Seismic data acquired from mature industrial areas frequently contain a large amount of local strong noise with high amplitude due to the continuous operation of production equipment.However,such local strong noise can be hardly suppressed using conventional denoising methods.This study integrated dilated convolution(DC) and U-Net into a DC-UNet network for suppressing local strong noise.For the circular DC blocks at the front end of the DC-Unet network,a circularly expanded DC kernel was used to extract the features of strong noise at different scales,with the receptive field being expanded.Meanwhile,an encoder was used at the back end of the network to extract the features of strong noise and restore the details of strong noise.Subsequently,the DC-UNet network was employed to perform a nonlinear mapping from noisy data to noise.On this basis,strong noise was suppressed by subtracting the learned strong noise from the noisy data.As indicated by the experimental results of synthetic and real data obtained from the training using the PyTorch framework in the GPU environment,the DC-UNet network can effectively suppress the local strong noise and improve the signal-to-noise ratio compared with DnCNN,U-Net,and PCA-UNet networks.
周慧, 孙成禹, 刘英昌, 蔡瑞乾. 基于DC-UNet卷积神经网络的强噪声压制方法[J]. 物探与化探, 2023, 47(5): 1288-1297.
ZHOU Hui, SUN Cheng-Yu, LIU Ying-Chang, CAI Rui-Qian. A method for strong noise suppression based on DC-UNet. Geophysical and Geochemical Exploration, 2023, 47(5): 1288-1297.
Shahdoosti H R, Rahemi Z. Edge-preserving image denoising using a deep convolutional neural network[J]. Signal Process, 2019, 159(3):20-32.
doi: 10.1016/j.sigpro.2019.01.017
Feng X Q, Yang C C, Long Z Y. The filtering method in f-x-y domain based on singular value decomposition[J]. Geophysical and Geochemical Exploration, 2005, 29(2):171-173.
Jia C M, Jiang G Q, Liu Z C, et al. Denising method based on sparse hyperbolic Radon transform in the frequency domain[J]. Geophysical and Geochemical Exploration, 2016, 40(3):527-533.
Yang L Q, Song H B, Hao T Y, et al. Method of 2-D wavelet transform in attenuating random noise[J]. Geophysical Prospecting for Petroleum, 2005, 44(1):4-6.
[9]
Zhang K, Zuo W, Chen Y, et al. Beyond a Gaussian Denoiser:Residual learning of deep CNN for image denoising[J]. IEEE Transactions on Image Processing, 2017, 26(7):3142-3155.
doi: 10.1109/TIP.2017.2662206
pmid: 28166495
Han W X, Zhou Y D, Chi Y. Deep learning convolutional neural networks for random noise attenuation in seismic data[J]. Geophysical Prospecting for Petroleum, 2018, 57(6):862-869.
doi: 10.3969/j.issn.1000-1441.2018.06.008
[11]
Alwon S. Generative adversarial networks in seismic data processing[C]// SEG Technical Program Expanded Abstracts, 2018.
Yu S W, Ma J W. Seismic noise suppression based on deep learning[C]// SEG Beijing 2018 International Geophysical Conference and Exhibition, 2018.
[13]
Wang F, Chen S. Residual learning of deep convolutional neural network for seismic random noise attenuation[J]. IEEE Geosciences and Remote Sensing Letters, 2019, 16(8):1314-1318.
doi: 10.1109/LGRS.8859
Luo R Z, Li Y Y. Random scismic noisc attenuation based on RUnet convolutional neural network[J]. Gcophysical Prospecting for Petroleum, 2020, 59(1):51-59
[16]
Li H, Yang W, Yong X. Deep learning for ground-roll noise attenuation[C]// SEG Technical Program Expanded Abstracts, 2018.
[17]
Yu F, Koltun V. Multi-scale context aggregation by dilated convolutions[C]// ICLR, 2016.
[18]
张华博. 基于深度学习的图像分割研究与应用[D]. 成都: 电子科技大学, 2018.
[18]
Zhang H B. Research and application of image segmentation by deep learning[D]. Chengdu: University of Electronic Science and Technology of China, 2018.
[19]
薛海洋. 基于深度学习的图像分割算法研究[D]. 南宁: 广西大学, 2020.
[19]
Xue H Y. Research on image segmentation algorithm based on deep learning[D]. Nanning: Guangxi University, 2020.