Application of Unmanned Aerial Systems to Blast Monitoring in Open Pit Mines

Current techniques used for monitoring the blasting process in open pit mines are manual, intermittent, and inefficient and can expose technicians to hazardous conditions. This thesis presents the application of unmanned aerial systems (UAS) for monitoring and improving blasting processes in open pit mines.A laboratory setup was developed to fast-track development of UAS applications for blast monitoring. The first application investigated was the measurement of post-blast rock fragmentation because it affects the productivity and efficiency of all downstream operations. The results of lab development showed that automated UAS fragmentation measurement produced similar accuracy and required significantly less time when compared to manual photograph analysis. The progress and results in the lab motivated field studies in open pit mines. These field studies investigated the accuracy of the 3D model generated, estimated image scale, and fragmentation in comparison with sieving analysis. Again, UAS techniques were found to produce results with similar accuracy to sieving; however, significant manual editing was required during photograph analysis. To increase the accuracy and efficiency of UAS fragmentation measurement, deep learning models were developed and trained to measure fragmentation from muck pile images. Model validation on muck pile orthophotos found that the deep learning methods achieved good accuracy compared to manual image labelling. Validation on screened piles showed that the deep learning measurement performed similar to manual photograph analysis accuracy when compared with sieving. Based on benefits found for UAS fragmentation measurement, other applications in blast monitoring and blasting process improvement in open pit mines were investigated. The UAS-based monitoring was done in three different stages during field experiments in different open pit mines. In the pre-blasting stage, production blasthole patterns were mapped to investigate drillhole alignment. To monitor the blasting process, a high-speed camera was mounted on the UAS to investigate blast initiation, sequencing, misfired holes and stemming ejection. In the post-blast stage, the muck pile was monitored to estimate fragmentation, assess muck pile configuration, and bench floor condition. The collected aerial data provided detailed information and high spatial and temporal resolution on the quality of the blasting process and highlighted significant opportunities for process improvement.Ph.D