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Journal of ZheJiang University (Engineering Science)  2020, Vol. 54 Issue (5): 978-984    DOI: 10.3785/j.issn.1008-973X.2020.05.016
Earth Science     
Seismic data processing based on convolutional autoencoder
Jin-sheng JIANG(),Hao-ran REN*(),Han-ye LI
Key Laboratory of Geoscience Big Data and Deep Resource of Zhejiang Province, School of Earth Sciences, Zhejiang University, Hangzhou 310027, China
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Abstract  

Data-driven methods represented by deep learning were introduced into seismic data processing, which can speed up the processing process of seismic data and obtain more accurate information about underground media. The convolutional autoencoder (CAE) method can extract data features by using the spatial correlation of seismic data while compressing it, which avoids the assumption dependence of mathematical and physical model. The underground velocity structures of different geological models were designed, and the training and test sets with different features were obtained through wave equation forward modeling. CAE method is different from the traditional model-driven method of seismic random noise attenuation and seismic data interpolation. CAE method can identify and extract the valid seismic signals directly from seismic data with random noise and missing traces, to attenuate random noise and reconstruct the original seismic data. Experimental results verified the effectiveness of this method. CAE method gets rid of the artificial threshold, therefore has higher processing efficiency.



Key wordsconvolutional autoencoder      seismic data interpolation      seismic random noise attenuation      deep neural network      sparse representation     
Received: 11 June 2019      Published: 05 May 2020
CLC:  P 3  
Corresponding Authors: Hao-ran REN     E-mail: jiangjs217@zju.edu.cn;rhr@zju.edu.cn
Cite this article:

Jin-sheng JIANG,Hao-ran REN,Han-ye LI. Seismic data processing based on convolutional autoencoder. Journal of ZheJiang University (Engineering Science), 2020, 54(5): 978-984.

URL:

http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2020.05.016     OR     http://www.zjujournals.com/eng/Y2020/V54/I5/978


基于卷积自编码器的地震数据处理

引入以深度学习为代表的数据驱动方法,加速地震数据的处理流程,获得更精确的地下介质信息. 卷积自编码器方法在地震数据压缩降维的同时,利用数据的空间局部相关性自动提取信号特征,避免数学物理模型的假设依赖. 通过设计不同地质模型的地下速度结构,利用波动方程正演模拟构建大量不同特征的地震数据训练集和测试集. 与模型驱动的地震随机噪声压制和地震道插值方法不同,数据驱动下的卷积自编码器方法能够从含随机噪声地震数据和地震道缺失数据中,直接识别和提取出其中的有效地震信号,从而压制随机噪声以及重建原始地震数据,实验结果验证了该方法的有效性. 卷积自编码器方法不需要人工阈值控制,具有更高的处理效率.


