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Abstract

Summary

This paper demonstrates the success in applying a Vision Transformer-based Seismic Foundation Model (Sheng et al., 2023) to obtain a coarse-scale dip field which is then incorporated into a multi-scale volumetric flattening procedure ( ). This method provides greater precision, consistency and scalability for automated horizon detection than has hitherto been possible with previous methods like CNNs.

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/content/papers/10.3997/2214-4609.202576019
2025-11-10
2026-02-08

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References

  1. Lomask, J. [2023] Horizon detection with CNN-based multiscale volumetric flattening. SEG Technical Program Expanded Abstracts, 758–762 (Abstract).
     [Google Scholar]
  2. Lomask, J., Guitton, A., Fomel, S., Claerbout, J., and Valenciano, A. [2006] Flattening without picking. Geophysics, 71(4), P13–P20.
     [Google Scholar]
  3. Sheng, H., Wu, X., Si, X., Li, J., Zhang, S., and Duan, X. [2025] Seismic foundation model: A next generation deep-learning model in geophysics. Geophysics, 90, IM59–IM79.
     [Google Scholar]
/content/papers/10.3997/2214-4609.202576019
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Seismic Foundation Model based Coarse-scale Dip Estimation for Use in Multiscale Volumetric Flattening-based Horizon Detection
Conference Proceedings 2025, 1 (2025); https://doi.org/10.3997/2214-4609.202576019
/content/papers/10.3997/2214-4609.202576019
/content/papers/10.3997/2214-4609.202576019
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