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[1]Quantitative Evaluation of the Remediation Process in Engineering Rock Fractures Using Electrical-resistivity Method:Transportation Infrastructure Geotechnology,2025,12
[2]Three-dimensional discrete fracture network identification based on deep learning and reversible jump Markov chain Monte Carlo algorithm:International Journal of Rock Mechanics and Mining Sciences,2025,197
[3]Deep learning-based inversion framework for fractured media characterization by assimilating hydraulic tomography and thermal tracer tomography data: Numerical and field study:Engineering Geology,2025,350
[4]Experimental investigation of thawing behavior of saline soils using resistivity method.[J:Journal of Geophysics and Engineering,2024,21(3):854-867.
[5]陈赐海，邓亚平，Deep learning-based inversion framework by assimilating hydrogeological and geophysical data for an enhanced geothermal system characterization and thermal performance prediction.[J:Energy,2024,320
[6]Deng Yaping，Characterization of discrete fracture networks with deep-learning based hydrogeophysical inversion:Journal of Hydrology,2024,631):130819.
[7]Deng Yaping，Effects of grain size, solution salinity and pH on the electrical response of oil-bearing carbonate sands:Near Surface Geophysics,2022,20):253-264.
[8]Yang Ze，Deng Yaping，Characterizing temporal behavior of a thermal tracer in porous media by time-lapse electrical resistivity measurements:Hydrogeology Journal,2021,29):1173-1188.
[9]Deng Yaping，Application of spectral induced polarization for characterizing surfactant-enhanced DNAPL remediation in laboratory column experiments:Journal of Contaminant Hydrology,2020,230):103603.
[10]Deng Yaping，Complex conductivity of oil-contaminated clayey soil:Journal of Hydrology,2018,561):930-942.
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