Mineral exploration is becoming an activity increasingly based on drilling. It is a very costly process that is crucial for the profitability of subsurface resources exploitation. To compensate for the huge expenses associated with drilling, the industry increasingly uses advanced remote sensing techniques. The two key notions in this context are Uncrewed Aerial Vehicles (UAV) and hyperspectral imaging (HSI). UAV technology allows placing highly maneuverable low-altitude deployment systems, and HSI allows acquiring electromagnetic spectra across hundreds of narrow wavebands which enables material identification due to distinctive spectral signatures.
UAV-based HSI provides geologic targeting capabilities by remotely sensing hydrothermal alteration zones. Some minerals show certain reactions to light in particular wavelengths in the visible, near-infrared and shortwave infrared (VNIR-SWIR) ranges. Analyzing such wavelengths allows geologists to create the map of indicator minerals, including white micas and clays, with centimeter resolution (Koerting et al., 2024). Hyperspectral sensors analyze the vibrational properties of molecules bonds in rock-forming minerals, making it possible to discover potential zones of ore concentration using HSI without the help of ground sampling.
Nowadays, there is a common scientific opinion that drone-borne HSI technology is essential to create the connection between broadscale satellite imagery and localized ground data. Satellites provide a large coverage of the Earth but their relatively low resolution cannot reveal structural elements necessary for drilling. At the same time, drones can work at altitudes where it is possible to collect data with high resolution and to keep a safe distance from the ground (Dadrass Javan et al., 2024). Scientists claim that the use of drones equipped with hyperspectral sensors helps to fill this gap and create alteration maps with scales relevant for exploration.
The use of drones equipped with hyperspectral sensors will have a considerable effect on drilling expenses. Taking into account that exploration boreholes require the largest amount of expenditures, the drilling of barren ground causes great financial costs. According to the scientific literature, UAV hyperspectral surveys help geologists to detect zones without mineralization early and optimize drilling plan (Koerting et al., 2024). By improving the geological model before the mobilization of the rig, companies can lower the dry-hole rates significantly.
It is worth mentioning that the cost-saving effect increases considerably when HSI data are combined with geophysical sensors. The integration of high-resolution HSI with drone-borne magnetic survey helps to develop reliable prospectivity models (Jackisch et al., 2019). Using modern computational tools, it is possible to analyze the huge volume of data quickly and make real-time decisions. As workflows develop, the correlation of surface hyperspectral signature collected by drones with subsurface scan of drill core creates highly reliable predictive models (Thiele et al., 2024).
To sum up, the use of hyperspectral drones surveys for mineral exploration represents a revolution in terms of cost-effectiveness. With the help of hyperspectral sensors, it is possible to map alteration zones in a precise way and connect satellite observations with ground works. The scientific opinion regarding the use of UAV-based hyperspectral imaging confirms its significant reduction of blind drilling and probing barren ground.
References
Dadrass Javan, F., Samadzadegan, F., Toosi, A., & van der Meijde, M. (2024). Unmanned Aerial Geophysical Remote Sensing: A Systematic Review. Remote Sensing, 17(1), 110. https://doi.org/10.3390/rs17010110
Jackisch, R., Madriz, Y., Zimmermann, R., et al. (2019). Drone-Borne Hyperspectral and Magnetic Data Integration: Otanmäki Fe-Ti-V Deposit in Finland. Remote Sensing, 11(18), 2084. https://doi.org/10.3390/rs11182084
Koerting, F., Asadzadeh, S., Hildebrand, J. C., et al. (2024). VNIR-SWIR Imaging Spectroscopy for Mining: Insights for Hyperspectral Drone Applications. Mining, 4, 1013–1057. https://doi.org/10.3390/mining4040057
Thiele, S. T., Kirsch, M., Lorenz, S., et al. (2024). Maximising the value of hyperspectral drill core scanning through real-time processing and analysis. Frontiers in Earth Science, 12. https://doi.org/10.3389/feart.2024.1433662
