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.

Academic Appointments


Boards, Advisory Committees, Professional Organizations


  • Associate Editor, Geophysics (2023 - Present)
  • Associate Editor, Computers and Geosciences (2022 - Present)

All Publications


  • 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 Liu, M., Grana, D., Mukerji, T. 2024; 129 (7)
  • Hierarchical Homogenization With Deep-Learning-Based Surrogate Model for Rapid Estimation of Effective Permeability From Digital Rocks JOURNAL OF GEOPHYSICAL RESEARCH-SOLID EARTH Liu, M., Ahmad, R., Cai, W., Mukerji, T. 2023; 128 (2)
  • Joint Inversion of Geophysical Data for Geologic Carbon Sequestration Monitoring: A Differentiable Physics-Informed Deep Learning Model Journal of Geophysical Research: Solid Earth Liu, M., Vashisth, D., Grana, D., Mukerji, T. 2023; 128 (3)

    View details for DOI 10.1029/2022JB025372

  • Multiscale Fusion of Digital Rock Images Based on Deep Generative Adversarial Networks GEOPHYSICAL RESEARCH LETTERS Liu, M., Mukerji, T. 2022; 49 (9)
  • Uncertainty quantification in stochastic inversion with dimensionality reduction using variational autoencoder GEOPHYSICS Liu, M., Grana, D., de Figueiredo, L. P. 2022; 87 (2): M43-M58

    View details for DOI 10.1190/geo2021-0138.1

  • Probabilistic physics-informed neural network for seismic petrophysical inversion GEOPHYSICS Li, P., Liu, M., Alfarraj, M., Tahmasebi, P., Grana, D. 2024; 89 (2): M17-M32
  • Frequency-domain electromagnetic induction for the prediction of electrical conductivity and magnetic susceptibility using geostatistical inversion and randomized tensor decomposition GEOPHYSICS Liu, M., Narciso, J., Grana, D., Van De Vijver, E., Azevedo, L. 2023; 88 (6): E159-E171
  • Computation of effective elastic moduli of rocks using hierarchical homogenization JOURNAL OF THE MECHANICS AND PHYSICS OF SOLIDS Ahmad, R., Liu, M., Ortiz, M., Mukerji, T., Cai, W. 2023; 174
  • Joint Inversion of Geophysical Data for Geologic Carbon Sequestration Monitoring: A Differentiable Physics‐Informed Neural Network Model Journal of Geophysical Research: Solid Earth Liu, M., Vashisth, D., Grana, D., Mukerji, T. 2023; 128 (3)

    View details for DOI 10.1029/2022JB025372

  • Randomized Tensor Decomposition for Large-Scale Data Assimilation Problems for Carbon Dioxide Sequestration MATHEMATICAL GEOSCIENCES Liu, M., Grana, D., Mukerji, T. 2022
  • Prediction of CO2 Saturation Spatial Distribution Using Geostatistical Inversion of Time-Lapse Geophysical Data IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING Grana, D., Liu, M., Ayani, M. 2021; 59 (5): 3846-3856
  • Stochastic inversion method of time-lapse controlled source electromagnetic data for CO2 plume monitoring INTERNATIONAL JOURNAL OF GREENHOUSE GAS CONTROL Ayani, M., Grana, D., Liu, M. 2020; 100
  • Petrophysical characterization of deep saline aquifers for CO2 storage using ensemble smoother and deep convolutional autoencoder ADVANCES IN WATER RESOURCES Liu, M., Grana, D. 2020; 142
  • A comparison of deep machine learning and Monte Carlo methods for facies classification from seismic data GEOPHYSICS Grana, D., Azevedo, L., Liu, M. 2020; 85 (4): WA41–WA52
  • Seismic facies classification using supervised convolutional neural networks and semisupervised generative adversarial networks GEOPHYSICS Liu, M., Jervis, M., Li, W., Nivlet, P. 2020; 85 (4): O47–O58
  • Time-lapse seismic history matching with an iterative ensemble smoother and deep convolutional autoencoder GEOPHYSICS Liu, M., Grana, D. 2020; 85 (1): M15–M31
  • Generation and evolution of overpressure caused by hydrocarban generation in the Jurassic source rocks of the central Junggar Basin, northwestern China AAPG BULLETIN Guo, X., He, S., Liu, K., Yang, Z., Yuan, S., Liu, M. 2019; 103 (7): 1553–74
  • Accelerating geostatistical seismic inversion using TensorFlow: A heterogeneous distributed deep learning framework COMPUTERS & GEOSCIENCES Liu, M., Grana, D. 2019; 124: 37–45
  • Stochastic nonlinear inversion of seismic data for the estimation of petroelastic properties using the ensemble smoother and data reparameterization GEOPHYSICS Liu, M., Grana, D. 2018; 83 (3): M25–M39
  • Recycling of oceanic crust from a stagnant slab in the mantle transition zone: Evidence from Cenozoic continental basalts in Zhejiang Province, SE China LITHOS Li, Y., Ma, C., Robinson, P. T., Zhou, Q., Liu, M. 2015; 230: 146–65