Isatis.neo | Geostatistics made accessible
Isatis.neo is the leading and most comprehensive software solution for geostatistics. Featuring an intuitive user interface, it results from Geovariances’ dual commitment to developing breakthrough technology and making premium 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, the Petroleum Edition offers a preconfigured workflow for Time-to-Depth Conversion with comprehensive uncertainty analysis.
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 complete set of powerful and intuitive statistical and geostatistical tools in a fully flexible package letting you design your process to best address your specific issues. And if you need further analyses, Isatis.neo enables you to write python coding into your batch processes, allowing for a high degree of customization required for optimized workflows.
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.
Go beyond with Machine Learning
With Isatis.neo, you may combine geostatistics with Machine Learning techniques to solve real and complex problems.
Hear from our customers
“O recurso mineral de 2023, estimado pelo Isatis.neo, aumentou Life of Mine e o valor dos ativos minerais da EuroChem. /// The 2023 mineral resource, estimated through Isatis.neo, increased Life of Mine and Eurochem's mineral asset value.”
"Con Isatis.neo, usted puede implementar rápidamente flujos de trabajo en proyectos con múltiples dominios y variables a modelar. | With Isatis.neo, you can quickly implement workflows in projects involving multiple domains and variables."
"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."
Discover Isatis.neo at our upcoming events
Geovariances is to talk at the Datamine UK User Conference & Workshop 2023 in Bristol on October 23 and 24.
Join Benoît Poupeau's talk and discover Isatis.neo's main capabilities and novelties, and what makes it the state-of-the-art software for...
Geovariances CEO and Datamine VP Geology Gustavo Usero is to talk at Datamine Perth Geology Symposium 2023
Join Gustavo Usero's talks and learn how geostatistics and Machine Learning can be used to optimize ore control. Discover a new Studio RM ...
Geovariances’ consultant is to talk at EEGS / AEG 2023 Virtual Symposium on “Life of Mine – Maintaining Sustainability Through Geoscience”
Roberto Rolo is to present how Geovariances combines Machine Learning and geostatistics to optimize ore control models in mining operation...
See all news
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)
Assessment of ambient dose equivalent rate distribution patterns in a forested-rugged terrain using field-measured and modeled dose equivalent rates | M. Yasumiishi, P. Masoudi, T. Nishimura, K. Ochi, X. Ye, J. Aldstadt, M. Komissarov - Radiation Measurements, Volume 168, 2023, 106978, ISSN 1350-4487, https://doi.org/10.1016/j.radmeas.2023.106978.
A novel geostatistical index of uncertainty for short-term mining plan | G. M. C. Dias, M. M. Rocha & V. M. Silva (2023) - CIM Journal, DOI: 10.1080/19236026.2022.2145077
Application de la géostatistique dans l’analyse de risque géotechnique lié à la liquéfaction du sol | Gestion des Données et Nouvel Environnement numérique en Géotechnique - Journée technique CFMS 15 nov 2022
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