地面微震资料去噪方法研究
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摘要
微地震监测技术就是通过观测、分析生产活动中所产生的微小地震事件来监测生产活动的影响、效果及地下状态的地球物理技术,分为井中监测与地面监测两种。与井中微震监测相比,地面微震监测具有无需监测井、布线灵活、成本低等优点,具有更广阔的应用前前景。然而,地而微震资料噪声复杂,微震有效信号淹没于噪声之中,导致资料信噪比较低。这给资料的处理解释带来较大困难。因此,研究适合提高地面微震资料信噪比的去噪方法,对于地面微震监测具有重要意义。
本文首先对实际微震资料进行了全面分析,对微震有效信号和资料中典型的噪声特点以及分布规律进行总结。通过对已有微震去噪方法的研究,总结出各种方法的适用范围与局限性,从而为研究适合于地面微震资料的去噪方法奠定基础。
小窗口SVD方法是针对微震资料中普遍存在的倾斜线性干扰而采用的一种改进SVD去噪方法。该方法首先求取小窗口内微震记录的参考道,利用参考道与小窗口内各道记录做互相关求取各道的时间偏移量。然后根据偏移量将小窗口内微震记录做拉平处理,对拉平后的微震记录进行SVD分解,选取适当奇异值进行资料重构,最后通过反拉平重构资料完成去噪。实践表明,根据实际微震资料特点设计合理的滑动小时窗和参考道迭代次数,可有效压制微震资料中具有一定斜率的线性干扰。
单道SVD方法是针对单道微震记录周期性较强的特点提出的一种改进SVD去噪方法。方法首先利用单道微震记录来构建分解矩阵,然后对矩阵进行奇异值分解,通过对奇异值分布规律的分析,选取适当的奇异值实现矩阵的重构,最后通过SVD反变换得到得构信号,从而达到削弱噪声、突出有效信号的目的。实践表明,该方法能有效压制单道微震记录中的周期噪声,是一种适用于地面微震资料的去噪方法。
改进时变斜度/峰度法是针对微震有效信号与噪声对称性或高斯性的差异而提出的一种基于高阶统计量的去噪方法。该方法采用长短时窗内时变斜度或时变峰度的归一化差值作为滤波因子对微震资料进行去噪,从而削弱了时窗内由于噪声的非对称性或非高斯性对去噪效果产生的影响,确保了信号得到更好突出,资料的信噪比得到明显改善。
基于形态学的去噪方法主要用来削弱微震资料中的脉冲干扰。该方法首先采用形态学理论的基本形态开运算和闭运算组成的开-闭和闭-开的形态滤波器,然后采用两者组成组合滤波器对微震资料进行去噪,从而克服形态滤波过程中的统计偏倚现象。实践表明,基于形态学的去噪方法对微震资料中的脉冲噪声具有较好的压制作用。
通过对理论模型和实际微震资料处理结果的分析表明,本课题研究的去噪方法可有效的压制微震资料中的噪声,在较大程度上改善了微震资料的信噪比。另外,微震资料噪声复杂,通过单一的去噪方法很难得到理想的效果。因此,新方法的研究和已有方法的联合去噪是解决地面微震资料低信噪比的两个发展方向。
Microseismic monitoring is a geophysical technique, which is used to monitor the influence of producing activity and underground state through observing and analysing microseismic events caused by producing activity. Microseismic monitoring is divided into borehole monitoring and ground monitoring. Compared with borehole seismic monitoring, ground monitoring has more advantages, such as no monitoring well, flexible wiring, low cost, all of these makes microseismic monitoring has a much wider foreground. However, the noise of ground microseismic data is complex, microseismic signals are submerged in noise, which lead to the low noise ratio of microseismic data. This brings more difficulty to the data processing and interpretati-on. Therefore, research suitable denoising methods for improve the SNR of ground microseismic data has great significant for microseismic monitoring.
At first, the characteristics and distribution regularities of microseismic signal and typical noise are summarized by making a comprehensive analysis to the actual microseismic data. And then, the scope and limitations of methods are summed up by researching the existing microseismic denoising methods. Which lay the foundation for researching denoising method on microseismic data.
The small window svd method is an improved svd denoising method, which is proposed aimd at linear interference in microseismic data. At first, the method calcul-ates the standard trace in small window, gets time offsets by using cross-correlation calculation between standard trace and microseismic traces in small window. And then, flatting the microseismic data in small window according to time offsets, making svd decomposition on flatted microseismic data, selecting suitable singular values to refactor microseismic data. At last, accomplishing microseismic data filer by reverse flattening operation. The practice shows that design reasonable small window according to the characteristic of actual microseismic data and iterations of standard trace can suppress slope interference in microseismic data effectively.
The single-trace svd method is an improved svd denoising method, which is proposed aimd at periodic interference in single-trace microseismic data. At First, it uses a single-channel seismic records to build the decomposing matrix. And then, selects appropriate singular values to rebulid the matrix through analysing the distribution law of the singular values about matrix. Finally, achieves the purpose of removing noise and highlighting effective signals through rebuliding signals by using svd inverse transform. The practice shows that the method can remove the periodic noise in single-trace microseismic records effectively. It is a denoising method suitable for the ground microseismic data.
The improved time-varying skewness/kurtosis is a denoising method based on higher-order statistics, which is proposed aimd at the difference of symmetry or gaussianity between signal and noise. It uses normalized difference of time-varying skewness or kurtosis between small time window and long time window as the filter coefficient. Which weaken the effects of the denoising effect on asymmetry or nongaussianity of noise. To ensure that the signal better outstanding, signal-to-noise ratio of microseismic data is improved obviously.
Fractal denoising is mainly used to suppress pulse interference in the microseis-mic data. At first, it constitutes open-close morphological filter and close-open morphological filter by using primary algorithm of fractal theory:morphological opening operation and morphological closing operation. And then, uses junction filter to denoise microseismic data, which is consists of open-close morphological filter and close-open morphological, so as to overcome statistical bias phenomenon in the process of morphological filtering. The practice shows that fractal denoising method can suppress the impulse noise in microseismic data effectively.
