Matching objective and subjective information in groundwater inverse analysis by Akaike's Bayesian Information Criterion

Abstract

In order to overcome the illposedness of groundwater inverse analysis it is inevitable to introduce prior information of some form and thus Bayesian statistics. One of the essential problems in Bayesian inverse formulation is the optimum matching between the objective information (i.e., the observation) and the subjective information (i.e., the prior information). In this study, Akaike's Bayesian Information Criterion (ABIC) is introduced to overcome this problem. ABIC is also effective in the model identification problem, and this aspect is also emphasized. The effectiveness of the method is illustrated by analyses on an actual aquifer system. Both steady and transient state analyses are carried out. The paper also provides the background of ABIC in some detail.