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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/npg-2016-39
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/npg-2016-39
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  02 Sep 2016

02 Sep 2016

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This preprint was under review for the journal NPG but the revision was not accepted.

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

Xian-Qiong Cheng1, Qi-He Liu2, and Ping Ping Li1 Xian-Qiong Cheng et al.
  • 1College of Geophysics, Chengdu University of Technology, Chengdu, P.R. China
  • 2The School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, P.R. China

Abstract. Crustal thickness is an important factor affecting lithosphere structure and therefore deep geodynamics. In this paper, we propose to apply deep learning neural networks called stacked sparse auto-encoder to obtain crustal thickness for eastern Tibet and western Yangtze craton. Firstly taking phase and group velocities simultaneously as input and theoretical crustal thickness as output, we construct twelve deep neural networks trained by 70,000 and tested by 30,000 theoretical models. We then invert observed phase and group velocities by these twelve neural networks. Based on test errors and misfits with other crustal thickness models, we select the optimized one as crustal thickness for study areas. Compared with other ways detected crustal thickness such as seismic wave reflection and receiver function, we conclude that deep learning neural network is a promising, believable and inexpensive tool for geophysical inversion.

Xian-Qiong Cheng et al.

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Xian-Qiong Cheng et al.

Xian-Qiong Cheng et al.

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Short summary
In this paper we resolve classic geophysical problem based on newly developed computer and information science. Since many classic geophysical problems are nonlinear, researches treating them as linearity are approximate. When we treat inverting moho depth as full nonlinearity we attain more satisfactory results with lower costs and higher accuracy. Results we have attained can provide important data for discussing origin and development of earthquake, also for distribution of mineral resources.
In this paper we resolve classic geophysical problem based on newly developed computer and...
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