The analysis of the theoretical model and actual microseismic data shows that the filtering methods researched in subject can suppress the noise in microseismic data effectively, and also improve the signal-to-noise ratio of microseismic data in a large extent. In addition, the noise of microseismic data is complex, it is difficult to obtain ideal denoising effect by using a method alone. So research new methods and combination of existing methods are two development direction for solving the low signal-to-noise ratio of microseismic data.
本文首先对实际微震资料进行了全面分析,对微震有效信号和资料中典型的噪声特点以及分布规律进行总结。通过对已有微震去噪方法的研究,总结出各种方法的适用范围与局限性,从而为研究适合于地面微震资料的去噪方法奠定基础。
小窗口SVD方法是针对微震资料中普遍存在的倾斜线性干扰而采用的一种改进SVD去噪方法。该方法首先求取小窗口内微震记录的参考道,利用参考道与小窗口内各道记录做互相关求取各道的时间偏移量。然后根据偏移量将小窗口内微震记录做拉平处理,对拉平后的微震记录进行SVD分解,选取适当奇异值进行资料重构,最后通过反拉平重构资料完成去噪。实践表明,根据实际微震资料特点设计合理的滑动小时窗和参考道迭代次数,可有效压制微震资料中具有一定斜率的线性干扰。
单道SVD方法是针对单道微震记录周期性较强的特点提出的一种改进SVD去噪方法。方法首先利用单道微震记录来构建分解矩阵,然后对矩阵进行奇异值分解,通过对奇异值分布规律的分析,选取适当的奇异值实现矩阵的重构,最后通过SVD反变换得到得构信号,从而达到削弱噪声、突出有效信号的目的。实践表明,该方法能有效压制单道微震记录中的周期噪声,是一种适用于地面微震资料的去噪方法。
改进时变斜度/峰度法是针对微震有效信号与噪声对称性或高斯性的差异而提出的一种基于高阶统计量的去噪方法。该方法采用长短时窗内时变斜度或时变峰度的归一化差值作为滤波因子对微震资料进行去噪,从而削弱了时窗内由于噪声的非对称性或非高斯性对去噪效果产生的影响,确保了信号得到更好突出,资料的信噪比得到明显改善。
基于形态学的去噪方法主要用来削弱微震资料中的脉冲干扰。该方法首先采用形态学理论的基本形态开运算和闭运算组成的开-闭和闭-开的形态滤波器,然后采用两者组成组合滤波器对微震资料进行去噪,从而克服形态滤波过程中的统计偏倚现象。实践表明,基于形态学的去噪方法对微震资料中的脉冲噪声具有较好的压制作用。
通过对理论模型和实际微震资料处理结果的分析表明,本课题研究的去噪方法可有效的压制微震资料中的噪声,在较大程度上改善了微震资料的信噪比。另外,微震资料噪声复杂,通过单一的去噪方法很难得到理想的效果。因此,新方法的研究和已有方法的联合去噪是解决地面微震资料低信噪比的两个发展方向。
Microseismic monitoring is a geophysical technique, which is used to monitor the influence of producing activity and underground state through observing and analysing microseismic events caused by producing activity. Microseismic monitoring is divided into borehole monitoring and ground monitoring. Compared with borehole seismic monitoring, ground monitoring has more advantages, such as no monitoring well, flexible wiring, low cost, all of these makes microseismic monitoring has a much wider foreground. However, the noise of ground microseismic data is complex, microseismic signals are submerged in noise, which lead to the low noise ratio of microseismic data. This brings more difficulty to the data processing and interpretati-on. Therefore, research suitable denoising methods for improve the SNR of ground microseismic data has great significant for microseismic monitoring.
At first, the characteristics and distribution regularities of microseismic signal and typical noise are summarized by making a comprehensive analysis to the actual microseismic data. And then, the scope and limitations of methods are summed up by researching the existing microseismic denoising methods. Which lay the foundation for researching denoising method on microseismic data.
The small window svd method is an improved svd denoising method, which is proposed aimd at linear interference in microseismic data. At first, the method calcul-ates the standard trace in small window, gets time offsets by using cross-correlation calculation between standard trace and microseismic traces in small window. And then, flatting the microseismic data in small window according to time offsets, making svd decomposition on flatted microseismic data, selecting suitable singular values to refactor microseismic data. At last, accomplishing microseismic data filer by reverse flattening operation. The practice shows that design reasonable small window according to the characteristic of actual microseismic data and iterations of standard trace can suppress slope interference in microseismic data effectively.
The single-trace svd method is an improved svd denoising method, which is proposed aimd at periodic interference in single-trace microseismic data. At First, it uses a single-channel seismic records to build the decomposing matrix. And then, selects appropriate singular values to rebulid the matrix through analysing the distribution law of the singular values about matrix. Finally, achieves the purpose of removing noise and highlighting effective signals through rebuliding signals by using svd inverse transform. The practice shows that the method can remove the periodic noise in single-trace microseismic records effectively. It is a denoising method suitable for the ground microseismic data.
The improved time-varying skewness/kurtosis is a denoising method based on higher-order statistics, which is proposed aimd at the difference of symmetry or gaussianity between signal and noise. It uses normalized difference of time-varying skewness or kurtosis between small time window and long time window as the filter coefficient. Which weaken the effects of the denoising effect on asymmetry or nongaussianity of noise. To ensure that the signal better outstanding, signal-to-noise ratio of microseismic data is improved obviously.
Fractal denoising is mainly used to suppress pulse interference in the microseis-mic data. At first, it constitutes open-close morphological filter and close-open morphological filter by using primary algorithm of fractal theory:morphological opening operation and morphological closing operation. And then, uses junction filter to denoise microseismic data, which is consists of open-close morphological filter and close-open morphological, so as to overcome statistical bias phenomenon in the process of morphological filtering. The practice shows that fractal denoising method can suppress the impulse noise in microseismic data effectively.
The analysis of the theoretical model and actual microseismic data shows that the filtering methods researched in subject can suppress the noise in microseismic data effectively, and also improve the signal-to-noise ratio of microseismic data in a large extent. In addition, the noise of microseismic data is complex, it is difficult to obtain ideal denoising effect by using a method alone. So research new methods and combination of existing methods are two development direction for solving the low signal-to-noise ratio of microseismic data.
