
Mingliang Liu
Postdoctoral Scholar, Energy Resources Engineering
Bio
I am currently a postdoctoral scholar working with Tapan Mukerji on digital rock physics at Stanford University. My research focuses on geophysics inverse problems, seismic reservoir characterization, history matching, digital rock physics, data assimilation and deep learning. I completed my PhD degree in geophysics from the University of Wyoming in 2021 under the supervision of Dario Grana, and earned the bachelor and master degree from China University of Geosciences (Wuhan) in 2013 and 2016, respectively.
Honors & Awards
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Research Scholarship, International Association for Mathematical Geosciences (2021)
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Outstanding Ph.D. Student, University of Wyoming (2021)
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James L. Allen Scholarship, Society of Exploration Geophysicists (2019)
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Technical Program Travel Grant, Society of Exploration Geophysicists (2019)
Professional Education
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Bachelor of Science, China Univ Of Geosciences (2013)
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Master of Engineering, China Univ Of Geosciences (2016)
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Doctor of Philosophy, University of Wyoming (2021)
All Publications
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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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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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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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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