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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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NPG | Articles | Volume 26, issue 1
Nonlin. Processes Geophys., 26, 13-23, 2019
https://doi.org/10.5194/npg-26-13-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Nonlin. Processes Geophys., 26, 13-23, 2019
https://doi.org/10.5194/npg-26-13-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 01 Mar 2019

Research article | 01 Mar 2019

Denoising stacked autoencoders for transient electromagnetic signal denoising

Fanqiang Lin et al.
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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 Fanqiang Lin on behalf of the Authors (16 Jan 2019)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (17 Jan 2019) by Luciano Telesca
RR by Anonymous Referee #3 (30 Jan 2019)
RR by Anonymous Referee #2 (09 Feb 2019)
ED: Publish as is (09 Feb 2019) by Luciano Telesca
Publications Copernicus
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Short summary
The deep-seated information is reflected in the late-stage data of the second field. By introducing the deep learning algorithm integrated with the characteristics of the secondary field data, we can map the contaminated data in late track data to a high-probability position. By comparing several filtering algorithms, the SFSDSA method has better performance and the denoising signal is conducive to further improving the effective detection depth.
The deep-seated information is reflected in the late-stage data of the second field. By...
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