Early detection of impending slope failure in open pit mines using spatial and temporal analysis of real aperture radar measurements

Abstract

Slope monitoring in open pit mines is an essential component of day-to-day operations and plays a key role in assisting geotechnical engineers and mine operators in maintaining mine safety and production schedules. Pit slope monitoring techniques have advanced significantly within the past decade, most notably in ground-based radar technology. Ground-based radar allows real-time monitoring of slope deformation across a broad coverage area, alerting mine staff to wall movements exceeding established thresholds. Lineof-sight measurements derived from the radar can be presented as 3D point clouds for the scan area, allowing mine staff to view the distribution of slope movements across the pit wall with each progressive scan.This paper presents a new methodology for spatial and temporal analysis of deformation point clouds captured by ground-based radar. The methodology builds on two existing early warning methods, the Fukuzono inverse-velocity method and the SLOpe gradient (SLO) method, which are based on the analysis of point measurement data derived from traditional geodetic prism monitoring. However, similar methodologies that fully utilise the spatial and temporal characteristics of ground-based radar data are yet to be developed.Radar data from historical slope failures captured by GroundProbe Slope Stability Radar (SSR) at a number of hard rock mines was utilised in the development of the new spatial and temporal analysis methodology. A slope failure that occurred at an open pit copper mine is presented throughout as a case example. The spatial analysis component of the methodology uses a benchmark point (or pixel), based on an alarm threshold specific to each failure case, and averaged deformation increments based on a percentage of the deformation measured by the benchmark pixel at the time of alarm. The temporal analysis component of the methodology examines deformation and velocity trends for all spatial analysis cases. The results of the spatial and temporal analysis were then used to evaluate the inverse-velocity and SLO time of failure prediction methods. Overall, the proposed methodology will improve, and provide a more systematic means of interpreting spatial and temporal ground-based radar data, aiding geotechnical engineers in managing slope movement alarms and alarm responses to provide a safer working environment for mine employees.The use of ground-based radar for monitoring pit slope wall instabilities has become the state of practice in many of today's open pit mine operations. Ground-based radar systems allow real-time monitoring of active slope instabilities, providing 3D slope deformation point clouds derived from line-of-sight measurements. These 3D point clouds allow mine staff to view the distribution of slope movements across the pit wall with each progressive scan.

Development of an early-warning time-of-failure analysis methodology for open-pit mine slopes utilizing ground-based slope stability radar monitoring data

“…A systematic multi-pixel selection technique is then implemented, termed "the percent deformation method", where mul- tiple pixels are selected based on a percentage of the deformation of the benchmark pixel; see Dick et al (2013) for a detailed description. Two data trends are subsequently analyzed and compared throughout the implementation of the method: the single benchmark pixel and the 50% deformation increment.…”

Failure Prediction of Open-Pit Mine Landslides Containing Complex Geological Structures Using the Inverse Velocity Method

“…Astonishingly, although the INV method was developed based on laboratory tests more than 30 years ago, it does not appear to have achieved implementation and verification for real-time slope failure prediction in the mining industry or mining-related technical literature until 2001 [24]. Since the early 21st century, in order to develop approaches to evaluate the failure time (t f ) of landslides, published examples (from the investigation of some large open-pit slope failures) of successful implementation are proposed to predict impending failure based on the results of the conventional application of INV methods [25][26][27][28].…”

Application of Optical Flow Technique and Photogrammetry for Rockfall Dynamics: A Case Study on a Field Test

“…In the slope monitoring of open pit mines, real-time monitoring techniques such as ground-based radar [1], Lidar [2,3], IoT sensors [4] and vision-based technology [5][6][7] have been rapidly developed to investigate the instability of rock slopes. Among these techniques, vision-based technology, which includes inspection using captured records, image analysis, photogrammetry and real-time computer vision, has been regarded as the most effective and economical way to identify areas where inactive rock slopes have started to move [5][6][7][8][9][10].…”