An AI-assisted conditioning and geological interpretation workflow for usage in implicit geological modeling
Researchers present an AI toolkit that interprets seismic horizons and faults to speed geological modeling.
The paper presents machine-learning methods, developed under the Horizon Europe GO-Forward and MOOI WarmingUP GOO projects, to accelerate implicit geological modeling. Self-supervised and semi-supervised contrastive CNNs reduce noise and interpolate onshore seismic data from about 300 to 3,500 meters. Horizons and faults are then interpreted with minimal human-labeled training data. A demonstration interprets the top of the Dutch Maassluis Formation in the Leeuwarden and Waalwijk 3D seismic cubes.
- Toolkit interprets horizons and faults in onshore seismic data.
- Contrastive CNNs reduce noise and interpolate sparse signals.
- Methods require little human-generated training data.
- Demo covers the Maassluis Formation in two Dutch 3D cubes.
Full article202 words · extracted from arxiv.org · click to collapse
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.
Text extracted automatically; images, tables and formatting may be missing. Original: https://arxiv.org/abs/2610.09871