arXiv：cs.LG· Stefan Carpentier, Jan Diederik van Wees, Eva de Boever, Jan Niederau, Camille Chapeland, Suzanne Atkins, Boris Boullenger, Jens Wollenweber·· 4 小时前AI 评分28
AI 辅助条件化与地质解释工作流：用于隐式地质建模
An AI-assisted conditioning and geological interpretation workflow for usage in implicit geological modeling
AI 导读
一个 AI 辅助地震解释工具包被用于浅至深层（约 300-3500 m）陆上地震数据的层位与断层解释，先以自监督和半监督对比学习 CNN 做降噪与插值，再以（半）自监督方法减少人工训练数据。该工具包在 Leeuwarden 和 Waalwijk 三维地震数据体中解释了荷兰 Maassluis 组顶部，结果表明 AI 辅助解释工作流已成熟到可整合进应用地质建模与决策。
正文
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.
| Comments: | 17 page, 19 figures |
| Subjects: | Geophysics (physics.geo-ph); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.09871 [physics.geo-ph] |
| (or arXiv:2610.09871v1 [physics.geo-ph] for this version) | |
| https://doi.org/10.48550/arXiv.2610.09871 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Stefan Carpentier [view email]
[v1]
Wed, 7 Oct 2026 11:32:47 UTC (2,868 KB)
来源：arXiv：cs.LG · arxiv.org