Mingliang Liu
Physical Science Research Scientist
Energy Science & Engineering
Bio
Mingliang Liu is a Research Scientist at the Stanford Center for Earth Resources Forecasting (SCERF). His research focuses on multiscale subsurface characterization and the sustainable development of Earth resources.
Boards, Advisory Committees, Professional Organizations
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Associate Editor, Geophysics (2023 - Present)
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Associate Editor, Computers and Geosciences (2022 - Present)
All Publications
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Geostatistical Inversion for Subsurface Characterization Using Stein Variational Gradient Descent With Autoencoder Neural Network: An Application to Geologic Carbon Sequestration
JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH
2024; 129 (7)
View details for DOI 10.1029/2024JB029073
View details for Web of Science ID 001259343800001
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Hierarchical Homogenization With Deep-Learning-Based Surrogate Model for Rapid Estimation of Effective Permeability From Digital Rocks
JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH
2023; 128 (2)
View details for DOI 10.1029/2022JB025378
View details for Web of Science ID 000936298000001
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Joint Inversion of Geophysical Data for Geologic Carbon Sequestration Monitoring: A Differentiable Physics-Informed Deep Learning Model
Journal of Geophysical Research: Solid Earth
2023; 128 (3)
View details for DOI 10.1029/2022JB025372
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Multiscale Fusion of Digital Rock Images Based on Deep Generative Adversarial Networks
GEOPHYSICAL RESEARCH LETTERS
2022; 49 (9)
View details for DOI 10.1029/2022GL098342
View details for Web of Science ID 000795770000001
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Uncertainty quantification in stochastic inversion with dimensionality reduction using variational autoencoder
GEOPHYSICS
2022; 87 (2): M43-M58
View details for DOI 10.1190/geo2021-0138.1
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Probabilistic physics-informed neural network for seismic petrophysical inversion
GEOPHYSICS
2024; 89 (2): M17-M32
View details for DOI 10.1190/GEO2023-0214.1
View details for Web of Science ID 001244894900001
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Frequency-domain electromagnetic induction for the prediction of electrical conductivity and magnetic susceptibility using geostatistical inversion and randomized tensor decomposition
GEOPHYSICS
2023; 88 (6): E159-E171
View details for DOI 10.1190/GEO2022-0443.1
View details for Web of Science ID 001164124700012
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Computation of effective elastic moduli of rocks using hierarchical homogenization
JOURNAL OF THE MECHANICS AND PHYSICS OF SOLIDS
2023; 174
View details for DOI 10.1016/j.jmps.2023.105268
View details for Web of Science ID 000951654900001
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Joint Inversion of Geophysical Data for Geologic Carbon Sequestration Monitoring: A Differentiable Physics‐Informed Neural Network Model
Journal of Geophysical Research: Solid Earth
2023; 128 (3)
View details for DOI 10.1029/2022JB025372
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Randomized Tensor Decomposition for Large-Scale Data Assimilation Problems for Carbon Dioxide Sequestration
MATHEMATICAL GEOSCIENCES
2022
View details for DOI 10.1007/s11004-022-10005-1
View details for Web of Science ID 000802314500001
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Prediction of CO2 Saturation Spatial Distribution Using Geostatistical Inversion of Time-Lapse Geophysical Data
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
2021; 59 (5): 3846-3856
View details for DOI 10.1109/TGRS.2020.3018910
View details for Web of Science ID 000642096400017
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Stochastic inversion method of time-lapse controlled source electromagnetic data for CO2 plume monitoring
INTERNATIONAL JOURNAL OF GREENHOUSE GAS CONTROL
2020; 100
View details for DOI 10.1016/j.ijggc.2020.103098
View details for Web of Science ID 000567840400002
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Petrophysical characterization of deep saline aquifers for CO2 storage using ensemble smoother and deep convolutional autoencoder
ADVANCES IN WATER RESOURCES
2020; 142
View details for DOI 10.1016/j.advwatres.2020.103634
View details for Web of Science ID 000550807600004
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A comparison of deep machine learning and Monte Carlo methods for facies classification from seismic data
GEOPHYSICS
2020; 85 (4): WA41–WA52
View details for DOI 10.1190/GEO2019-0405.1
View details for Web of Science ID 000583755100064
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Seismic facies classification using supervised convolutional neural networks and semisupervised generative adversarial networks
GEOPHYSICS
2020; 85 (4): O47–O58
View details for DOI 10.1190/GEO2019-0627.1
View details for Web of Science ID 000583755100035
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Time-lapse seismic history matching with an iterative ensemble smoother and deep convolutional autoencoder
GEOPHYSICS
2020; 85 (1): M15–M31
View details for DOI 10.1190/GEO2019-0019.1
View details for Web of Science ID 000506219100026
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Generation and evolution of overpressure caused by hydrocarban generation in the Jurassic source rocks of the central Junggar Basin, northwestern China
AAPG BULLETIN
2019; 103 (7): 1553–74
View details for DOI 10.1306/1213181614017139
View details for Web of Science ID 000475472700002
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Accelerating geostatistical seismic inversion using TensorFlow: A heterogeneous distributed deep learning framework
COMPUTERS & GEOSCIENCES
2019; 124: 37–45
View details for DOI 10.1016/j.cageo.2018.12.007
View details for Web of Science ID 000458938800004
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Stochastic nonlinear inversion of seismic data for the estimation of petroelastic properties using the ensemble smoother and data reparameterization
GEOPHYSICS
2018; 83 (3): M25–M39
View details for DOI 10.1190/GEO2017-0713.1
View details for Web of Science ID 000443596300068
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Recycling of oceanic crust from a stagnant slab in the mantle transition zone: Evidence from Cenozoic continental basalts in Zhejiang Province, SE China
LITHOS
2015; 230: 146–65
View details for DOI 10.1016/j.lithos.2015.05.021
View details for Web of Science ID 000357839000011