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The energy transition will require 6.5 billion tons of additional materials ; 95 percent of this is steel , copper and aluminum , where known reserves are sufficient to meet demand . The other five percent are critical materials where there is a gap between predicted demand and known reserves , thus requiring exploration . There are three main challenges with existing mining exploration that have created a technology gap for AI and other innovations :
1 . Material exploration is mostly field-based activity and is inefficient ; existing mining exploration has a less than one percent success rate
2 . Materials are more difficult to access , as easy-to-access reserves have been depleted and many new mines will be underground
3 . We need critical materials to build the low carbon economy , but we don ’ t know where those materials are
Empowering the prediscovery stage
The initial stages of a mine ’ s life cycle include the concept and pre-discovery phases , which can take around seven years . Although exploration stages are low-value , high cost , and high risk , they have high potential . Paired with the rising demand for minerals and metals , exploration spending has risen significantly in recent years .
Traditional exploration is a field-based activity , were teams of geologists and mining explorers test for surface signs of minerals and then via boreholes . In general , miners are going in blind with little information of where and how to test . As a result , the process is labor intensive , time-consuming , and highly inefficient . There is a less than one percent success rate for exploration activities converting into commercial mines .
Technologies that are seeking to solve exploration challenges include :
• Advanced sensing like LiDAR or hyperspectral imaging technologies can provide more details and accurate estimations of ore quality , characterization , and location . For example , PlotLogic ’ s stacked LiDAR , hyperspectral imaging , and AI or machine learning analysis software increased the life of one of BHP ’ s iron ore mines in Australia by five years
• AI machine learning data analysis can utilize large sets of primary and historic data , often held by mining companies , to predict site locations and site characteristics . VerAI ’ s proprietary AI software and sensor system , for instance , discovers mineral deposits with 100 times more accuracy , 20 times faster , and 20 times cheaper than current industry benchmarks
• Surgical and precision drilling , such as Novomera ’ s surgical sensor-enabled drilling and software system , is estimated to reduce waste by up to 95 percent and costs by 50 percent . Novomera ’ s Near Borehole Imaging Tool ( NBIT ) is used to collect subsurface data , define ore-body geometry , and calculate the precision drill trajectory ; the smart drill can coursecorrect as it travels down narrow veins and can then extract the deposit , with the waste is then backfilled to enable real-time environmental remediation
Scaling new technologies
These innovations are in the early stage , often in the pilot stages and yet to commercialize . The key buyers for these technologies are also the investors and partners , who are all major miners . Incumbents holding such an advantage include Australia ’ s BHP , Brazil ’ s Vale , and
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