CFSG EMEA | Training course
Become an expert in geostatistics for mineral resource estimation. Learn theory and practice through exercises with Isatis.neo.
The Specialized Training Cycle in Geostatistics, known as CFSG, is a high-level training program in mining geostatistics delivered by the Geostatistics Team from Mines Paris and Geovariances. Its objective is to give you a comprehensive and deep knowledge of geostatistics for mineral resource estimation so that, when you return to work, you will be able to build the block models that your company needs for confident mine planning. By attending the program, you learn the theory behind the techniques presented to you and practice them through numerous exercises and a real-life project.
CFSG EMEA is intended for individuals whose time zone is aligned with Paris. It will be delivered online in 6 modules over 9 weeks throughout 2024, starting in February. The first module (module A) is the CFSG core module presenting the fundamentals of mining geostatistics for resource estimation.
- Half of the training program is devoted to methodological presentations, the other half to practical exercises to deepen the understanding of concepts.
– The methodological courses are given by Mines Paris professors.
– 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 get a training certificate that officially recognizes the full completion of the module.
- Course material and a temporary software license are provided.
- A minimum number of 5 participants is required for a module actually to take place.
Who should attend
The CFSG training program is meant for mining geologists and engineers willing to achieve a high level of geostatistics and boost their careers.
Module A – 2 x 10 days
Learn the fundamentals of mining geostatistics for resource estimation and build your first block model. Learn how to improve estimation using multivariate geostatistics. Get insights into geostatistical simulations.
Module A is ideal for newcomers to mining geostatistics.
PART 1 – STATISTICS FOR MINERAL RESOURCES
- The different types of quantities
– Quantitative (i.e., grade or petrophysical properties) or categorical (i.e., geological facies and rock types)
– Missing information, limit of detection (LOD), limit of quantification (LOQ)
– Additive variables
– Variables defined on a space (i.e., drillholes, maps, and block models)
– Support of information (size and shape) and volume of selection (i.e., Selective Mining Unit)
- Univariate statistics
– 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
- Multivariate statistics
– Scatter plots
– Marginal and conditional distributions
– Linear and empirical regression
– Transformations (linear combinations, i.e., Principal Component Analysis)
- Selectivity curves
– Rules for selection: cutoff and support (sample vs. Selective Mining Unit)
– Tonnage, Average Grade, Metal, and Conventional benefit
– Support effect
– Information effect
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.
PART 2 – MODELING THE SPATIAL CONTINUITY
- Sampling for spatialized variables
– Clustered and preferential sampling
– Sampling geometry: scattered, seismic lines, drill holes, regular grids
– Declustering and weighted statistics
- 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
Several exercises to learn to import data, achieve exploratory data analysis, compute experimental variograms, and adjust variogram models with Isatis.neo Mining Edition.
PART 3 – IN-SITU RESOURCE ESTIMATION
– 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
– Universal Kriging in more general cases
– The Variogram Model
– The Neighborhood: Moving vs. Global
- Properties of kriging
– Support of the sample (different sizes)
– Support of the target: block, convolution, gradient
- Cross-validation (leave-one-out, K-fold) and Kriging validation
- Extensions: Kriging with Variance of Measurement Errors, Filtering
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.
PART 4 – ADVANCED IN-SITU RESOURCE ESTIMATION AND SIMULATION
- Multivariate modeling
– Experimental statistics: scatter plots, correlation table, regressions (linear and non-linear)
– Transforms: Principal Component Analysis (PCA) and Multiple Factor Analysis (MAF), Indicator Residuals
– Simple and cross variograms
– Modeling: Linear Model of Coregionalization
- Multivariate estimation
– Cokriging: Simple and Ordinary. Simplifications
– Collocated Co-kriging
– Factorial Kriging Analysis
- Selectivity curves for recoverable resources
. Rules for selection: cutoff and support (sample vs. Selective Mining Unit)
. Tonnage, Average Grade, Metal, and Conventional Benefit
. Support effect of the variable
. Information effect
– Limitations of Linear Kriging
– Non-linear estimation vs. simulations
- Gaussian Simulations
– Why resort to the Gaussian Model?
– Transform from Raw to Gaussian quantities: Normal score and Anamorphosis
– Properties (univariate, multivariate), covariance function
– Simulation techniques: Sequential Gaussian Simulations, Turning Bands Simulations, Conditioning Kriging
Several exercises to learn how to analyze contacts between domains, compute and adjust multivariate variograms, and run point and block simulations, Direct Block Simulations, and post-processing (statistics, simulation reduction, grade-tonnage curves) with Isatis.neo Mining Edition.
Module B to F
These 5 optional modules are designed to delve into more advanced geostatistics:
- Module B: Recoverable resources with non-linear methods (5 days)
Learn how to compute recoverable resources considering mining selectivity and quantify the uncertainties.
- Module C: Non-stationary geostatistics (5 days)
Learn how to constrain the block model with geological trends.
- Module D: Facies simulations (5 days)
Learn how to achieve reliable and realistic facies modeling.
- Module E: Domaining (5 days)
Get introduced to a powerful machine-learning-based technique for geological domaining.
- Module F: Coupling Machine-Learning and geostatistical techniques using Python (5 days)
Learn innovative and machine-learning-based techniques that improve performance.
More information regarding the content and the schedule of each module will be provided to you soon. Each module can be attended independently of each other. However, it is important 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.
The course is delivered in English and requires a good level of this language. Sound notions of mathematics are 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.
Modules can be attended à la carte, according to your needs, independently from each other. Module A is mandatory if you have never used Isatis.neo or have no prior experience in geostatistics.
Benefit from the trainers’ high expertise in geostatistics
From Mines Paris – PSL