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April 23, 2021

Data science in the mining industry


Artur Haponik

CEO & Co-Founder

Reading time:

8 minutes

In our previous blog post, we talked about artificial intelligence in the mining sector. Today, we want to dig deeper and examine what role data science plays in the modern mining industry. Shortly, we could say that it is a true backbone of modern mining. In fact, without advanced data analytics techniques, mining would have never been efficient or safe, for that matter. What is data analytics used for in the modern mining industry? And what are the crucial challenges this sector has to overcome? Let’s find out!

First off, let’s talk about crucial challenges the mining industry has to face nowadays.

The mining industry: Key challenges and risks

Here, we are going to use the 2020 Global Mining Survey Report conducted by KPMG. According to this report, the global mining sector struggles with ten significant challenges and risks:

  • Commodity prices
  • Permitting risk
  • Access to capital, including liquidity
  • Community relations
  • Global trade war
  • Economic downturn/uncertainty
  • Political instability
  • Environmental risks
  • Tailings management
  • Ability to access and replace reserves

Now, data science can offer vital support in dealing with at least four of these problems. Consider the ability to access and replace reserves. Thanks to data science tools, mining companies can analyze the environment, assess potential threats and risks, and devise the most effective strategy tailored to the specific situation. For example, in the previous article, we showed you how drones are frequently used in similar assignments.

Although it’s not necessarily a mining-related application, data science techniques can be easily utilized to analyze the current economic and political situation. And thanks to predictive analytics, mining companies can estimate future prices of the extracted resources. This way, they can assess the profitability of every specific mining project.

If the analysis result is far from optimistic, you can simply abandon a given mining endeavor and look for more profitable places. In dynamic and uncertain pandemic times, this application of data science can be a true game-changer for the entire industry.

data science in the mining, mine

Data-related risks. Is the industry ready for data science?

Of course, problems don’t end here. KPMG’s report indicates only industry-wide issues. What about data science and data-related risks? According to, this industry struggles with data selection. In other words, it’s difficult for industry leaders to decide which data ought to be collected and analyzed in order to obtain useful conclusions and recommendations. That’s primarily due to vast amounts of data available in the mining world.

In some of our blog posts, we’ve told you that too much information is not necessarily a good thing. It’s a data scientists’ role to decide which datasets should be taken into consideration. Next, these datasets should be properly organized and cleaned so that they can be useful from the business point of view. In some industries, like, for instance, marketing, it’s a quick and straightforward process. In others, like mining, it’s much more complicated, and as it turns out, many companies operating in this sector struggle with data selection.

Moreover, as reports, consolidating data across different systems, platforms, and entities is also vital. In fact, it can be a root of all evil, as both data warehouses and data science techniques require cleaned and organized data in order to work effectively. All of that would mean that the mining sector has to introduce itself to some significant changes if it wants to grow and use the full potential of data science.

mine, coal
In essence, this is what we do at Addepto. Our role is to help companies struggling with data analytics to make the most of it without the need to build an internal data team from scratch. We help organizations all over the world master data they possess, select the most important datasets and sources, and organize everything so that it’s useful from the data science’s standpoint.

As a result, just within months, companies (including mining ones) can take a massive leap forward and overcome challenges they’ve been struggling with for years.

How is data science used in the mining industry?

In short, we could say that data science can help mining companies improve literally every stage of their work. Consider excavation. Let’s go back to seismic surveys.

Seismic surveys

According to Encyclopædia Britannica[1], it’s a method of investigating subterranean structures, particularly related to exploration for petroleum, natural gas, and mineral deposits. Now, how does it work? This technique is based on determining the time interval that elapses between the initiation of a seismic wave at a selected shot point and the arrival of reflected or refracted impulses received by seismic detectors.

Now, when it comes to seismic surveys, seismic air guns are necessary to initiate the wave. Upon arrival at the detectors, the amplitude and timing of waves are recorded to give a seismogram (record of ground vibrations).



As a result of seismic surveys, companies conducting them obtain useful information that next can be used to create accurate maps of structures identifying areas where diverse resource deposits can be found. It’s frequently a starting point for every other mining-related activity.

With data science tools, you can upload insight coming from seismic surveys to make more accurate decisions regarding future excavations and optimal excavation techniques.

Geological modeling

This aspect of data science in mining is strictly related to seismic surveys and other exploration activities. As we can read in the 3D Geological Modeling in Mineral Deposits paper, “geological 3D modeling is very important because it gives detailed information on management in the most optimal way to mine”.

Thanks to geological modeling, mining companies can create complex geological models and geological objects. In order to make such maps, some relevant data sources are indispensable:

  • Geological maps
  • Geological records analysis
  • Structural information
  • Geophysical and geochemical data

Geological modeling helps mining exploration companies and geological institutions spot all geophysical anomalies and potential problems. With geological modeling, mining companies can also estimate the reserve’s worth and decide on the best drilling methods.

Data science helps with geological modeling because it facilitates the process of creating such models. With data analytics tools, companies can consider all the relevant data sources and make accurate models in a significantly shorter time.

geological maps

Process flow

In fact, we can say that data analytics can help mining companies with the entire process flow. In a simplified way, this process consists of three key elements:

  • Extraction
  • Intermediate transportation
  • Final transportation (to the plant, factory, or distribution center)

Now, with the support of advanced data science algorithms, mining companies can leverage the value, volume, and velocity of data they process. In both these articles, we’ve talked about the ways in which excavation can be improved with data science. In short, thanks to DS, this process is much more effective and quick.

When it comes to intermediate transportation, the synergy between LHD (load, haul, dump) loaders and operators is crucial. Data science can help optimize this cooperation so that both LHD vehicles and operators work as effectively as possible. And what about final transportation (from the mine to the plant or other facility)?

We have to go back here to, as this is a bit more complicated subject. As the authors of the article, we quoted earlier indicate, logistics can be a severe problem for many mining companies. even calls this stage “the most inefficient part of the mining process”. Strong words! This means that there’s still room for improvement, even though that opinion comes from 2017. Let’s think for a few moments about how data science can improve the last stage of the mining process. With data science methods and the use of AGVs and drones, mining companies can devise the most effective way to transport their yields, optimize routes, and make more informed decisions regarding transportation and logistics-related processes.

data science in the mining, miner

Other uses of data science in the mining sector

Naturally, it’s impossible to analyze all the aspects of data science in mining in just one article. Other fields where data science can play a crucial role are:

  • Workers’ safety: Thanks to IoT and other sensors, mining companies can monitor underground conditions 24/7 and act immediately in case of an emergency or other hazardous situation.
  • Predictive maintenance: This solution allows you to keep all your machines, LHD vehicles, and equipment in perfect condition. With predictive maintenance, you can achieve two goals. First, you can avoid potential problems and glitches. And second, because your equipment is always properly taken care of, overall maintenance costs are decreased.
  • Increasing deposit’s value: With DS, you can examine the specific deposit’s value before you start drilling and pick the best mining method. As a result, you can make the most of every reserve you exploit.

If you run a mining or manufacturing company, and you’re interested in implementing data science into your business model–feel free to drop us a line! Data analytics is our major specialization.

Every month, we help tens of companies process, analyze, and draw insights from data they possess. We will gladly help you as well!


[1] Encyclopedia Britannica, Seismic survey,, accessed April 21, 2021


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