Articles | Volume 26, issue 1
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

Related authors

The development and test research of a multichannel synchronous transient electromagnetic receiver
Fanqiang Lin, Xuben Wang, Kecheng Chen, Depan Hu, Song Gao, Xue Zou, and Cai Zeng
Geosci. Instrum. Method. Data Syst., 7, 209–221,,, 2018
Short summary

Related subject area

Subject: Time series, machine learning, networks, stochastic processes, extreme events | Topic: Solid earth, continental surface, biogeochemistry
Application of Lévy processes in modelling (geodetic) time series with mixed spectra
Jean-Philippe Montillet, Xiaoxing He, Kegen Yu, and Changliang Xiong
Nonlin. Processes Geophys., 28, 121–134,,, 2021
Short summary
Seismic section image detail enhancement method based on bilateral texture filtering and adaptive enhancement of texture details
Xiang-Yu Jia and Chang-Lei DongYe
Nonlin. Processes Geophys., 27, 253–260,,, 2020
Short summary
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, Huaiqing Zhang, Nian Yu, Ruiheng Li, and Qiong Zhuang
Nonlin. Processes Geophys., 26, 445–456,,, 2019
Short summary
Negentropy anomaly analysis of the borehole strain associated with the Ms 8.0 Wenchuan earthquake
Kaiguang Zhu, Zining Yu, Chengquan Chi, Mengxuan Fan, and Kaiyan Li
Nonlin. Processes Geophys., 26, 371–380,,, 2019
Short summary
Mahalanobis distance-based recognition of changes in the dynamics of a seismic process
Teimuraz Matcharashvili, Zbigniew Czechowski, and Natalia Zhukova
Nonlin. Processes Geophys., 26, 291–305,,, 2019

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,, 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,, 2014. 
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.