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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- Solving the frequency-domain acoustic VTI wave equation using physics-informed neural networks C. Song et al. 10.1093/gji/ggab010
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- DEEP LEARNING-BASED NUMERICAL DISPERSION MITIGIATION IN SEISMIC MODELLING K. Gadylshina et al. 10.33764/2618-981X-2021-2-2-17-25
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63 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
- High-frequency wavefield extrapolation using the Fourier neural operator C. Song & Y. Wang 10.1093/jge/gxac016
- Executing realistic earthquake simulations in unreal engine with material calibration Y. Sun et al. 10.1016/j.cag.2024.104091
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- Artificial intelligence for mineral exploration: A review and perspectives on future directions from data science F. Yang et al. 10.1016/j.earscirev.2024.104941
- Approximation of modal wavenumbers and group speeds in an oceanic waveguide using a neural network A. Varon et al. 10.1121/10.0019704
- Deep learning, machine learning and internet of things in geophysical engineering applications: An overview K. Dimililer et al. 10.1016/j.micpro.2020.103613
- Deep neural network reducing numerical dispersion for post-processing of seismic modeling results K. Gadylshina et al. 10.18303/2619-1563-2022-1-99
- 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
- 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
- DD-Net: Dual Decoder Network With Curriculum Learning for Full Waveform Inversion X. Zhang et al. 10.1109/TGRS.2024.3358492
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- Deep-neural-networks-based approaches for Biot–squirt model in rock physics F. Xiong et al. 10.1007/s11600-022-00740-8
- 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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- SeismicTransformer: An attention-based deep learning method for the simulation of seismic wavefields Y. Xiang et al. 10.1016/j.cageo.2024.105629
- 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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- Time Domain Reflectometry Waveform Interpretation With Convolutional Neural Networks Z. Wang et al. 10.1029/2022WR033895
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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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- Physics-informed differentiable method for piano modeling R. Simionato et al. 10.3389/frsip.2023.1276748
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- 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
- One-dimensional magnetotelluric parallel inversion using a ResNet1D-8 residual neural network W. Ling et al. 10.1016/j.cageo.2023.105454
- Systematic literature review on seismic diffraction imaging G. Zakarewicz et al. 10.1016/j.earscirev.2024.104792
- Numerical dispersion mitigation neural network for seismic modeling K. Gadylshin et al. 10.1190/geo2021-0242.1
- Accelerating 2D and 3D frequency-domain seismic wave modeling through interpolating frequency-domain wavefields by deep learning W. Cao et al. 10.1190/geo2021-0435.1
- A review of approaches for submarine landslide-tsunami hazard identification and assessment J. Roger et al. 10.1016/j.marpetgeo.2024.106729
- Nonlinear seismic inversion by physics-informed Caianiello convolutional neural networks for overpressure prediction of source rocks in the offshore Xihu depression, East China Y. Cheng & L. Fu 10.1016/j.petrol.2022.110654
- Predicting transmission loss in underwater acoustics using convolutional recurrent autoencoder network W. Mallik et al. 10.1121/10.0013894
- Ground-Motion Evaluation of Hybrid Seismic Velocity Models R. Ajala & P. Persaud 10.1785/0320220022
- Uncertainty Quantification in Intelligent-Based Electrical Resistivity Tomography Image Reconstruction With Monte Carlo Dropout Strategy L. Xixi et al. 10.1109/TGRS.2023.3262835
- Instantaneous Physics‐Based Ground Motion Maps Using Reduced‐Order Modeling J. Rekoske et al. 10.1029/2023JB026975
- 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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Latest update: 20 Nov 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...
