吉林大学学报(地球科学版) ›› 2019, Vol. 49 ›› Issue (4): 1145-1159.doi: 10.13278/j.cnki.jjuese.20180128
代丽艳1,2, 董宏丽1,2, 李学贵1,3
Dai Liyan1,2, Dong Hongli1,2, Li Xuegui1,3
摘要： 随着常规油气藏资源不断枯竭，非常规油气藏的勘探开发已逐渐成为一种必然趋势，从而使得微地震监测技术快速发展。微地震事件的发生持续时间短、声波频率高，使得实际采集到的微地震数据信噪比较低。本文首先简要介绍了微地震监测技术能够在非常规油气藏开发中保证高效的增产，以及微地震噪声压制在微地震监测技术数据处理流程中是关键一步，直接影响着后续微地震研究的准确性和可靠性；并对地面微地震监测数据中的噪声源进行分析，归纳了地面微地震监测中常见噪声：强脉冲干扰、工业交流电干扰、钻井干扰、声波干扰、规则干扰等，分析了其各自的基本特点。然后，概述了地面微地震数据去噪方法已取得的成果，按频率、传播方向、空间分布区域等特性进行分类，分析各种去噪方法在实际应用过程中针对的噪声类型，以及在去噪过程中对有效信号造成的影响等。最后，基于深度学习具有更强的复杂函数表征能力，分析了3种典型深度学习模型的结构及特点；结合在其他相关领域数据去噪问题的成功应用，深度学习可以解决目前微地震数据噪声压制存在的问题，可以作为微地震数据去噪的一种新方法；考虑到目前微地震数据样本数量可能影响深度学习在微地震监测中大规模的应用，本文提出用生成式对抗网络来构建微地震数据样本库以解决该问题，并将其用于后续深度学习过程中的模型训练。
中图分类号:
| [1] 邹才能,杨智,朱如凯,等.中国非常规油气勘探开发与理论技术进展[J].地质学报,2015,89(6):979-1007. Zou Caiceng, Yang Zhi, Zhu Rukai, et al. China's Unconventional Oil and Gas Exploration and Development and Theoretical and Technological Progress[J]. Acta Geologica Sinica, 2015, 89(6):979-1007. [2] 刘振武,撒利明,巫芙蓉,等.中国石油集团非常规油气微地震监测技术现状及发展方向[J].石油地球物理勘探,2013,48(5):843-853. Liu Zhenwu, Sa Liming, Wu Furong, et al. Current Status and Development Direction of Unconventional Oil and Gas Microseismic Monitoring Technology of CNPC[J]. Oil Geophysical Prospecting, 2013, 48(5):843-853. [3] 邹才能,陶士振,杨智,等.中国非常规油气勘探与研究新进展[J].矿物岩石地球化学通报,2012,33(4):312-322. Zou Caineng, Tao Shizhen, Yang Zhi, et al. New Advance in Unconventional Petroleum Exploration and Research in China[J]. Bulletin of Mineralogy, Petrology and Geochemistry, 2012, 33(4):312-322. [4] 董世泰,高红霞.微地震监测技术及其在油田开发中的应用[J].石油仪器,2004,18(5):5-8. Dong Shitai, Gao Hongxia. Microseismic Monitering Technology and Its Application to Oilfield Development[J]. Petroleum Instruments, 2004, 18(5):5-8. [5] 梁冰,朱广生.油气田勘探开发中的微震监测方法[M].北京:石油工业出版社,2004:1-23. Liang Bing, Zhu Guangsheng. Microseismic Monitoring Method in Oil and Gas Field Exploration and Development[M]. Beijing:Petroleum Industry Press, 2004:1-23. [6] 蒋腾飞.微地震数据去噪方法研究[D].荆州:长江大学,2015. Jiang Tengfei. Microseismic Data Denoising Method[D]. Jingzhou:Yangtze University, 2015. [7] 张山,刘清林,赵群,等.微地震监测技术在油田开发中的应用[J].石油物探,2002,41(2):226-231. Zhang Shan, Liu Qinglin, Zhao Qun, et al. Microseismic Monitoring Technology in Oil Field Development[J]. Geophysical Prospecting for Petroleum, 2002, 41(2):226-231. [8] 邵晓光,董宏丽,代丽艳.微地震监测技术综述[J].吉林大学学报(信息科学版),2018,36(1):55-61. Shao Xiaoguang, Dong Hongli, Dai Liyan. Microseismic Monitoring Technology Review[J]. Journal of Jilin University (Information Science Edition), 2018, 36(1):55-61. [9] 吴建光,张平,吕昊,等.基于震幅叠加的微地震事件定位在地面监测中的应用[J].吉林大学学报(地球科学版),2017,47(1):255-264. Wu Jianguang, Zhang Ping, Lü Hao, et al. Application of Microseismic Event Location Using Amplitude Summation in Surface Monitoring[J]. Journal of Jilin University (Earth Science Edition), 2017, 47(1):255-264. [10] 宋维琪.