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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Cited articles

Ali, A., Fan, Y., and Shu, L.: Automatic modulation classification of digital modulation signals with stacked autoencoders, Digit. Signal Process., 71, 108–116, https://doi.org/10.1016/j.dsp.2017.09.005, 2017. 
Becker, S. and Plumbley, M.: Unsupervised neural network learning procedures for feature extraction and classification, Appl. Intell., 6, 185–203, https://10.1007/bf00126625, 1996. 
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.