Ensemble space inversion (ENSI) enables efficient, regularisation-constrained calibration of complex, highly-parameterised models. This tutorial demonstrates how linear analysis can be undertaken in partnership with the
Ensemble space inversion (ENSI) is implemented through the PEST_HP suite (version 18). Using ENSI you can calibrate a complex model quickly. The calibration subspace is comprised
Building and history-matching a three-dimensional model is a difficult procedure. The third dimension increases parameter requirements, model run times, and model output uncertainty. Ideally, predictive
This tutorial explores the use of “conceptual points” as a precursor to model parameterisation. Expected hydraulic properties are provided at these conceptual points. Just as
This tutorial explores construction of the interface between PEST/PEST++and a simple MODFLOW/MODPATH model, and how to then subject that model to history-matching and uncertainty analysis–including
View the webinar recording introducing the course here. This free guided self-study course and weekly Q & A sessions with the GMDSI team provided guidance and
GMDSI and the USGS have co-funded the development of a series of jupyter notebooks that use python scripting to demonstrate many aspects of applied decision-support
Using the PLPROC parameter preprocessor supplied with the PEST suite, moveable polylinear and polygonal structural features such as faults and aquitard windows can be inserted
Date: 9th May 2023 (Recorded – see button below) John Doherty, Jeremy White and Catherine Moore presented issues that occupy the boundaries between uncertainty analysis
Date: 28th February 2023 Webinar recording This webinar had two presenters. The first is Chris Li (from CDM Smith). Chris delivered a talk entitled ‘The “Cinderella Syndrome”
GMDSI is managed by the National Centre for Groundwater Research and Training (NCGRT) and administered by Flinders University.