  In groundwater contaminant remediation and risk assessment, it is important
to identify parameters of the contaminant source and hydraulic conductivity
field by solving an inverse problem. However, if the dimensionality of the
inverse problem is high, it is usually computationally expensive to obtain
accurate estimation and uncertainty assessment of these parameters. This is
particularly the case when Markov Chain Monte Carlo (MCMC) sampling is used. In
this paper, an efficient approach entitled inverse iterative simulation (iIS)
is proposed to efficiently identify the contaminant source characteristics,
together with the hydraulic conductivity field. The iIS algorithm utilizes a
simple approach borrowed from Ensemble Smother (ES) to update model parameters
and an inverse Gaussian process (iGP) approach to improve the accuracy of
parameter updating. Two numerical experiments are tested. For the low
dimensional case (with 11 parameters), the iIS algorithm can obtain parameter
estimation very close to that of MCMC method. For the high dimensional case
(with 108 parameters), the iIS algorithm can obtain accurate parameter
estimation with very low computational cost.
