Articles | Volume 26, issue 4
Nonlin. Processes Geophys., 26, 445–456, 2019
https://doi.org/10.5194/npg-26-445-2019
Nonlin. Processes Geophys., 26, 445–456, 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 et al.

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Huaiqing Zhang on behalf of the Authors (28 Sep 2019)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (30 Sep 2019) by Norbert Marwan
RR by Anonymous Referee #2 (08 Oct 2019)
RR by Anonymous Referee #1 (15 Oct 2019)
ED: Publish subject to minor revisions (review by editor) (17 Oct 2019) by Norbert Marwan
AR by Huaiqing Zhang on behalf of the Authors (19 Oct 2019)  Author's response    Manuscript
ED: Publish subject to minor revisions (review by editor) (22 Oct 2019) by Norbert Marwan
AR by Anna Wenzel on behalf of the Authors (24 Oct 2019)  Author's response    Manuscript
ED: Publish as is (24 Oct 2019) by Norbert Marwan
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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.