Experience of using KNA in Datamine (Snowden) Supervisor for selection of parameters for block model estimation

Hands-on KNA Tools in Datamine Supervisor

The objective of this workshop is to introduce participants to the sequential parameter selection for grade estimation in block models using Kriging Neighborhood Analysis in Datamine (Snowden) Supervisor. The methodology will be demonstrated on real data, applicable to both kriging and inverse distance weighting.

Improving the accuracy of geological models and understanding of estimation methodology

The workshop is aimed at developing practical skills for geologists and resource estimation specialists, as well as students and teachers studying in-depth modeling. Participants will learn how to correctly select variograms, block sizes, search parameters and the number of sampling points to improve the reliability of block models.