The Role of Artificial Intelligence and Machine Learning in Mining Industry

With the introduction of autonomous vehicles approximately ten years ago, the mining industry began utilizing machine learning, Artificial Intelligence (AI), and autonomous technology. By lowering costs, increasing productivity, enhancing safety, minimizing worker exposure to hazardous situations, and promoting continuous production sector numerous financial advantages. Economic, financial, technological, workforce, and societal obstacles have, however, hampered the adoption of these technologies. Both of these phases require a great deal of data collection and utilization, and it may take two to twenty years to determine the deposit's true value using antiquated techniques, traditional methods involving human labour. Typically, prospecting begins by utilizing maps, satellite images, aerial photos, and other accessible data to identify an abnormality characteristics development. Major Findings: This article explains the state of technology adoption in the mining sector and identifies possible future application areas. The first step in finding a profitable mineral deposit is prospecting, which involves assessing the resource in light of the market and economy to determine whether or not additional investment is feasible for the particular prospect. Prospecting are entails surveying the target area and gathering geological, economic, and geophysical data. Borehole logging, laboratory work, sampling, and additional prospect investigation are all part of exploration.