Noise Attenuation for CSEM Data via Deep Residual Denoising Convolutional Neural Network and Shift-Invariant Sparse Coding

To overcome the interference of noise on the exploration effectiveness of the controlled-source electromagnetic method (CSEM), we improved the deep learning algorithm by combining the denoising convolutional neural network (DnCNN) with the residual network (ResNet), and propose a method based on the...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Vol. 15; no. 18; p. 4456
Main Authors: Wang, Xin, Bai, Ximin, Li, Guang, Sun, Liwei, Ye, Hailong, Tong, Tao
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.09.2023
Subjects:
Algorithms
Artificial neural networks
China
Coding
controlled-source electromagnetic method (CSEM)
Data mining
Deep learning
denoising
denoising convolutional neural network (DnCNN)
Dictionaries
Electromagnetism
Invariants
Libraries
Long short-term memory
Machine learning
Methods
Neural networks
Noise reduction
Remote sensing
residual network (ResNet)
Seismic engineering
shift-invariant sparse coding (SISC)
Signal to noise ratio
Square waves
Wavelet transforms
China
ISSN:2072-4292, 2072-4292
Online Access:Get full text
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Summary:To overcome the interference of noise on the exploration effectiveness of the controlled-source electromagnetic method (CSEM), we improved the deep learning algorithm by combining the denoising convolutional neural network (DnCNN) with the residual network (ResNet), and propose a method based on the residual denoising convolutional neural network (ResDnCNN) and shift-invariant sparse coding (SISC) for denoising CSEM data. Firstly, a sample library was constructed by adding simulated noises of different types and amplitudes to high-quality CSEM data collected. Then, the sample library was used for model training in the ResDnCNN, resulting in a network model specifically designed for denoising CSEM data. Subsequently, the trained model was employed to denoise the measured data, generating preliminary denoised data. Finally, the preliminary denoised data was processed using SISC to obtain the final denoised high-quality data. Comparative experiments with the ResNet, DnCNN, U-Net, and long short-term memory (LSTM) networks demonstrated the significant advantages of our proposed method. It effectively removed strong noise such as Gaussian, impulse, and square wave, resulting in an improvement of the signal-to-noise ratio by nearly 20 dB. Testing on CSEM data from Sichuan Province, China, showed that the apparent resistivity curves plotted using our method were smoother and more credible.
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ISSN:2072-4292
2072-4292
DOI:10.3390/rs15184456