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 learning for fast simulation of seismic waves in complex media B. Moseley et al. 10.5194/se-11-1527-2020
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68 citations as recorded by crossref.
- Sensing prior constraints in deep neural networks for solving exploration geophysical problems X. Wu et al. 10.1073/pnas.2219573120
- Seismic savanna: machine learning for classifying wildlife and behaviours using ground‐based vibration field recordings A. Szenicer et al. 10.1002/rse2.242
- RCNN: Recurrent Convolutional Neural Network for Solving 3-D Wave Equation C. Li et al. 10.1109/LGRS.2023.3328421
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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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- 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
- Multitasking neural network to jointly map discrete fracture structures and matrix transmissivity by inverting hydraulic data acquired in 2D fractured aquifers. XNET-fracture M. Vu & A. Jardani 10.1016/j.advwatres.2023.104463
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- High-Precision Intelligence Denoising of Potential Field Data Based on RevU-Net Z. Zhou et al. 10.1109/LGRS.2023.3241295
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- 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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- 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
- Rapid Seismic Waveform Modeling and Inversion With Neural Operators Y. Yang et al. 10.1109/TGRS.2023.3264210
- Multi-task neural network in hydrological tomography to map the transmissivity and storativity simultaneously: HT-XNET M. Vu & A. Jardani 10.1016/j.jhydrol.2022.128167
- 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
- Removing Time Dispersion from Elastic Wave Modeling with the pix2pix Algorithm Based on cGAN T. Xu et al. 10.3390/rs15123120
- Physics‐Informed Neural Networks (PINNs) for Wave Propagation and Full Waveform Inversions M. Rasht‐Behesht et al. 10.1029/2021JB023120
- Machine Learning in Earthquake Seismology S. Mousavi & G. Beroza 10.1146/annurev-earth-071822-100323
- Wavefield Reconstruction Inversion via Physics-Informed Neural Networks C. Song & T. Alkhalifah 10.1109/TGRS.2021.3123122
- A Three-Dimensional Geological Structure Modeling Framework and Its Application in Machine Learning S. Wang et al. 10.1007/s11004-022-10027-9
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- Numerical dispersion mitigation neural network for seismic modeling K. Gadylshin et al. 10.1190/geo2021-0242.1
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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
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- Data‐Driven Design of Wave‐Propagation Models for Shale‐Oil Reservoirs Based on Machine Learning F. Xiong et al. 10.1029/2021JB022665
- 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: 17 Mar 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...