引文
[1]陆基孟,王永刚.地震勘探原理[M].北京：中国石油大学出版社,2011
[2]张向林,陶果,刘新茹.油气地球物理勘探技术进展[J].地球物理学进展,2006,21(1)：143-151
[3]张玮,詹仕凡,张少华,等.石油地球物理勘探技术进展与发展方向[J].中国工程科学,2010,12(44)：97-101
[44]管清友,李君臣.中国页岩气革命远未到来[J].中国报道,2011,30(2)：324-336
[5]贾利春,陈勉,金衍.国外页岩气井水里压裂裂缝监测技术进展[J].天然气与石油,2010,30(1)：44-47
[6]Shawn Maxwell. Microseismic Hydraulic Fracture Imaging:The Path Toward Optimizing Shale Gas Production[J]. Shales,2011,30(3):340-346
[7]Robert C Downie, Le Calvez J H, Ken Kerrihard. Real-Time Microseismic Monitoring of Simultaneous Hydraulic Fracturing Treatments in Adjacent Horizontal Wells in the Woodford Shale[J]. CSEG Convention 2009,26(1): 484-492
[8]宋维琪,陈泽东,毛中华.水力压裂裂缝微地震检测技术[M].山东：中国石油大学出版社,2008
[9]Sylvette Bonnefoy-Claudet, Fabrice Cotton, Pierre-Yves Bard. The nature of noise wavefileld and its applications for site effects studies [J]. Earth-Science Reviews,2006,79:205-227
[10]Li Zhengguang, Yang Runhai, Zhao Jinming,et al. Rock-braking test research on earthquake sequence type[J]. Journal of Seismological Research,2005, 28(4):388-393
[11]Zhang Shan, Liu Qinglin, Zhao Qun, et al. Aplication of microseismic monitoring technology in development of oil field[J]. Geophysical Prospecliong for Petroleum,2002,41(2):226-231
[12]梁兵,朱广生.油气出勘探开发中的微地震监测方法[M].北京：工业出版社,2004
[13]Rut ledge J T, Phillips W S, House L S, et al. Microseismic mapping of a Cotton Valley hydraulic fracture using decimated downhole arrays, Expanded Abstracts[M]. New Orleans(Louisiana):Ann Internat Mtg Soc Explor Geophys, 1998,338-341
[144]刘继民,刘建中,刘志鹏,等.用微地震法监测压裂裂缝转向过程[J].石油勘探与开发,2005,32(2)：75—77
[15]刘建中,王秀鹃,孙玉玲.人工压裂形成多裂缝的可能性研究[J].石油勘探与开发, 2002,29(3)：103-106
[16]石道涵,张昊,黄远.嵌入式人工压裂实时监测技术的应用[J].石油仪器,2005,19(3)：51-52
[17]宋元合,张少标,丁振红,等.微震监测技术在濮城油田沙三上油藏的应用[J].石油仪器,2003,11(4)：11-13
[18]刘建宏,崔勤元,王亚鹏,等.微震监测技术在南泥湾油田长六油藏的应用[J].西北地质,2004,37(2)：80-82
[19]刘建安,马红星,暮立俊,等.井下微地震裂缝测试技术在长庆油田的应用[J].油气井测试,2005,14(2)：54—56
[20]李民河,廖健德,赵增义,等.微地震波裂缝监测技术在油田裂缝研究中的应用-以克拉玛依八区下乌尔禾组油藏为例[J].油气地质与采收率,2004,11(3)：16-18
[21]周婕,高敬文,何平,等.微地震裂缝监测系统在鄯善油田的应用[J].油气井测试,2005,14(6)：62-64
[22]刘云彬,安广柱,张烈辉.用微地震波法确定松辽盆地头台油田地应力场特征[J].大庆石油地质与开发,2006,25(3)：29-30
[23]伍藏原,李汝勇,张明益等.微地震监测气驱前缘技术在牙哈凝析气田的应用[J].天然气地球科学,2005,16(3)：390-393
[24]刘百红,秦绪英,郑四连,等.微地震监测技术及其在油田中的应用现状[J].勘探球物理学进展,2005,28(5)：325-329
[25]刘建中,王春耘,刘继民,等.用微地震法监测油田生产动态[J].石油勘探与开发,2004,31(2)：71-73
[26]张山,刘清林,赵群,等.微地震监测技术在油田开发中的应用,石油物探,2002,41(2)：226-231
[27]王惠清,刘进军,周高鹏,等.微地震方法监测水力压裂改善措施效果[J].新疆石油学院学报,2004,16(4)：41-43
[28]李庆忠.走向精确勘探的道路[M].北京：石油工业出版社,1994
[29]张军华.地震资料去噪方法-原理、算法、编程及应用[M].山东：中国石油大学出版社,2011
[30]Stork A I, Ito H. Source parameter scaling for small earthquake observed at the Western Nagano 800m deep borehole, central Japan. The Bulletin of the Seismological Society of America,2004,94(5):1781-1794
[31]加尔彼林,著.地震勘探偏振法[M].何樵登,杨宝俊,译.北京：石油工业出版社,1989
[32]宋维琪,孙英杰,朱卫星.微地震资料频域相干-时间域偏振滤波方法[J].石油地球物理勘探,2008,43(2)：161-167
[33]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
[34]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)：367-370
[35]国九英,周兴元.f-x域等道距道内插[J].石油地球物理勘探,1996,31(1)：28-34
[36]许大为,潘一山,李国臻,等.基于小波变换的矿山微震信号滤波方法研究[J].矿业业工程,2007,5(4)：66-68
[37]Farnoush F A, Mike B, Jyoti B. Noise suppression in surface microseismic data[J].The Leading Edge,2012,31(12):1496-1501
[38]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[J].石油物探,2012,51(6)：620-625
[39]严永新,张永华,陈祥,等.微地震技术在裂缝监测中的应用研究[J].地学前沿,2013,20(1)：1-5
[40]Jupe A, Cowles J, Jones R. Micro-seismic monitoring:Listen and see the reservoir[J]. World Oil,1998,219(12):171-174
[41]张玎,张景和.微震裂缝监测技术在低渗透油田的应用[J].资源环境与工程,2008,22(1)：45-52
[42]Liu J Z, Wang C Y, Liu J M, et al. Micro-seismic monitor on the operation of oil fields[J]. Petroleum Exploration and Development,2004,31(2):71-73