微地震监测新技术与新方法[M].东营:中国石油大学出版社,2014. Song Weiqi. The New Technology and Method for Microseismic Monitoring[M]. Dongying:China Petroleum University Press, 2014. [11] 郭晓中.地面微地震资料处理方法研究[D].青岛:中国石油大学(华东),2012. Guo Xiaozhong. Ground Microseismic Data Processing Method[D]. Qingdao:China University of Petroleum (East China), 2012. [12] Zheng Jing, Lu Jiren, Jiang Tianqi, et al. Microseismic Event Denoising via Adaptive Directional Vector Median Filters[J]. Acta Geophys, 2017, 65(1):47-54. [13] 朱小三,高锐,李秋生,等.深反射地震数据的噪音衰减方法综述[J].地球物理学进展,2013,28(6):2878-2900. Zhu Xiaosan, Gao Rui, Li Qiusheng, et al. Review of the Noises Attenuation of Deep Reflection Seismic Data[J]. Progress in Geophysics, 2013,28(6):2878-2900. [14] 王纪强.基于DSP的微地震数据采集仪的研制[D].青岛:山东科技大学,2005. Wang Jiqiang. Development of Micro-Seismic Data Acquisition Instrument Based on DSP[D]. Qingdao:Shandong University of Science and Technology, 2005. [15] 梁北援,程百利,吴壮坤,等.微破裂向量扫描技术的自动化数据处理[J].地球物理学进展,2017,32(1):377-386. Liang Beiyuan, Cheng Baili, Wu Zhuangkun, et al. Micro-Fracturing Vector Scanning Technology for Automated Data Processing[J]. Progress in Geophysics, 2017, 32(1):377-386. [16] 王伟,高静怀,陈文超,等.基于结构自适应中值滤波器的随机噪声衰减方法[J].地球物理学报,2012,55(5):1732-1741. Wang Wei, Gao Jinghuai, Chen Wenchao, et al. Randon Seismic Noise Suppression via Structure-Adaptive Median Filter[J]. Chinese Journal of Geophysics, 2012, 55(5):1732-1741. [17] 唐金良,曹辉,王立华,等.中值滤波在井间地震资料处理中的应用[J].石油物探,2005,44(1):47-50. Tang Jinliang, Cao Hui, Wang Lihua, et al. Application of Median Filtering in the Processing of Crosswell Seismic Data[J]. Geophysical Prospecting for Petroleum, 2005, 44(1):47-50. [18] 刘太伟.地面微地震资料去噪方法研究[D].青岛:中国石油大学(华东),2013. Liu Taiwei. Ground Microseismic Data Denoising Method[D]. Qingdao:China University of Petroleum (East China), 2013. [19] 高少武,赵波,贺振华,等.基于余弦函数的自适应单频干扰消除[J].地球物理学进展,2009,24(5):1762-1767. Gao Shaowu, Zhao Bo, He Zhenhua, et al. Eliminatio of Adaptive Mono-Frequency Interference Based on Cosine Function[J]. Progress in Geophysics, 2009, 24(5):1762-1767. [20] 宋维琪,吕世超,郭晓中,等.提高微地震资料信噪比的频率域极化滤波[J].石油物探,2011,50(4):361-366. Song Weiqi, Lü Shichao, Guo Xiaozhong, et al. Improve Frequency-Domain Polarization Filtering of Signal-to-Noise Ratio of Microseismic Data[J]. Geophysical Prospecting for Petroleum, 2011, 50(4):361-366. [21] 朱卫星,宋洪亮,曹自强,等.自适应极化滤波在微地震信号处理中的应用[J].勘探地球物理进展,2010,33(5):367-371. Zhu Weixing, Song Hongliang, Cao Ziqiang, et al. The Application of Adaptive Polarization Filter in Microseismic Signal Processing[J]. Progress in Exploration Geophysics, 2010, 33(5):367-371. [22] 朱卫星.微地震信号的震相分离[D].青岛:中国石油大学(华东),2008. Zhu Weixing. The Seismic Phase Separation of the Microseismic Signal[D]. Qingdao:China University of Petroleum (East China), 2008. [23] 朱卫星,宋维琪,修金磊,等.