An AI-assisted conditioning and geological interpretation workflow for usage in implicit geological modeling
Abstract
Implicit modeling and Relative Geologic Time are geological modeling techniques that enable more efficient, faster, less biased and more reproducible modeling results. For optimal operation, these techniques require many well-constrained input data. In the framework of the Horizon Europe GO-Forward and MOOI WarmingUP GOO projects and to accelerate Implicit modeling, Machine Learning (ML) methods have been tested and implemented in a toolkit for the interpretation of (onshore) seismic data from the shallow to deep range (+- 300 - 3500 m). The goal is to rapidly characterise this depth domain by efficient interpretation of horizons and faults in seismic data. The first step is to improve the signal by applying AI techniques like self-supervised and semi-supervised contrastive learning CNN's for noise reduction and interpolation. Next, horizons and faults are interpreted with minimal use of human-generated training data by using (semi-) self-supervised methods. The resulting developed toolkit supports the application of the implemented algorithms in an efficient workflow. As a first demonstration, the top of the Dutch Maassluis Formation has been interpreted in the Leeuwarden and Waalwijk 3D seismic cubes. Overall, this study demonstrates that AI-assisted interpretation workflows have reached a level of maturity that allows their integration into applied geological modeling and decision-making.
Related
- Learning to Guide Ontology-Driven Geological Interpretation with Graph Neural Networks
- Emirates Exploration Imager (EXI) Reveals Crater and Boulder Distribution on Deimos: Insights Into Its Dynamical and Geological Evolution
- Escaping Geological Generative Prior Distributions in Bayesian Travel Time Tomography
- Two-stage assessment of regional geological hazard susceptibility under mining disturbance
- Rock carving depth reflects carvers’ geological insight
BibTeX
@misc{carpentier2026aiassisted,
title = {{An AI-assisted conditioning and geological interpretation workflow for usage in implicit geological modeling}},
author = {Stefan Carpentier and Jan Diederik van Wees and Eva de Boever and Jan Niederau and Camille Chapeland and Suzanne Atkins and Boris Boullenger and Jens Wollenweber},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.09871},
eprint = {2610.09871},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.09871},
note = {CC BY-SA 4.0},
}
RIS
TY - UNPB
TI - An AI-assisted conditioning and geological interpretation workflow for usage in implicit geological modeling
AU - Stefan Carpentier
AU - Jan Diederik van Wees
AU - Eva de Boever
AU - Jan Niederau
AU - Camille Chapeland
AU - Suzanne Atkins
AU - Boris Boullenger
AU - Jens Wollenweber
PY - 2026
DA - 2026/10/07
PB - arXiv
DO - 10.48550/arxiv.2610.09871
UR - https://arxiv.org/abs/2610.09871
AB - Implicit modeling and Relative Geologic Time are geological modeling techniques that enable more efficient, faster, less biased and more reproducible modeling results. For optimal operation, these techniques require many well-constrained input data. In the framework of the Horizon Europe GO-Forward and MOOI WarmingUP GOO projects and to accelerate Implicit modeling, Machine Learning (ML) methods have been tested and implemented in a toolkit for the interpretation of (onshore) seismic data from the shallow to deep range (+- 300 - 3500 m). The goal is to rapidly characterise this depth domain by efficient interpretation of horizons and faults in seismic data. The first step is to improve the signal by applying AI techniques like self-supervised and semi-supervised contrastive learning CNN's for noise reduction and interpolation. Next, horizons and faults are interpreted with minimal use of human-generated training data by using (semi-) self-supervised methods. The resulting developed toolkit supports the application of the implemented algorithms in an efficient workflow. As a first demonstration, the top of the Dutch Maassluis Formation has been interpreted in the Leeuwarden and Waalwijk 3D seismic cubes. Overall, this study demonstrates that AI-assisted interpretation workflows have reached a level of maturity that allows their integration into applied geological modeling and decision-making.
ER -