Optimal sampling to minimize uncertainty is a common problem in several industries, in mining, the necessity to accurately estimate resources while using the least possible number of drill holes is especially important given its costs.
Our answer is to simultaneously use geostatistics to model and feed the system and artificial intelligence to optimally locate and recommend new drill holes positions with the objective of maximizing terrain information.
This approach reduces operational costs by minimizing the number of new perforations subject to obtaining the confidence levels needed in each stage of the mine life.
Our solution allows experts to easily evaluate and estimate different scenarios. Our software blends geostatistics modeling and the most popular resources estimation methods with artificial intelligence algorithms specially oriented to optimization, prediction and classification of random variables.
This is an uncommon combination which we believe will be useful delivering great insight and value to experts.
Our software as well as proposing new locations for the next drill holes will display uncertainty reductions of the mining model.
Also every optimization algorithm is adjustable according to experts necessities and priorities.