Training Opportunities 2022

We will be offering a series of remote introductory, advanced, and workflow geostatistical modeling classes in partnership with the Centre of Computational Geostatistics. Modules are available on subjects ranging from an Introduction to Geostatistical Modeling to Practical Techniques for Machine Learning Applied to Geometallurgical Data. Each module will be offered from 8:30 to 12 noon Mountain time. A PDF with condensed course summary and pricing information:

2022 Course Pricing PDF Click Here to Register and Attend 2022 Short Courses

Module DatesModule
May 2 (8:30 AM MDT) Introduction to Modern Geostatistics: high level overview of modern geostatistics including workflows for long range resources, drill hole spacing and classification, grade control and geometallurgical modeling. This is suitable to a wide audience including managers, staff from other disciplines who want to know about geostatistics and those that want a refresher.
May 3 (8:30 AM MDT) Exploratory Data Analysis and Outliers: exploratory data analysis including multivariate and outlier management, statistical displays and summary statistics for univariate and multivariate continuous and categorical variables. Outlier management by visual, statistical and geostatistical-simulation based methods are presented with examples.
May 4 (8:30 AM MDT) Variograms: calculation, interpretation, and modeling for continuous and categorical variables with local varying anisotropy. The practical steps to obtain a geologically realistic and suitable variogram for all required variables are covered. Combining general geological knowledge with sparse drill data for the best possible variogram is reviewed. Change of support will be summarized.
May 5 (8:30 AM MDT) Estimation and Kriging: estimation including validation and setup for different model applications (implicit modeling/visualization, final estimates, interim estimates and probabilistic prediction). The theory will be developed. Attention will be given to practical application, locally varying anisotropy, parameter selection and validation of the results. Measures of performance are reviewed.
June 13 (8:30 AM MDT) Simulation Fundamentals: the fundamental principles of simulation and, in particular, Gaussian simulation are covered including prerequisite steps such as the normal score transform. Unconditional simulation and conditioning by kriging are presented. Alternative implementations such as turning bands and locally varying anisotropy will be reviewed.
June 14 (8:30 AM MDT) Simulation with a Trend: stationary and local trend modeling and removal for the simulation of non-stationary variables has emerged as a staple of modern geostatistics. The theory, implementation details and examples of optimizing trend models and modeling with a trend will be covered. The use of Gaussian mixture models and stepwise conditional transform is presented
June 15 (8:30 AM MDT) Parameter Uncertainty and Checking: the multivariate spatial bootstrap will be presented for quantifying and transferring parameter uncertainty. The second half of the module will focus on checking simulated realizations including the assessment of accuracy and precision. Other checks such as statistical reproduction and swath plots are reviewed.
June 16 (8:30 AM MDT) Hierarchical Truncated pluriGaussian Simulation: theory, implementation details and practical application of HTPG are covered for categorical variable simulation. The details of incorporating a trend, variogam inference, latent variable imputation and model assembly are covered. A comparison to other categorical modeling techniques is included.
September 6 (8:30 AM MDT) Multivariate Decorrelation: the theory and implementation of principal component analysis and variants such as sphering, minimum/maximum autocorrelation factors and projection pursuit multivariate transformation (PPMT) are presented for multivariate model building. Guidance on technique selection will be given and examples shown.
September 7 (8:30 AM MDT) Probabilistic Porphyry Modeling Workflow: the practice and a full worked case study will be presented for geometric and continuous property simulation with flattening transformations. The solution will include all prerequisite steps, surface and geometry modeling, boundary modeling, model construction, model validation, classification and resource calculation.
September 8 (8:30 AM MDT) Data Imputation: the theory and workflows for the management of missing multivariate data are presented with examples. The treatment of biased data and data of differing quality will also be addressed with examples. Alternative techniques will be reviewed and guidance presented on current best practice.
September 9 (8:30 AM MDT) Probabilistic Tabular and Vein Modeling Workflow: the practice and a full worked case study will be presented for geometric and continuous property simulation with flattening transformations. The solution will include all steps of EDA, surface and geometry modeling, boundary modeling, model construction, model validation, classification and resource calculation.
November 7 (8:30 AM MST) Machine Learning for Geometallurgical Modeling: practical techniques and applications for machine learning of metallurgical properties is covered. Techniques to manage non additive variables, unequally sampled data, limited test work are presented. Appropriate workflows are developed and presented; this class is focused on the application of machine learning and is suitable for metallurgists, resource modelers, and others interested in the approach for geometallurgical data.
November 8 (8:30 AM MST) Drillhole Spacing Workflow: the concepts, practice and a full worked case study to optimize drill hole spacing (and placement) considering local factors and value of information are presented. The solution will include all steps of resampling and resimulation, model construction, model validation and analysis of uncertainty versus drill hole spacing.

Public courses included pre-course material, three hours of lectures, and post course material including data and complete solutions for each module completed in RMSP.



In-House Training Overview

Our public classes are one venue for training. We also offer in-house (virtual) training and customize with your data to meet your needs. Contact us for more information.

  • Fundamentals of Geostatistics (2 - 4 days) a general class going through statistics, declustering, variograms, kriging, simulation and special topics related to resources and reserves estimation. Fundamental concepts of geostatistics will be covered in lectures and brief software demonstrations. Topics include problem formulation, stationarity, prerequisite statistics, declustering, variograms, kriging, simulation (surfaces, categorical and continuous variables) and special topics related to resources and reserves estimation.
  • Advanced Geostatistics (3 - 5 days) is an advanced class going through modern multivariate and probabilistic geostatistical techniques. Applied geostatistical tools and workflows will be presented to go from data through surfaces, boundaries, rock types and multivariate modeling to generate probabilistic resource estimates. Multivariate property modeling with cokriging, data imputation and multivariate transforms will be covered. Time will be divided between lectures, demonstrations and brief exercises.
  • Probabilistic Resource Modeling (2 - 4 days) that goes through the workflow from data through surfaces, boundaries, rock types and multivariate modeling to generate probabilistic resource estimates.
  • Advanced Multivariate Geostatistics (2 - 4 days) that goes through modern workflows including data analysis, transformations, model building and post processing with a focus on geometallurgy.
  • Applied Mining Geostatistics (2 - 4 days) aimed at current practice of kriging and best practice resource estimation touching on checking, validation and classification.
  • Fundamentals of Mining and Mineral Processing (2 - 4 days) provides an overview for professionals that may not have a classical mining background.
  • Geostatistical Reservoir Modeling (2 - 4 days) aimed at petroleum reservoir modeling including facies modeling, property modeling, geophysical data integration, production data integration and uncertainty management.
  • Characterization and Management of SAGD Reservoirs with Geostatistical and Optimization Techniques (3 days) is focused on special topics related to modeling and decision making for in situ projects in Northern Alberta.

Interested in training? Sign up for our mailing list below or email us:

  contact@resmodsol.com