Declustering – an essential part of resource evaluation. Author: Ian Glacken
Mining data, by its very nature, is rarely collected in a regular pattern; it is human nature, and very good business sense, to take more samples in the higher-grade parts of orebodies. As a consequence of this data for resource evaluation is almost always clustered. While the most common method of grade estimation, ordinary kriging (OK), inherently declusters the input data through the point-to-point covariance matrix, other estimation methods, such as inverse distance modelling, do not decluster the data for the purposes of estimation, and this can sometimes lead to biased results.