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 have used Isatis.neo full on for a big multi-domain multi-element model from compositing to reporting, using gaussian and raw, multiple block sizes, etc. and I’m very impressed. Fast migrations, fast estimation, good reporting, validation, and visualization functionality."
"I found Isatis.neo Resources Workflow extremely useful and helpful. I managed to produce high-level geostatistical models very quickly."
"Isatis.neo was amazing; it did everything I could think of and prompted me to do things I hadn't thought of. I haven't come across any other geostatistics software which comes close to its functionality."
Geovariances is pleased to bring you a conversation with Daniel Guibal and Michael Cunningham, both long-time users of Isatis, now happy users of Isatis.neo. They recently used the software to provide a new JORC resource estimate of the Ausgold Katanning Gold Project.
"O curso Prática em Geoestatística com uso do software Isatis.neo, promovido pela Universidade Federal do Rio Grande do Sul em parceia com a Geovariances, foi muito intenso e produtivo. Agradeço toda a equipe pelo suporte e pela qualidade do conteúdo."
WHAT IS HAPPENING IN YOUR INDUSTRY?
CFSG 2023: 2nd session of the online training program in geostatistics by the Paris School of Mines. Book your seat now!
Once again, Mines Paris' Geostatistics Team and Geovariances bring together their experts to offer online their Specialized Training Cycle...
Geovariances se complace en organizar un curso dedicado exclusivamente a las estudiantes universitarias.
A Universidade do Rio Grande do Sul e a Geovariances realizarão em parceria os cursos de pós-graduação em geoestatística
A Geovariances orgulha-se de ser parceira do Departamento de Engenharia de Minas da Universidade Federal do Rio Grande do Sul...
See all news
Seminario web | Cómo mejorar la precisión del modelo de recursos con datos inexactos – Sesión Latin America
Amélioration de l’estimation des teneurs en alumine d’un gisement de kaolin à l’aide des simulations par bandes tournantes | XVe Journées de géostatistique 2021 - V. Bouchet (Imerys), M.C. Febvey (Geovariances), Hélène Binet (Geovariances), Armand Dubus (Geovariances)
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
Geostatistical Modeling of Overburden Lithofacies to Optimize Continuous Mining in the Ptolemais Lignite Mines, Greece | Modis, K.; Sideri, D.; Roumpos, C.; Binet, H.; Pavloudakis, F.; Paraskevis, N. - Minerals 2022, 12, 1109
Comparison of geostatistical and machine learning models for predicting geochemical concentration of iron: case of the Nkout iron deposit (south Cameroon) | André William Boroh, Sylvain Kouayep Lawou, Martin Luther Mfenjou, Ismaïla Ngounouno - Journal of African Earth Sciences, Volume 195, 2022
Implication of geological domains data for modeling and estimating resources from NKout iron deposit (South-Cameroun) | Implication of geological domains data for modeling and estimating resources from NKout iron deposit (South-Cameroun)
Full paper: On measuring the spatial sampling density of a deposit for mineral resource classification | Full paper - On measuring the spatial sampling density of a deposit for mineral resource classification by Marie-Cécile Febvey, Benjamin Martin and Jacques Rivoirard
Poster: On measuring the spatial sampling density of a deposit for mineral resource classification | On measuring the spatial sampling density of a deposit for mineral resource classification by Marie-Cécile Febvey, Benjamin Martin and Jacques Rivoirard