GMDSI sponsors three PhD students who have spent time in industry, and intend to return to industry. Their research will focus on improving concepts and technologies that support the use of models in real-world decision-making.
- improved uncertainty analysis where hydraulic properties are structurally controlled
- appropriate complexity for decision-support modelling in different geological and decision-support contexts
- greater numerical efficiency in history-matching and in direct predictive hypothesis testing
- optimisation of context-specific methodologies for data assimilation and uncertainty analysis
- optimisation under uncertainty.
We are proud to introduce:
Tomás holds a masters degree in hydrogeology. He has had 11 years experience in groundwater modelling and site characterisation for the evaluation of open pit mine dewatering/depressurisation strategies, estimation of lithium reserves in Salars, and environmental impact assessment. His research interests include optimisation of groundwater modelling workflows, including strategies for appropriate model simplification, parameterisation, history matching, and uncertainty analysis.
Neil has spent the past 13 years developing numerical groundwater models for water resource assessment and environmental approvals of coal, iron ore and gold mines, as well as coal-bed methane extraction. He skills and interests include programming, linear/non-linear optimisation, uncertainty analysis, drilling supervision, and construction of monitoring bores/VWPs.
- Spatial averaging implied in aquifer test interpretation: The meaning of estimated hydraulic properties. Authors: Neil Manewell, John Doherty and Phil Hayes.
- Complex or Simple – Does a Model Have to be One or the Other? Authors: Rui Hugman and John Doherty
- Probabilistic Contaminant Source Assessment – Getting the Most Out of Field Measurements. Authors: Rui Hugman, Francesca Lotti and John Doherty
Banner photo: BHP