Posted by Paul Moore on April 1st, 2020
Last year, Geovariances launched the Isatis.neo Mining Edition, the geostatistical software that, according to its own statements, increases performance and enables reliable resource estimation as well as comprehensive geostatistical studies. The company just released the 2020.02 version of Isatis.neo, with tools derived from the earlier Isatis that have been the most popular with users.
Among them are the following:
- Model sub-cell for a sub-block model that is adapted to domain boundaries
- Grade capping for a more robust estimate
- Resource estimation using Multiple Indicator Kriging (MIK)
- Degree of variation analysis from both sides of the domain boundaries
- The intelligent selection of a representative subset of simulations for an efficient risk analysis
It is now possible to create sub-block models from scratch. The sub-cell formation occurs from both sides of the domain boundaries. Users can run estimates on sub-block models or migrate estimates or simulation results from regular block to sub-block models for further processing.
It allows the higher or lower values in strong-tailed distributions to be limited to ignore outliers and make the estimate more robust. Isatis.neo allows users to test a series of lower or upper cut values to determine the most appropriate value for the deposit or record. Pre and post cap statistics are calculated for each test value to aid decision making.
Users can now rate resources using Multiple Indicator Kriging (MIK). This nonlinear resource estimation technique is commonly used when the sample quality distribution is skewed or the drilling is far away. It consists of the kriging of several indicators that make up the grade tonnage values (amount of metal Q, tonnage T and mean grade M above the limit values) and the E-type estimate (the expectation of the distribution defined by the kriged indicators and the Histogram interpolation) can be derived parameters) can be derived.
The simulation reduction tool allows you to select a representative subset of simulations to characterize the risk associated with a project. This tool, first developed in Isatis, makes it possible to reduce the original number of realizations to a few more manageable elements while maintaining the statistics of the original simulation set.
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