Automated Geological and Structural Mapping of Open Pit Mines using Unmanned Aerial Vehicle (UAV) Systems and Machine Learning

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An accurate characterization and mapping of geological features (e.g. lithological units, alteration zones, faults) exposed on pit walls, is critical for mine planning and design. As a result, geological and structural maps and models require frequent updates throughout the life of mine by acquiring and integrating new geological information. The accuracy of these geological and structural models is essential for short-term and long-term mine planning as well as for geomechanical stability of pit slopes. Conventional geological and structural data acquisition and analysis is usually performed manually by direct visual inspection and measurement of exposed rock surfaces. This process is time consuming, labour-intensive, and may be unsafe as mine personnel are exposed to hazardous conditions including falling rocks and moving equipment. This research project aims to use remote sensing techniques together with machine learning algorithms to automate mapping of geological features in open pit mines. Unmanned Aerial Vehicle (UAV) systems equipped with hyperspectral and RGB cameras will be used for surveying large pit walls in two open pit mines (Kinross Gold's Bald Mountain and McEwen Mining's Gold Bar). Using UAV for data acquisition allows collecting high resolution images of the pit walls from areas inaccessible to mine personnel. The collected hyperspectral and RGB images will be used to train supervised machine learning models for detecting, classifying and mapping target geological features on the pit walls. In addition, the collected data will be used to predict mechanical properties of rock units exposed on the pit walls by developing correlations between hyperspectral data and rock mechanical properties. The results of this project can improve the development and consistency of geological and structural pit wall mapping which can have significant economic and safety impacts on mining operations.