[43]Maxwell S. Microseismic:growth born from success [J]. The Leading Edge,2010, 29(3):338-343
[44]李智敏,苟先太,金炜东,等.微地震信号的频率特征[J].岩土工程学报,2008,30(6)：830-834
[45]张军华,吕宁,出连玉,等.地震资料去噪方法技术综合评述[J].地球物理学进展,2006,21(2)：546-553
[46]Brian Russell, Dan Hampson, Joong Chun. Noise elimination and the radon transform, Parti[J]. The Leading Edge,1990,9(11):18-23
[47]Brian Russell,Dan Hampson, Joong Chun. Noise elimination and the radon transform, Part2[J]. The Leading Edge,1990,9(11):31-37
[48]蔡希玲,张俊桥.复杂地表区噪声分析技术与压噪方法应用[J].石油地球物理勘探,2002,37(1)：1-4
[49]ZHU Wei-hong, LIU Qing-lin, Panos G. Linear noise at tenuation using local radial trace median filtering[J]. The Leading Edge,2004,23(8):728-730
[50]Douglas Alsdorf. Noise reduction in seismic datausing fourier correlation coefficient filtering[J]. Geophysics,1997,62(5):1617-1627
[51]刘法启,张关泉.小波变换与F-K算法在滤波中的应用[J].石油地球物理勘探, 1996,31(6)：782-792
[52]王振国,周熙襄,李晶.基于小波变换的最小光滑滤波去噪[J].石油地球物理勘探,2002,37(6)：594-600
[53]PHILLIPS R D, WATSON L T, WYNNE R H, et al. Feature reduction using a singular value decomposition for the iterative guided spectral class rejection hybrid classifier[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2009,64(1):107-116
[54]AHMED S M, ALZOUBI Q, ABOZAHHAD M. Ahybrid ECG compression algorithm based on singular value decomposition and discrete wavelet transform[J]. Journal of Medical Engineering and Technology,2007,31(1):54-61
[55]VANLANDUIT S, CAUBERGHE B,GUILLAUMB P. Reduction of large fequency response function data sets using robust singular value decomposi t ion [J]. Computers and Structures,2006,84(12):808-822
[56]VOZALIS M G, MARGARITIS K G. Using SVD and demographic data for the enhance_ment of generalized collaborative filtering[J]. Information Sciences,2007, 177(15):3017-3037
[57]WILLIAMS T, AHMADI M, MILLER WC. Design of 2D FIR and IIR digital filters with canonical signed digit coefficients using singular value decomposition and genetic algorithms[J]. Circuits Systems and SignalProcessing,2007,26(1): 69-89
[58]LEHTOLA L, KARsIKAs M, KOSKINEN M,et al. Effects of noise and filtering on SVD-based morphological parameters of the T wave in the ECG[J]. Journal of Medical Engineering&Technology,2008,32(5):400-407
[59]MARK B, JIANG D. Noise reduction in multiple-echo data sets using singular value decomposi t ion [J]. Magnetic Resonance Imaging,2006,24(2):849-856
[60]LIU Hongxing, LI Jian, ZHAO Ying, et al. Improved singular value decomposition technique for detecting and extracting periodic impulse component in a vibration signal [J]. Chinese Journal of Mechanical Engineering,2004,17(3): 340-345
[61]CEMPEL C. Generalized singular value decomposition in multidimensional condition monitoring of machines-A proposal of comparative diagnostics [J]. Mechanical Systems and Signal Processing,2009,23(3):701-711
[62]Freire S L M, Ulrych T J. Application of singular value decomposition to vertical seismic profiling[J]. Geophysics,1988,53(6):778-785
[63]Liu Xuewei. Ground roll suppression using the Karhunen-Loeve transform [J]. Geophysics,1999,64(2):564-566
[64]苑益军,徐林.叠前线性干扰自动追踪SVD压制方法[J].现代地质,2007,21(41)：733-737
[65]陈遵德.地震记录的奇异值分布特征[J].石油物探快讯,1990,5：44-52
[66]陈遵德,段天友,朱广生.SVD滤波方法的改进与应用[J].石油地球物理勘探,1994,29(6)：783-792
[67]钟本善,何昌礼,杨忠民.复杂构造地区的SVD去噪技术[J].都理工学院学报,2000,27(1)：93-95
[68]沈鸿雁,李庆春.频域奇异值分解(SVD)地震波场去噪[J].石油地球物理勘探,2010,45(2)：185-189
[69]梁霖,徐光华,刘弹,等.小波-奇异值分解在异步电机转子故障特征提取中的应用.中国电机工程学报,2005,25(19)：111-115
[70]Sergio L M Freire, Tad J Ulrych. Application of singular value decomposition to vertical seismic profiling[J]. Geophysics,1998,53(6):778-785
[71]Jackson G M, Mason I M, Greenhalgh S A. Principal component transforms of triaxial recordings by singular value decomposition[J]. Geophysics,1991, 56(4):528-533