微地震信号的偏振:位置对比法震相分离技术[J].石油地球物理勘探,2009,44(4):425-429. Zhu Weixing, Song Weiqi, Xiu Jinlei, et al. The Seismic Signal Polarization:Position Correlation Method of Seismic Phase Separation Technique[J]. Oil Geophysical Prospecting, 2009, 44(4):425-429. [24] 朱卫星,张春晓,邱铁成,等.微地震信号的变速FK滤波:自适应极化滤波方法[J].地球物理学进展,2009,24(5):1776-1786. Zhu Weixing, Zhang Chunxiao, Qiu Tiecheng, et al. The Seismic Signal Technology with Variable Velocity FK Filtering:The Auto-Adapted Polarization Filtering[J]. Progress in Geophysics, 2009, 24(5):1776-1786. [25] 宋维琪,刘太伟.地面微地震资料τ-p变换噪声压制[J].石油地球物理勘探,2015,50(1):48-53. Song Weiqi, Liu Taiwei. Surface Microseismic Noise Suppression with τ-p Transform[J]. Oil Geophysical Prospecting, 2015, 50(1):48-53. [26] 陈伟.微地震波场分离技术研究[D].青岛:中国石油大学(华东),2009. Chen Wei. The Study of Micro-Seismic Wave Field Separation Technology[D]. Qingdao:China University of Petroleum (East China), 2009. [27] Juan I S, Danllo R V, Mauriclo Sacchi. Microseismic Data Denoising via an Apex-Shifted Hyperbolic Radon Transform[J]. SEG Technical Program Expanded Abstracts, 2013, doi:10.1190/segam2013-1414.1. [28] Forghani-Arani F, Willis M, Haines S S, et al.An Effective Noise-Suppression Technique for Surface Microseismic Data[J]. Geophysics, 2013, 78(6):KS85-KS95. [29] 段家银.基于盲信号理论的微震监测数据信噪分离方法研究[D].荆州:长江大学,2016. Duan Jiayin. Research on Signal-Noise Separation of Microseismic Monitoring Data Based on Blind Signal Theory[D]. Jingzhou:Yangtze University, 2016. [30] Liang C, Thornton M P, Morton P, et al. Improving Signal-to-Noise Ratio of Passsive Seismic Data with an Adaptive FK Filter[C]//2009 SEG International Exposition and Annual Meeting. Houston:Society of Exploration Geophysicists, 2009:1703-1707. [31] 慕阳,肖宏跃,蒋全科,等.利用小波阀值降噪技术提高微地震信号的信噪比[J].勘察科学技术,2016(2):22-26. Mu Yang, Xiao Hongyue, Jiang Quanke, et al. Using Wavelet Threshold Noise Reduction Technology to Improve the Signal-to-Noise Ratio of Microseismic Signals[J]. Site Investigation Science and Technology, 2016(2):22-26. [32] 于腾.基于改进小波变换的微地震信号噪声压制技术研究[D].长春:吉林大学,2014. Yu Teng. Research onNoise Suppression of Microseismic Signals Based on Improved Wavelet Transform[D]. Changchun:Jilin University, 2014. [33] 仝中飞,王德利,刘冰.基于Curvelet变换阈值法的地震数据去噪方法[J].吉林大学学报(地球科学版),2008,38(增刊1):48-52. Tong Zhongfei, Wang Deli, Liu Bing. Seismic Data Denoise Based on Curvelet Transform with the Threshold Method[J]. Journal of Jilin University (Earth Science Edition), 2008, 38(Sup.1):48-52. [34] 姜宇东,杨勤勇,何柯,等.基于曲波变换的地面微地震资料去噪方法研究[J].石油物探,2012,51(6):620-624. Jiang Yudong, Yang Qinyong, He Ke, et al. Surface Microseismic Date Denoising Method Based on Curvelet Transform[J]. Geophysical Prospecting for Petroleum, 2012, 51(6):620-624. [35] 何柯,周丽萍,于宝利,等.基于补偿阈值的曲波变换地面微地震弱信号检测方法[J].