Articles | Volume 26, issue 1
https://doi.org/10.5194/npg-26-13-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-26-13-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Mar 2019
Research article |
| 01 Mar 2019
| 01 Mar 2019
Denoising stacked autoencoders for transient electromagnetic signal denoising
Fanqiang Lin
School of Information Science and Technology, Chengdu University of Technology, Chengdu, 610059, China
Key Lab of Geo-Detection and Information Techniques of Ministry of Education, Chengdu, 610059, China
Kecheng Chen
CORRESPONDING AUTHOR
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
School of Information Science and Technology, Chengdu University of Technology, Chengdu, 610059, China
Xuben Wang
College of Geophysics, Chengdu University of Technology, Chengdu, 610059, China
Key Lab of Geo-Detection and Information Techniques of Ministry of Education, Chengdu, 610059, China
Hui Cao
College of Geophysics, Chengdu University of Technology, Chengdu, 610059, China
Key Lab of Geo-Detection and Information Techniques of Ministry of Education, Chengdu, 610059, China
Danlei Chen
School of Information Science and Technology, Chengdu University of Technology, Chengdu, 610059, China
Fanzeng Chen
School of Information Science and Technology, Chengdu University of Technology, Chengdu, 610059, China
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Cited
20 citations as recorded by crossref.
- TEM1Dformer: A Novel 1-D Time Series Deep Denoising Network for TEM Signals D. Pan et al. 10.1109/JSEN.2023.3330468
- Automated Transient Electromagnetic Data Processing for Ground-Based and Airborne Systems by a Deep Learning Expert System M. Asif et al. 10.1109/TGRS.2022.3202304
- The Potential of Machine Learning for a More Responsible Sourcing of Critical Raw Materials P. Ghamisi et al. 10.1109/JSTARS.2021.3108049
- CG-DAE: a noise suppression method for two-dimensional transient electromagnetic data based on deep learning S. Yu et al. 10.1093/jge/gxad035
- Noise Attenuation for CSEM Data via Deep Residual Denoising Convolutional Neural Network and Shift-Invariant Sparse Coding X. Wang et al. 10.3390/rs15184456
- Denoising magnetic resonance spectroscopy (MRS) data using stacked autoencoder for improving signal‐to‐noise ratio and speed of MRS J. Wang et al. 10.1002/mp.16831
- Efficient processing power harmonic noise with fluctuation frequency in urban transient electromagnetic surveys S. Huang et al. 10.1063/5.0040092
- TEMDnet: A Novel Deep Denoising Network for Transient Electromagnetic Signal With Signal-to-Image Transformation K. Chen et al. 10.1109/TGRS.2020.3034752
- Denoising of Transient Electromagnetic Data Based on the Minimum Noise Fraction-Deep Neural Network Y. Sun et al. 10.1109/LGRS.2022.3180433
- A method for reducing transient electromagnetic Noise: Combination of variational mode decomposition and wavelet denoising algorithm T. Qi et al. 10.1016/j.measurement.2022.111420
- Time-Domain Electromagnetic Noise Suppression Using Multivariate Variational Mode Decomposition K. Xing et al. 10.3390/rs16050806
- A synthetic denoising algorithm for full-waveform induced polarization based on deep learning W. Liu et al. 10.1190/geo2022-0234.1
- Integrated TEM and GPR Data Interpretation for High-Resolution Measurement of Urban Underground Space J. Chen et al. 10.1109/TIM.2021.3134995
- A transient electromagnetic signal denoising method based on an improved variational mode decomposition algorithm G. Feng et al. 10.1016/j.measurement.2021.109815
- Comparative Research on Noise Reduction of Transient Electromagnetic Signals Based on Empirical Mode Decomposition and Variational Mode Decomposition H. Wei et al. 10.1029/2020RS007135
- DL-RMD: a geophysically constrained electromagnetic resistivity model database (RMD) for deep learning (DL) applications M. Asif et al. 10.5194/essd-15-1389-2023
- TEM-NLnet: A Deep Denoising Network for Transient Electromagnetic Signal With Noise Learning M. Wang et al. 10.1109/TGRS.2022.3148340
- Rapid and High-Resolution Detection of Urban Underground Space Using Transient Electromagnetic Method J. Lin et al. 10.1109/TII.2021.3104012
- Research Trends and Case Studies of Deep Learning Applications in Geo-electric and Electromagnetic Surveys J. Jeong et al. 10.32390/ksmer.2022.59.4.379
- Deep learning-based protoacoustic signal denoising for proton range verification J. Wang et al. 10.1088/2057-1976/acd257
20 citations as recorded by crossref.