[72]陆文凯,牟永光.一种改进的SVD滤波器[J].石油地球物理勘探,1996,31(5)：736-741
[73]陈遵德.基于奇异值分解的倾角扫描叠加[J].江汉石油学院学报,1992,14(1)：45-50
[74]陆文凯,李衍达.SVD分解提高地震资料的信噪比和分辨率[J].石油地球物理勘探,1998,33(增1)：145-149
[75]赵犁丰,姚玉玲.SVD方法在信号重构中的应用[J].青岛海洋大学学报,1999,29(1)：101-106
[76]刘志鹏,赵伟,陈小宏,等.局部频率域SVD压制随机噪声方法[J].石油地球物理勘探,2012,47(2)：202-206
[77]张贤达.时间序列分析-高阶统计量方法[M].北京：清华大学出版社,1996
[78]Kianoush N,Ahmad R. Application of Higher Order Statistics to Surface Electromyogram Signal Classification[J]. Transaction on Biomedical Engineering,2007,54(10):1762-1768
[79]Mendel M. Tutorial on Higher-order Statistics (spectra) in Signal Processing and System Theory:Theory Results and Some Applications[J].Proc. IEEE,1991, 79 (3):278-305
[80]谢红梅,赵健,齐华,等.基于高阶统计量的非高斯噪声中的信号检测方法研究[J].西安工业学院学报,2001,21(1)：1-5
[81]杨晨阳,屈剑明,李少洪,等.高阶统计量在检测中的应用[J].信号处理,1995,11(4)：288-294
[82]李跃强,全同贵,周支元,等.峰度特征值在视频水印中的应用[J].计算机工程与应用,2009,45(14)：90-92
[83]陈昊,张建忠,王玉荣.基于模型的风速时间序列峰度分析[J].中国电力,2011,44(1)：90-93
[84]吴晓朝,王国良,闫了了.基于峰度-小波包分析的雷达信号去噪方法[J].火力与指挥控制,2011,36(2)：163-165
[85]邢贞贞,韩立国,王宇,等.高阶统计量方法在地震信号分析中的应用[J].吉林大学学报,2007,37(增刊)：139-142
[86]李希亮,刘希强,董晓娜,等.高阶统计量方法在地球物理学中的应用与展望[J].西北地震学报,2010,32(2)：201-205
[87]李志华.高阶统计量方法在地球物理学中的应用[J].工程地球物理学报,2006,3(5)：386-391
[88]石殿祥,李岩,殷福亮,等.基于高阶累积量的非最小相位地震子波提取[J].石油地球物理勘探,1999,34(5)：491-499
[89]谢桂生,石玉梅,尹成.高阶累积量子波估计与处理[J].石油物探,2000,39(4)：19-25
[90]Lazear G L. Mixed-phase wavelet estimation using fourth order eumulants[J]. Geophysics,1993,58(7):1042-1051
[91]唐兵,尹成.基于高阶统计的非最小相位地震子波恢复[J].地球物理学报,2001,44(3)：404-410
[92]尹成,熊晓军,张白林,等.四阶累积量在剩余静校正中的应用研究[J].天然气工业,2004,24(12)：48-50
[93]杜宁平,史军,朱红涛,等.高阶统计量分析在油气预测中的应用[J].海洋地质动态,2004,20(8)：27-29
[94]杨宇山,李媛媛,刘天佑.高阶统计量在地震弱信号及“磁亮点”识别中的应用[J].石油地球物理学报,2005,40(1)：103-107
[95]杨宇山,李媛媛,王法刚,等.高阶统计量弱信号识别方法在识别地质孔洞中的应用[J].长江科学院院报,2005,22(4)：30-33
[96]熊晓军,贺振华,尹成,等.高阶统计量在油气地球物理勘探中的新应用[J].地质科技情报,2005,24(2)：77-84
[97]尹成,伍志明,邓怀群.高阶统计量方法在地震勘探中的应用[J].地球物理学进展,2003,18(3)：546-550
[98]Lu W K, Li Y D, Zhang S W, et al. Higher-order-statistics and supert race-based coherehce-estimational gorithm[J]. Geophsics,2005,70(3):13-18
[99]Walden A T. Non-Gaussian reflectivity, entropy and deconvolution[J]. Geophsics,2005,70(3):73-78
[100]王书明,朱培民,李宏伟,等.地球物理学中的高阶统计量方法[M].北京：科学出版社,2006
[101]胡永泉,尹成,潘树林,等.改进的时变斜度峰度法微地震信号识别技术[J].石油物探,2012,51(6)：625-632
[102]秦树人.工程信号处理[M].北京：高等教育出版社,2008
[103]孙衍才.地震资料保幅去噪综述[J].内蒙古石油化工,2011,21(12)：15-19
[104]曾灏,范小冬.叠前地震资料的去噪处理[J].石油地球物理勘探,1993,28(2)：190-198
[105]安镇文.分形与混沌理论在地震学中的应用与探讨[J].地球物理学进展,1994,9(2)：84-89
[106]叶兴茂,黄卫宁.分形几何学在物探中的应用现状及今后研究中应注意的几个问题[J].国外地质勘探技术,1995,15(3)：9-13
[107]戴元红,陈鸿昶,乔德江,等.基于短时能量比的语音端点检测算法的研[J].通信技术,2009,42(2)：181-183
[108]林玉野,高晓颖,曹炬.基于局部能量比的空中红外弱小目标检测方法[J].航天技术,2007,25(5)：48-50
[1()9]左国平,王彦春,隋荣亮.利用能量比法拾取地震初至的一种改进方法[J].石油物探,2004,43(4)：345-347
[110]胡茑庆,邱忠,谢光军,等.涡轮泵故障检测的频段能量比SOM算法[J].国防科技大学学报,2005,27(6)：93-96
[111]张军华,赵勇,赵爱国.用小波变换与能量比方法联合拾取初至波[J].物探化探计算技术,2002,24(4)：309-312
[112]卢毅,韩志锟,陈瑜.基于能量比预处理的FFT窄带滤波方法[J].东南大学学报,2010,40(5)：948-951
[113]张白林,潘树林,尹成.地震资料数字处理方法[M].北京：石油工业出版社,2008
[114]谢金娥,刘志成,吕秋玲.一种优化预测滤波压制随机噪声的方法[J].地球物理学进展,2012,27(3)：1213-1218
[115]赵德斌,黄真萍,王春梅.f-x域奇异值分解预测滤波法随机噪声衰减[J].石油物探,1998,37(3)：29-33
[116]彭真明,肖慈殉,王斌.改进的f-x域预测滤波及其应用[J].物探化探计算技术,1999,21(2)：145-150
[117]刘婉莹,孟凡顺,刘喜武.地震数据分数阶域空间预测滤波方法研究[J].中国海洋大学学报,2008,38(6)：1008-1012
[118]冯兴强,郭建平,杨长春.三维地震资料空变f-x-y域预测滤波[J].信号处理,2007,23(4)：593-597
[119]盛明兰,蓝章礼,周建庭.梯度均值预测滤波算法及其应用[J].计算机科学,2009,36(7)：300-302
[120]董劲男,秦贵和,张晋东,等.基于多项式预测滤波理论的实时信号传输[J].吉林大学学报,2008,38(4)：890-896
[121]Matheron G. Random Sets and Integral Geometry [M]. New York:Wiley Press,1975
[122]Serra J. Image Analysis and Mathematical Morphology [M]. New York:Academic Press,1982
[123]张建成,吴新杰.形态滤波在实时信号处理中应用的研究[J].传感技术学报,2007,20(4)：828-831
