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July 18, 2022

How is AI enhancing the mining industry?


Edwin Lisowski

CSO & Co-Founder

Reading time:

8 minutes

It is no exaggeration to say that mining and ores excavation is one of the world’s oldest professions. Even ancient nations were looking for silver, gold, and other materials used to make coins, jewelry, and other everyday objects. Today, however, the face of the mining industry is completely different. For starters, we have much more advanced technology at our disposal. And secondly, we’re looking for other materials, for example, oil. And when we talk about cutting-edge technology, we cannot leave artificial intelligence and machine learning behind! These two technologies are true game-changers in a number of industries, and mining is no exception.

If we had to squeeze this entire subject into just one sentence, we ought to say that thanks to AI in the mining industry, much more efficient extraction of ores and raw materials is possible. If we could add one more sentence, we’d say that AI also helps in the automation of different aspects of mining-related work.

But let’s be more specific, and see how AI enhances the modern mining industry. Let’s go underground!

Intelligent revolution in the mining industry

In early 2017, McKinsey Global Institute published a white paper called “Beyond the supercycle: How technology is reshaping resources”. Inside, we can find a great deal of valuable information on how AI enhances the modern mining industry.

For starters, when it comes to resource exploration and extraction, authors name four critical technologies that aid miners in their everyday work:

  • Automation
  • Data collection
  • Mobile computing
  • Analytics

That’s, of course, not the whole story. After all, we also have AI, machine learning, and cognitive computing. All these technologies support mining companies worldwide in determining when, where, and how to mine most efficiently. However, to make that possible, you need a lot of diverse data sources providing you with high-quality information.

When we talk about the mining industry, these data sources are, for instance:

  • Drill data: According to McKinsey, modern drills are equipped with top-tier sensors located at drill bits’ tips. These sensors are able to measure ore grade in real time, making excavation more effective.
  • Sample analysis: In fact, this stage is crucial. For obvious reasons, mining companies are looking for high-quality mineral deposits. Sample analysis backed by intelligent algorithms can help you get reliable results in much shorter time, compared to traditional (manual) methods.
  • Survey reports: Thanks to these reports, mining companies can take other relevant information into account, just to mention commodity prices, the ability to access and replace reserves, environmental risks, and political instability.
  • Geological data

Aspects of AI in mining

Now, we can take a look at some of the most common aspects and applications of artificial intelligence in this sector.

Error elimination

That’s the role mainly for data analysis. Thanks to this discipline, miners can better understand the environment, especially the terrain. As a result, they can make more informed decisions regarding where to begin excavation to be efficient and safe. Therefore, errors and potentially dangerous situations are limited to a minimum. Thanks to data science, mining companies can save a lot of time and resources on locating new mines and ore reserves.
Obviously, that’s possible only when you have access to high-quality data.

In order to obtain it, mining companies frequently decide to conduct seismic surveys. Although the seismic activity is, to many of us, strictly related to earthquakes, these surveys have (almost) nothing to do with them and are extremely useful in the mining sector. Shortly put, a seismic survey is a non-invasive method of gathering information about the location and characteristics of geological structures beneath the Earth’s surface[1]. That’s a perfect source of information that can be used to feed data analytics and machine learning algorithms. Let’s take a look at one of these systems:

Infosys NIA

Nia is an AI platform developed by Infosys to help mining companies. Nia takes diverse types of data coming from different sources in order to improve the mining process. For example, Nia analyzes geological, topography, geo-mechanical, engineering, and mineralogy data. Thanks to Nia, each stage of the mining process is enhanced. Nia supports companies in predicting potential ore reserves and analyzes samples to estimate the specific mineral deposit’s total surface area. To make sure Nia gets better and better, Infosys equipped it with machine learning features so that their predictions and calculations improve over time.

