This five-day course, led by John Doherty (author of PEST), covers modern inversion and uncertainty analysis techniques for subsurface flow models. Hands-on sessions show how to implement theory using the PEST and PEST++ suites.
A key focus is the distinction between numerical modelling that is undertaken to understand how groundwater systems work and decision-support groundwater modeling. While the latter must inform real-world management, it must do so under uncertainty—since subsurface properties can never be perfectly known. The goal is not perfect prediction, but bias-free decision-making that acknowledges risk.
Topics include:
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History matching and data assimilation
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Appropriate model complexity and parameterization
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Non-stationary geostatistics
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Data Space Inversion (DSI) – a powerful, efficient method for uncertainty analysis without parameter tuning