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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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Bengio, Y., Lamblin, P., Popovici, D., Larochelle, H., and Montreal, U.: Greedy layer-wise training of deep networks, Adv. Neur. In., 19, 153–160, 2007. 
Caruana, R., Lawrence, S., and Giles, L.: Overfitting in neural nets: backpropagation, conjugate gradient, and early stopping, in: Proceedings of International Conference on Neural Information Processing Systems, 402–408, 2000. 
Chen, B., Lu, C. D., and Liu, G. D.: A denoising method based on kernel principal component analysis for airborne time domain electro-magnetic data, Chinese J. Geophys.-Ch., 57, 295–302, https://doi.org/10.1002/cjg2.20087, 2014. 
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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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