Coupled hydrogeophysical inversion to identify non-Gaussian hydraulic conductivity field by jointly assimilating geochemical and time-lapse geophysical data-Reference-Cited by-同舟云学术

Coupled hydrogeophysical inversion to identify non-Gaussian hydraulic conductivity field by jointly assimilating geochemical and time-lapse geophysical data

Published:2019-11 Issue: Volume:578 Page:124092
ISSN:0022-1694
Container-title:Journal of Hydrology
language:en
Short-container-title:Journal of Hydrology

Author:

Kang Xueyuan,Shi Xiaoqing^ORCID,Revil André,Cao Zhendan,Li Liangping^ORCID,Lan Tian,Wu Jichun

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Water Science and Technology

Reference59 articles.

1. EarthImagerTM 2D and 3D, Resistivity and IP Inversion Software;AGI,2010

2. Specific storage and hydraulic conductivity tomography through the joint inversion of hydraulic heads and self-potential data;Ahmed;Adv. Water Resour.,2016

3. The electrical resistivity log as an aid in determining some reservoir characteristics;Archie;Trans. Am. Ind. Min. Metall. Pet. Eng.,1942

4. The Upper Quaternary Deposits of the Changjiang Coastal Plain, Shanghai Area;Baeteman,1989

5. Dynamics of Fluids in Porous Materials;Bear,1972

Cited by 34 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Coupled hydrogeophysical inversion through ensemble smoother with multiple data assimilation and convolutional neural network for contaminant plume reconstruction;Stochastic Environmental Research and Risk Assessment;2024-08-25

2. Monitoring moisture dynamics in multi-layer cover systems for mine tailings reclamation using autonomous and remote time-lapse electrical resistivity tomography;CAN GEOTECH J;2024

3. Groundwater inverse modeling: Physics-informed neural network with disentangled constraints and errors;Journal of Hydrology;2024-08

4. Improving deep groundwater aquifer characterization with deep learning inversion of audio-frequency magnetotelluric data;Journal of Hydrology;2024-08

5. A Deep Learning‐Based Data Assimilation Approach to Characterizing Coastal Aquifers Amid Non‐Linearity and Non‐Gaussianity Challenges;Water Resources Research;2024-06-30