物探与化探,2016,40(1):55-60. He Ke, Zhou Liping, Yu Baoli, et al. Curvelet Transform Ground Weak Seismic Weak Signal Detection Method Based on Compensation Threshold[J]. Geophysical and Geochemical Exploration, 2016, 40(1):55-60. [36] 何柯.微地震弱信号检测方法研究[D].青岛:中国石油大学(华东),2013. He Ke. Microseismic Weak Signal Detection Method Research[D]. Qingdao:China University of Petroleum (East China), 2013. [37] Akram J, Chen Z, Eaton D, et al. Time-Frequency Denoising of Microseismic Data[C]//2016 SEG International Exposition and Annual Meeting. Dallas:Society of Exploration Geophysicists, 2016:2750-2754. [38] Zhao Haitao, Li Yue, Zhang Chao. SNR Enhancement for Downhole Microseismic Data Using CSST[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(8):1139-1143. [39] 刘昕,陈祖斌,王东鹤,等.基于非下采样Shearlet变换的微地震随机噪声压制[J].煤炭技术,2016,35(1):128-129. Liu Xin, Chen Zubin, Wang Donghe, et al. Micro-Seismic Random Noise Suppression Based on Non-Subsampled Shearlet Transform[J]. Coal Technology, 2016, 35(1):128-129. [40] 刘昕. 基于高阶累积量和Shearlet变换的微地震数据噪声压制研究[D].长春:吉林大学,2016. Liu Xin. Research on Noise Suppression of Microseismic Data Based on Higher-Order Cumulant and Shearlet Transform[D]. Changchun:Jilin University, 2016. [41] 赵海涛.基于循环平移Shearlet变换自适应阈值消减微震勘探随机噪声[D].长春:吉林大学,2017. Zhao Haitao. Based on Cyclic Translation Shearlet Transform Adaptive Threshold Subtraction Microseismic Exploration Random Noise[D]. Changchun:Jilin University, 2017. [42] 梁小强.基于谱多流行聚类的Shearlet变换微地震数据噪声压制研究[D].长春:吉林大学,2017. Liang Xiaoqiang. Shearlet Transform Microseismic Data Noise Suppression Based on Spectral Multi-popular Clustering[D]. Changchun:Jilin University, 2017. [43] 王鹏,常旭,王一博,等.基于时频稀疏性分析法的低信噪比微震事件识别与恢复[J].地球物理学报,2014,57(8):2656-2665. Wang Peng, Chang Xu, Wang Yibo, et al. Automatic Event Detection and Event Recovery in Low Microseismic Signals Based on Time-Frequency Sparseness[J]. Chinese Journal of Geophysics, 2014, 57(8):2656-2665. [44] RodriguezI V, Bonar D, Sacchi M. Microseismic Data Denoising Using a 3C Group Sparsity Constrained Time-Frequency Transform[J]. Geophysics, 2012, 77(2):V21-V29. [45] 李稳,刘伊克,刘保金.基于稀疏分布特征的井下微地震信号识别与提取方法[J].地球物理学报,2016,59(10):3869-3882. Li Wen, Liu Yike, Liu Baojin. Downhole Signal Recognition and Extraction Based on Sparse Distribution Features[J]. Chinese Journal of Geophysics, 2016, 59(10):3869-3882. [46] 邵婕,孙成禹,唐杰,等.基于字典训练的小波域稀疏表示微地震去噪方法[J].石油地球物理勘探,2016,51(2):254-260. Shao Jie, Sun Chengyu, Tang Jie, et al. Micro-Seismic Data Denoising Based on Sparse Representations over Learned Dictionary in the Wavelet Domain[J]. Oil Geophysical Prospecting, 2016, 51(2):254-260. [47] Rodriguez I V, Bonar D, Sacchi M D. Microseismic Record de-Noising Using a Sparse Time-Frequency Transform[C]//SEG Technical Program Expanded Abstracts 2011. San Antonio:SEG, 2011:1693-1698. [48] 常凯,张海江,林叶.