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Engineering and Technology

D-Index
55
Citations
9709
World Ranking
3050
National Ranking
908

Overview

Jef Caers is a researcher affiliated with Stanford University in the United States. Their scholarly work spans multiple fields including Engineering, Environmental Science, and Earth and Planetary Sciences, with a strong emphasis on subfields such as Artificial Intelligence, Ocean Engineering, Environmental Engineering, Geochemistry and Petrology, and Atmospheric Science.

The main topics addressed in Caers's research include Reservoir Engineering and Simulation Methods, Geochemistry and Geologic Mapping, Soil Geostatistics and Mapping, Geological Modeling and Analysis, Groundwater Flow and Contamination Studies, Cryospheric Studies and Observations, and Landslides and Related Hazards.

Caers has contributed to a range of scientific publications, including:

  • Stochastic modeling of subglacial topography exposes uncertainty in water routing at Jakobshavn Glacier, 2020, Journal of Glaciology
  • A Monte Carlo-based framework for risk-return analysis in mineral prospectivity mapping, 2020, Geoscience Frontiers
  • Antarctic Topographic Realizations and Geostatistical Modeling Used to Map Subglacial Lakes, 2020, Journal of Geophysical Research Earth Surface
  • Quantifying the Effect of Precipitation on Landslide Hazard in Urbanized and Non-Urbanized Areas, 2021, Geophysical Research Letters
  • Automated Monte Carlo-based quantification and updating of geological uncertainty with borehole data (AutoBEL v1.0), 2020, Geoscientific Model Development

Frequent co-authors include Zhen Yin, Lijing Wang, Emma MacKie, David Zhen Yin, and Chen Zuo.

Caers's work appears frequently across various publication venues such as arXiv (Cornell University), Computers & Geosciences, Natural Resources Research, Water Resources Research, and Geoscientific Model Development.

Caers also has authored books, including "Data Science for the Geosciences" published by Cambridge University Press in 2023.

Best Publications

  • Multiple-point Geostatistics: Stochastic Modeling with Training Images

    Gregoire Mariethoz;Jef Caers

  • Multiple-point Geostatistics: A Quantitative Vehicle for Integrating Geologic Analogs into Multiple Reservoir Models

    Jef Caers;Tuanfeng Zhang

  • Stochastic Simulation of Patterns Using Distance-Based Pattern Modeling

    Mehrdad Honarkhah;Jef Caers

  • Modeling Uncertainty in the Earth Sciences

    Jef Caers

  • Conditional Simulation with Patterns

    G. Burc Arpat;Jef Caers

  • Representing Spatial Uncertainty Using Distances and Kernels

    Céline Scheidt;Jef Caers

  • History Matching Under Training-Image-Based Geological Model Constraints

    Jef Caers

  • Geological realism in hydrogeological and geophysical inverse modeling: A review

    Niklas Linde;Philippe Renard;Tapan Mukerji;Jef Caers

  • The Probability Perturbation Method: A New Look at Bayesian Inverse Modeling

    Jef Caers;Todd Hoffman

  • Uncertainty Quantification in Reservoir Performance Using Distances and Kernel Methods--Application to a West Africa Deepwater Turbidite Reservoir

    Céline Scheidt;Jef Caers

  • Geostatistical Reservoir Modelling using Statistical Pattern Recognition

    Jef Caers

  • Quantifying geological uncertainty for flow and transport modeling in multi-modal heterogeneous formations

    Luc Feyen;Luc Feyen;Jef Caers

  • Modeling of a Deepwater Turbidite Reservoir Conditional to Seismic Data Using Principal Component Analysis and Multiple-Point Geostatistics

    Sebastien Strebelle;Karen Payrazyan;Jef Caers

  • Dynamic data integration for structural modeling: model screening approach using a distance-based model parameterization

    Satomi Suzuki;Guillaume Caumon;Jef Caers

  • Combining geologic‐process models and geostatistics for conditional simulation of 3‐D subsurface heterogeneity

    H. A. Michael;H. Li;A. Boucher;T. Sun

  • MS-CCSIM

    Pejman Tahmasebi;Muhammad Sahimi;Jef Caers

  • Quantifying Asymmetric Parameter Interactions in Sensitivity Analysis: Application to Reservoir Modeling

    Darryl Fenwick;Céline Scheidt;Jef Caers

  • History matching and uncertainty quantification of facies models with multiple geological interpretations

    Hyucksoo Park;Céline Scheidt;Darryl Fenwick;Alexandre Boucher

  • Sequential simulation with patterns

    Jef Caers;Guven Burc Arpat

  • Simulation of Earth textures by conditional image quilting

    K. Mahmud;K. Mahmud;G. Mariethoz;G. Mariethoz;J. Caers;P. Tahmasebi

  • Direct forecasting of subsurface flow response from non-linear dynamic data by linear least-squares in canonical functional principal component space

    Aaditya Satija;Jef Caers

  • Identifying discrete geologic structures that produce anomalous hydraulic response : An inverse modeling approach

    Michael J. Ronayne;Steven M. Gorelick;Jef Caers

  • A Distance-based Prior Model Parameterization for Constraining Solutions of Spatial Inverse Problems

    Satomi Suzuki;Jef Caers

  • Petroleum Geostatistics

    Unknown

Frequent Co-Authors

Frédéric Nguyen
Frédéric Nguyen University of Liège
Tapan Mukerji
Tapan Mukerji Stanford University
Rosemary Knight
Rosemary Knight Stanford University
Pejman Tahmasebi
Pejman Tahmasebi University of Wyoming
Jan Beirlant
Jan Beirlant KU Leuven
Alain Dassargues
Alain Dassargues University of Liège
Dustin M. Schroeder
Dustin M. Schroeder Stanford University
Jan Vanderborght
Jan Vanderborght Forschungszentrum Jülich
Anthony R. Kovscek
Anthony R. Kovscek Stanford University
John E. McCray
John E. McCray Colorado School of Mines

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