MRE – Mineral Resource Estimation by linear geostatistics | Training course

• Build a solid foundation in geostatistical methods for mineral resource estimation.
  – Module 1: Learn and practice the standard workflow for resource estimation in a univariate context. This module covers in-depth data analysis, detailed variographic analyses, block modeling, grade distribution interpolation using kriging, estimation validation and unbiased grade-tonnage curves for short-term resources.
  – Module 2: Advance into the multivariate context by exploring multivariate statistical tools like Principal Component Analysis and applying kriging and co-kriging methods for estimating multi-element orebodies and getting multivariate models respecting ratio between main metal and oxides and elements.
• Gain skills to optimize decision-making in resource estimation by combining theory and hands-on practice using Isatis.neo Mining Edition.

– Primary estimation of long-term resources.
– Estimation of short-term resources.
– Production of resource models for mine design.
– Spatial analysis of drillhole data.

• The course is structured into two parts, Module A and Module B, each lasting two days. Participants can attend either module independently based on their needs.
• It combines methodological presentations and practical exercises using a real data set to enhance the understanding of key concepts.
• Participants will engage in computer exercises with Isatis.neo Mining Edition.
• Course material and a temporary software license will be provided.

Professionals seeking a sound theoretical and practical knowledge of mining geostatistics. 

• MODULE 1: UNIVARIATE GEOSTATISTICS
  – The importance of geostatistics in Mineral Resource Estimation.
  – Extrapolatory Data Analysis (EDA) and spatial analysis of data.
  – Data stationarity analysis.
  – Data regularization: compositing and declustering.
  – Variographical analysis: variogram cloud and parameters of experimental variogram, directional variogram.
  – Variogram modeling: automatic, semi-automatic, manual and interactive.
  – Theory of kriging and mining-related variants: ordinary and block kriging, weight distribution analysis.
  – Building a sound sample neighborhood: Kriging Neighbourhood Analysis (KNA)
  – Cross-validation
  – Estimation validation
  – Grade-tonnage table and curves
• MODULE 2: MULTIVARIATE GEOSTATISTICS
  – Principal Component Analysis (PCA).
  – Kriging non-stationary variable: kriging with external drift or universal kriging.
  – Grade correlation analysis.
  – Cross-variogram and cross-covariance on purely heterotopic datasets.
  – Correlated grades interpolation by Ordinary cokriging, Collocated cokriging and Rescaled cokriging.

• A basic understanding of resource concepts such as grade, tonnage, and cut-off is recommended.
• To extend your knowledge, we recommend attending the complementary advanced short course Recoverable Resource Estimation