基于样条插值与曲波变换压缩感知的井下微地震监测数据重建[J].物探化探计算技术,2016,38(6):788-795. Chang Kai, Zhang Haijiang, Lin Ye. Downhole Microseismic Monitoring Data Reconstruction Based on Spline Interpolation and Curvelet-Based Compressive Sensing[J]. Computing Techniques for Geophysical and Geochemical Exploration, 2016, 38(6):788-795. [49] 宋维琪,张宇.基于压缩感知理论的微地震资料噪声压制[J].地球物理学进展,2017,32(4):1636-1642. Song Weiqi, Zhang Yu. Noise Suppression of the Microseismic Data Based on the Compressive Sampling Theory[J]. Progress in Geophysics, 2017, 32(4):1636-1642. [50] 秦晅,宋维琪.基于同步压缩变换微地震弱信号提取方法研究[J].石油物探,2016,55(1):60-66. Qin Xuan,Song Weiqi. Weak Signal Extraction Method of Microseismic Data Based on Synchrosqueezing Transform[J]. Geophysical Prospecting for Petroleum, 2016, 55(1):60-66. [51] 秦晅,蔡建超,刘少勇,等.基于经验模态分解互信息熵与同步压缩变换的微地震信号去噪方法研究[J].石油物探,2017,56(5):658-666. Qin Xuan, Cai Jianchao, Liu Shaoyong, et al. Denoising of Microseismic Signals Based on Empirical Mode Decomposition Mutual Information Entropy and Simultaneous Compression Transform[J]. Geophysical Prospecting for Petroleum, 2017, 56(5):658-666. [52] 贾瑞生,赵同彬,孙红梅,等.基于经验模态分解及独立成分分析的微震信号降噪方法[J].地球物理学报,2015,58(3):1013-1023. Jia Ruisheng, Zhao Tongbin, Sun Hongmei, et al. Microseismic Signal Denoising Method Based on Empirical Mode Decomposition and Independent Component Analysis[J]. Chinese Journal of Geophysics, 2015, 58(3):1013-1023. [53] Han Jiajun, Mirko B. Microseismic and Seismic Denoising via Ensemble Empirical Mode Decomposition and Adaptive Thresholding[J]. Geophysics, 2015, 80(6):KS69-KS80. [54] 段家银,段天友,刘豪,等.变模态分解在微地震信号去噪中的应用[J].石化技术,2015,22(7):217. Duan Jiayin, Duan Tianyou, Liu Hao, et al. The Application of Variable Modal Decomposition in Denoising of Microseismic Signals[J]. Petrochemical Industry Technology, 2015, 22(7):217. [55] 周柏彤,刘增力,朱健晨.关于多种模态分解方法的分离效果的差别探讨[J].信息技术,2016(12):87-92. Zhou Baitong, Liu Zengli, Zhu Jianchen. Discussion on the Sifferences in the Separation Effect of Various Modal Decomposition Methods[J]. Information Technology, 2016(12):87-92. [56] 胡永泉,尹成,潘树林,等.基于单道奇异值分解的微地震资料去噪方法[J].石油天然气学报,2013,35(4):64-69. Hu Yongquan, Yin Cheng, Pan Shulin, et al. Denoising Method for Microseismic Data Based on Single-Channel SVD[J]. Journal of Oil and Gas Technology, 2013, 35(4):64-69. [57] Liang Xiaoqiang, Li Yue, Zhang Chao. Noise Suppression for Microseismic Data by Non-Subsampled Shearlet Transform Based on Singular Value Decomposition[J]. Geophysical Prospecting, 2019, 48:151-158. [58] Iqbal N,Zerguine A, Kaka S L, et al. Automated SVD Filtering of Time-Frequency Distribution for Enhancing the SNR of Microseismic/Microquake Events[J]. Journal of Geophysics & Engineering, 2016, 13(6):964-973. [59] 詹毅,赵波.自动追踪SVD压制线性干扰方法的改进[J].