Goldspot Discoveries

It’s a Canadian company that uses data-driven science, AI, and ML in order to create intelligent systems capable of discovering potential gold reserves. Just like in Nia’s case, Goldspot uses their smart solutions to process and analyze relevant data. Moreover, they have a useful feature called LithoLens. It is a core imaging technology that automatically examines old core images to create fresh and accurate geological logs. In other words, they take old core photos and transform them into new, georeferenced core images. In order to make this job even better, they use deep learning algorithms to enhance the photos and extract valuable geological information.

Safety & Compliance

Safety is critical in the mining setup. Mining companies exert every effort to make sure their sites, employees, and machines are fully safe and can work without any complications or delays. To make that possible, regular and thorough inspections are necessary. This is where technologies like IoT, computer vision, and digital twins step into play. You can read more about that in our recent article about digital twins.

In short, intelligent technologies are used to keep all the machines, vehicles, and other equipment in perfect shape. Predictive maintenance is another vital field, as it allows you to save time on repairs and maintenance. As a result, mining companies can ensure their facilities are fully safe and managed properly. Similar systems can be used to monitor all the relevant parameters underground, so that potential hazardous/emergency situations can also be curbed.


While we are on the safety issue, let’s talk for a few moments about wearables. They are extensively used in a number of sectors, and for a good reason! Wearables with IoT sensors can easily be used to monitor workers’ behavior and condition. With wearables, it can be done 24/7 without the need to extend your workforce. Such systems simply inform when an emergency occurs. Thanks to the Internet of Things, these sensors are capable of generating real-time data that is then automatically analyzed to spot irregularities or hazardous behavior.


This is an example of a company that provides similar solutions to the high-risk industries, including:

  • Mining
  • Construction
  • Defense
  • Fire & rescue

Thoroughtec offers a comprehensive simulator-based training platform, where, thanks to wearable sensors, managers can monitor worker behavior continuously. These sensors generate data that is then automatically analyzed to spot worrisome trends and situations[2].

Increasing reserve’s value aka smart sorting

For obvious reasons, mining companies try to make the most of every reserve they exploit. Intelligent, AI-based solutions come in handy here as well. Consider Tomra, a Norwegian company that works on mining sorting systems helping to extend the life of mining operations and increase the overall value of the deposit. How is that possible? They use intelligent sensor-based solutions that constitute a very effective separation and sorting technology. As a result, mining companies can make the most of every mining facility, increase profits, and make their work more effective.

Such an approach is frequently referred to as smart sorting. It’s a solution that usually utilizes several high-tech technologies, especially IoT and deep learning. AI algorithms powered by color sensors and X-ray data are used to improve diverse mining processes. And deep learning makes them even more efficient and useful.

AVGs in the mining industry

Automated Guided Vehicles are more and more common in every branch of industry. The mining industry is no exception. Here, we have:

  • Mining vehicles
  • Drones
  • Autonomous drilling systems
  • Autonomous hauling fleets

Such vehicles offer a lot of significant benefits for the industry. First off, thanks to technologies like machine learning and computer vision, it is possible to use AVGs to automatically determine the type of discovered minerals and the quality of the specific reserve. But that’s just the beginning. Because these vehicles and machines can be operated and managed fully remotely, they contribute to the safety of mining as they can be used in the most dangerous spots and stages of work. This way, your employees are fully safe, and the work can be continued.

Light vehicles and drones can also be used for on-site inspections and mine planning. And this is exactly what Drone Deploy does.

Drone deploy

This company uses aerial data coming from drones for stockpile management, on-site inspections, progress reporting, and logistics overseeing (for example, drones are extremely helpful when it comes to hauling logistics). With the support of drones, your company can visualize and assess mining and geological conditions in order to plan further actions.

As you can see, artificial intelligence plays a more and more important role in the mining industry. It makes miners work more effectively and safely. It helps in optimizing and automating diverse, time-consuming tasks, such as minerals sorting. And finally, thanks to the analysis of seismic surveys with data science, mining companies can get a more thorough understanding of the environment they’re working in.

If you are interested in applying AI and machine learning in your mining or manufacturing company–we are at your service! Feel free to drop us a line, and let the work begin!




Machine Learning