Isatis.neo | Geostatistics made accessible
Isatis.neo is a smart and powerful software solution in geostatistics. Featuring an intuitive user interface, it results from Geovariances’ dual commitment to developing breakthrough technology and making first-class geostatistics accessible to more users.
Designed for every business dealing with spatialized data, Isatis.neo exceeds industry standards in geostatistics. The software enables thorough data analysis and visualization, produces high-quality maps and models, and allows you to carry out extensive uncertainty and risk analyses that optimize your decision-making process.
Available in a Standard Edition, Isatis.neo is also offered in two special versions, Petroleum Edition and Mining Edition, to better meet the specific requirements of these two industries. In addition to business-oriented tools, each version offers a preconfigured workflow for an optimized way to tackle classical although challenging issues:
– Mineral Resource Estimation including Ore Control and Reconciliation for the Mining Edition,
– Time-to-Depth Conversion with comprehensive uncertainty analysis for the Petroleum Edition.
Improve your performance
Geostatistics can appear daunting if you are not familiar with the approach. This is why we have been working to make Isatis.neo straightforward to use so that you only focus on your geostatistical analysis, not on how to use the tool. Your performance is boosted thanks to its intuitive interface, but also by cutting-edge parallelized algorithms and powerful scripting procedures that allow fast and easy model updating.
Tailor your project to your needs
Isatis.neo provides a wide choice of proven and state-of-the-art statistical and geostatistical tools in a fully flexible package letting you design your own process to best address your specific issues. And if you need further analyses, Isatis.neo gives you access to the power of Python functionalities and coding through its Calculator to generate your own variables and functions.
Make better decisions
Isatis.neo makes you benefit from Geovariances’ technical excellence in geostatistics. The software derives from robust, tried and tested Isatis software and 35 years of know-how in developing geostatistics-based software solutions in partnership with the French Mining School of Paris. With Isatis.neo, you are certain to hold the keys for data and risk-informed decision making.
Optimize your process
Our software users have all different skill levels in geostatistics. This is why we wanted Isatis.neo to be partly workflow-driven to give you the best and optimized way to your objectives. We have developed business-oriented pre-configured workflows for that purpose:
– Resources in Isatis.neo Mining Edition,
– Conversions & Uncertainties in Isatis.neo Petroleum Edition.
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."
Discover Isatis.neo at our upcoming events
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...
CFSG 2022: new online format, new teachers for the training program in geostatistics by the Paris School of Mines. Book your seat now!
The Geostatistics Team from MINES ParisTech and Geovariances partner together to offer a new CFSG online, their high-level training progra...
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Roadshow Américas 2022 | Além da geoestatística: Machine Learning, Python, e mais para recursos robustos e precisos
Roadshow Americas 2022 | Beyond geostatistics: Machine Learning, Python, and more for robust and accurate resources
Roadshow Americas 2022 | Más allá de la geoestadística: Machine Learning, Python y más para obtener recursos sólidos y precisos
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)
Utilisation d’un algorithme de classification par Machine Learning pour la caractérisation géomécanique des sols | CFMS 2020 - par Marie-Cecile Febvey
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)
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
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
Spatio-temporal optimization of groundwater monitoring network at Pickering Nuclear Generating Station (video) | Pre-recorded video of a technical paper presented during Geostats 2021 | Yvon Desnoyers and Pedram Masoudi, Geovariances, Mike Grey, Kinectrics.