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
"Great piece of software helping the Resource Geologist to investigate its dataset to take the best decision while estimating."
"I have had a chance to test some parts of Isatis.neo and am amazed at how the menus and many functionalities are nicely streamlined. Everything seems to be carefully and logically arranged, especially for new users. For the old Isatis users, I think it is just a matter of..."
"We've got excellent results with the clustering tool available in Isatis.neo. The domains created with Isatis.neo have been validated with the reconciliation of mined areas and the results of the technological studies."
Training very good in all aspects: organization, material, applicability of the methodology and technical knowledge of the consultant. We will certainly have more challenges and we know that we may count on you.
Geovariances technical support is personalized, which I really enjoy. Many software companies provide a totally impersonal "call center" type of support, which certainly discourages us to use the support and the software itself. Not Geovariances.
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A Universidade do Rio Grande do Sul e a Geovariances realizarão em parceria os cursos de pós-graduação em geoestatística oferecidos pela universidade
A Geovariances orgulha-se de ser parceira do Departamento de Engenharia de Minas da Universidade Federal do Rio Grande do Sul para os curs...
La Universidad do Rio Grande do Sul y Geovariances van a realizar en sociedad los cursos de postgrado en Geoestadística ofrecidos por la universidad
Geovariances tiene orgullo de ser socio del Departamento de Ingeniería Minera de la Universidad Federal de Rio Grande do Sul para los cur...
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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
Application of Pluri-Gaussian simulations and conditional simulation for geological modelling and estimation of a nickel deposit in New Caledonia | Febvey, M C, Desassis, N, Le Guen, M and Isatelle, F, 2019 - Proceedings Mining Geology 2019, pp 135–149 (The Australasian Institute of Mining and Metallurgy: Melbourne)
How the use of stratigraphic coordinates improves grade estimation | Rubio, Ricardo Hundelshaussen, Koppe, Vanessa Cerqueira, Costa, João Felipe Coimbra Leite, & Cherchenevski, Pablo Koury. (2015) - Rem: Revista Escola de Minas, 68(4), 471-477. https://doi.org/10.1590/0370-44672015680057
Development of a Methodology combining Clustering and Conditional Simulation for the Definition of Underwater Sampling Models | Bandopadhyay, Sukumar, and Victor Tenorio. Proceedings of the 37th International Symposium on the Application of Computers and Operations Research in the Mineral Industry - APCOM 2015 (2015): n. pag. Print.
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