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A Novel Methodology for the Stochastic Integration of Geophysical and Hydrogeological Data in Geologically Consistent Models
Date de parution
2023
In
Water Resources Research
Vol.
59
No
7
Mots-clés
- Aare Valley
- Switzerland
- Data integration
- Geology
- Geophysics
- Hydrology
- Stochastic models
- Stochastic systems
- Data assimilation
- integration and fusion
- Ensemble smoother
- Geological models
- Ground-water hydrology
- Hydrogeological
- Hydrogeophysics
- Multiple data
- Novel methodology
- Stochastic hydrologies
- algorithm
- data assimilation
- estimation method
- groundwater resource
- hydrogeology
- methodology
- stochasticity
- Groundwater
Aare Valley
Switzerland
Data integration
Geology
Geophysics
Hydrology
Stochastic models
Stochastic systems
Data assimilation
integration and fusio...
Ensemble smoother
Geological models
Ground-water hydrolog...
Hydrogeological
Hydrogeophysics
Multiple data
Novel methodology
Stochastic hydrologie...
algorithm
data assimilation
estimation method
groundwater resource
hydrogeology
methodology
stochasticity
Groundwater
Résumé
<jats:title>Abstract</jats:title><jats:p>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.</jats:p>
Identifiants
Type de publication
journal article
