iTOUGH2 solves the inverse problem by automatically calibrating a TOUGH2 model (or any other model) against observed data. Any TOUGH2 input parameter can be estimated based on any observation for which a corresponding TOUGH2 output can be calculated. An objective function measures the difference between the model calculation and the observed data, and a minimization algorithm proposes new parameter sets that iteratively improve the match. Once the best estimate parameter set is identified, iTOUGH2 performs an extensive error analysis which provides statistical information about residuals, estimation uncertainties, and the ability to discriminate among model alternatives. Furthermore, an uncertainty propagation analysis allows one to quantify prediction errors.
While specifically developed for the calibration of TOUGH2 models, iTOUGH2 also supports the PEST/JUPITER protocol, i.e., iTOUGH2 capabilities can be used for the analysis of any user-supplied model or series of models.
iTOUGH2 runs in three application modes:
iTOUGH2 is documented in the following reports:
The procedure of inverse modeling using iTOUGH2 is visualized in the flow chart below. Click on a box to obtain additional information.
Page updated: January 4, 2012