Geostatistics for Mineral Resource Estimation
Geostatistics is the most efficient and powerful framework to characterise, estimate and manage your mineral resource.
Geologists or mining engineers can apply geostatistics at all stages of the mine life cycle: from exploration to development, production and even for site remediation. Geostatistics offers a wide range of methodologies adapted to all commodities and styles of deposits.
Geovariances’ scientific rigour, continuous innovation and geostatistical expertise guarantee the quality of your evaluations at different stages of the development of your projects (feasibility studies, bankable studies, desktop reviews, etc.).
HEAR FROM OUR CUSTOMERS
“I used Isatis for optimizing drillhole spacing. This saved considerably on drilling costs and on time.”
"Minestis is revolutionary in that it completely democratizes advanced geostatistics at the mine site with minimal input from a consultancy or head office. Combined with Isatis,these two software packages can add immense value to any company with concomitant minimization of risk"
"J'ai beaucoup apprécié l'expertise en formation de Geovariances lors d'une première expérience d'un cours organisé à Institut des Sciences de la Terre à Dakar sur l'estimation des ressources minérales."
"Minestis reconciles in a dynamic way the geological and the geostatistical block models. The software brings important innovations in data analysis, modeling, resource / reserve estimation and taking into account geological uncertainty."
"Minestis structured workflow combined with Geovariances tailored support has made conditional simulations accessible to a broad range of users and increased their use."
WHAT IS HAPPENING IN YOUR INDUSTRY?
With its brand-new module for Ore Control, Minestis 2018 is more than ever the quintessential software for Mineral Resource Estimation, of...
Can you afford to bypass advanced geostatistics for the sake of productivity? Is it really a smart business decision?
A little extra time and human resources devoted to the use of above standard geostatistical procedures will prove highly beneficial from a...
With the "Sampling Density Variance" tool, Isatis proposes an innovative methodology allowing robust resource classification, independant ...
Co-kriging of log ratios: a worked alternative method | Clint Ward, Cliffs, Ute Mueller ECU
Local Uncertainty Benchmarking – A coal case study | Written by C. Mawdesley, D. Barry, O. Bertoli and R. Saha
Sensitivity study of the estimation variance approximation of a quotient | Comparison with Conditional Simulations in the Mn Deposit of Bangombé (Gabon)
Data classification using geostatistical hierarchical clustering for robust and dynamic domaining
Key Functionalities new module Studio RM 2016 | Presented by Olivier Bertoli at the UC2016 event organized in the UK by Datamine
Recoverable resource estimation for an underground manganese project using multivariate conditional simulation with scenario reduction
Production reconciliation of a multivariate uniform conditioning technique for mineral resource modelling of a porphyry copper gold deposit
Application of nonlinear geostatistical indicator kriging in lithological categorization of an iron ore deposit
Multivariate block simulations of a lateritic nickel deposit and post-processing of a representative subset
Modeling the Geometry of a Mineral Deposit Domain with a Potential Field