Application of the Iterative Ensemble Smoother Method and Cloud Computing: A Groundwater Modeling Case Study
| Autor: | Alexis Valenza, Kevin Hayley, Bruce Hutchison, Emma White, Jens Schumacher |
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| Rok vydání: | 2019 |
| Předmět: |
Mathematical optimization
lcsh:Hydraulic engineering 010504 meteorology & atmospheric sciences Groundwater flow Computer science Geography Planning and Development 0207 environmental engineering Open-pit mining Aquifer 02 engineering and technology mining Aquatic Science 01 natural sciences Biochemistry lcsh:Water supply for domestic and industrial purposes lcsh:TC1-978 Iterative Ensemble Smoother Pit water Uncertainty quantification 020701 environmental engineering Uncertainty analysis 0105 earth and related environmental sciences Water Science and Technology lcsh:TD201-500 groundwater modeling geography geography.geographical_feature_category business.industry predictive uncertainty analysis cloud computing business Groundwater model Numerical stability |
| Zdroj: | Water Volume 11 Issue 8 Water, Vol 11, Iss 8, p 1649 (2019) |
| ISSN: | 2073-4441 |
| DOI: | 10.3390/w11081649 |
| Popis: | Numerical groundwater modelling to support mining decisions is often challenging and time consuming. Simulation of open pit mining for model calibration or prediction requires models that include unsaturated flow, large magnitude hydraulic gradients and often require transient simulations with time varying material properties and boundary conditions. This combination of factors typically results in models with long simulation times and/or some level of numerical instability. In modelling practice, long run times and instability can result in reduced effort for predictive uncertainty analysis, and ultimately decrease the value of the decision-support modelling. This study presents an early application of the Iterative Ensemble Smoother (IES) method of calibration-constrained uncertainty analysis to a mining groundwater flow model. The challenges of mining models and uncertainty quantification were addressed using the IES method and facilitated by highly parallelized cloud computing. The project was an open pit mine in South Australia that required predictions of pit water levels and inflow rates to guide the design of a proposed pumped hydro energy storage system. The IES calibration successfully produced 150 model parameter realizations that acceptably reproduced groundwater observations. The flexibility of the IES method allowed for the inclusion of 1493 adjustable parameters and geostatistical realizations of hydraulic conductivity fields to be included in the analysis. Through the geostatistical realizations and IES analysis, alternative conceptual models of fractured rock aquifer orientation and connections could be conditioned to observation data and used for predictive uncertainty analysis. Importantly, the IES method out-performed finite difference methods when model simulations contained small magnitude numerical instabilities. |
| Databáze: | OpenAIRE |
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