David Zhen Yin is the co-founder and program director of Stanford Mineral-X to lead the research of sustainable critical minerals explorations for renewable energy transitions. He is also the principal scientist at Stanford Center for Earth Resources Forecasting and Co-PI of the Stanford-KoBold collaboration. He develops data-scientific approaches for prediction, uncertainty quantification, and decision-making in critical earth resources exploration and development.

David developed broad experience working with complex projects involving academia and industry and broad knowledge of the fields. His research delivered several key technologies transferred as in-house technologies in Chevron, Equinor, and KoBold. In addition, his research developments have been implemented on various subjects, from Antarctica bed topography modeling, critical mineral explorations in Canada/China/US, and the North Sea and Gulf of Mexico projects.

Before joining Stanford, David was a Research Associate at Edinburgh Time-Lapse Project in Scotland, leading a geophysical monitoring research project in collaboration with Equinor from 2016 to 2018. He was also a technology consultant at Equinor's Research Center in Bergen, Norway. Then, he was a Chevron CoRE Postdoctoral Fellow at Stanford from 2018 to 2021.

David received his Ph.D. in Geosciences from Heriot-Watt University, Edinburgh, UK, in 2016. His research interests include data science for geosciences, geological uncertainty quantification, and decision-making under uncertainty. He has authored one book and tens of articles in peer-reviewed journals and international conferences.

Academic Appointments

Administrative Appointments

  • Senior Research Scientist, Stanford University (2024 - Present)
  • Co-founder & Program Director, Stanford Mineral-X (2022 - Present)
  • Research Scientist, Stanford University (2021 - 2024)
  • Chevron CoRE Postdoctoral Fellow, Stanford University (2018 - 2021)
  • Research Associate, Heriot-Watt University (2016 - 2018)

Honors & Awards

  • Chevron CoRE (Center of Research Excellence) Fellowship, Chevron (2018)
  • Frans and Alice Hammons Award, SEG (2014)

Boards, Advisory Committees, Professional Organizations

  • Co-chair, Stanford Earth Postdoc Advisory Council (2019 - 2022)

All Publications

  • Mapping high-resolution basal topography of West Antarctica from radar data using non-stationary multiple-point geostatistics (MPS-BedMappingV1) GEOSCIENTIFIC MODEL DEVELOPMENT Yin, Z., Zuo, C., MacKie, E. J., Caers, J. 2022; 15 (4): 1477-1497
  • Quantifying Uncertainty in Downscaling of Seismic Data to High-Resolution 3-D Lithological Models IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING Yin, Z., Amaru, M., Wang, Y., Li, L., Caers, J. 2022; 60
  • Stochastic modeling of subglacial topography exposes uncertainty in water routing at Jakobshavn Glacier JOURNAL OF GLACIOLOGY MacKie, E. J., Schroeder, D. M., Zuo, C., Yin, Z., Caers, J. 2021; 67 (261): 75–83
  • A Monte Carlo-based framework for risk-return analysis in mineral prospectivity mapping GEOSCIENCE FRONTIERS Wang, Z., Yin, Z., Caers, J., Zuo, R. 2020; 11 (6): 2297–2308
  • Automated Monte Carlo-based quantification and updating of geological uncertainty with borehole data (AutoBEL v1.0) GEOSCIENTIFIC MODEL DEVELOPMENT Yin, Z., Strebelle, S., Caers, J. 2020; 13 (2): 651–72
  • A Tree-Based Direct Sampling Method for Stochastic Surface and Subsurface Hydrological Modeling WATER RESOURCES RESEARCH Zuo, C., Yin, Z., Pan, Z., MacKie, E. J., Caers, J. 2020; 56 (2)
  • Simulation of hydrogen generation via in-situ combustion gasification of heavy oil INTERNATIONAL JOURNAL OF HYDROGEN ENERGY Song, P., Li, Y., Yin, Z., Ifticene, M., Yuan, Q. 2024; 49: 925-936
  • Unraveling the uncertainty of geological interfaces through data-knowledge-driven trend surface analysis COMPUTERS & GEOSCIENCES Wang, L., Peeters, L., MacKie, E. J., Yin, Z., Caers, J. 2023; 178
  • GStatSim V1.0: a Python package for geostatistical interpolation and conditional simulation GEOSCIENTIFIC MODEL DEVELOPMENT MacKie, E. J., Field, M., Wang, L., Yin, Z., Schoedl, N., Hibbs, M., Zhang, A. 2023; 16 (13): 3765-3783
  • A nearest neighbor multiple-point statistics method for fast geological modeling COMPUTERS & GEOSCIENCES Zuo, C., Pan, Z., Yin, Z., Guo, C. 2022; 167
  • A workflow for building surface-based reservoir models using NURBS curves, coons patches, unstructured tetrahedral meshes and open-source libraries Computers & Geosciences Zhang, Z., Yin, Z., Yan, X. 2018; 121: 11
  • Evaluation of inter-well connectivity using well fluctuations and 4D seismic data JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING Yin, Z., MacBeth, C., Chassagne, R., Vazquez, O. 2016; 145: 533–47
  • Enhancement of dynamic reservoir interpretation by correlating multiple 4D seismic monitors to well behavior Interpretation-A Journal of Subsurface Characterization Yin, Z., Ayzenberg, M., MacBeth, C., Feng, T., Chassagne, R. 2015; 3 (2): SP35–SP52
  • A method to update fault transmissibility multipliers in the flow simulation model directly from 4D seismic JOURNAL OF GEOPHYSICS AND ENGINEERING Benguigui, A., Yin, Z., MacBeth, C. 2014; 11 (2)
  • Joint interpretation of interwell connectivity by integrating 4D seismic with injection and production fluctuations SPE EUROPEC Yin, Z., MacBeth, C., Chassagne, R. 2015

    View details for DOI 10.2118/174365-MS

  • Improving 4D Seismic Interpretation and Seismic History Matching Using the Well2seis Technique First EAGE Workshop on Practical Reservoir Monitoring Yin, Z., Ayzenberg, M., MacBeth, C. 2017