Discover how to combine machine learning and geostatistics to better interpret geological data and improve your models.
In just 45 minutes, learn how machine learning techniques can complement geostatistical approaches to better analyze complex datasets and support decision-making in mining and geoscience projects.
Through practical examples and a live demonstration, you will see how Python and Isatis.neo can be used together to integrate machine learning methods into geostatistical workflows.
Date & format
Thursday, March 24, 2026, 11:00 am (Paris CET)
Duration: ~45 minutes + live Q&A
Live online session – all registrants will receive the replay video after the event..
Ce webinaire est également réalisé en français jeudi 26 mars à 11h00. Cliquez ici pour vous y inscrire.
Why attend
Machine learning is transforming the way geoscientists analyze and interpret complex datasets. When combined with geostatistics, it opens new possibilities for improving geological understanding, defining domains, and predicting key variables in mining and geoscience projects.
In this webinar, you will be introduced to the fundamentals of machine learning and learn how these techniques can be applied to geoscience and mining problems. Through practical examples and a live demonstration, you will see how Python and Isatis.neo can be used together to integrate machine learning methods into geostatistical workflows.
Whether you are new to machine learning or looking to better understand its role in geoscience applications, this session will provide practical insights and clear examples.
What you’ll learn
In this webinar, you will discover:
- A live demonstration using Python and Isatis.neo.
- How machine learning can support geoscience and mining workflows.
- The difference between supervised and unsupervised learning.
- Dimensionality reduction techniques (PCA, MAF, MDS) for simplifying complex datasets.
- How clustering methods can help define geological domains.
- How supervised learning methods can be used for regression and classification.
- An overview of commonly used algorithms, including k-nearest neighbors, linear and logistic regression, discriminant analysis, decision trees, support vector machines, random forests, and neural networks.
- Practical examples of machine learning applied to geoscience datasets.
Who should attend
This webinar is ideal for geostatisticians, geologists, mining engineers, exploration managers, resource estimation professionals, and technical teams looking to explore machine learning in a practical, mining-focused context.
Speaker
Pedram Masoudi has been a consultant and trainer at Geovariances since 2019. His work focuses on geostatistical modeling and the integration of geological and geophysical data using Isatis.neo and Python. His expertise includes mineral resource estimation (JORC-compliant), petroleum reservoir characterization, geological facies modeling, geotechnical studies, and mapping of contaminated soils, particularly in radioprotection contexts.
Don’t miss this opportunity to learn from our experts!
Join us to discover how machine learning techniques can complement geostatistics and help you better interpret and model geoscience data.
A replay will be available for all registered participants.