- TEM1Dformer: A Novel 1-D Time Series Deep Denoising Network for TEM Signals D. Pan et al. 10.1109/JSEN.2023.3330468
- Automated Transient Electromagnetic Data Processing for Ground-Based and Airborne Systems by a Deep Learning Expert System M. Asif et al. 10.1109/TGRS.2022.3202304
- The Potential of Machine Learning for a More Responsible Sourcing of Critical Raw Materials P. Ghamisi et al. 10.1109/JSTARS.2021.3108049
- CG-DAE: a noise suppression method for two-dimensional transient electromagnetic data based on deep learning S. Yu et al. 10.1093/jge/gxad035
- Noise Attenuation for CSEM Data via Deep Residual Denoising Convolutional Neural Network and Shift-Invariant Sparse Coding X. Wang et al. 10.3390/rs15184456
- Denoising magnetic resonance spectroscopy (MRS) data using stacked autoencoder for improving signal‐to‐noise ratio and speed of MRS J. Wang et al. 10.1002/mp.16831
- Efficient processing power harmonic noise with fluctuation frequency in urban transient electromagnetic surveys S. Huang et al. 10.1063/5.0040092
- TEMDnet: A Novel Deep Denoising Network for Transient Electromagnetic Signal With Signal-to-Image Transformation K. Chen et al. 10.1109/TGRS.2020.3034752
- Denoising of Transient Electromagnetic Data Based on the Minimum Noise Fraction-Deep Neural Network Y. Sun et al. 10.1109/LGRS.2022.3180433
- A method for reducing transient electromagnetic Noise: Combination of variational mode decomposition and wavelet denoising algorithm T. Qi et al. 10.1016/j.measurement.2022.111420
- Time-Domain Electromagnetic Noise Suppression Using Multivariate Variational Mode Decomposition K. Xing et al. 10.3390/rs16050806
- A synthetic denoising algorithm for full-waveform induced polarization based on deep learning W. Liu et al. 10.1190/geo2022-0234.1
- Integrated TEM and GPR Data Interpretation for High-Resolution Measurement of Urban Underground Space J. Chen et al. 10.1109/TIM.2021.3134995
- A transient electromagnetic signal denoising method based on an improved variational mode decomposition algorithm G. Feng et al. 10.1016/j.measurement.2021.109815
- Comparative Research on Noise Reduction of Transient Electromagnetic Signals Based on Empirical Mode Decomposition and Variational Mode Decomposition H. Wei et al. 10.1029/2020RS007135
- DL-RMD: a geophysically constrained electromagnetic resistivity model database (RMD) for deep learning (DL) applications M. Asif et al. 10.5194/essd-15-1389-2023
- TEM-NLnet: A Deep Denoising Network for Transient Electromagnetic Signal With Noise Learning M. Wang et al. 10.1109/TGRS.2022.3148340
- Rapid and High-Resolution Detection of Urban Underground Space Using Transient Electromagnetic Method J. Lin et al. 10.1109/TII.2021.3104012
- Research Trends and Case Studies of Deep Learning Applications in Geo-electric and Electromagnetic Surveys J. Jeong et al. 10.32390/ksmer.2022.59.4.379
- Deep learning-based protoacoustic signal denoising for proton range verification J. Wang et al. 10.1088/2057-1976/acd257
Latest update: 19 Apr 2024
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...
