Innovative hyperspectral imaging for Lithium exploration

The current transition towards a sustainable future is rooted in the use of green technologies. These technologies have led to a large rise in demand for previously obscure minerals and metals. One such sought-after element is lithium (Li), due to its use in Li-ion batteries for electric vehicles. Recycling alone cannot meet the present demand for this material, thus exploration of new resources and the improvement of existing mining activities are required. Conventional exploration methods are typically performed by a team of geoscientists and involve geological fieldwork, geophysical surveys and extensive drilling. These methods are time-consuming, expensive, vulnerable to weather conditions or in-accessible terrains and often carry a considerable environmental impact. In the case of Li exploration, the typical geochemical analyses and assaying methods fall short in accurately identifying and quantifying the presence of Li. Therefore, we suggest a non-invasive, hyperspectral imaging (HSI) approach for the detection of mineralized outcrops. HSI is a fast developing technology that allows for rapid mineral mapping, facilitating mineral exploration at various scales. In this contribution, we demonstrate our approach at the Uis Li-bearing pegmatite mine in Namibia by utilizing a variety of platforms and sensors in order to map key minerals associated with Li-mineralization. We collected hand samples from the main pit to identify relevant minerals. In addition, we captured hyperspectral data covering the main pit in the visible and near infrared (VNIR) and short-wave infrared (SWIR) range of the electromagnetic spectrum with a tripod mounted hyperspectral sensor. We acquired RGB photos of the main pit for Structure-from-Motion (SfM) multi-view stereo (MVS) photogrammetry to create a three-dimensional (3D) model. By using computer vision and machine learning techniques, we combined the hyperspectral data with the 3D information to produce a 3D point cloud with hyperspectral attributes. This workflow was performed using the open-source python based toolbox hylite and allowed us to produce a geometrically correct and spatially continuous 3D map of Li-bearing minerals. We validated our result with drill-core data as well as laser induced breakdown spectroscopy (LIBS) measurements and X-ray fluorescence (XRF) analyses of the hand samples. Additionally, we recently acquired drone-borne SWIR data over the same mine, allowing for more flexible data acquisition in order to mitigate access limitation. Taking a HSI approach enables us to rapidly and efficiently map complex terrains in a non-invasive and sustainable exploration scheme.