Stochastic inverse modeling is a MODFLOW run option that takes each run in a stochastic simulation and performs parameter estimation on the run to find the optimal values based on observation data. This option is very time consuming compared with a regular stochastic simulation and a parameter estimation run because you are doing parameter estimation for each stochastic run times.
Stochastic inverse modeling can be performed only when using material sets or HUF arrays as chosen in the Stochastic Options dialog. You can use any of the parameter estimation codes (PEST, MF2KPES, UCODE).
The following occurs during the stochastic inverse process:
Run MODFLOW for the stochastic simulation iteration.
Set the starting head equal to the resulting heads from the MODFLOW run.
Run parameter estimation.
Run final MODFLOW (if needed for PEST and UCODE).
Move to next stochastic simulation iteration.
The starting heads are interpolated to decrease the run times during the inverse portion.
Related Links
T-PROGS
Using Indicator Simulations With Stochastic Modeling