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

被引:5
作者
Wang, Xin [1 ,2 ]
Bai, Ximin [3 ]
Li, Guang [1 ,2 ,4 ]
Sun, Liwei [1 ]
Ye, Hailong [3 ]
Tong, Tao [3 ]
机构
[1] East China Univ Technol, Nanchang Key Lab Intelligent Sensing Technol & Ins, Nanchang 330013, Peoples R China
[2] Cent South Univ, Key Lab Metallogen Predict Nonferrous Met & Geol E, Minist Educ, Changsha 410083, Peoples R China
[3] Jiangxi Inst Co Ltd Survey & Design, Nanchang 330095, Peoples R China
[4] East China Univ Technol, Fundamental Sci Radioact Geol & Explorat Technol L, Nanchang 330013, Peoples R China
关键词
controlled-source electromagnetic method (CSEM); denoising convolutional neural network (DnCNN); residual network (ResNet); shift-invariant sparse coding (SISC); deep learning; denoising; ELECTROMAGNETIC METHOD; INVERSION; TRANSIENT; DECOMPOSITION;
D O I
10.3390/rs15184456
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
引用
收藏
页数:19
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