Webinar | How to achieve a robust classification of mineral resources using kriging results
Discover how to use kriging results to support a rapid, reliable, and defensible classification of mineral resources.
In just 45 minutes, learn how geostatistical tools can help classify resources in a way that is consistent with reporting standards and better aligned with geological uncertainty.
Through practical explanations and a live demonstration, you will see how kriging results and related quantitative criteria can support mineral resource classification workflows.
🞉 Date & format
Tuesday, April 21, 2026, 11:00 am (Paris CEST)
Duration: ~45 minutes + live Q&A
Live online session – all registrants will receive the replay video after the event.
Ce webinaire aura également lieu en français le même jour à 14h00. Cliquez ici pour vous y inscrire.
🞉 Why attend
Mineral resource classification is essential for effective mine planning, risk management, and transparent communication with stakeholders. By categorizing blocks according to the level of uncertainty in estimated grades, companies can better understand confidence levels, manage operational risk, and support informed decision-making.
In this webinar, you will learn how kriging results can be used to support a more robust and quantitative approach to classification. Rather than relying only on distance-based rules, geostatistical criteria can help link classification decisions to sampling density, grade continuity, and estimation uncertainty.
Whether you are reviewing existing classification methods or looking for more defensible, JORC-aligned approaches, this session will provide practical guidance and concrete examples.
🞉 What you’ll learn
In this webinar, you will discover:
- The fundamentals of mineral resource classification and the differences between Inferred, Indicated, and Measured categories.
- The strengths and limitations of traditional distance-based methods compared with kriging-based approaches.
- How to use kriging results and conditional simulations to quantify geological risk as a function of drillhole spacing and grade variability.
- Which numerical criteria can support a rapid and defensible classification workflow.
- How the Spatial Sampling Density Variance (SSDV) approach can help classify resources based on sampling spacing and variogram models.
- A live demonstration showing how these concepts can be applied in practice.
🞉 Who should attend
This webinar is ideal for geostatisticians, resource geologists, mining engineers, resource estimation professionals, Competent Persons / Qualified Persons, and technical teams involved in mineral resource evaluation and reporting.
Don’t miss this opportunity to learn from our experts!
Join us to discover how kriging-based approaches can improve the robustness, transparency, and consistency of mineral resource classification.
A replay will be available for all registered participants.
🞉 Lecturer
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 covers mineral resource estimation (JORC-compliant), petroleum reservoir characterization, geological facies modeling, geotechnical studies, and the mapping of contaminated soils, particularly in radioprotection contexts.