MinE 612

Principles of Geostatistics

Description

Geostatistical methods are presented for characterizing the spatial distribution of regionalized variables, such as ore grades, porosity, permeability, and contaminant concentrations. This class focuses on the geostatistical methodologies for quantifying spatial variability with variograms/covariance functions, estimation with kriging techniques, and stochastic simulation with Gaussian, indicator, and annealing-based methods. Important subjects such as uncertainty quantification, volume-variance relations, and modelling multiple variables will also be addressed. Case studies will be presented from mining, petroleum, and environmental engineering. Students will undertake a variety of theoretical and practical assignments.

Learning Outcomes

Understand the importance of geological heterogeneity modeling for resources and reserves
Describe the sequence of steps required for constructing a fit-for-purpose geostatistical model
Appreciate how to calculate, interpret and model a variogram for spatial variability quantification
Implement a kriging estimator for optimal block model estimates
Explain how uncertainty is a consequence of natural variability and sparse data
Express how optimal decisions can be made in the presence of geological uncertainty

• Details
• Pre-Requisites
• Co-Requisites
• GA - Graduate Attributes
• Course Files

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Undergraduate Program(s)

Sections & Respective Instructors

A1 - 2025/2026 - Fall - Clayton Deutsch

A1 - 2024/2025 - Fall - Clayton Deutsch

A1 - 2023/2024 - Fall - Clayton Deutsch

A1 - 2020/2021 - Fall - Jeffery Boisvert