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

Key points

• 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).1
• The goal is to rapidly characterise this depth domain by efficient interpretation of horizons and faults in seismic data.1
• 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.1
• 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.1

Sources (1)

• [1]An AI-assisted conditioning and geological interpretation workflow for usage in implicit geological modeling
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 11:32 AM

  Implicit modeling and Relative Geologic Time are geological modeling techniques that enable more efficient, faster, less biased and more reproducible modeling results.

  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).

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