Shallow Profile Data Denoising Method Based on Improved Cycle-consistent Generative Adversarial Network
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摘要: 为解决浅地层剖面数据噪声多、分辨率低问题，本文将环形生成对抗网络的方法应用于浅地层剖面资料的去噪，实现智能去噪。首先，选择具有特殊对称生成对抗网络循环机制以及“循环一致性”损失的环形生成对抗性网络，并对其进行结构改进，提升网络学习和训练的性能。然后，基于优化的浅地层剖面样本集训练网络，实现对于浅地层剖面数据随机噪声的去除，提升数据的信噪比。通过对实验和实际资料的试算，以及与传统带通滤波方法的对比，验证本文方法对浅地层剖面数据去噪的有效性和适应性。Abstract: This study applied the cycle-consistent generative adversarial network method to the denoising of shallow profile data to realize intelligent denoising. This could help resolve the problem of noise and low resolution of shallow profile data. To do this, the cycle generative adversarial network with special symmetric generation countermeasure network cycle mechanism and "cycle consistency loss" was selected. We improved the performance of the network learning and training by optimizing the network structure. Next, based on the optimized shallow profile sample set training network, random noise was removed from the shallow profile data and the signal-to-noise ratio of the data was improved. The effectiveness and adaptability of this method for denoising shallow profile data were verified by trial calculations of experimental and actual data and by comparison with the traditional band-pass filtering method.
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Keywords:
- artificial intelligence /
- shallow profile data /
- random noise /
- data denoising
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表 1 两种去噪方法PSNR对比
Table 1 PSNR comparison of two denoising methods
去噪方法 PSNR/dB 带通滤波去噪 18.729 CycleGAN去噪 25.247 
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[1] 李一保, 张玉芬, 刘玉兰, 等. 浅地层剖面仪在海洋工程中的应用[J]. 工程地球物理学报, 2007,(1): 4−8. LI Y B, ZHANG Y F, LIU Y L, et al. Application of subbottom profiler to ocean engineering[J]. Chinese Journal of Engineering Geophysics, 2007, (1): 4−8. (in Chinese).
[2] 刘玉萍, 丁龙翔, 杨志成, 等. 利用浅剖资料进行海底底质分析[J]. 物探与化探, 2016,40(1): 66−72. LIU Y P, DING L X, YANG Z C, et al. Seabed sediment analysis using sub-bottom profile data[J]. Geophysical and Geochemical Exploration, 2016, 40(1): 66−72. (in Chinese).
[3] 李平, 杜军. 浅地层剖面探测综述[J]. 海洋通报, 2011,30(3): 344−350. LI P, DU J. Review on the probing of sub-bottom profiler[J]. Marine Science Bulletin, 2011, 30(3): 344−350. (in Chinese).
[4] 顾兆峰, 张志珣, 刘怀山. 南黄海西部地区浅层气地震特征[J]. 海洋地质与第四纪地质, 2006,(3): 65−74. GU Z F, ZHANG Z X, LIU H S. Seismic features of shallow gas in the western area of the Yellow Sea[J]. Marine Geology & Quaternary Geology, 2006, (3): 65−74. (in Chinese).
[5] 颜中辉, 栾锡武, 潘军, 等. 海上浅地层剖面处理的关键去噪技术[J]. 海洋地质前沿, 2016,32(9): 64−70. YAN Z H, LUAN X W, PAN J, et al. Key denoising techniques in marine sub-bottom shallow profiling[J]. Marine Geological Frontiers, 2016, 32(9): 64−70. (in Chinese).
[6] 施凤. 浅地层剖面数据精处理关键技术研究[D]. 武汉: 武汉大学, 2017. [7] 冯强强, 温明明, 吴衡, 等. 海洋浅地层剖面资料的数据处理方法[J]. 海洋地质前沿, 2013,29(11): 49−53, 66. FENG Q Q, WEN M M, WU H, et al. Data processing methods for marine sub-bottom profiles[J]. Marine Geological Frontiers, 2013, 29(11): 49−53, 66. (in Chinese).
[8] JEONG B J, LEE Y H. Design of weighted order statistic filters using the perceptron algorithm[J]. IEEE Transactions on Signal Processing, 1994, 42(11): 3264−3269. doi: 10.1109/78.330393
[9] GONZALEZ R C, WOODS R E. 数字图像处理[M]. 阮秋琦, 阮宇智, 译. 北京: 电子工业出版社, 2003. [10] MA Y Y, LI G F, WANG Y J, et al. Random noise attenuation by f-x spatial projection-based complex empirical mode decomposition predictive filtering[J]. Applied Geophysics, 2015, 12(1): 47−54. doi: 10.1007/s11770-015-0467-3
[11] 潘军, 栾锡武, 孙运宝, 等. SRME技术在海洋浅水高分辨率地震勘探中的应用[J]. 地球物理学进展, 2015,30(1): 429−434. PAN J, LUAN X W, SUN Y B, et al. Application of SRME technology in marine shallow water high resolution seismic exploration[J]. Progress in Geophysics, 2015, 30(1): 429−434. (in Chinese).
[12] 王兆湖, 王建民, 高振山, 等. 叠前自适应f-x域相干噪音衰减技术及应用[J]. 地球物理学进展, 2013,28(5): 2605−2610. WANG Z H, WANG J M, GAO Z S, et al. Pre-stack self-adaptive f-x domain coherent noise attenuation technology and application[J]. Progress in Geophysics, 2013, 28(5): 2605−2610. (in Chinese).
