Development of rapid and automated remote sensing methods for ground engagement equipment enabling selective mining

采矿业正在通过采用先进技术和日益自动化的采矿作业来应对许多挑战(例如，复杂和低品位的矿藏，更严格的环境法规)。通过组合实施高分辨率多传感器数据采集系统以及包含基于分析的决策解决方案的自动化数据处理系统，可以显著降低采矿项目的运营成本。然而，数据采集和测量能力的限制阻碍了选择性采矿系统的采用(实现具有成本效益和可持续的采矿作业)，影响了分析解决方案的稳健性。这项提议的主要目标是通过开发基于实时数据采集的分析决策解决方案来实现选择性挖掘。这将包括高分辨率高光谱和激光雷达数据的获取和分析，这些数据是通过活跃矿井工作面的地面交战设备获得的。这些信息将用于矿石硬度的表征、岩石破碎分析和矿石与废石的区分。研究结果将适用于加拿大和世界各地的大量采矿作业。该项目将首先在实验室规模进行，然后进行现场实验(与行业合作伙伴合作)，产生分析解决方案的原型实施，并包括使用真实数据集进行广泛的测试和验证。

The mining industry is responding to many challenges (e.g., complex and lower grade ore deposits, tighter environmental regulations) by adopting advanced technologies and increasingly automating mining operations. The operating costs of a mining project can be significantly reduced through the combined implementation of a high-resolution multi-sensor data acquisition system, along with an automated, data-processing system that incorporates analytics-based decision-making solutions. However, the adoption of selective mining systems (enabling cost-effective and sustainable mining operations) has been hampered by limitations in data acquisition and measurement capabilities, with an impact in the robustness of analytics solutions.The main goal of this proposal is to enable selective mining by developing analytics-based decision-makingsolutions based on real-time data acquisition. This will include the acquisition and analysis of high-resolutionhyperspectral and LiDAR data, obtained through ground engagement equipment in an active mine face. The information will be used for characterization of ore hardness; rock fragmentation analysis and discrimination of ore from waste rock. The results of the research will be applicable to a large number of mining operations in Canada and across the world. The project will be conducted in lab scale first, followed by field experiments (in cooperation with industry partner), producing prototype implementations of the analytical solutions, and including extensive testing and validation using real datasets.