Articles | Volume 11, issue 4
Solid Earth, 11, 1527–1549, 2020
https://doi.org/10.5194/se-11-1527-2020
Solid Earth, 11, 1527–1549, 2020
https://doi.org/10.5194/se-11-1527-2020

Research article 24 Aug 2020

Research article | 24 Aug 2020

Deep learning for fast simulation of seismic waves in complex media

Ben Moseley et al.

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Cited articles

Ahmed, E., Saint, A., Shabayek, A. E. R., Cherenkova, K., Das, R., Gusev, G., Aouada, D., and Ottersten, B.: A survey on Deep Learning Advances on Different 3D Data Representations, arXiv [preprint], https://arxiv.org/abs/1808.01462, 2018. a
Aki, K. and Richards, P. G.: Quantitative seismology, W. H. Freeman and Co., New York, New York, 1980. a, b
Araya-Polo, M., Jennings, J., Adler, A., and Dahlke, T.: Deep-learning tomography, The Leading Edge, 37, 58–66, 2018. a
Bergen, K. J., Johnson, P. A., De Hoop, M. V., and Beroza, G. C.: Machine learning for data-driven discovery in solid Earth geoscience, Science, 363, eaau0323, https://doi.org/10.1126/science.aau0323, 2019. a
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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.