A Novel Methodology for the Stochastic Integration of Geophysical and Hydrogeological Data in Geologically Consistent Models.

To address groundwater issues, it is often necessary to develop geological and hydrogeological models. Combining geological, geophysical and hydrogeological data available on a site to build such models is often a challenge. This paper presents a methodology to integrate such data within a geologically consistent model with robust error estimation. The methodology combines the Ensemble Smoother with Multiple Data Assimilation (ESMDA) algorithm with a hierarchical geological modeling approach (ArchPy). Geophysical and hydrogeological field data are jointly assimilated in a stochastic ESMDA framework. To speed up the inversion process, forward responses are computed in lower‐dimensional spaces relevant to each physical problem. By doing so, the final models take into account multiple data sources and regional conceptual geological knowledge. This study illustrates the applicability of this novel approach using actual data from the upper Aare Valley, Switzerland. The results of cross‐validation show that the combination of different data types, each sensitive to different spatial dimensions, enhances the quality of the model within a reasonable computing time. The proposed methodology allows the automatic generation of groundwater models with robust uncertainty estimation and could be applied to a wide variety of hydrogeological issues. Plain Language Summary: When dealing with groundwater, it is necessary to develop underground models. However, taking into account all the different data types on a site is time‐consuming, and there is a lack of uncertainty quantification. In this study, we develop an approach that automatically combines different types of data, including boreholes, geophysical data, and hydrogeological measurements. All data are combined using a stochastic algorithm and produce an ensemble of plausible and data‐compatible models. These models can be used, for example, to forecast groundwater availability, pollutant distribution, or the effect of climate change on groundwater. Key Points: A methodology is proposed to assimilate geological, hydrogeological, and geophysical data in consistent stochastic modelsThe methodology combines a hierarchical geological modeling technique with the ensemble smoother with multiple data assimilationThe applicability of the methodology is demonstrated using actual field data from the upper Aare valley aquifer in Switzerland [ABSTRACT FROM AUTHOR]