Estimation of groundwater flow parameters using least squares

In this paper, we present several variations on the least squares approach of estimating steady-state flow parameters in a groundwater model. We test the approach on numerically generated data, and also apply it to field data from the MADE (MAcro Dispersion Experiment) data set obtained at Columbus AFB, Mississippi. Our results are based on a least squares cost functional with a finite difference scheme used to solve the steady-state flow equation (Darcyʹs Law). An implicit filtering optimization algorithm is implemented in parallel using PVM.