关键词: 卷积自编码器,  地震道插值,  地震随机噪声压制,  深度神经网络,  稀疏表达 
Fig.1 Architecture of convolutional autoencoder
Fig.2 Training and testing process of convolutional autoencoder
Fig.3 Velocity models to generate training sets
Fig.4 Comparison of random noise attenuation results
Fig.5 Comparison of seismic data interpolation results
[1]   张英强, 陆文凯.基于迭代收缩高分辨率Radon变换的地震道插值[C]//中国地球物理学会第二十九届年会.昆明: 中国地球物理学会, 2013: 632-633.
ZHANG Ying-qiang, LU Wen-kai. Seismic trace interpolation method based on iterative shrinking high-resolution radon transform [C]// The 29th Annual Meeting of the Chinese Geophysical Society. Kunming: Chinese Geophysical Society, 2013: 632-633.
[2]   谭军, 宋鹏, 李金山, 等 基于速度加权叠加和AVO分析的叠前地震数据插值方法[J]. 石油物探, 2017, 56 (6): 812- 819
TAN Jun, SONG Peng, LI Jin-shan, et al Pre-stack seismic data interpolation based on velocity-weighted stack and AVO analysis[J]. Geophysical Prospecting for Petroleum, 2017, 56 (6): 812- 819
doi: 10.3969/j.issn.1000-1441.2017.06.006
[3]   王立明, 刘保华, 许江, 等 一种有效的地震道插值方法[J]. 地球物理学进展, 2012, 27 (6): 2561- 2569
WANG Li-ming, LIU Bao-hua, XU Jiang, et al An effective seismic trace interpolation method[J]. Progressin Geophys, 2012, 27 (6): 2561- 2569
doi: 10.6038/j.issn.1004-2903.2012.06.033
[4]   SHAN H, MA J, YANG H Comparisons of wavelets, contourlets and curvelets in seismic denoising[J]. Journal of Applied Geophysics, 2009, 69 (2): 103- 115
doi: 10.1016/j.jappgeo.2009.08.002
[5]   薛昭, 董良国, 单联瑜 Radon变换去噪方法的保幅性理论分析[J]. 石油地球物理勘探, 2012, 47 (6): 858- 867
XUE Zhao, DONG Liang-guo, SHAN Lian-yu Amplitude preservation theoretical analysis of radon transforms denoising method[J]. OilGeophysical Prospecting, 2012, 47 (6): 858- 867
[6]   彭才, 常智, 朱仕军 基于曲波变换的地震数据去噪方法[J]. 石油物探, 2008, 47 (5): 461- 464
PENG Cai, CHANG Zhi, ZHU Shi-jun Noise elimination method based on curvelet transform[J]. Geophysical Prospecting for Petroleum, 2008, 47 (5): 461- 464
doi: 10.3969/j.issn.1000-1441.2008.05.006
[7]   吴招才, 刘天佑 地震数据去噪中的小波方法[J]. 地球物理学进展, 2008, 23 (2): 493- 499
WU Zhao-cai, LIU Tian-you Wavelet transform methods in seismic data noise attenuation[J]. Progress in Geophysics, 2008, 23 (2): 493- 499
[8]   颜中辉, 栾锡武, 王赟, 等 基于经验模态分解的分数维地震随机噪声衰减方法[J]. 地球物理学报, 2017, 60 (7): 2845- 2857
YAN Zhong-hui, LUAN Xi-wu, WANG Yun, et al Seismic random noise attenuation based on empirical mode decomposition of fractal dimension[J]. Chinese Journal of Geophysics, 2017, 60 (7): 2845- 2857
doi: 10.6038/cjg20170729
[9]   NEELAMANI R, BAUMSTEIN A I, GILLARD D G, et al Coherent and random noise attenuation using the curvelet transform[J]. The Leading Edge, 2008, 27: 240- 248
doi: 10.1190/1.2840373
[10]   HINTON G, DENG L, YU D, et al Deep neural networks for acoustic modeling in speech recognition[J]. IEEE Signal Processing Magazine, 2012, 29 (6): 82- 97
doi: 10.1109/MSP.2012.2205597
[11]   LECUN Y, BENGIO Y, HINTON G Deep learning[J]. Nature, 2015, 521 (7553): 436
doi: 10.1038/nature14539
[12]   GUO Y, LIU Y, OERLEMANS A, et al Deep learning for visual understanding: a review[J]. Neurocomputing, 2016, 187: 27- 48
doi: 10.1016/j.neucom.2015.09.116
[13]   GRAMSTAD O, NICKELM. Automated interpretation of top and base salt using deep-convolutional networks [C]// 88th Annual International Meeting, SEG. Anaheim: Society of Exploration Geophysicists, 2018: 1956-1960.
[14]   WU X, SHI Y, FOMEL S. Convolutional neural networks for fault interpretation in seismic images [C]// 88th Annual International Meeting, SEG. Anaheim: Society of Exploration Geophysicists, 2018: 1946-1950.
[15]   ALFARRAJ M, ALREGIB G. Petrophysicalproperty estimation from seismic data using recurrent neural networks [C]// 88th Annual International Meeting, SEG. Anaheim: Society of Exploration Geophysicists, 2018: 2141-2146.
[16]   ALWON S. Generative adversarial networks in seismic data processing [C]// 88th Annual International Meeting, SEG. Anaheim: Society of Exploration Geophysicists, 2018: 1991-1995.
[17]   LI H, YANG W, YONG X. Deep learning for ground-roll noise attenuation [C]// 88th Annual International Meeting, SEG. Anaheim: Society of Exploration Geophysicists, 2018: 1981-1985.
[18]   王钰清, 陆文凯, 刘金林, 等 基于数据增广和CNN的地震随机噪声压制[J]. 地球物理学报, 2019, 62 (1): 421- 433
WANG Yu-qing, LU Wen-kai, LIU Jin-lin, et al Random seismic noise attenuation based on data augmentation and CNN[J]. Chinese Journal of Geophysics, 2019, 62 (1): 421- 433
doi: 10.6038/cjg2019M0385
[19]   HINTON G E, SALAKHUTDINOV R R Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313 (5786): 504- 507
doi: 10.1126/science.1127647
[20]   SHELHAMER E, LONG J, DARRELL T Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39 (4): 640- 651
doi: 10.1109/TPAMI.2016.2572683
[21]   BADRINARAYANAN V, KENDALL A, CIPOLLA R SegNet: adeep convolutional encoder-decoder aqrchitecture for scene segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39 (12): 2481- 2495
doi: 10.1109/TPAMI.2016.2644615
[22]   贾文娟, 张煜东 自编码器理论与方法综述[J]. 计算机系统应用, 2018, 27 (5): 1- 9
JIA Wen-juan, ZHANG Yu-dong Survey on theories and methods of autoencoder[J]. Computer Science and Applications, 2018, 27 (5): 1- 9
[23]   MASCI J, MEIER U, CIRESAN D, et al. Stacked convolutional auto-encoders for hierarchical feature extraction [C]// Artificial Neural Networks and Machine Learning–ICANN 2011. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011: 52-59.
[24]   BENGIO Y, COURVILLE A, VINCENT P Representation learning: areview and new perspectives[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35 (8): 1798- 1828
doi: 10.1109/TPAMI.2013.50
[25]   刘洋, 李炳秀, 王典, 等 基于正则化条件的地震数据局部信噪比估计方法[J]. 地球物理学报, 2017, 60 (5): 1979- 1987
LIU Yang, LI Bing-xiu, WANG Dian, et al Local SNR estimation method based on regularization for seismic data[J]. Chinese Journal of Geophysics, 2017, 60 (5): 1979- 1987
doi: 10.6038/cjg20170529
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