When you think of the words “data” and “mine”, no doubt the idea of data mining comes first. However, just as much as we find value in mining the rich resources of data, so too can we apply the advanced techniques for dealing with data to real-world mining — that is, extracting natural resources from the earth. The world is just as dependent on natural resources as it is data resources, so it makes sense to see how the evolving areas of artificial intelligence and machine learning have an impact on the world of mining and natural resource extraction.
Mining has always been a dangerous profession, since extracting minerals, natural gas, petroleum, and other resources requires working in conditions that can be dangerous for human life. Increasingly, we are needing to go to harsher climates such as deep under the ocean or deep inside the earth to extract the resources we still need. It should come as little surprise then that mining and resource extraction companies are looking to robotics, autonomous systems, and AI applications of all sorts to minimize risk, maximize return, and also lessen the environmental impact that mining has on our ecosystem.
The role of machine learning in mining
The mining industry uses a lot of big, expensive machinery to perform a wide range of operations at the mine site as well as farther away when the materials need to be processed. Much of this machinery has many sensors that provide large volumes of data that give insights into how the very expensive machines are operating, the conditions in which they operate, and also insights into their performance on specific tasks. Keeping machines up and running is essential to making sure that the mining operation can continue. Any downtime or unnecessary maintenance will result in significant cost and complications for the mining operation.
In addition, the use of more advanced analytics prevents the need for humans to have to travel deep into the mine in potentially dangerous situations to try to evaluate problems and determine what is happening. Furthermore, the use of predictive analytics enables more strategic and efficient operations from maintenance to purchasing equipment.
Reducing the environmental impact
Resource extraction is inextricably linked to the environment around it. It’s near impossible to take something out of the earth without impacting the nature and topology surrounding that extraction. However, it is possible to minimize that impact, and machine learning and AI are helping to do just that. Antoine explains that there is very little benefit to dig in the wrong place or extract resources inefficiently. Machine learning systems are helping to analyze geology and topography with greater precision to identify what exactly you’re digging for and find the most optimal way in which to extract it, maximizing the benefit for the operator and minimizing the overall environmental impact.
Other uses of machine learning and AI can be applied to using drone footage or satellite imagery to keep a constant watch on waste and output piles to make sure that they are keeping in compliance with environmental regulations as well as minimizing any potential safety hazards. Rather than just spot checking these locations on a regular basis, these AI-enabled computer vision systems can keep a constant watch, spotting potential problems, and alerting management to solve problems before they become hazardous.