A multipopulation genetic algorithm to solve the inverse problem in hydrogeology

Abstract

The inverse problem of groundwater flow is treated with an automatic method that can produce several alternative solutions at once. During their joint optimization, these solutions can exchange information in order to maintain some diversity and thus avoid a systematic premature convergence toward a single local minimum. Although genetic algorithms are capable of doing this, they have not often been used in groundwater inverse optimization. First, a specific multipopulation genetic algorithm is developed. It is then tested on two synthetic cases of steady state flow with transmissivity values extending over 4 orders of magnitude. The first test is nonparametric and optimizes as many parameters as those used to define the reference case. The second test uses a sort of “pilot point” parametrization. The optimization is carried out on a limited number of perturbations that are interpolated and superimposed on an initial transmissivity field. In view of the good quality of the results, these initial attempts provide incentives to further develop genetic algorithms in groundwater inverse problems.