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.
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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42 citations as recorded by crossref.
- 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
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- Approximation of modal wavenumbers and group speeds in an oceanic waveguide using a neural network A. Varon et al. 10.1121/10.0019704
- Seismic Wave Propagation and Inversion with Neural Operators Y. Yang et al. 10.1785/0320210026
- Simulating Multicomponent Elastic Seismic Wavefield Using Deep Learning C. Song et al. 10.1109/LGRS.2023.3250522
- Deep learning, machine learning and internet of things in geophysical engineering applications: An overview K. Dimililer et al. 10.1016/j.micpro.2020.103613
- 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
- Deep neural network reducing numerical dispersion for post-processing of seismic modeling results K. Gadylshina et al. 10.18303/2619-1563-2022-1-99
- Solving the frequency-domain acoustic VTI wave equation using physics-informed neural networks C. Song et al. 10.1093/gji/ggab010
- 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
- Small-data-driven fast seismic simulations for complex media using physics-informed Fourier neural operators W. Wei & L. Fu 10.1190/geo2021-0573.1
- 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
- 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
- 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
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- Uncertainty Quantification in Intelligent-Based Electrical Resistivity Tomography Image Reconstruction With Monte Carlo Dropout Strategy L. Xixi et al. 10.1109/TGRS.2023.3262835
- Efficient low-fidelity aeroacoustic permanence calculation of propellers F. Yunus et al. 10.1016/j.ast.2022.107438
- 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
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Latest update: 25 Sep 2023
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...