Jef Caers

Jef Caers

Stanford University

H-index: 53

North America-United States

About Jef Caers

Jef Caers, With an exceptional h-index of 53 and a recent h-index of 36 (since 2020), a distinguished researcher at Stanford University, specializes in the field of data science for geoscience, uncertainty quantification, mineral exploration, decision making.

His recent articles reflect a diverse array of research interests and contributions to the field:

Uncovering heterogeneous effects in computational models for sustainable decision-making

The Intelligent Prospector v1. 0: geoscientific model development and prediction by sequential data acquisition planning with application to mineral exploration

Data science for the geosciences

My journey out of fossil fuel-funded research

BetaZero: Belief-state planning for long-horizon POMDPs using learned approximations

Formulating and Solving the Data-Consistent Geophysical Inverse Problem for Subsurface Modeling Applications

Statistical modeling of three-dimensional redox architecture from non-colocated redox borehole and transient electromagnetic data

Optimizing Carbon Storage Operations for Long-Term Safety

Jef Caers Information

University

Position

Professor of Geological Sciences

Citations(all)

10441

Citations(since 2020)

4075

Cited By

8036

hIndex(all)

53

hIndex(since 2020)

36

i10Index(all)

144

i10Index(since 2020)

84

Email

University Profile Page

Stanford University

Google Scholar

View Google Scholar Profile

Jef Caers Skills & Research Interests

data science for geoscience

uncertainty quantification

mineral exploration

decision making

Top articles of Jef Caers

Title

Journal

Author(s)

Publication Date

Uncovering heterogeneous effects in computational models for sustainable decision-making

Environmental Modelling & Software

Mariia Kozlova

Robert J Moss

Julian Scott Yeomans

Jef Caers

2024/1/1

The Intelligent Prospector v1. 0: geoscientific model development and prediction by sequential data acquisition planning with application to mineral exploration

Geoscientific Model Development

John Mern

Jef Caers

2023/1/11

Data science for the geosciences

Lijing Wang

David Zhen Yin

Jef Caers

2023/7/31

My journey out of fossil fuel-funded research

Nature Geoscience

Jef Caers

2023/6

BetaZero: Belief-state planning for long-horizon POMDPs using learned approximations

arXiv preprint arXiv:2306.00249

Robert J Moss

Anthony Corso

Jef Caers

Mykel J Kochenderfer

2023/5/31

Formulating and Solving the Data-Consistent Geophysical Inverse Problem for Subsurface Modeling Applications

Alex Miltenberger

Lijing Wang

Tapan Mukerji

Jef Caers

2023/5/27

Statistical modeling of three-dimensional redox architecture from non-colocated redox borehole and transient electromagnetic data

Hydrogeology Journal

Lijing Wang

Hyojin Kim

Birgitte Hansen

Anders V Christiansen

Troels N Vilhelmsen

...

2023/9

Optimizing Carbon Storage Operations for Long-Term Safety

arXiv preprint arXiv:2304.09352

Yizheng Wang

Markus Zechner

Gege Wen

Anthony Louis Corso

John Michael Mern

...

2023/4/19

Unraveling the uncertainty of geological interfaces through data-knowledge-driven trend surface analysis

Computers & Geosciences

Lijing Wang

Luk Peeters

Emma J MacKie

Zhen Yin

Jef Caers

2023/9/1

Stochastic inversion of gravity data accounting for structural uncertainty

Mathematical Geosciences

Noah Athens

Jef Caers

2022/2

Probability Intersection: New Avenues for Inversion and Conceptual Model Evaluation

Frontiers in Hydrology 2022

Alex Miltenberger

Jef Caers

Haruko M Wainwright

Nicholas E Thiros

Sebastian Uhlemann

...

2022/6

Data-driven model falsification and uncertainty quantification for fractured reservoirs

Engineering

Junling Fang

Bin Gong

Jef Caers

2022/11/1

Efficacy of information in mineral exploration drilling

Natural Resources Research

J Caers

C Scheidt

Z Yin

L Wang

T Mukerji

...

2022/6

A POMDP model for safe geological carbon sequestration

arXiv preprint arXiv:2212.00669

Anthony Corso

Yizheng Wang

Markus Zechner

Jef Caers

Mykel J Kochenderfer

2022/10/25

A Hidden Markov Model Approach to Quantify Uncertainty of High-Resolution 3D Lithofacies Models Using Seismic Data

AGU Fall Meeting Abstracts

Jonas Kloeckner

David Zhen Yin

Diego Machado Marques

Jef Caers

2022/12

Hierarchical Bayesian Inversion of Global Variables and Large‐Scale Spatial Fields

Water Resources Research

Lijing Wang

Peter K Kitanidis

Jef Caers

2022/5

Sequential value of information for subsurface exploration drilling

Natural Resources Research

T Hall

C Scheidt

L Wang

Z Yin

T Mukerji

...

2022/10

Parameter inversion with sequential neural density estimators: an enhanced machine learning-based inversion

AGU Fall Meeting Abstracts

Lijing Wang

Celine Scheidt

David Zhen Yin

Alex Miltenberger

Kate Maher

...

2022/12

Quantifying uncertainty in downscaling of seismic data to high-resolution 3-D lithological models

IEEE Transactions on Geoscience and Remote Sensing

Zhen Yin

Maisha Amaru

Yizheng Wang

Lewis Li

Jef Caers

2022/2/24

Intelligent prospector v1. 0: geoscientific model development and prediction by sequential data acquisition planning with application to mineral exploration

Geoscientific Model Development Discussions

John Mern

Jef Caers

2022/8/8

See List of Professors in Jef Caers University(Stanford University)

Co-Authors

H-index: 72
Anthony R. Kovscek

Anthony R. Kovscek

Stanford University

H-index: 65
Hamdi Tchelepi

Hamdi Tchelepi

Stanford University

H-index: 51
Niklas Linde

Niklas Linde

Université de Lausanne

H-index: 45
Philippe Renard

Philippe Renard

Université de Neuchâtel

H-index: 41
Gregoire Mariethoz

Gregoire Mariethoz

Université de Lausanne

H-index: 34
Frédéric Nguyen

Frédéric Nguyen

Université de Liège

academic-engine