principal stresses similar to measurements recently conducted at two sites in the mine, confirming the previously estimated virgin stress state. Spatial correlations between plastic failure in the model and seismicity in the hanging wall and footwall were found. However, these correlations were not consistent throughout either material for any evaluated set of material properties; either the plastic failure in the footwall or hanging wall corresponded well with seismicity. This may be because a set of rock mass properties which represent rock mass failure at this scale have not been evaluated or that some underlying failure mechanisms causing seismicity are not represented in the models, for example, failure along discontinuities. Some events larger than moment magnitude of 1.2 in the hanging wall, in particular shear source mechanisms events, do not correspond well with plastic failure from the model. These results potentially indicate that geological structures, which are not represented in these models, influence mine behaviour. The improved understanding of input data, rock mass behaviour, and failure mechanisms as a result of these models has a direct impact upon mine excavation design and future rock behaviour investigations, and will be used in the continued research, as well as in mine planning. https://papers.acg.uwa.edu.au/p/1410_24_Vatcher/ Mine-scale numerical modelling, seismicity and stresses at Kiirunavaara Mine, Sweden

Evolution of seismicity at Kiruna Mine

“…Similar observation was made also for many other cases of the levels in studied blocks. EI > 1 (log EI > 0) (stress increase) is observed on the edges of the block around the production level and below in Phase B, and on the edges around the production level and below in Phase C. In other cases the area with EI > 1 is spreading and moving under the production levels on the footwall side in Phases B and C. The indications of increased stresses on the footwall side below the production levels are in line with the results from the numerical modelling (Vatcher et al 2014).…”

Production-associated risk factors of seismicity in the Kiirunavaara mine

“…Initial stresses applied in the model were based on the relationships developed by Sandström (2003) for the Kiirunavaara mine. This initial stress state resulted in an induced stress field at depth in numerical models that closely matched independent stress measurements (Vatcher et al 2014).…”

Enhancing Machine Learning Algorithms to Assess Rock Burst Phenomena

“…The use of microseismic monitoring in the rockburst prediction has been a common topic by many researchers (Dou 2018;Cai 2015;). The use of numerical modeling (Vatcher 2014;Tianwei 2015;Board 2007;Vardar 2019;Khademian. 2016;Poeck 2016;Khademian 2019;He 2016;Manouchehriana 2018;Mitri 1999;Jiang 2010;Sharan 2007) in the rockburst prediction and its combination with other techniques is also a research topic that is investigated by many researchers, but it's main use focuses on the establishing of the burst prone areas and still there is not a universally accepted methodology of simulating accurately dynamic phenomena.…”