Jeff Boisvert, PhD, PEng

Professor, Faculty of Engineering - Civil and Environmental Engineering Dept


Professor, Faculty of Engineering - Civil and Environmental Engineering Dept


Area of Study / Keywords

Mining Engineering and Wildland Fire Management


Research Website Link


Research Interests

  • Geostatistical modeling of nonstationary domains.
  • Geostatistical models for environmental applications including CO2 sequestration and storage.
  • Geostatistical models for temporal data.
  • Use of numerical models in mine planning.
  • Integration of multivariate data sets in numerical modeling.
  • Spatial modeling of variables that impact wildland fires and help in prediction.
  • Automated wildland fire hazard and triage assessments.
  • Fire growth modeling.

Research Currently in Progress

  • Reproduction of non-linear geological features such as channels, veins, complex folding, etc. This research uses the shortest path distance to integrate non-stationary anisotropy.
  • Generation of anisotropy fields from available data such as point measurements, surfaces, interpreted volumes or geological analogues.
  • Selection and use of training images with multiple point simulation algorithms.
  • Assessment of variable importance with geometallurgical data (often >100 variables).
  • Wildland fire management using remotely collected data from drones, observers, and satellites to improve decision making. 


MIN E 422 - Environmental Impact of Mining Activities

Environmental impact of mining projects and activities. Topics include: environmental impact assessment (EIA) processes, sustainable development, mine closure, reclamation planning, social responsibility of mining, regulations, guidelines, surface subsidence, tailings disposal, erosion and acid rock drainage. Corequisite: MIN E 413.

MIN E 613 - Non-Parametric and Multivariate Geostatistics

Cell based methods for geology modeling, including indicator formalism for categorical data and truncated Gaussian simulation. Object based and process-based approaches for fluvial reservoirs. Indicators for continuous variable estimation and simulation. Multivariate geostatistics including models of coregionalization, cokriging, Gaussian cosimulation, Markov-Bayes simulation and multivariate data transformation approaches. Introduction to advanced simulation approaches including direct simulation, simulated annealing and multiple point simulation. Prerequisite: Consent of instructor.

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