Articles | Volume 26, issue 2
https://doi.org/10.5194/npg-26-61-2019
https://doi.org/10.5194/npg-26-61-2019
Research article
 | 
17 Apr 2019
Research article |  | 17 Apr 2019

Inverting Rayleigh surface wave velocities for crustal thickness in eastern Tibet and the western Yangtze craton based on deep learning neural networks

Xianqiong Cheng, Qihe Liu, Pingping Li, and Yuan Liu

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Inverting Rayleigh surface wave velocities for eastern Tibet and western Yangtze craton crustal thickness based on deep learning neural networks
Xian-Qiong Cheng, Qi-He Liu, and Ping Ping Li
Nonlin. Processes Geophys. Discuss., https://doi.org/10.5194/npg-2016-39,https://doi.org/10.5194/npg-2016-39, 2016
Revised manuscript not accepted
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Cited articles

Bassin, C., Laske, G., and Masters, G.: The current limits of resolution for surface wave tomography in north America, EOS T. Am. Geophys. Un., 81, F897, 2000. 
Bengio, Y.: Learning deep architectures for AI, Foundations and trends in Machine Learning, 2, 1–127, 2009. 
Bengio, Y., Lamblin, P., Popovici, D., and Larochelle, H.: Greedy layer-wise training of deep networks, in: Proceedings of the Advances in Neural Information Processing Systems, Vancouver, BC, Canada, 153–160, 2006. 
Bishop, C. M.: Neural Networks for Pattern Recognition, Oxford University Press, Oxford, UK, 1995. 
Chen, S. and Wilson, C. J. L.: Emplacement of the Longmen Shan Thrust – Nappe Belt along the eastern margin of the Tibetan Plateau, J. Struct. Geol., 18, 413–430, 1996. 
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This paper is based on a deep learning neural network to invert the Rayleigh surface wave velocity of the crustal thickness, which is a new geophysical inversion solution that proved to be effective and practical. Through comparative experiments, we found that deep learning neural networks can more accurately reveal the non-linear relationship between phase velocity and crustal thickness than traditional shallow networks. Deep learning neural networks are more efficient than Monte Carlo methods.