Mineral Exploration/ Ore zone estimation
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It’s easier to find the area with largest deposits through the use of machine learning. In this way, we also do the process efficiently.
Machine learning algorithms can be used for mineral prospectivity mapping. Framing the exploration task as a supervised learning problem, the geological, geochemical and geophysical information can be used as training data, and known mineral occurences can be used as training labels. The goal is to parameterize the complex relationships between the data and the labels such that mineral potential can be estimated in under-explored regions using available geoscience data.
Benefits for the company
In the modern era of diminishing returns on fixed exploration budgets, challenging targets, and ever-increasing numbers of multi-parameter datasets, proper management and integration of available data is a crucial component of any mineral exploration program.
Feasability
low
Type of expertise/ AI domain
Supervised Learning, Estimation, Prediction using Support Vector Machine, Convolutional Neural network
Internal data required
Spatial Variation and Distribution of Mineral Grade, Geological, Geochemical and Geophysical information
Artificial intelligence and machine learning can help mining companies find minerals to extract, a critical component of any smart mining operation. Although this is a fairly new application of AI and machine learning, many mining companies are excited about the prospect. Goldspot Discoveries Inc. is a company that aims to make finding gold more of a science than art by using machine learning. Similarly, Goldcorp and IBM Watson are collaborating to use artificial intelligence to review all the geological info available to find better drilling locations for gold in Canada. These efforts to be more precise when finding areas to mine by using machine learning can help the mining industry be more profitable.