Parent Command
>> OPTION

Subcommand
>>>> CORRELATION
>>>> EIGENVALUE
>>>> ITERATION
>>>> LIST
>>>> SENSITIVITY
>>>> SEQUENTIAL
>>>> TRUNCATE

Description
This command invokes the automatic parameter selection option of iTOUGH2. The parameters defined in block > PARAMETER are screened according to two selection criteria (see commands >>>> SENSITIVITY and >>>> CORRELATION) or will be truncated based on the eigenvalues of the Fisher information matrix (see commands >>>> EIGENVALUE, or >>>> TRUNCATE). Only the most sensitive and/or most independent parameters are subjected to the optimization process. If command >>> SUPER is used, superparameters will be generated and optimized (see Tonkin and Doherty [WRR, 41, W10412, doi:10.1029/2005WR003995, 2005]). Only the most sensitive and/or most independent parameters are subjected to the optimization process. The selection procedure is repeated every few iterations. Automatic parameter selection allows one to submit a larger set of parameters to the estimation process. If a parameter is not sensitive enough to be estimated from the available data, it is automatically removed from the set of parameters being updated. This makes the inversion faster because fewer parameters have to be perturbed for calculating the Jacobian matrix (the full Jacobian is only calculated every few iteration when the selection criteria are reevaluated). The inversion is also more robust. Parameters that are not sensitive or highly correlated tend to be changed drastically during an iTOUGH2 iteration which may cause unnecessary numerical difficulties. If they are (temporarily) removed from the parameter set, they remain at their initial current value. An additional advantage of using this option is the fact that sensitivities, estimation uncertainties, and parameter correlations are calculated for all the specified parameters, regardless of whether they are updated during the optimization. Due to the non-linearity of the inverse problem at hand, sensitivity coefficients and parameter correlations constantly change during the optimization. Therefore, the selection criteria must be reevaluated from time to time (see command >>>> ITERATION (s)), i.e., parameters may be deactivated and reactivated during the course of an inversion.

Example
> COMPUTATION
>> OPTION
>>> use SUPERparameters
>>>> pick superparameters based on : 4 largest EIGENVALUES
>>>> repeat selection every : 3 rd ITERATION
<<<<
<<<

See Also
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