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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- Ground Motion Simulation Via Generative Adversarial Network K. Chen et al. 10.1007/s11770-025-1154-7
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- Deep attributes: innovative LSTM-based seismic attributes G. Roncoroni et al. 10.1093/gji/ggae053
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- 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
- Integration of PSPU-Net gravity inversion neural network with gravity data for enhanced 3D basement relief estimation X. Liu et al. 10.1190/geo2024-0150.1
- Small-data-driven fast seismic simulations for complex media using physics-informed Fourier neural operators W. Wei & L. Fu 10.1190/geo2021-0573.1
- Physics‐Informed Neural Networks for Elliptical‐Anisotropy Eikonal Tomography: Application to Data From the Northeastern Tibetan Plateau Y. Chen et al. 10.1029/2023JB027378
- On cost-efficient parallel iterative solvers for 3D frequency-domain seismic multisource viscoelastic anisotropic wave modeling G. Ma et al. 10.1190/geo2023-0368.1
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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
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- Reducing Frequency Bias of Fourier Neural Operators in 3D Seismic Wavefield Simulations Through Multistage Training Q. Kong et al. 10.1785/0220250085
- Convolutional neural networks with SegNet architecture applied to three-dimensional tomography of subsurface electrical resistivity: CNN-3D-ERT M. Vu & A. Jardani 10.1093/gji/ggab024
- High-Precision Intelligence Denoising of Potential Field Data Based on RevU-Net Z. Zhou et al. 10.1109/LGRS.2023.3241295
- Real-time reconstruction of ground motion during small magnitude earthquakes: A pilot study Y. Kim et al. 10.1016/j.soildyn.2025.109707
- Time Domain Reflectometry Waveform Interpretation With Convolutional Neural Networks Z. Wang et al. 10.1029/2022WR033895
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- 3-D Joint Inversion of Gravity and Magnetic Data Based on a Deep Learning Network With Automatic Recognition of Structural Similarity Z. Zhou et al. 10.1109/TGRS.2023.3330988
- Reduced-order modelling for complex three-dimensional seismic wave propagation J. Rekoske et al. 10.1093/gji/ggaf049
- Generalizable data driven full waveform inversion for complex structures and severe topographies M. Saadat et al. 10.1016/j.petsci.2024.05.002
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- Physics-informed differentiable method for piano modeling R. Simionato et al. 10.3389/frsip.2023.1276748
- Simulating Multicomponent Elastic Seismic Wavefield Using Deep Learning C. Song et al. 10.1109/LGRS.2023.3250522
- Deep neural Helmholtz operators for 3-D elastic wave propagation and inversion C. Zou et al. 10.1093/gji/ggae342
- Accelerating Bayesian microseismic event location with deep learning A. Spurio Mancini et al. 10.5194/se-12-1683-2021
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- A versatile framework to solve the Helmholtz equation using physics-informed neural networks C. Song et al. 10.1093/gji/ggab434
- Machine learning‐accelerated gradient‐based Markov chain Monte Carlo inversion applied to electrical resistivity tomography M. Aleardi et al. 10.1002/nsg.12211
- Bayesian Geophysical Inversion Using Invertible Neural Networks X. Zhang & A. Curtis 10.1029/2021JB022320
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- A review of approaches for submarine landslide-tsunami hazard identification and assessment J. Roger et al. 10.1016/j.marpetgeo.2024.106729
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- Multi-frequency wavefield modeling of acoustic VTI wave equation using physics informed neural networks A. Sandhu et al. 10.3389/feart.2023.1227828
- DEEP LEARNING-BASED NUMERICAL DISPERSION MITIGIATION IN SEISMIC MODELLING K. Gadylshina et al. 10.33764/2618-981X-2021-2-2-17-25
- Deep learning for fast simulation of seismic waves in complex media B. Moseley et al. 10.5194/se-11-1527-2020
- A 3D complexity-adaptive approach to explore sparsity in elastic wave propagation C. Haindl et al. 10.1190/geo2020-0490.1
- Wave propagation modeling using machine learning-based finite difference scheme D. Ji et al. 10.1016/j.jcp.2025.113870
- Data‐Driven Design of Wave‐Propagation Models for Shale‐Oil Reservoirs Based on Machine Learning F. Xiong et al. 10.1029/2021JB022665
- Supervised pre-training and prior-guided fine-tuning for deep learning-based scatter interpolation and geophysical inversion Z. Guo et al. 10.1093/gji/ggaf200
- Development and application of neural network technology in solving geodynamic problems O. Maximova 10.21285/2227-2917-2023-3-516-525
- Seismic Wavefields Modeling With Variable Horizontally Layered Velocity Models via Velocity-Encoded PINN J. Zou et al. 10.1109/TGRS.2024.3411472
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Latest update: 24 Oct 2025
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...
