OBN Data Reconstruction with Physics Informed Neural Networks

F. Brandolin; A. Alfaraj; T. Alkhalifah; C. Tsingas

doi:10.3997/2214-4609.2025647027

OBN Data Reconstruction with Physics Informed Neural Networks
Authors F. Brandolin¹, A. Alfaraj², T. Alkhalifah¹ and C. Tsingas²
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¹ King Abdullah University of Science and Technology (KAUST) ² EXPEC Advanced Research Center, Saudi Aramco
Publisher: European Association of Geoscientists & Engineers
Source: Conference Proceedings, Third EAGE Seabed Seismic Today Workshop, Nov 2025, Volume 2025, p.1 - 4
DOI: https://doi.org/10.3997/2214-4609.2025647027

Abstract

Summary

Building on the idea of describing seismic wavefields as a superposition of local plane waves, we propose to interpolate seismic data by utilizing a physics informed neural network (PINN). In the proposed framework, two feed-forward neural networks are jointly trained using the local plane wave differential equation as well as the available data as two terms in the objective function. In this work, we present the first experimental study on reconstructing seismic data acquired from ocean-bottom nodes.

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2025-11-24

2026-02-15

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References

Brandolin, F., Ravasi, M. & Alkhalifah, T. (2024) PINNslope: seismic data interpolation and local slope estimation with physics informed neural networks. Geophysics, 89(4), V331–V345. https://doi.org/10.1190/geo2023-0323.1.
[Google Scholar]
Raissi, M., Perdikaris, P. & Karniadakis, G.E. (2019) Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics, 378, 686–707. https://doi.org/10.1016/j.jcp.2018.10.045
[Google Scholar]

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OBN Data Reconstruction with Physics Informed Neural Networks

Conference Proceedings 2025, 1 (2025); https://doi.org/10.3997/2214-4609.2025647027

/content/papers/10.3997/2214-4609.2025647027

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OBN Data Reconstruction with Physics Informed Neural Networks

Abstract

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