Characterization of the non-Gaussian hydraulic conductivity field via deep learning-based inversion of hydraulic-head and self-potential data

•A deep learning-based hydrogeophysical inversion framework is developed.•Using limited head data may lose the fine structure of channelized aquifer.•Integrating hydraulic-head and self-potential data can improve the characterization of non-Gaussian hydraulic conductivity. Accurate characterization...

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Vydáno v:Journal of hydrology (Amsterdam) Ročník 610; s. 127830
Hlavní autoři: Han, Zheng, Kang, Xueyuan, Wu, Jichun, Shi, Xiaoqing
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.07.2022
Témata:
aquifers
Convolutional variational autoencoder
electric field
geophysics
groundwater flow
hydraulic conductivity
Hydraulic tomography
hydrodynamics
Joint hydrogeophysical inversion
Non-Gaussian hydraulic conductivity
Self-potential
spatial variation
tomography
uncertainty
Non-Gaussian hydraulic conductivity
Self-potential
Hydraulic tomography
Convolutional variational autoencoder
Joint hydrogeophysical inversion
ISSN:0022-1694, 1879-2707
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Shrnutí:•A deep learning-based hydrogeophysical inversion framework is developed.•Using limited head data may lose the fine structure of channelized aquifer.•Integrating hydraulic-head and self-potential data can improve the characterization of non-Gaussian hydraulic conductivity. Accurate characterization of the spatial heterogeneity of hydraulic properties such as hydraulic conductivity (K) is essential for understanding groundwater flow and contaminant transport processes. Deep learning-based ensemble smoother methods have proven to be effective in estimating non-Gaussian K fields by assimilating hydrodynamic measurements. However, traditional hydrogeological investigations (e.g., hydraulic tomography) often suffer from the limited number of invasive borehole data (e.g., hydraulic head), making the non-Gaussian K inversion problem strongly underdetermined. As a non-invasive and high-sampling-density geophysical approach, the self-potential (SP) method can measure fluctuations of the electrical field caused by subsurface fluid movement. Thus, the recorded SP signals are electro-kinetic responses of the groundwater flow and can provide complementary information for delineating non-Gaussian heterogeneity. In this paper, we performed a joint hydrogeophysical inversion of hydraulic-head and SP data to characterize the non-Gaussian K field within a deep learning-based inversion framework. We conducted three synthetic cases in a 3-D non-Gaussian aquifer with 16,000 unknown K, to assess the ability of the proposed joint inversion framework for K imaging by assimilating different types of data. The results show that using limited hydraulic-head data alone may capture the large-scale features of the non-Gaussian reference field but might lose fine features. By integrating the hydraulic-head and SP data, the non-Gaussian K field can be reconstructed with an improved accuracy and reduced estimation uncertainty. The low-cost SP method opens up new opportunities for non-Gaussian K characterization and fluid-dynamic monitoring.
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ISSN:0022-1694
1879-2707
DOI:10.1016/j.jhydrol.2022.127830