石油地球物理勘探,2008,43(2):158-160. Zhan Yi, Zhao Bo. Improvement of Suppressing Linear Interference by Automatically Tracing SVD[J]. Oil Geophysical Prospecting, 2008, 43(2):158-160. [60] 夏森,王维波,李树荣,等.微地震信号的参数辨识建模及其Kalman滤波[J].地球物理学进展,2016,31(5):2005-2010. Xia Sen, Wang Weibo, Li Shurong, et al. Parameter Identification Modeling of Microseismic Signals and Kalman Filtering[J]. Advances in Geophysics, 2016, 31(5):2005-2010. [61] Xia S, Wang W, Li S, et al. Application of Kalman Filter in Microseismic Data Denoising Based on Identified Signal Model[C]//Proceedings of the 28th Chinese Control and Decision Conference (2016 CCDC). Yinchuan:Northeastern University and IEEE Singapore Industrial Electronics Branch, 2016:4381-4385. [62] 宋维琪,何欣,吕世超.应用卡尔曼滤波识别微地震信号[J].石油地球物理勘探,2009,44(增刊1):34-38. Song Weiqi, He Xin, Lü Shichao. Application of Kalman Filter Identifying Micro Seismic Signals[J]. Oil Geophysical Prospecting, 2009, 44(Sup.1):34-38. [63] Baziw E, Weir-Jones I. Application of Kalman Filtering Techniques for Microseismic Event Detection[J]. Pure and Applied Geophysics, 2002, 159(1):449-471. [64] 何柯,于宝利,周丽萍,等. 基于独立分量分析的地面微地震监测噪声压制方法研究[C]//中国石油学会2015年物探技术研讨会论文集.武汉:中国石油学会石油物探专业委员会,2015:5. He Ke, Yu Baoli, Zhou Liping, et al. Noise Suppression Based on Independent Component Analysis for Terrestrial Microseismic Monitoring[C]//China Petroleum Institute 2015 Geophysical Technology Conference Proceedings. Wuhan:Petroleum Geophysical Exploration Committee of China Petroleum Association, 2015:5. [65] 宋维琪,李艳清,刘磊.独立分量分析与压缩感知微地震弱信号提取方法[J].石油地球物理勘探,2017,52(5):984-989. Song Weiqi, Li Yanqing, Liu Lei. Microseismic Weak Signal Extraction Based on the Independent Component Analysis and Compressive Sensing[J]. Oil Geophysical Prospecting, 2017, 52(5):984-989. [66] 刁瑞,吴国忱,尚新民,等.地面阵列式微地震数据盲源分离去噪方法[J].物探与化探,2017,41(3):521-526. Diao Rui, Wu Guochen, Shang Xinmin, et al. The Blind Separation Denoising Method for Surface Array Microseismic Data[J]. Geophysical and Geochemical Exploration, 2017, 41(3):521-526. [67] 朱卫星.相关滤波在微地震数据处理中的应用[J].勘探地球物理进展,2007,30(2):130-134. Zhu Weixing. Application of Correlation Filter in Processing of Microseismic Data[J]. Progress in Exploration Geophysics, 2007, 30(2):130-134. [68] 刘玉海,尹成,潘树林,等.基于互相关函数法的地面微地震信号检测研究[J].石油物探,2012,51(6):633-637. Liu Yuhai, Yin Cheng, Pan Shulin, et al. Ground Microseismic Signal Detection Based on Cross-Correlation Function[J]. Geophysical Prospecting for Petroleum, 2012, 51(6):633-637. [69] 刘玉海,尹成,潘树林,等.一种改进的相邻道互相关函数法地面微地震信号压噪研究[J].石油天然气学报,2012,34(11):76-78. Liu Yuhai, Yin Cheng, Pan Shulin, et al. An Improved Method for Noise Reduction of Terrestrial Microseismic Signals by Adjacent Channel Cross-Correlation Function[J]. Journal of Oil and Gas Technology, 2012, 34(11):76-78. [70] 张旭亮,桂志先,王鹏,等.