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

使用无人机 (UAV) 系统和机器学习自动绘制露天矿地质和结构测绘

• 批准号: 561041-2020
• 负责人: Esmaeili, Kamran
• 金额: $ 19.52万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=721850
• 关键词: Automated; Geological; Structural; Mapping; Open

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.

地质特征的准确表征和映射（例如，岩性单元，改建区，断层）暴露在坑壁上，对于矿山规划和设计至关重要。结果，地质和结构地图和模型需要通过获取和整合新的地质信息在整个矿山的整个生命中进行频繁的更新。这些地质和结构模型的准确性对于短期和长期的矿山规划以及坑斜率的地质力学稳定性至关重要。常规的地质和结构数据获取和分析通常是通过直接视觉检查和测量裸露的岩石表面手动执行的。这个过程耗时，劳动力密集，可能是不安全的，因为矿工会面临危险条件，包括掉落的岩石和移动的设备。该研究项目旨在将遥感技术与机器学习算法一起使用，以自动化开放坑矿中地质特征的映射。配备高光谱和RGB摄像机的无人机（UAV）系统将用于测量两个开放坑矿山（Kinross Gold's Bald Mountain和McEwen Mining's Gold Bar）中的大坑壁。使用无人机进行数据采集允许从无法接近我的人员的区域收集高分辨率图像。收集的高光谱和RGB图像将用于训练监督的机器学习模型，以检测，分类和绘制坑壁上的目标地质特征。此外，收集的数据将用于预测通过高光谱数据和岩石机械性能之间的相关性，预测岩壁上暴露于坑壁上的机械性能。该项目的结果可以改善地质和结构性坑壁图的发展和一致性，这些地质和结构壁图可能对采矿业务产生重大的经济和安全影响。