David Zhen Yin is the co-founder and program director of Stanford Mineral-X Initiative to lead the research of sustainable critical minerals explorations for renewable energy transitions. He is also a research 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.
Phys Sci Res Assoc, Earth & Planetary Sciences
Research Scientist, Stanford University (2022 - Present)
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)
- Mapping high-resolution basal topography of West Antarctica from radar data using non-stationary multiple-point geostatistics (MPS-BedMappingV1) GEOSCIENTIFIC MODEL DEVELOPMENT 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 2022; 60
- Stochastic modeling of subglacial topography exposes uncertainty in water routing at Jakobshavn Glacier JOURNAL OF GLACIOLOGY 2021; 67 (261): 75–83
- A Monte Carlo-based framework for risk-return analysis in mineral prospectivity mapping GEOSCIENCE FRONTIERS 2020; 11 (6): 2297–2308
- Automated Monte Carlo-based quantification and updating of geological uncertainty with borehole data (AutoBEL v1.0) GEOSCIENTIFIC MODEL DEVELOPMENT 2020; 13 (2): 651–72
- A Tree-Based Direct Sampling Method for Stochastic Surface and Subsurface Hydrological Modeling WATER RESOURCES RESEARCH 2020; 56 (2)
- GStatSim V1.0: a Python package for geostatistical interpolation and conditional simulation GEOSCIENTIFIC MODEL DEVELOPMENT 2023; 16 (13): 3765-3783
- A nearest neighbor multiple-point statistics method for fast geological modeling COMPUTERS & GEOSCIENCES 2022; 167
A workflow for building surface-based reservoir models using NURBS curves, coons patches, unstructured tetrahedral meshes and open-source libraries
Computers & Geosciences
2018; 121: 11
View details for DOI 10.1016/j.cageo.2018.09.001
- Evaluation of inter-well connectivity using well fluctuations and 4D seismic data JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2016; 145: 533–47
- Enhancement of dynamic reservoir interpretation by correlating multiple 4D seismic monitors to well behavior Interpretation-A Journal of Subsurface Characterization 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 2014; 11 (2)
Joint interpretation of interwell connectivity by integrating 4D seismic with injection and production fluctuations
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
View details for DOI 10.3997/2214-4609.201700035