基于K-L变换的微地震资料去噪的分析及应用[J].工程地球物理学报,2013,10(1):81-84. Zhang Xuliang, Gui Zhixian, Wang Peng, et al. The Analysis and Application of Micro-Seismic Data Denoising Based on K-L Transform[J]. Chinese Journal of Engineering Geophysics, 2013, 10(1):81-84. [71] 吴红梅,樊骥.基于K-L变换的微地震资料去噪方法[J].复杂油气藏,2013,6(3):33-35. Wu Hongmei, Fan Ji. A Denoising Method Based on K-L Transform for Microseismic Data[J]. Complex Hydrocarbon Reservoirs, 2013, 6(3):33-35. [72] 朱峰.低信噪比微地震监测方法与技术研究[D].荆州:长江大学,2015. Zhu Feng. Low Signal-to-Noise Ratio Microseismic Monitoring Methods and Techniques[D]. Jingzhou:Yangtze University, 2015. [73] 徐丽娜.神经网络控制[M].北京:电子工业出版社, 2003. Xu Lina. Neural Network Control[M]. Beijing:Electronic Industry Press, 2003. [74] 唐超,胡光锐.利用神经网方法进行地震信号处理的反褶积新方法[J].信号处理,1994(4):233-237. Tang Chao, Hu Guangrui. A New Deconvolution Method for Seismic Signal Processing Using Neural Network Method[J]. Journal of Signal Processing, 1994(4):233-237. [75] Zhang Xiaopu, Lin Jun, Chen Zubin, et al. An Efficient Neural-Network-Based Microseismic Monitoring Platform for Hydraulic Fracture on an Edge Computing Architecture[J]. Sensors, 2018, 18(6):1828. [76] 杨立强,宋海斌,郝天珧.基于BP神经网络的波阻抗反演及应用[J].地球物理学进展,2005,20(1):34-37. Yang Liqiang, Song Haibin, Hao Tianyao. Application of Impedance Inversion Based on BP Neural Network[J]. Progress in Geophysics, 2005, 20(1):34-37. [77] 尚雪义,李夕兵,董陇军,等.一种基于势函数的微地震事件去噪和聚类方法:CN107479093A[P].2017-12-15. Shang Xueyi, Li Xibing, Dong Longjun, et al. A Potential Function Based Microseismic Event Denoising and Clustering Method:CN107479093A[P]. 2017-12-15. [78] 张建萍.基于计算智能技术的聚类分析研究与应用[D].济南:山东师范大学,2014. Zhang Jianping. Research and Application of Clustering Analysis Based on Computational Intelligence Technology[D]. Jinan:Shandong Normal University, 2014. [79] Tanachapong W, Sirapat C, Khamron S. Efficient Algorithms Based on the K-Means and Chaotic League Championship Algorithm for Numeric, Categorical, and Mixed-Type Data Clustering[J]. Expert Systems with Applications, 2017, 90:146-167. [80] Lecun Y, Bengio Y, Hinton G. Deep Learning[J]. Nature, 2015, 521:436. [81] Schmidhuber J. Deep Learning in Neural Network:An Overview[J]. Neural Netw, 2015, 61:85-117. [82] 余凯,贾磊,陈雨强,等.深度学习的昨天、今天和明天[J].计算机研究与发展,2013,50(9):1799-1804. Yu Kai, Jia Lei, Chen Yuqiang, et al. Deep Learning:Yesterday, Today, and Tomorrow[J]. Journal of Computer Research and Development, 2013, 50(9):1799-1804. [83] 雷亚国,贾峰,周昕,等.基于深度学习理论的机械装备大数据健康监测方法[J].机械工程学报,2015,51(21):49-56. Lei Yaguo, Jia Feng, Zhou Xin, et al. A Deep Learning-Based Method for Machinery Health Monitoring with Big Data[J]. Journal of Mechanical Engineering, 2015, 51(21):49-56. [84] 骞宇澄,刘昭策.深度学习的实现与发展:从神经网络到机器学习[J].