This can help better estimate deposits with multiple populations of mineralization existing within a single estimation domain. Surpac users can restrict the samples selected in a manner that is sensitive to the magnitude of the sample grade. The block model inverse distance and ordinary kriging estimation methods allows Surpac users to define a number of anisotropic search distances for specified grade ranges. To avoid this kind of issues, it is recommended to restrict the influence area of the high grade. The result of this is the smearing of the high grade along the deposit. The use of a single search distance is tending to bias the estimation and the high grade value will impact on any grade calculated at the maximum search distance set up. This method provides a way of handling the high grade values in the estimation. In a composite file, there may be high grade values disseminated in the domain. Here is a tip to search ellipsoid based on grade range when working in Surpac 6.7
Its optimized performance allows the use of much larger models with very high sampling density, which has historically been difficult to collect and analyze in a single model. With multi-threading, Surpac 6.7 uses all of the available CPU cores to significantly increase the speed of high resolution modeling, analyzing much larger models in a fraction of the time. Significant increases in the processing speed of the block model enable geologists and engineers to see faster results of larger models with greater coverage or finer resolution. The release of GEOVIA Surpac™ 6.7 earlier this year features design improvements for grade estimation by enhancing search ellipses and weighting length of composites.
Today’s post comes from Claver Gnamien, one of our Mining Knowledge Consultants.
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