[124]陈辉,郭科,胡英.数学形态学在地震信号处理中的应用研究[J].地球物理学进展,2009,24(6)：1995-2002
[125]李春枝,何荣建,田光明.数学形态滤波在振动信号分析中的应用研究[J].计算机工程与科学,2008,30(9)：126-128
[126]汤井田,李晋,肖晓,等.数学形态滤波与大地电磁噪声压制[J].地球物理学报,2012,55(5)：1784-1792
[127]李海山,吴国忱,印兴耀.形态分量分析在地震数据重建中的应用[J].石油地球物理勘探,2012,47(2)：236-243
[128]王润秋,郑贵娟,付洪洲,等.地震资料处理中的形态滤波去噪方法[J].石油地球物理勘探,2005,40(3)：277-282
[129]舒泓,王毅.基于数学形态滤波和Hilbert变换的电压闪变测量[J].中国电机工程学报,2008,28(1)：111-114
[130]程扬军,黄纯,何朝晖,等.基于自适应顺序形态滤波的电能质量去噪方法[J].计算机仿真,2009,26(12)：218-220
[131]Maragos P, Schafer R W. Morphological filters-Part I:Their set theoretic analysis and relations to linear shift-invariant filters[J]. IEEE Trans. On ASSP,1987,35(8):1153-1169
[132]Maragos P, Schafer R W. Morphological filters-Part II:Their relations to median, order-statistic, and stack filters[J]. IEEE Trans. On ASSP,1987, 35(8):1170-1184
[133]刁成嘉.面向对象技术导论[M].北京：机械工业出版社,2006
[134]李宋琛.Linux面向对象窗口高级编程[M].北京：科学出版社,2001
[135]王自强,刘海燕,李媛洲.Linux下图形用户界面程序的开发与实现[J].计算机应用与软件,2005,22(6)：81-83
[136]王浩南,刘益成.基于嵌入式Linux系统下的Qt开发[J].电脑开发与应用,2010,23(1)：11-13
[137]孙一帆,郭唐永,邹彤,等.使用Qt库开发地震数据处理系统的分析与设计[J].大地测量与地球动力学,2011,31(增刊)：138-141
[138]万长胜,金革,王坚,等.用Qt进行面向对象软件的设计与开发[J].核电子学与探测技术,2004,24(3)：311-313
[2]张向林,陶果,刘新茹.油气地球物理勘探技术进展[J].地球物理学进展,2006,21(1)：143-151
[3]张玮,詹仕凡,张少华,等.石油地球物理勘探技术进展与发展方向[J].中国工程科学,2010,12(44)：97-101
[44]管清友,李君臣.中国页岩气革命远未到来[J].中国报道,2011,30(2)：324-336
[5]贾利春,陈勉,金衍.国外页岩气井水里压裂裂缝监测技术进展[J].天然气与石油,2010,30(1)：44-47
[6]Shawn Maxwell. Microseismic Hydraulic Fracture Imaging:The Path Toward Optimizing Shale Gas Production[J]. Shales,2011,30(3):340-346
[7]Robert C Downie, Le Calvez J H, Ken Kerrihard. Real-Time Microseismic Monitoring of Simultaneous Hydraulic Fracturing Treatments in Adjacent Horizontal Wells in the Woodford Shale[J]. CSEG Convention 2009,26(1): 484-492
[8]宋维琪,陈泽东,毛中华.水力压裂裂缝微地震检测技术[M].山东：中国石油大学出版社,2008
[9]Sylvette Bonnefoy-Claudet, Fabrice Cotton, Pierre-Yves Bard. The nature of noise wavefileld and its applications for site effects studies [J]. Earth-Science Reviews,2006,79:205-227
[10]Li Zhengguang, Yang Runhai, Zhao Jinming,et al. Rock-braking test research on earthquake sequence type[J]. Journal of Seismological Research,2005, 28(4):388-393
[11]Zhang Shan, Liu Qinglin, Zhao Qun, et al. Aplication of microseismic monitoring technology in development of oil field[J]. Geophysical Prospecliong for Petroleum,2002,41(2):226-231
[12]梁兵,朱广生.油气出勘探开发中的微地震监测方法[M].北京：工业出版社,2004
[13]Rut ledge J T, Phillips W S, House L S, et al. Microseismic mapping of a Cotton Valley hydraulic fracture using decimated downhole arrays, Expanded Abstracts[M]. New Orleans(Louisiana):Ann Internat Mtg Soc Explor Geophys, 1998,338-341
[144]刘继民,刘建中,刘志鹏,等.用微地震法监测压裂裂缝转向过程[J].石油勘探与开发,2005,32(2)：75—77
[15]刘建中,王秀鹃,孙玉玲.人工压裂形成多裂缝的可能性研究[J].石油勘探与开发, 2002,29(3)：103-106
[16]石道涵,张昊,黄远.嵌入式人工压裂实时监测技术的应用[J].石油仪器,2005,19(3)：51-52
[17]宋元合,张少标,丁振红,等.微震监测技术在濮城油田沙三上油藏的应用[J].石油仪器,2003,11(4)：11-13
[18]刘建宏,崔勤元,王亚鹏,等.微震监测技术在南泥湾油田长六油藏的应用[J].西北地质,2004,37(2)：80-82
[19]刘建安,马红星,暮立俊,等.井下微地震裂缝测试技术在长庆油田的应用[J].油气井测试,2005,14(2)：54—56
[20]李民河,廖健德,赵增义,等.微地震波裂缝监测技术在油田裂缝研究中的应用-以克拉玛依八区下乌尔禾组油藏为例[J].油气地质与采收率,2004,11(3)：16-18
[21]周婕,高敬文,何平,等.微地震裂缝监测系统在鄯善油田的应用[J].油气井测试,2005,14(6)：62-64
[22]刘云彬,安广柱,张烈辉.用微地震波法确定松辽盆地头台油田地应力场特征[J].大庆石油地质与开发,2006,25(3)：29-30
[23]伍藏原,李汝勇,张明益等.微地震监测气驱前缘技术在牙哈凝析气田的应用[J].天然气地球科学,2005,16(3)：390-393
[24]刘百红,秦绪英,郑四连,等.微地震监测技术及其在油田中的应用现状[J].勘探球物理学进展,2005,28(5)：325-329
[25]刘建中,王春耘,刘继民,等.用微地震法监测油田生产动态[J].石油勘探与开发,2004,31(2)：71-73
[26]张山,刘清林,赵群,等.微地震监测技术在油田开发中的应用,石油物探,2002,41(2)：226-231
[27]王惠清,刘进军,周高鹏,等.微地震方法监测水力压裂改善措施效果[J].新疆石油学院学报,2004,16(4)：41-43
[28]李庆忠.走向精确勘探的道路[M].北京：石油工业出版社,1994
[29]张军华.地震资料去噪方法-原理、算法、编程及应用[M].山东：中国石油大学出版社,2011