电子技术与软件工程,2017(11):30-31. Yan Yucheng, Liu Zhaoce. The Realization and Development of Deep Learning:From Neural Networks to Machine Learning[J]. Electronic Technology & Software Engineering, 2017(11):30-31. [85] 刘建伟,刘媛,罗雄麟.深度学习研究进展[J].计算机应用研究,2014,31(7):1921-1930. Liu Jianwei, Liu Yuan, Luo Xionglin. Research and Development on Deep Learning[J]. Application Research of Computers, 2014, 31(7):1921-1930. [86] 安杏杏,董宏丽,张勇,等.输油管道泄漏检测技术综述[J].吉林大学学报(信息科学版),2017,35(4):424-429. An Xingxing, Dong Hongli, Zhang Yong, et al. Overview of Oil Pipeline Leak Detection Technology[J]. Journal of Jilin University (Information Science Edition), 2017, 35(4):424-429. [87] 郭丽丽,丁世飞.深度学习研究进展[J].计算机科学,2015,42(5):28-33. Guo Lili, Ding Shifei. Research Progress on Deep Learning[J]. Computer Science, 2015, 42(5):28-33 [88] 尹宝才,王文通,王立春.深度学习研究综述[J].北京工业大学学报,2015,41(1):48-59. Yin Baocai, Wang Wentong, Wang Lichun. Review of Deep Learning[J]. Journal of Beijing University of Technology, 2015, 41(1):48-59. [89] Lawrence S, Giles C L, Tsoi A C, et al. Face Recognition:A Convolutional Neural-Network Approach[J]. IEEE Transactions on Neural Networks, 1997, 8(1):98-113. [90] Jain V, Seung H S. Natural Image Denoising with Convolutional Networks[J]. Advances in Neural Information Processing Systems 21(NIPS 2008), 2008, 21:769-776. [91] 李传朋,秦品乐,张晋京.基于深度卷积神经网络的图像去噪研究[J].计算机工程,2017,43(3):253-260. Li Chuanpeng, Qin Pinle, Zhang Jinjing. Research on Image Denoising Based on Deep Convolutional Neural Network[J]. Computer Engineering, 2017, 43(3):253-260. [92] Kuremoto T, Kimura S, Kobayashi K, et al. Time Series Forecasting Using a Deep Belief Network with Restricted Boltzmann Machines[J]. Neurocom-puting, 2014, 137(15):47-56. [93] Yu D, Deng L. Deep Learning and Its Applications to Signal and Information Processing[J]. IEEE Signal Processing Magazine, 2011, 28(1):145-154. [94] 段艳杰,吕宜生,张杰,等.深度学习在控制领域的研究现状与展望[J].自动化学报,2016,42(5):643-654. Duan Yanjie, Lü Yisheng, Zhang Jie, et al. Deep Learning for Control:The State of the Art and Prospects[J]. Acta Automatica Sinica, 2016, 42(5):643-654. [95] Lu C, Wang Z Y, Qin W L, et al. Fault Diagnosis of Rotary Machinery Components Using a Stacked Denoising Autoencoder-Based Health State Identification[J]. Signal Processin, 2017, 130:377-388. [96] Vincent P, Larochelle H, Lajoie I, et al. Stacked Denoising Autoencoders:Learning Useful Representations in a Deep Network with a Local Denoising Criterion[J]. Journal of Machine Learning Research, 2010, 11(12):3371-3408. [97] Goodfellow I J,Pouget-Abadie J, Mirza M, et al. Generative Adversarial Networks[J]. Advances in Neural Information Processing Systems, 2014, 3:2672-2680. |
| [1] | 吴建光, 张平, 吕昊, 曾晓献. 基于震幅叠加的微地震事件定位在地面监测中的应用[J]. 吉林大学学报(地球科学版), 2017, 47(1): 255-264. |
| [2] | 李海山, 吴国忱, 印兴耀. 形态分量分析在去除地震资料随机噪声中的应用[J]. J4, 2012, 42(2): 554-561. |
|