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.