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Coupled hydrogeophysical inversion to identify non-Gaussian hydraulic conductivity field by jointly assimilating geochemical and time-lapse geophysical data
Kang, X.; Shi, X.; Revil, A.; Cao, Z.; Li, L.; Lan, T.; Wu, J. (2019). Coupled hydrogeophysical inversion to identify non-Gaussian hydraulic conductivity field by jointly assimilating geochemical and time-lapse geophysical data. J. Hydrol. (Amst.) 578: 124092. https://dx.doi.org/10.1016/j.jhydrol.2019.124092
In: Journal of Hydrology. Elsevier: Tokyo; Oxford; New York; Lausanne; Shannon; Amsterdam. ISSN 0022-1694; e-ISSN 1879-2707, meer
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| Author keywords |
Non-Gaussian; Parameters estimation; Multi-source data assimilation; Electrical resistivity tomography; Ensemble smoother |
| Auteurs | | Top |
- Kang, X.
- Shi, X.
- Revil, A.
- Cao, Z.
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| Abstract |
Reliable inversion of spatial heterogeneity of hydraulic conductivity is crucial to understand subsurface fluids migration. The Ensemble Smoother – Direct Sampling method (ES-DS) has proven to be an effective method to identify non-Gaussian hydraulic conductivity distributions by incorporating a variety of traditional hydrodynamic measurements, e.g., piezometric head. However, inversion problems for non-Gaussian parameters often suffer from a sparsity of the available data from direct sampling in boreholes. As a non-intrusive, cost-effective, and high sampling density method, time-lapse geophysical technique has not yet drawn much attention as a useful source of information for delineating the underlying non-Gaussian heterogeneity. In this study, we integrated coupled hydrogeophysical modeling and the ES-DS algorithm to estimate non-Gaussian hydraulic conductivity field by assimilating both geochemical and time-lapse geophysical datasets. Four synthetic Cases for a salt injection experiment, monitored by both sampling analysis and electrical resistivity tomography, are conducted to assess the ability of the proposed approach to characterize hydraulic properties by assimilating different types of data. Results show that using geochemical or geophysical data alone only allow a rough reconstruction of subsurface heterogeneity of aquifers but might lose the fine structure. By incorporating multi-source datasets, the main patterns of the non-Gaussian reference fields can be reflected with an improved resolution. The time-lapse geophysical methods open up new opportunities to accurately characterize non-Gaussian aquifers and monitor the dynamic processes of subsurface fluids. |
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