Geochemical models are used to seek answers about composition and evolution of groundwater, spill remediation, viability of geothermal resources and other important geoscientific applications. To understand these processes, it is useful to evaluate geochemical model response to different input parameter combinations. Running the model with varying input parameters creates a large amount of output data. It is a challenge to screen this data from the model to identify the significant relationships between input parameters and output variables.

For addressing this problem we develop a Visual Analytics approach in an ongoing collaboration between Geoinformatics and Hydrogeology sections of GFZ. We implement our approach as an interactive data exploration tool called GCex. It is designed so that a diverse set of tasks such as inverse modeling, sensitivity analysis and model parameter refinement can be supported.

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