Article
Key Technological Gaps in Mineral Exploration and Mining
March 11, 2025
The mining industry faces key technological gaps in exploration, data integration, automation, and sustainability. While advances in geophysics and robotics are accelerating, data analysis technology lags behind. MinersAI is helping bridge this gap by building a foundational data layer with embedded analytics and AI, enabling the integration, processing, and interpretation of vast geological datasets.
Exploration Technology
Exploration traditionally relies on geological mapping, geochemistry, and relatively simple geophysical techniques for the identification of mineralization and pre-targeting studies. Technological advances in geophysical methods could help significantly advance the ability to discover new mineralization zones under vegetative and sedimentary cover (i.e. hiding under plants, soils or relatively recent deposits of sediment). Next-generation airborne gravity gradiometry (Atomionics), airborne hyperspectral data, and higher resolution magnetics are all components that can help advance under-cover discoveries, as well as help more robustly map remote parts of the planet to look for potential mineralization. The new data types are typically much denser which also creates challenges in handling and interpreting the vast quantities of newly generated data, but modern cloud compute and emerging AI technologies are being leveraged to help solve this problem (e.g. MinersAI).
Data Integration and Analysis
National and local geological surveys, exploration companies, and the mining industry all generate vast amounts of data but lack the necessary tools to integrate and analyze this information effectively. There is a significant gap in the use of big data analytics, machine learning, and artificial intelligence (AI) to process and interpret geological data. Advanced data analytics can optimize exploration strategies, predict probable locations of mineralization, and enhance decision-making processes. MinersAI is one company building a foundational data layer of geoscience, mining, and cultural data with embedded analytics and AI to solve this exact problem.
Automation and Robotics
Mining operations frequently involve hazardous and labor-intensive tasks, driving the need for automation and robotics to improve safety, increase productivity, and reduce operational costs. Autonomous drilling rigs from companies like Komatsu are streamlining precision drilling, while Sandvik is pioneering robotic mining solutions capable of operating in extreme environments. Additionally, unmanned aerial vehicles (UAVs) from Australian Droid and Robot (ADR) are transforming surveying and monitoring tasks, providing real-time data collection in previously inaccessible areas. Together, these innovations are reshaping modern mining, reducing human risk, and enhancing efficiency across operations.
Sustainability and Environmental Impact
The environmental footprint of mining activities is a major concern. Traditional extraction and processing methods can lead to significant land degradation, water pollution, and greenhouse gas emissions. There is a gap in the development of environmentally friendly technologies, such as bio-mining, waterless ore processing, and cleaner energy solutions, to minimize the ecological impact of mining operations. Additionally, technologies to extract as much metal or mineral volume per volume of raw ore will be critical to flatten the cost curve for commodities in the next few decades. Targeted drilling is another technology that could reduce the environmental impact of mining operations by only removing narrow zones of rock surrounding the ore of interest. Novamera is a key company deploying targeted drilling technologies.