Hydrogeophysical Characterization of Nonstationary DNAPL Source Zones by Integrating a Convolutional Variational Autoencoder and Ensemble Smoother

Detailed characterization of dense nonaqueous phase liquid (DNAPL) source zone architecture (SZA) is essential for designing efficient remediation strategies. However, it is difficult to characterize a highly irregular and localized SZA, because traditional drilling investigations provide limited in...

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Vydáno v:	Water resources research Ročník 57; číslo 2
Hlavní autoři:	Kang, Xueyuan, Kokkinaki, Amalia, Kitanidis, Peter K., Shi, Xiaoqing, Lee, Jonghyun, Mo, Shaoxing, Wu, Jichun
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Washington John Wiley & Sons, Inc 01.02.2021
Témata:	Aquifers Contaminants convolutional variational autoencoder Data data collection Datasets Deep learning deep‐learning parameterization Depletion DNAPL source zone characterization Drilling ensemble smoother Estimation accuracy Frameworks Geology Geophysical data geophysics geostatistics Hydrogeology Identification methods Infiltration Iterative methods joint hydrogeophysical inversion Learning Learning behaviour Liquids Mathematical models Modelling Morphology Multiphase Nonaqueous phase liquids Numerical experiments Organic contaminants Parameterization Performance evaluation Physics Remediation Saturation Statistical methods Statistical models Training water
ISSN:	0043-1397, 1944-7973
On-line přístup:	Získat plný text
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