In India, mining industry contributes around 10 to 11 percent of the industrial sector’s GDP, which is close to $106 billion. Though the mining industry is hugely profitable, it faces multiple challenges around power, infrastructure, health, safety, capital, commodity prices and environmental consequences, among others.
It is interesting to note that a single drill hole can create 200 megabytes of data, which means an exploration project can produce terabytes of data. All of these data points are extremely important but to sort through them manually will be extremely difficult even for an entire team of capable geologists. Using Machine Learning, it is now possible to spot the areas showing similar patterns to past discoveries.
RockMass Technologies, for instance, has deployed the latest generation of sensors to capture real time data to identify potential failure planes on rock surfaces, using handheld hardware to analyze rock surfaces and providing the data to the user within minutes. This is done with software that analyzes data 18 times faster than current manual methods, making accurate and quick assessment of potential risks.
Goldspot Discoveries, a Canada-based company has also applied AI in mineral exploration. They were able to anticipate 86 percent of current gold deposits in the Canadian Abitibi gold belt by using only 4 percent of the geological, mineral and topological data of the surface area.
On the other hand, Motion Metrics is using fragmentation analysis, an AI-based prototype for accurate measuring of rock fragmentation within the shovel bucket. In this process, data which is collected can give valuable feedback to blasting engineers and increase productivity through payload monitoring.
Similarly, TOMRA, a Norwegian multinational corporation has developed mineral and ore sorting equipment which uses sensors to separate important mineral ores from waste rock.
It is clear that automation and use of AI and Machine Learning can significantly help save costs, increase efficiency and have tons of other benefits for companies. What’s holding us back is large amount of quality data. However, with multitude of companies working on scaling the use of AI in mining, it is just matter of time before AI becomes more prevalent in the mining industry.
Credits : Akhil Handa