[30]Stork A I, Ito H. Source parameter scaling for small earthquake observed at the Western Nagano 800m deep borehole, central Japan. The Bulletin of the Seismological Society of America,2004,94(5):1781-1794
[31]加尔彼林,著.地震勘探偏振法[M].何樵登,杨宝俊,译.北京：石油工业出版社,1989
[32]宋维琪,孙英杰,朱卫星.微地震资料频域相干-时间域偏振滤波方法[J].石油地球物理勘探,2008,43(2)：161-167
[33]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
[34]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)：367-370
[35]国九英,周兴元.f-x域等道距道内插[J].石油地球物理勘探,1996,31(1)：28-34
[36]许大为,潘一山,李国臻,等.基于小波变换的矿山微震信号滤波方法研究[J].矿业业工程,2007,5(4)：66-68
[37]Farnoush F A, Mike B, Jyoti B. Noise suppression in surface microseismic data[J].The Leading Edge,2012,31(12):1496-1501
[38]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[J].石油物探,2012,51(6)：620-625
[39]严永新,张永华,陈祥,等.微地震技术在裂缝监测中的应用研究[J].地学前沿,2013,20(1)：1-5
[40]Jupe A, Cowles J, Jones R. Micro-seismic monitoring:Listen and see the reservoir[J]. World Oil,1998,219(12):171-174
[41]张玎,张景和.微震裂缝监测技术在低渗透油田的应用[J].资源环境与工程,2008,22(1)：45-52
[42]Liu J Z, Wang C Y, Liu J M, et al. Micro-seismic monitor on the operation of oil fields[J]. Petroleum Exploration and Development,2004,31(2):71-73
[43]Maxwell S. Microseismic:growth born from success [J]. The Leading Edge,2010, 29(3):338-343
[44]李智敏,苟先太,金炜东,等.微地震信号的频率特征[J].岩土工程学报,2008,30(6)：830-834
[45]张军华,吕宁,出连玉,等.地震资料去噪方法技术综合评述[J].地球物理学进展,2006,21(2)：546-553
[46]Brian Russell, Dan Hampson, Joong Chun. Noise elimination and the radon transform, Parti[J]. The Leading Edge,1990,9(11):18-23
[47]Brian Russell,Dan Hampson, Joong Chun. Noise elimination and the radon transform, Part2[J]. The Leading Edge,1990,9(11):31-37
[48]蔡希玲,张俊桥.复杂地表区噪声分析技术与压噪方法应用[J].石油地球物理勘探,2002,37(1)：1-4
[49]ZHU Wei-hong, LIU Qing-lin, Panos G. Linear noise at tenuation using local radial trace median filtering[J]. The Leading Edge,2004,23(8):728-730
[50]Douglas Alsdorf. Noise reduction in seismic datausing fourier correlation coefficient filtering[J]. Geophysics,1997,62(5):1617-1627
[51]刘法启,张关泉.小波变换与F-K算法在滤波中的应用[J].石油地球物理勘探, 1996,31(6)：782-792
[52]王振国,周熙襄,李晶.基于小波变换的最小光滑滤波去噪[J].石油地球物理勘探,2002,37(6)：594-600
[53]PHILLIPS R D, WATSON L T, WYNNE R H, et al. Feature reduction using a singular value decomposition for the iterative guided spectral class rejection hybrid classifier[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2009,64(1):107-116
[54]AHMED S M, ALZOUBI Q, ABOZAHHAD M. Ahybrid ECG compression algorithm based on singular value decomposition and discrete wavelet transform[J]. Journal of Medical Engineering and Technology,2007,31(1):54-61
[55]VANLANDUIT S, CAUBERGHE B,GUILLAUMB P. Reduction of large fequency response function data sets using robust singular value decomposi t ion [J]. Computers and Structures,2006,84(12):808-822
[56]VOZALIS M G, MARGARITIS K G. Using SVD and demographic data for the enhance_ment of generalized collaborative filtering[J]. Information Sciences,2007, 177(15):3017-3037
[57]WILLIAMS T, AHMADI M, MILLER WC. Design of 2D FIR and IIR digital filters with canonical signed digit coefficients using singular value decomposition and genetic algorithms[J]. Circuits Systems and SignalProcessing,2007,26(1): 69-89
[58]LEHTOLA L, KARsIKAs M, KOSKINEN M,et al. Effects of noise and filtering on SVD-based morphological parameters of the T wave in the ECG[J]. Journal of Medical Engineering&Technology,2008,32(5):400-407
[59]MARK B, JIANG D. Noise reduction in multiple-echo data sets using singular value decomposi t ion [J]. Magnetic Resonance Imaging,2006,24(2):849-856
[60]LIU Hongxing, LI Jian, ZHAO Ying, et al. Improved singular value decomposition technique for detecting and extracting periodic impulse component in a vibration signal [J]. Chinese Journal of Mechanical Engineering,2004,17(3): 340-345
