Articles | Volume 26, issue 1
https://doi.org/10.5194/npg-26-13-2019
https://doi.org/10.5194/npg-26-13-2019
Research article
 | 
01 Mar 2019
Research article |  | 01 Mar 2019

Denoising stacked autoencoders for transient electromagnetic signal denoising

Fanqiang Lin, Kecheng Chen, Xuben Wang, Hui Cao, Danlei Chen, and Fanzeng Chen

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Latest update: 12 Oct 2024
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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.