Robin Thibaut

Computational Geoscientist

Bayesian Uncertainty Quantification

ML/AI

Computational geoscientist specializing in Bayesian uncertainty quantification and ML for geothermal systems. I develop finite-element thermal simulation–calibrated, Python-first workflows that fuse well, geophysical, and hydrologic data for subsurface characterization and quantified uncertainty analysis.

Core Expertise

Programming & ML: Python (NumPy, Pandas, scikit-learn, PyTorch, TensorFlow), Uncertainty Quantification (Bayesian, BEL), Experimental Design, ML/AI, Time-series/Spatial Modeling

Geoscience Modeling: Finite element thermal simulation, MODFLOW, MT3DMS, MODPATH, ModelMuse, CRTOMO, RES2DINV, SGEMS, Geophysical Data Integration (ERT/IP), Hydrologic/Groundwater Modeling

Data & Engineering: SQL, Snowflake, Git, Data Engineering/ETL, Cloud (Google Cloud Platform), Scientific Visualization, Linux/Windows/macOS

Highlighted Publications

Comparing Well and Geophysical Data for Temperature Monitoring
Published in Water Resources Research | DOI: 10.1029/2022WR033045
A Framework for Bayesian Experimental Design in Environmental Geosciences
Published in Journal of Hydrology | DOI: 10.1016/j.jhydrol.2021.126903 | Repo: skbel
Minimum Gradient Support Inversion for Resistivity/IP Data
Published in Journal of Applied Geophysics | DOI: 10.1016/j.jappgeo.2021.104286 | Repo: MGS-public

Open Source Software & Resources

Software Packages:

SKBEL - Bayesian Evidential Learning framework DOI: 10.5281/zenodo.6205242
MGS-public - Minimum Gradient Support inversion for resistivity/IP data
Python Scientific Project Template - Standardized structure for scientific Python projects

Datasets:

4D ERT Monitoring Dataset - Temperature monitoring with geophysical data
Wellhead Protection Area Prediction Dataset - Groundwater modeling and protection

Featured Blog Posts

GitHub Stats

Robin's GitHub stats
Top Languages

Connect & Collaborate

ResearchGate: Robin Thibaut
Stack Overflow: Profile
Twitter: @RobinThibaut
Researcher Profile: Web of Science
ORCID: ORCID

news

Oct 02, 2024	SLC Bound! 🏔️
Aug 02, 2023	Berkeley Bound! 🌁
Mar 28, 2023	PhD defended!
Mar 09, 2023	Public PhD defense. "Machine Learning for Bayesian Experimental Design in the Subsurface"

latest posts

Oct 19, 2022	Comparing Well and Geophysical Data for Temperature Monitoring Within a Bayesian Experimental Design Framework
May 03, 2022	Principal Component Analysis from the ground up with Python
May 01, 2022	A Gentle Introduction to Python with PyCharm

selected publications

Efficiency and heat transport processes of low-temperature aquifer thermal energy storage systems: new insights from global sensitivity analyses

Luka Tas, Niels Hartog, Martin Bloemendal, and 5 more authors

Geothermal Energy, Jan 2025

Open access, published 07 January 2025

DOI Bib

@article{tas2025,
  author = {Tas, Luka and Hartog, Niels and Bloemendal, Martin and Simpson, David and Robert, Tanguy and {\textbf{Robin} \textbf{Thibaut}} and Zhang, Le and Hermans, Thomas},
  title = {Efficiency and heat transport processes of low-temperature aquifer thermal energy storage systems: new insights from global sensitivity analyses},
  journal = {Geothermal Energy},
  volume = {13},
  pages = {Article number: 2},
  year = {2025},
  month = jan,
  note = {Open access, published 07 January 2025},
  doi = {10.1186/s40517-024-00310-0},
}

Comparing Well and Geophysical Data for Temperature Monitoring Within a Bayesian Experimental Design Framework

\textbfRobin Thibaut, Nicolas Compaire, Nolwenn Lesparre, and 3 more authors

Water Resources Research, Nov 2022

DOI Bib HTML

@article{Thibaut2022,
  author = {\textbf{Robin Thibaut} and Compaire, Nicolas and Lesparre, Nolwenn and Ramgraber, Maximilian and Laloy, Eric and Hermans, Thomas},
  doi = {10.1029/2022WR033045},
  issn = {0043-1397},
  issue = {11},
  journal = {Water Resources Research},
  month = nov,
  title = {Comparing Well and Geophysical Data for Temperature Monitoring Within a Bayesian Experimental Design Framework},
  volume = {58},
  url = {https://onlinelibrary.wiley.com/doi/10.1029/2022WR033045},
  year = {2022},
}

A new framework for experimental design using Bayesian Evidential Learning: The case of wellhead protection area

\textbfRobin Thibaut, Eric Laloy, and Thomas Hermans

Journal of Hydrology, Dec 2021

DOI Bib HTML

@article{thibaut_new_2021,
  author = {\textbf{Robin Thibaut} and Laloy, Eric and Hermans, Thomas},
  doi = {10.1016/j.jhydrol.2021.126903},
  issn = {00221694},
  journal = {Journal of Hydrology},
  keywords = {Bayesian Evidential Learning,Experimental design,Groundwater modelling,Machine learning},
  month = dec,
  pages = {126903},
  title = {A new framework for experimental design using Bayesian Evidential Learning: The case of wellhead protection area},
  volume = {603},
  url = {https://linkinghub.elsevier.com/retrieve/pii/S0022169421009537},
  year = {2021},
}