Mastering the Art of Unsupervised Learning: Techniques and Guidance
Unsupervised learning, a cornerstone of artificial intelligence, has emerged as a powerful technique that enables machines to uncover patterns and insights from unlabelled data.
Mining requires large investment and is a costly undertaking. Identifying and finding the right place to mine is a critical step in mining operations. Locating good and large amount of material to extract from the ground is the first step but it is not as easy as it sounds. Mineral prospecting and exploration are complex and involves a long process traditionally conducted by a geologist to identify the type and location of the mineral reserve.
With discoveries of deposits far in between and cost increasing, mining companies are now utilizing the technology of artificial intelligence and machine learning to improve precision of mineral deposit discovery. There is no more room for guesswork. With AI, exploration can be calculated, and accuracy improved because digging in the wrong direction can mean millions of resources wasted.
Different technology is at work to identify new and potentially valuable areas to mine or drill. Geophysical, geochemical, geological data, regional structure, pattern matching, and known deposits were some traditional data points considered. With AI more data points can be incorporated to streamline the mineral exploration system and improve the odds of making an economic discovery.
With today’s technology additional data points from remote sensing, gravity, aeromagnetic and radiometric surveys, digital terrain, computer vision system and predictive analytics can be combined with traditional data for AI to analyze vast quantities of information to predict the location and delineate new targets for prospective resource area.
The last few years more companies are incorporating AI in their operation including mining exploration with varied use-cases. AI-based mining exploration algorithms have been used to identify mineral deposits in greenfield sites, and drones used for autonomous drilling to minimize cost. 3D maps have been developed to identify visual mapping for more complex and large exploration sites. Cloud-based image analysis to categorize and segment geological imagery and videos provides more time to geologist interpret results than collating and working on data. There are also use cases to help evaluate risks and potential environmental impact to develop solutions factoring in the ecological costs of the operation.
Mining is disruptive and destructive to the environment. Artificial intelligence is helping to transform the mining industry into a safer, more profitable, and more environmentally friendly industry. Improving resource discovery accuracy through AI can minimize disruptions and avoid unnecessary damage to the environment.