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Abstract

Summary

We propose a method to generate high-detail, pixel sharp velocity attributes through combined inversion of stacked seismic amplitude data and existing velocity models. The resulting models can be used to obtain highly realistic and geologically relevant synthetic data. The main use case presented is the modelling of training data for ML applications towards imaging and velocity model building workflows, with example applications.

© EAGE Publications BV
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/content/papers/10.3997/2214-4609.2025642033
2025-10-06
2026-02-15

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References

  1. Kolbjørnsen, O., A.K.Evensen (2019). Digital super-resolution in seismic amplitude processing. SEG technical program expanded abstracts. ISSN 1949-4645. doi: 10.1190/segam2019‑3214317.1
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  2. Gerard T.Schuster, YuqingChen, and ShihangFeng, (2024), “Review of physics-informed machine-learning inversion of geophysical data,” GEOPHYSICS89: T337–T356.
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  3. M.Zhu, S.Feng, Y.Lin, & L.Lu., (2023) Fourier-DeepONet: Fourier-enhanced deep operator networks for full waveform inversion with improved accuracy, generalizability, and robustness. Computer Methods in Applied Mechanics and Engineering, 416, 116300, 2023.
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/content/papers/10.3997/2214-4609.2025642033
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Relevant Training Data for ML - Leveraging the Existing Image
Conference Proceedings 2025, 1 (2025); https://doi.org/10.3997/2214-4609.2025642033
/content/papers/10.3997/2214-4609.2025642033
/content/papers/10.3997/2214-4609.2025642033
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