Efficient updating of kriging estimates and variances
The software provides you with an auditable trail of parameters and allows repeating the whole process in a click.The reporting facilities are fully compliant with the mining reporting codes requirements.Quickly get well-fitted multivariate/multi-directional variogram models. Estimate resources at global and local scales from Uniform Conditioning (UC), Localized Uniform Conditioning (LUC) and Turning Bands conditional simulations.Control the quality of the estimates through various statistics: histograms, swath plots and cross plots between data and estimates.Group borehole samples according to geological, structural and statistical criteria and identifies domains in an automatic way.Interpolate domain boundaries from previously identified domains through implicit modeling.Remote sensing is an efficient means of obtaining large-area land-cover data. This paper presents a geostatistical method to model spatial uncertainty in estimates of the areal extent of land-cover types.The area estimates are based on exhaustive but uncertain (soft) remotely sensed data and a sample of reference (hard) data.
Adopting Minestis means you access first-class algorithms that originate from Mines Paris Tech’s Center for Geostatistics and have been tried and tested for over 20 years through Isatis.
Besides, Minestis provides the techniques that help you optimizing sampling, or, on another level, operational or mineral processing decisions thanks to multivariate analysis (estimation/simulation).
"Minestis sequential and efficient workflows ensures that you are not missing any critical steps in the Resource Evaluation process.
Using sequential indicator simulation, a set of equally probable maps are generated from which uncertainties regarding land-cover patterns are inferred.
Collocated indicator cokriging, the geostatistical estimation method employed, explicitly accounts for the spatial cross-correlation between hard and soft data using a simplified model of coregionalization.