CFSG Module A: Linear Geostatistics for Local Resource Estimation | Training course

The CFSG (Specialized Training Cycle in Geostatistics) is a high-level training program in mining geostatistics delivered by the Geostatistics Team from Mines Paris and Geovariances. This program aims to equip you with an in-depth understanding of geostatistics for mineral resource estimation, enabling you to create block models your company needs for confident mine planning when you return to work. Throughout the training, you will learn the theoretical aspects of the techniques presented and practice them through various exercises and a real-world project.

CFSG is intended for individuals whose time zone is aligned with Europe (France). It will be delivered online in 4 modules over 7 weeks during the first semester of 2026, starting in March.

This module is the first one of the CFSG series. It is the CFSG core module, presenting the fundamentals of mining geostatistics for resource estimation.

WEEK 1 – STATISTICS FOR MINERAL RESOURCES

THEORY

• The different types of quantities
  – Quantitative (i.e., grade, density or metal quantity) or categorical (i.e., geological facies and rock types)
  – Missing information, limit of detection (LOD), limit of quantification (LOQ)
  – Variables defined on a space (i.e., drillholes, maps, and block models)
  – Additive variables
  – Support of information (size and shape) and volume of selection (i.e., Selective Mining Unit)
• Sampling for spatialized variables
  – Clustered and preferential sampling
  – Sampling geometry: scattered, seismic lines, drill holes, regular grids
  – Declustering and weighted statistics
• Univariate statistics
  – Histograms
  – Summary statistics:  mean, median, mode to capture the centrality, variance, inter-quartile interval, coefficient of variation to capture the dispersion, minimum, maximum, quantiles, box plots to capture the extremes
  – Base maps and swath plots
  – Transform of the variable: logarithm, log, indicator, capping, ranking, proportional effect
  – Continuous and discrete distributions: Gaussian, lognormal, uniform, triangular, exponential, gamma, Bernoulli, Binomial, Poisson
• Selectivity curves
  – Rules for selection: cutoff and support (sample vs. Selective Mining Unit)
  – Tonnage, Average Grade, Metal, and Conventional benefit
  – Support effect
  – Information effect

PRACTICE

Introduction to Isatis.neo Mining Edition to learn how to manage a project and various types of data sets. You will learn how to manage the statistical concept presented in theory in the software.

WEEK 2 – MODELING THE SPATIAL CONTINUITY

THEORY

• Exploratory Data Analysis (EDA)
  – Stationarity analysis using swath plots
  
• Measuring the spatial continuity
  – Spatial covariance and variograms
  – Variogram cloud, variogram map
  – Calculations in one, two, and three-dimensional spaces. The particular case of regular, gridded data.
  – Other empirical structural tools: robust variogram, madogram, rodogram
• Variogram model
  – The basic models: Nugget Effect, Exponential, Spherical, Gaussian, Cubic, Linear
  – Parameters and properties
  – The nested model and its multi-scale interpretation
  – Anisotropies: geometric, zonal, separable
  – Fitting strategy

PRACTICE

Several exercises to learn to import data, achieve exploratory data analysis, compute experimental variograms, and adjust variogram models with Isatis.neo Mining Edition.

WEEK 3 – KRIGING FOR LOCAL RESOURCE ESTIMATION

THEORY

• Estimator
  – Examples: Moving Mean, Nearest Neighbor, Inverse Distances
  – Precision vs. accuracy
  – Dichotomy between (deterministic) Drift and (stochastic) Residuals: (strictly) stationary, intrinsic, or non-stationary
  – Linear, Unbiased, Optimal
  – Estimation and quality of estimation (estimation error)
• Kriging (Best Linear Unbiased Estimation)
  – Simple Kriging with known mean
  – Ordinary Kriging in intrinsic cases
  – Block Kriging (change of support)
  – Extensions: Kriging with Variance of Measurement Errors, Filtering
• Neighborhood parameters
  – The Neighborhood: Moving vs. Global
  – Kriging Neighborhood Analysis (KNA)
• Validating resource models
  – Cross-validation (leave-one-out, K-fold)
  – Kriging estimation validation

PRACTICE

Several exercises to learn to build block models with Isatis.neo Mining Edition using Ordinary and Simple Kriging and to validate the model with cross-validation.

