Metraipolis › archive › dc_eeae2ec55b07

An AI-assisted conditioning and geological interpretation workflow for usage in implicit geological modeling — arXiv:2610.09871

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.09871
License (as archived): CC0-1.0
Archived: 2026-10-08T11:11:34.548Z
SHA-256 of the archived text: 94049462c764712d95f664c29ab5bca2d948fd52b95449689e34367e3e29177b
Stable URL of this page: https://metraipolis.com/documento/dc_eeae2ec55b07

How to cite

An AI-assisted conditioning and geological interpretation workflow for usage in implicit geological modeling — arXiv:2610.09871 — arXiv (en), https://arxiv.org/abs/2610.09871 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_eeae2ec55b07 (Metraipolis archive dc_eeae2ec55b07, sha256 94049462c764712d95f664c29ab5bca2d948fd52b95449689e34367e3e29177b)

BibTeX

@misc{dceeae2ec55b07,
  title = {An AI-assisted conditioning and geological interpretation workflow for usage in implicit geological modeling — arXiv:2610.09871},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.09871},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_eeae2ec55b07; sha256 94049462c764712d95f664c29ab5bca2d948fd52b95449689e34367e3e29177b},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.09871. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Geophysics (physics.geo-ph) ; Artificial Intelligence (cs.AI); Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2610.09871 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-08T10:14:06.135Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).