Abstract
Knowledge of stresses is important for many aspects of mine design, but conventional methods of measuring stresses produce estimates at only a limited number of points in space and time. Furthermore, stresses are known to be affected by geological structures, particularly faults, but mapping of how the stress field is affected by such structures is not currently possible. Therefore, there is a compelling reason to consider the use of techniques that can map such local stress field variations. The method of seismic stress inversion is utilised to address this limitation and its application is illustrated using seismic data collected from Nickel Rim South Mine (NRS) located in Sudbury, Ontario, Canada. NRS is a modern mine using blasthole open stoping with backfill as a means of bulk mining. The ellipsoidally shaped orebody is located between 1,160 and 1,710 m below ground surface, strikes east–west and is steeply dipping. Although the NRS host rock and orebody are relatively massive with high strength, the mine is structurally complicated. Many faults appear to influence the stress field, in addition to being the sources of seismic events. The seismic monitoring array in NRS has good coverage over the active volume of the mine and consists of uniaxial and triaxial geophones and accelerometers. This combination of sensors results in a large catalogue of events with good focal sphere coverage that permits source mechanism analyses to be performed. Extensive filtering has been applied to the seismic data to improve the quality, and for the stress inversion process the first motion stress inversion (MOTSI) software is used. MOTSI only uses the first motion polarities and estimates the stress tensor components with more complete uncertainties compared to other nonlinear methods. To facilitate development and refinement of the seismic stress inversion process, numerous clusters of seismic events over a period of seven months during the early stages of mining were initially analysed so as to minimise perturbations caused by the interaction between mining and geological structures. More than 500 manually processed events throughout the mine are utilised for the stress inversions. Results show that the clusters in the earlier stages of mining and further away from excavation boundaries demonstrate reasonable agreement with pre-mining stress estimates based on overcoring and breakouts. Keywords: microseismic monitoring, seismicity, stress mapping, seismic stress inversion
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