| Hydrogeophysical characterization of nonstationary DNAPL source zones by integrating a convolutional variational autoencoder and ensemble smoother X Kang, A Kokkinaki, PK Kitanidis, X Shi, J Lee, S Mo, J Wu Water Resources Research 57 (2), e2020WR028538, 2021 | 62 | 2021 |
| Coupled hydrogeophysical inversion to identify non-Gaussian hydraulic conductivity field by jointly assimilating geochemical and time-lapse geophysical data X Kang, X Shi, A Revil, Z Cao, L Li, T Lan, J Wu Journal of Hydrology 578, 124092, 2019 | 53 | 2019 |
| Coupled hydrogeophysical inversion of DNAPL source zone architecture and permeability field in a 3D heterogeneous sandbox by assimilation time-lapse cross-borehole electrical … X Kang, X Shi, Y Deng, A Revil, H Xu, J Wu Journal of Hydrology 567, 149-164, 2018 | 50 | 2018 |
| Improved characterization of DNAPL source zones via sequential hydrogeophysical inversion of hydraulic‐head, self‐potential and partitioning tracer data X Kang, A Kokkinaki, PK Kitanidis, X Shi, A Revil, J Lee, A Soueid Ahmed, ... Water Resources Research 56 (8), e2020WR027627, 2020 | 46 | 2020 |
| Characterization of the non-Gaussian hydraulic conductivity field via deep learning-based inversion of hydraulic-head and self-potential data Z Han, X Kang, J Wu, X Shi Journal of Hydrology 610, 127830, 2022 | 35 | 2022 |
| Integrating deep learning-based data assimilation and hydrogeophysical data for improved monitoring of DNAPL source zones during remediation X Kang, A Kokkinaki, C Power, PK Kitanidis, X Shi, L Duan, T Liu, J Wu Journal of Hydrology 601, 126655, 2021 | 34 | 2021 |
| Real-time monitoring of in situ chemical oxidation (ISCO) of dissolved TCE by integrating electrical resistivity tomography and reactive transport modeling Z Han, X Kang, K Singha, J Wu, X Shi Water Research 252, 121195, 2024 | 26 | 2024 |
| Characterization of discrete fracture networks with deep-learning based hydrogeophysical inversion Y Deng, X Kang, H Ma, J Qian, L Ma, Q Luo Journal of Hydrology 631, 130819, 2024 | 20 | 2024 |
| Residual NAPL morphology effects on electrical resistivity: insights from micromodel displacement experiments and pore network simulations S Qiang, X Shi, A Revil, X Kang, Y Liu, J Wu Water Resources Research 58 (12), e2022WR033233, 2022 | 17 | 2022 |
| Integration of deep learning‐based inversion and upscaled mass‐transfer model for DNAPL mass‐discharge estimation and uncertainty assessment X Kang, A Kokkinaki, X Shi, H Yoon, J Lee, PK Kitanidis, J Wu Water Resources Research 58 (10), e2022WR033277, 2022 | 17 | 2022 |
| Deep learning based optimization under uncertainty for surfactant-enhanced DNAPL remediation in highly heterogeneous aquifers J Du, X Shi, S Mo, X Kang, J Wu Journal of Hydrology 608, 127639, 2022 | 17 | 2022 |
| Optimized arrays for electrical resistivity tomography survey using Bayesian experimental design S Qiang, X Shi, X Kang, A Revil Geophysics 87 (4), E189-E203, 2022 | 16 | 2022 |
| Characterization of DNAPL source zones in clay-sand media via joint inversion of DC resistivity, induced polarization and borehole data X Kang, C Power, A Kokkinaki, A Revil, J Wu, X Shi, Y Deng Journal of Contaminant Hydrology 258, 104240, 2023 | 15 | 2023 |
| Deep learning-based inversion framework by assimilating hydrogeological and geophysical data for an enhanced geothermal system characterization and thermal performance prediction C Chen, Y Deng, H Ma, X Kang, L Ma, J Qian Energy 302, 131713, 2024 | 12 | 2024 |
| Monitoring in situ microbial growth and decay in soil column experiments by induced polarization Y Song, X Shi, A Revil, X Kang Geophysical Research Letters 49 (16), e2021GL097553, 2022 | 11 | 2022 |
| Modeling upscaled mass discharge from complex DNAPL source zones using a Bayesian neural network: Prediction accuracy, uncertainty quantification and source zone feature importance X Kang, A Kokkinaki, X Shi, J Lee, Z Guo, L Ni, J Wu Water Resources Research 60 (7), e2023WR036864, 2024 | 10 | 2024 |
| Evaluation of the benefits of improved permeability estimation on high-resolution characterization of DNAPL distribution in aquifers with low-permeability lenses Q Guo, X Shi, X Kang, S Hao, L Liu, J Wu Journal of Hydrology 603, 126955, 2021 | 9 | 2021 |
| Integrating hydraulic tomography, electrical resistivity tomography, and partitioning interwell tracer test datasets to improve identification of pool-dominated DNAPL source … Q Guo, X Shi, X Kang, Y Chang, P Wang, J Wu Journal of Contaminant Hydrology 241, 103809, 2021 | 9 | 2021 |
| Quantitative evaluation of the effect of pore fluids distribution on complex conductivity saturation exponents S Qiang, X Shi, A Revil, X Kang, Y Song, K Xing Journal of Geophysical Research: Solid Earth 129 (8), e2024JB028689, 2024 | 8 | 2024 |
| Improved solute transport modeling through joint estimation of hydraulic conductivity and dispersivities from tracer and ERT data Z Han, X Kang, J Wu, X Shi, J Jiang Advances in Water Resources 185, 104655, 2024 | 8 | 2024 |
Xiaoqing Shi（施小清）Professor of Hydrogeology, Nanjing UniversityVerified email at nju.edu.cn
