RRE – Recoverable Resource Estimation by nonlinear geostatistics | Training course

• MODULE 1: UC/LUC – UNIFORM CONDITIONING AND LOCALIZATION
  – Understand the limitation of kriging and smoothing effect in high drillholes spacing (during exploration) and the necessity of RRE methods to generate an unbiased grade-tonnage curve.
  – Understand the algorithm of UC and apply support change and UC using Isatis.neo.
  – Generate grade, tonnage, and metal quantities as a function of cut-off grades on the panels to generate grade-tonnage curves and tables based on the UC output.
  – Learn the localization technique proposed for the blocks or Selective Mining Units (SMU) within a panel, as well as the challenges in a multivariate and/or multidomain dataset. 
• MODULE 2: MIK – MULTIPLE INDICATOR KRIGING, CONDITIONAL EXPECTATION AND MULTI-GAUSSIAN KRIGING
  – Understand the application domains of MIK and conditional expectation in the framework of the RRE, and learn the theories of MIK and conditional expectation.
  – Learn how to apply MIK, conditional expectation, and their variants, and update grade-tonnage curves using Isatis.neo.
• MODULE 3: CONDITIONAL SIMULATIONS FOR CONTINUOUS VARIABLES
  – Learn the theory of geostatistical Turning Bands Simulation (TBS) and Sequential Gaussian Simulation (SGS) and understand their application limits.
  – Get insight on how to perform block and direct block simulation, then post-process the results in the framework of the RRE to generate grade-tonnage curves using Isatis.neo. 

• Estimation of long-term resources by recoverable resource estimation methods.
• Grade-tonnage curve estimation during exploration.

Professionals involved in feasibility studies or medium to long-term planning who wish to deepen their theoretical and practical knowledge of mining geostatistics. 

• MODULE 1: UC/LUC – UNIFORM CONDITIONING AND LOCALIZATION
  – Basic concepts of RRE
  – Gaussian anamorphosis modelling
  – Change of support: variance of core grade versus block grade
  – Uniform Conditioning
  – Information effect
  – Localized Uniform Conditioning
  – Dealing with multi-domain and Multivariate deposits
  – Grade-tonnage curves following UC/LUC
• MODULE 2: MIK – MULTIPLE INDICATOR KRIGING, CONDITIONAL EXPECTATION AND MULTI-GAUSSIAN KRIGING
  – Basic concepts of RRE
  – Workflow of MIK and its variants
  – MIK options and good practices in Isatis.neo
  – Understanding the advantages and disadvantages of MIK
  – Grade-tonnage curves following MIK
  – Basics of Conditional Expectation
  – The theory of Conditional Expectation and its variants
  – Ordinary multi-gaussian kriging
  – Understanding the advantages and disadvantages of Conditional Expectation
  – Available options in Isatis.neo geostatistical software: block and multivariate
  – Grade-tonnage curves following Conditional Expectation
• MODULE 3: CONDITIONAL SIMULATIONS FOR CONTINUOUS VARIABLES
  – Basic concepts of RRE
  – Introduction and general concepts of simulations
  – Gaussian transformation modelling
  – Conditional simulation methods in Isatis.neo software: TBS and SGS
  – Direct block simulations
  – Postprocessing results
  – Grade-tonnage curves following simulations

• Theory: slides will be provided.
• Practice: A training license for Isatis.neo geostatistical software will be attributed to the participants as well as a typical mining dataset.

• The course Mineral Resource Estimation covering the fundamental concepts of geostatistics for resource estimation offers an ideal basis for this advanced course. 
• Basic knowledge of linear geostatistics is recommended. 
• Basic knowledge of resource concepts would be appreciated: grade, tonnage, cut-off, etc.