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

Related authors

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
Short summary

Related subject area

Subject: Predictability, probabilistic forecasts, data assimilation, inverse problems | Topic: Solid earth, continental surface, biogeochemistry
Uncertainties, complexities and possible forecasting of Volcán de Colima energy emissions (Mexico, years 2013–2015) based on a fractal reconstruction theorem
Marisol Monterrubio-Velasco, Xavier Lana, and Raúl Arámbula-Mendoza
Nonlin. Processes Geophys., 30, 571–583, https://doi.org/10.5194/npg-30-571-2023,https://doi.org/10.5194/npg-30-571-2023, 2023
Short summary
The joint application of a metaheuristic algorithm and a Bayesian statistics approach for uncertainty and stability assessment of nonlinear magnetotelluric data
Mukesh, Kuldeep Sarkar, and Upendra K. Singh
Nonlin. Processes Geophys., 30, 435–456, https://doi.org/10.5194/npg-30-435-2023,https://doi.org/10.5194/npg-30-435-2023, 2023
Short summary
Modeling of terrain effect in magnetotelluric data from Garhwal Himalaya Region
Suman Saini, Deepak Kumar Tyagi, Sushil Kumar, and Rajeev Sehrawat
EGUsphere, https://doi.org/10.5194/egusphere-2023-2166,https://doi.org/10.5194/egusphere-2023-2166, 2023
Preprint withdrawn
Short summary
On parameter bias in earthquake sequence models using data assimilation
Arundhuti Banerjee, Ylona van Dinther, and Femke C. Vossepoel
Nonlin. Processes Geophys., 30, 101–115, https://doi.org/10.5194/npg-30-101-2023,https://doi.org/10.5194/npg-30-101-2023, 2023
Short summary
An approach for constraining mantle viscosities through assimilation of palaeo sea level data into a glacial isostatic adjustment model
Reyko Schachtschneider, Jan Saynisch-Wagner, Volker Klemann, Meike Bagge, and Maik Thomas
Nonlin. Processes Geophys., 29, 53–75, https://doi.org/10.5194/npg-29-53-2022,https://doi.org/10.5194/npg-29-53-2022, 2022
Short summary

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. 
Download
Short summary
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.