Articles | Volume 26, issue 4
https://doi.org/10.5194/npg-26-445-2019
https://doi.org/10.5194/npg-26-445-2019
Research article
 | 
26 Nov 2019
Research article |  | 26 Nov 2019

A fast approximation for 1-D inversion of transient electromagnetic data by using a back propagation neural network and improved particle swarm optimization

Ruiyou Li, Huaiqing Zhang, Nian Yu, Ruiheng Li, and Qiong Zhuang

Viewed

Total article views: 2,962 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,792 1,096 74 2,962 77 72
  • HTML: 1,792
  • PDF: 1,096
  • XML: 74
  • Total: 2,962
  • BibTeX: 77
  • EndNote: 72
Views and downloads (calculated since 02 Jul 2019)
Cumulative views and downloads (calculated since 02 Jul 2019)

Viewed (geographical distribution)

Total article views: 2,962 (including HTML, PDF, and XML) Thereof 2,408 with geography defined and 554 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 06 Jan 2025
Download
Short summary
The chaotic-oscillation inertia weight back propagation (COPSO-BP) algorithm is proposed for transient electromagnetic inversion. The BP's initial weight and threshold parameters were trained by COPSO, overcoming the BP falling into a local optimum. Inversion of the layered geoelectric model showed that the COPSO-BP method is accurate and stable and needs less training time. It can be used in 1-D direct current sounding, 1-D magnetotelluric sounding, seismic-wave impedance and source detection.