[13] YANG J, LIN N, ZHANG K, et al. Reservoir characterization using multi-component seismic data in a novel hybrid model based on clustering and deep neural network[J]. Natural Resources Research, 2021: 1−26.
[14] 杨晶, 丁仁伟, 林年添, 等. 基于深度学习的地震断层智能识别研究进展[J]. 地球物理学进展, 2022,37(1): 298−311. YANG J, DING R W, LIN N T, et al. Research progress of intelligent identification of seismic fault based on deep learning[J]. Progress in Geophysics, 2022, 37(1): 298−311. (in Chinese).
[15] 刘俊, 曹俊兴, 丁蔚楠, 等. 基于双向长短期记忆神经网络的储层孔隙度预测方法研究[J/OL]. 地球物理学进展: 1-9. (2021-07-23)[2021-12-18]. http://kns.cnki.net/kcms/detail/11.2982.P.20210723.1308.045.html. LIU J, CAO J X, DING W N, et al. Research on reservoir porosity prediction method based on bidirectional long-term and short-term memory neural network[J/OL]. Progress in Geophysics: 1-9. (2021-07-23)[2021-12-18]. http://kns.cnki.net/kcms/detail/11.2982.P.20210723.1308.045.html. (in Chinese).
[16] TUKEY J W. Exploratory data analysis[J]. Journal of the American Statistical Association, 1977, 28(1): 163−182.
[17] SUMAN S. Image denoising using new adaptive based median filter[J]. Signal & Image Processing an International Journal, 2014, 5(4): 1−13.
[18] QUAN H D, ZHANG H C, JIA H T. An improved adaptive median filtering algorithm[C]//International Symposium on Test Automation and Instrumentation, 2006: 269-272. [19] JAIN V, SEUNG H S. Natural image denoising with convolutional networks[C]//International Conference on Neural Information Processing Systems. Piscataway: IEEE Press, 2008: 769-776.
[20] 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, 2016, 26(7): 3142−3155.
[21] GOODFELLOW I J, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial nets[C]// Proceedings of the 27th International Conference on Neural Information Processing Systems, Montreal, 2014. Cambridge: MIT Press, 2014: 2672-2680.
[22] WANG H, LI Y, DONG X. Generative adversarial network for desert seismic data denoising[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020. DOI:10.1109/ TGRS.2020.3030692.
[23] DONG X T, LI Y. Denoising the optical fiber seismic data by using convolutional adversarial network based on loss balance[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020. DOI: 10.1109/TGRS.2020.3036065.
[24] ZHU J Y, PAKR T, ISOLA P, et al. Unpaired image-to-image translation using cycle-consistent adversarial networks[C]//Proceedings of the 2017 IEEE International Conference on Computer Vision, Venice, 2017. Washington: IEEE Computer Society, 2017: 2242-2251.
[25] 梁俊杰, 韦舰晶, 蒋正锋. 生成对抗网络GAN综述[J]. 计算机科学与探索, 2020,14(1): 1−17. LIANG J J, WEI J J, JIANG Z F. Generative adversarial networks GAN overview[J]. Journal of Frontiers of Computer Science & Technology, 2020, 14(1): 1−17. (in Chinese).
[26] 梁雪灿. 基于生成对抗网络的声学图像超分辨率研究[D]. 哈尔滨: 哈尔滨工程大学, 2019. [27] ISOLA P, ZHU J Y, ZHOU T H, et al. Image-to-image translation with conditional adversarial networks[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, 2017. Washington: IEEE Computer Society, 2017: 5967-5976.
[28] 王照, 陈恩庆. 基于深度残差生成对抗网络的本征图像分解算法[J]. 计算机应用与软件, 2022,39(3): 201−206. WANG Z, CHEN E Q. Eigen image decomposition algorithm for generating countermeasure network based on depth residual[J]. Computer Applications and Software, 2022, 39(3): 201−206. (in Chinese).
[29] NAYEF B H, ABDULLAH S N H S, SULAIMAN R, et al. Optimized leaky ReLU for handwritten Arabic character recognition using convolution neural networks[J]. Multimedia Tools and Applications, 2021, 81(2): 2065−2094.
[30] 杨久强, 林年添, 张凯, 等. 深度神经网络模型超参数选取及评价研究: 以含油气性多波地震响应特征提取为例[J]. 石油物探, 2022,61(2): 236−244. YANG J Q, LIN N T, ZHANG K, et al. Hyperparametric selection and evaluation of deep neural network models: A case study of feature extraction of multi-wave seismic response in an oil-gas reservoir[J]. Geophysical Prospecting for Petroleum, 2022, 61(2): 236−244. (in Chinese).
[31] 王议迎, 丁仁伟, 李建平, 等. 联合改进STA/LTA与MLoG算子的微震P波到时自动拾取方法[J]. 山东科技大学学报(自然科学版), 2021,40(6): 1−10. WANG Y Y, DING R W, LI J P, et al. Automatic pickup of microseismic P-wave arrival based on improved STA/LTA and MloG operators[J]. Journal of Shandong University of Science and Technology (Natural Science), 2021, 40(6): 1−10. (in Chinese).
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