High-quality mineral deposits in the developed world are mostly exhausted. Therefore, mining companies are increasingly driven to look for new deposits in remote locations. Mining in remote areas is costly and time-consuming when carried out in the field. Remote sensing enables quick and efficient mineral exploration data gathering, even for remote and rugged terrain. From electromagnetic imaging to topographical analysis, remote sensing and analysis provide a wealth of information to today's mining operators, significantly improving their chances of extracting valuable, necessary resources.
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Remote sensing began when mineral exploration outfits used aerial photography to detect signs of potential mineral deposits. While aerial photography is still in use today, the technology is much more sophisticated than the black-and-white photography used decades ago. Modern aerial photography technology produces high-resolution photographs that are used to make detailed maps and detect signature geological features that could indicate mineral deposits.
Mining operations also routinely use other types of imaging technology besides aerial photography. Sensor technology, based on airplanes or satellites, can provide much more detailed exploration than photography alone.
Different Remote Sensing Technologies
Modern remote sensing technologies offer a wide range of valuable data sets for identifying mineral deposits. The following technologies are commonly used in the mining industry:
- Hyperspectral imaging: Minerals, rocks, and other objects interact with electromagnetic waves based on their structure and composition. Remote sensing technology can capture electromagnetic radiation that is either reflected or emitted by mineral deposits. This information is then used to produce hyperspectral images that may reveal signatures of mineral deposits. Hyperspectral imagery can also indicate the composition of surface and subsurface materials.
- Light Detection and Ranging (LiDAR): By sending pulse laser light to the ground from an aircraft or satellite and measuring the reflected light, mineral exploration companies can produce detailed three-dimensional (3D) topography maps. These digital models can be analyzed for signature geological features that indicate mineral deposits.
- Radar: Similar to LiDAR, radar technology creates detailed surface topography maps. Unlike LiDAR, radar can be used at any time of day and in any weather condition.
- Thermal imaging: Heat from the Earth's surface can indicate signature geologies or mineralization processes.
Each sensing technology offers unique insights, and they are often used in concert to create a comprehensive model of a target area.
Remote Sensing Applications
In addition to searching for mineral deposits, one of the primary applications of remote sensing in mining is monitoring open pit operations. For example, radar images can be used to monitor any changes to the slope of an open pit mine to identify any potential risk of slope collapse. If remote sensing detects potential problems, they can be reported to mine management for remedial measures.
Some of the more unique remote sensing applications have to do with sustainability. One area of focus is ensuring that water resources are properly managed. This is critical from water management, compliance, and policy perspectives. Operators can monitor water distribution around tailings storage facilities using remote sensing technology. This allows them to detect the amount of surface moisture in a storage facility, surface seepage, evaporation, and other water management factors around a storage facility. Operators can use the information to perform remedial work and develop better designs for future tailings storage facilities.
Another sustainability application of remote sensing and mining is monitoring toxic gases. Infrared imaging can identify vegetation contaminated by harmful gases like sulfur dioxide and fluoride. Aerial photography can also detect evidence of harmful gas release by showing decreases in the density of tree canopies and loss of individual vegetation.
Multispectral imaging is also used to monitor the effects of groundwater pollution on vegetation, which has a different spectrum performance when contaminated. Multispectral imaging can identify the scope of pollution and inform remediation plans.
Latest Remote Sensing Research and Developments
One of the most exciting areas of remote sensing research is geospatial artificial intelligence (Geo AI), a field that combines remote sensing technology with machine learning algorithms. Geo AI can extract new insights from massive datasets to improve the precision and efficiency of data analysis generated by remote sensing technology. Recently, Geo AI researchers have focused on deep learning models capable of using high-resolution satellite photography and hyperspectral imaging to monitor vegetation and land cover. Deep learning models are also being developed to analyze radar data for terrain mapping. While Geo AI is the wave of the future, this area of research currently faces several challenges related to limited trading data, integrating multi-source data, and learning model sophistication.
One exciting ongoing development in remote sensing is the GoldenEye project, which is being funded by the European Commission. The international project is building an AI platform capable of monitoring and analyzing mining operations across Europe. The goal is to make mineral exploration safer, more efficient, cost-effective, and sustainable. The developed AI platform is expected to use remote sensing data from satellites, drones, and land-based sensors to facilitate exploration, extraction, and waste storage operations.
Conclusion
Remote sensing has become an essential technology for mining operations. Used to improve efficiency, safety, sustainability, and cost-effectiveness, it can capture all kinds of data, from aerial photography to hyperspectral imagery, and thanks to machine learning technology, the ability to process this data is rapidly improving. If Geo AI Efforts can overcome obstacles related to training data and model sophistication, remote sensing technologies could unlock vast resources in more sustainable ways.
References and Further Reading
K-Mine. (2023 July 4). Mastering mineral exploration: From concept to discovery - Part 5: The role of remote sensing in mineral exploration. https://k-mine.com/articles/mastering-mineral-exploration-from-concept-to-discovery-part-5-the-role-of-remote-sensing-in-mineral-exploration
Shepherd, P. (n.d.). The Application of Satellite Remote Sensing in the Mining Sector. SRK Consulting. https://www.srk.com/en/publications/the-application-of-satellite-remote-sensing-in-the-mining-sector
Borotkanych, N. (2022 November 10) Use Of Satellite Remote Sensing In The Mining Industry. EOS Data Analytics. https://eos.com/blog/use-of-satellite-remote-sensing-in-the-mining-industry/
Mekonnen, T. et al. (2021) August). Application of Remote Sensing in Mining. Global Science Education Journal 9(8):2385-2394. https://www.researchgate.net/publication/354208628_Application_of_Remote_Sensing_in_Mining
Sharifi, A. et al. (2024 April 29). Utilizing geospatial artificial intelligence for remote sensing applications. Environmental Earth Sciences 83(274). https://link.springer.com/article/10.1007/s12665-024-11584-4
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