Multi-objective Inverse Modelling Using Physically based Metrics

The heterogeneity in the soil medium makes it economically impossible to measure the soil characteristics across the whole soil profile. Inverse modelling configures a computer model to simulate the groundwater system and calibrates it to identify the soil characteristics in all grid-cells defined in the model. In classic inverse modelling approaches, residuals between simulated and measured groundwater head time series are converted into a single likelihood function to be maximized; therefore, the information content in the measured data is not fully exploited in those approaches. Moreover, the large number of grid-cells makes inverse modelling an ill-posed optimization problem with more unknown parameters than known measured values, leading to different parameter sets having a similar model performance. Despite recent advances in groundwater calibration practices, such as the regularization approach, there is a considerable room to improve groundwater model calibration using information content in the measured data. Regularization aims to add various sets of information to the calibration to tackle the non-uniqueness problem. However, this approach could associate with a considerable degree of subjectivity and uncertainty. This study seeks a novel approach to extract information content from measured data in the form of physically-based metrics that are often called signatures for improving the identifiability in the groundwater model calibration. Moreover, this study proposes a novel automated powerful inverse modelling strategy using a multi-objective approach to incorporate most of the information content through physically meaningful metrics to obtain more consistent models. The benchmark Freyberg 1988 synthetic case study, in which the true model parameter values are known, is used to demonstrate the applicability and potentials of the proposed framework. The reconstructed Freyberg case study in MODFLOW 2005 showed the framework has the ability to find consistent estimates of the groundwater model parameters.