Articles | Volume 11, issue 4
https://doi.org/10.5194/se-11-1527-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/se-11-1527-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 Aug 2020
Research article |
| 24 Aug 2020
| 24 Aug 2020
Deep learning for fast simulation of seismic waves in complex media
Department of Computer Science, University of Oxford, Oxford, UK
Tarje Nissen-Meyer
Department of Earth Sciences, University of Oxford, Oxford, UK
Andrew Markham
Department of Computer Science, University of Oxford, Oxford, UK
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- Deep attributes: innovative LSTM-based seismic attributes G. Roncoroni et al. 10.1093/gji/ggae053
- Solving the frequency-domain acoustic VTI wave equation using physics-informed neural networks C. Song et al. 10.1093/gji/ggab010
- AT-PINN: Advanced time-marching physics-informed neural network for structural vibration analysis Z. Chen et al. 10.1016/j.tws.2023.111423
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- Physics‐Informed Neural Networks for Elliptical‐Anisotropy Eikonal Tomography: Application to Data From the Northeastern Tibetan Plateau Y. Chen et al. 10.1029/2023JB027378
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- DD-Net: Dual Decoder Network With Curriculum Learning for Full Waveform Inversion X. Zhang et al. 10.1109/TGRS.2024.3358492
- Data-driven model discovery for plasma turbulence modelling I. Abramovic et al. 10.1017/S0022377822001192
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- Seismic Velocity Inversion via Physical Embedding Recurrent Neural Networks (RNN) C. Lu & C. Zhang 10.3390/app132413312
- Efficient low-fidelity aeroacoustic permanence calculation of propellers F. Yunus et al. 10.1016/j.ast.2022.107438
- Deep Learning for 3-D Inversion of Gravity Data L. Zhang et al. 10.1109/TGRS.2021.3110606
- Finite basis physics-informed neural networks (FBPINNs): a scalable domain decomposition approach for solving differential equations B. Moseley et al. 10.1007/s10444-023-10065-9
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Latest update: 26 Apr 2024
Short summary
Simulations of seismic waves are very important; they allow us to understand how earthquakes spread and how the interior of the Earth is structured. However, whilst powerful, existing simulation methods usually require a large amount of computational power and time to run. In this research, we use modern machine learning techniques to accelerate these calculations inside complex models of the Earth.
Simulations of seismic waves are very important; they allow us to understand how earthquakes...
