An AI-assisted conditioning and geological interpretation workflow for usage in implicit geological modeling

2026-10-07

AI Summary

This study addresses the challenges of low efficiency, significant subjective bias, and heavy reliance on manual labor in seismic interpretation for implicit geological modeling by proposing an AI-assisted interpretation toolkit that requires minimal labeled data. Methodologically, self-supervised and semi-supervised contrastive learning convolutional neural networks are employed to achieve signal enhancement, noise suppression, and data interpolation, enabling the automated extraction of horizons and faults. This workflow substantially improves both the modeling speed and reproducibility of shallow-to-deep seismic data. The successful application to the top boundary of the Maassluis Formation in the Netherlands validates the maturity and practical value of the proposed approach for real-world geological decision-making.

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.

Problem

• Implicit geological modeling
• Seismic data interpretation
• Horizon and fault interpretation
• Machine Learning

Innovation

• Implicit geological modeling
• Self-supervised learning
• Contrastive learning CNN
• Seismic interpretation
• Machine learning toolkit