WEEK 4 – MULTIVARIATE GEOSTATISTICS

THEORY

• Multivariate statistics
  – Experimental statistics: scatter plots, correlation table, regressions (linear and non-linear)
  – Marginal and conditional distributions
  – Linear and empirical regression
  – Transforms: Principal Component Analysis (PCA) and Multiple Factor Analysis (MAF), Indicator Residuals
• Multivariate modeling
  – Simple and cross variograms
  – Modeling: Linear Model of Coregionalization
• Multivariate estimation
  – Cokriging: Simple and Ordinary
  – Collocated Co-kriging
  – Rescaled Co-kriging
  – Factorial Kriging Analysis
• Non-stationary modeling
  – Dichotomy between (deterministic) Drift and (stochastic) Residuals: (strictly) stationary, intrinsic, or non-stationary
  – Exploratory Data Analysis: swath plots, cross plots, experimental variograms (quadratic behavior, or more)
  – Non-stationary Models: Drift and Stationary Residuals
  – Extension to complex drifts: Intrinsic Random Function of order k (Generalized covariances)
• Estimation
  – Kriging with local anisotropies
  – Universal Kriging in more general cases
  – Kriging with External Drift
  – Factorial Kriging Analysis

PRACTICE

Several exercises to learn how to analyze contacts between domains, compute and adjust multivariate variograms, run co-kriging variants and non-stationary modeling methods with Isatis.neo Mining Edition.

Each module can be attended independently of the others. However, it is essential to note that completion of Module A or having experience in geostatistics and Isatis.neo is a prerequisite for participation in any of these modules.

• Module B: Nonlinear Geostatistics for Recoverable Resource Estimation (optional)
  April 13-17, 2026 – 5 days
  Explore nonlinear techniques to model grade-tonnage curves and estimate recoverable resources while accounting for cutoffs and mining selectivity.
• Module C: Simulation of Continuous Variables for Uncertainty and Risk Analysis (optional)
  May 18-22, 2026 – 5 days
  Learn conditional simulations to generate probabilistic resource models and assess spatial uncertainty, quantify risk, and support informed decision-making.
• Module D: Simulation of Categorical Variables for Geology and Domain Modeling (optional)
  June 15-19, 2026 – 5 days
  Apply geostatistical simulation methods to model geological domains, lithology, and facies. Capture and represent spatial variability in categorical variables.

• Half of the training program is devoted to methodological presentations, and the other half to practical exercises to deepen understanding of the concepts.
  – Mines Paris professors give the methodological courses.
  – Geovariances consultants will drive the practical sessions from our French office.
  – For more convenience, these courses are recorded and made available to participants during the session and until one month after the end of the course.
• A typical training week would then be:
  – Monday, Tuesday, Wednesday, and Thursday: theoretical course (on a half-day) and hands-on practice with Isatis.neo Mining Edition (on the half-day following the theoretical course).
  – Friday: homework using Isatis.neo Mining Edition with compulsory rendering at the end of the day. Live corrections and comments from the teaching team. Validation of prior learning.
• CFSG is a full-time training. You are required to be present/connected for the duration of the sessions.
• CFSG is a certification training. The knowledge acquired in each module is validated through an examination. At the end of each module, you will receive a training certificate that officially confirms the module’s completion.
• Course material and a temporary software license are provided.
• A minimum of 8 participants is required for a module to proceed.

The CFSG training program is intended for mining geologists and engineers who are willing to achieve a high level of proficiency in geostatistics.

Module A is ideal for newcomers to mining geostatistics. Modules B to D are designed for individuals who wish to delve into more advanced geostatistics.

The course is delivered in English and requires a good level of this language. A sound understanding of mathematics is also recommended.

As the course is online, a good-quality internet connection is required. We also appreciate that the participant’s camera is turned on for the session.​

Benefit from the trainers’ high expertise in geostatistics

From Mines Paris – PSL

From Geovariances

What alumni say about CFSG