Journal cover Journal topic
Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union

Journal metrics

  • IF value: 1.329 IF 1.329
  • IF 5-year<br/> value: 1.394 IF 5-year
    1.394
  • CiteScore<br/> value: 1.27 CiteScore
    1.27
  • SNIP value: 0.903 SNIP 0.903
  • SJR value: 0.709 SJR 0.709
  • IPP value: 1.455 IPP 1.455
  • h5-index value: 20 h5-index 20
https://doi.org/10.5194/npg-2018-11
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
09 Mar 2018
Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Nonlinear Processes in Geophysics (NPG).
Inverting Rayleigh surface wave velocities for crustal thickness in eastern Tibet and the western Yangtze craton based on deep learning neural networks
Xianqiong Cheng1, Qihe Liu2, Pingping Li1, and Yuan Liu1 1College of Geophysics, Chengdu University of Technology, Chengdu, P.R. China
2School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, P.R. China
Abstract. Crustal thickness is an important factor affecting lithosphere structure and 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 of Rayleigh surface wave 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 adopt a new way for inversion of earth model parameters, and realize that deep learning neural network based on data driven with the highly nonlinear mapping ability can be widely used by geophysical inversion method, and our result has good agreement with high-resolution crustal thickness models. We conclude that deep learning neural network is a promising, efficient and believable tool for geophysical inversion.
Citation: Cheng, X., Liu, Q., Li, P., and Liu, Y.: Inverting Rayleigh surface wave velocities for crustal thickness in eastern Tibet and the western Yangtze craton based on deep learning neural networks, Nonlin. Processes Geophys. Discuss., https://doi.org/10.5194/npg-2018-11, in review, 2018.
Xianqiong Cheng et al.
Xianqiong Cheng et al.
Xianqiong Cheng et al.

Viewed

Total article views: 184 (including HTML, PDF, and XML)

HTML PDF XML Total BibTeX EndNote
131 46 7 184 7 7

Views and downloads (calculated since 09 Mar 2018)

Cumulative views and downloads (calculated since 09 Mar 2018)

Viewed (geographical distribution)

Total article views: 184 (including HTML, PDF, and XML)

Thereof 183 with geography defined and 1 with unknown origin.

Country # Views %
  • 1

Saved

Discussed

Latest update: 18 Jun 2018
Publications Copernicus
Download
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...
Share