[61]CEMPEL C. Generalized singular value decomposition in multidimensional condition monitoring of machines-A proposal of comparative diagnostics [J]. Mechanical Systems and Signal Processing,2009,23(3):701-711
[62]Freire S L M, Ulrych T J. Application of singular value decomposition to vertical seismic profiling[J]. Geophysics,1988,53(6):778-785
[63]Liu Xuewei. Ground roll suppression using the Karhunen-Loeve transform [J]. Geophysics,1999,64(2):564-566
[64]苑益军,徐林.叠前线性干扰自动追踪SVD压制方法[J].现代地质,2007,21(41)：733-737
[65]陈遵德.地震记录的奇异值分布特征[J].石油物探快讯,1990,5：44-52
[66]陈遵德,段天友,朱广生.SVD滤波方法的改进与应用[J].石油地球物理勘探,1994,29(6)：783-792
[67]钟本善,何昌礼,杨忠民.复杂构造地区的SVD去噪技术[J].都理工学院学报,2000,27(1)：93-95
[68]沈鸿雁,李庆春.频域奇异值分解(SVD)地震波场去噪[J].石油地球物理勘探,2010,45(2)：185-189
[69]梁霖,徐光华,刘弹,等.小波-奇异值分解在异步电机转子故障特征提取中的应用.中国电机工程学报,2005,25(19)：111-115
[70]Sergio L M Freire, Tad J Ulrych. Application of singular value decomposition to vertical seismic profiling[J]. Geophysics,1998,53(6):778-785
[71]Jackson G M, Mason I M, Greenhalgh S A. Principal component transforms of triaxial recordings by singular value decomposition[J]. Geophysics,1991, 56(4):528-533
[72]陆文凯,牟永光.一种改进的SVD滤波器[J].石油地球物理勘探,1996,31(5)：736-741
[73]陈遵德.基于奇异值分解的倾角扫描叠加[J].江汉石油学院学报,1992,14(1)：45-50
[74]陆文凯,李衍达.SVD分解提高地震资料的信噪比和分辨率[J].石油地球物理勘探,1998,33(增1)：145-149
[75]赵犁丰,姚玉玲.SVD方法在信号重构中的应用[J].青岛海洋大学学报,1999,29(1)：101-106
[76]刘志鹏,赵伟,陈小宏,等.局部频率域SVD压制随机噪声方法[J].石油地球物理勘探,2012,47(2)：202-206
[77]张贤达.时间序列分析-高阶统计量方法[M].北京：清华大学出版社,1996
[78]Kianoush N,Ahmad R. Application of Higher Order Statistics to Surface Electromyogram Signal Classification[J]. Transaction on Biomedical Engineering,2007,54(10):1762-1768
[79]Mendel M. Tutorial on Higher-order Statistics (spectra) in Signal Processing and System Theory:Theory Results and Some Applications[J].Proc. IEEE,1991, 79 (3):278-305
[80]谢红梅,赵健,齐华,等.基于高阶统计量的非高斯噪声中的信号检测方法研究[J].西安工业学院学报,2001,21(1)：1-5
[81]杨晨阳,屈剑明,李少洪,等.高阶统计量在检测中的应用[J].信号处理,1995,11(4)：288-294
[82]李跃强,全同贵,周支元,等.峰度特征值在视频水印中的应用[J].计算机工程与应用,2009,45(14)：90-92
[83]陈昊,张建忠,王玉荣.基于模型的风速时间序列峰度分析[J].中国电力,2011,44(1)：90-93
[84]吴晓朝,王国良,闫了了.基于峰度-小波包分析的雷达信号去噪方法[J].火力与指挥控制,2011,36(2)：163-165
[85]邢贞贞,韩立国,王宇,等.高阶统计量方法在地震信号分析中的应用[J].吉林大学学报,2007,37(增刊)：139-142
[86]李希亮,刘希强,董晓娜,等.高阶统计量方法在地球物理学中的应用与展望[J].西北地震学报,2010,32(2)：201-205
[87]李志华.高阶统计量方法在地球物理学中的应用[J].工程地球物理学报,2006,3(5)：386-391
[88]石殿祥,李岩,殷福亮,等.基于高阶累积量的非最小相位地震子波提取[J].石油地球物理勘探,1999,34(5)：491-499
[89]谢桂生,石玉梅,尹成.高阶累积量子波估计与处理[J].石油物探,2000,39(4)：19-25
[90]Lazear G L. Mixed-phase wavelet estimation using fourth order eumulants[J]. Geophysics,1993,58(7):1042-1051
[91]唐兵,尹成.基于高阶统计的非最小相位地震子波恢复[J].地球物理学报,2001,44(3)：404-410
[92]尹成,熊晓军,张白林,等.四阶累积量在剩余静校正中的应用研究[J].天然气工业,2004,24(12)：48-50
[93]杜宁平,史军,朱红涛,等.高阶统计量分析在油气预测中的应用[J].海洋地质动态,2004,20(8)：27-29
[94]杨宇山,李媛媛,刘天佑.高阶统计量在地震弱信号及“磁亮点”识别中的应用[J].石油地球物理学报,2005,40(1)：103-107
[95]杨宇山,李媛媛,王法刚,等.高阶统计量弱信号识别方法在识别地质孔洞中的应用[J].长江科学院院报,2005,22(4)：30-33
[96]熊晓军,贺振华,尹成,等.高阶统计量在油气地球物理勘探中的新应用[J].地质科技情报,2005,24(2)：77-84
[97]尹成,伍志明,邓怀群.高阶统计量方法在地震勘探中的应用[J].地球物理学进展,2003,18(3)：546-550
[98]Lu W K, Li Y D, Zhang S W, et al. Higher-order-statistics and supert race-based coherehce-estimational gorithm[J]. Geophsics,2005,70(3):13-18
[99]Walden A T. Non-Gaussian reflectivity, entropy and deconvolution[J]. Geophsics,2005,70(3):73-78
[100]王书明,朱培民,李宏伟,等.地球物理学中的高阶统计量方法[M].北京：科学出版社,2006
[101]胡永泉,尹成,潘树林,等.改进的时变斜度峰度法微地震信号识别技术[J].石油物探,2012,51(6)：625-632
[102]秦树人.工程信号处理[M].北京：高等教育出版社,2008
[103]孙衍才.地震资料保幅去噪综述[J].内蒙古石油化工,2011,21(12)：15-19
[104]曾灏,范小冬.叠前地震资料的去噪处理[J].石油地球物理勘探,1993,28(2)：190-198
[105]安镇文.分形与混沌理论在地震学中的应用与探讨[J].地球物理学进展,1994,9(2)：84-89
[106]叶兴茂,黄卫宁.分形几何学在物探中的应用现状及今后研究中应注意的几个问题[J].国外地质勘探技术,1995,15(3)：9-13
[107]戴元红,陈鸿昶,乔德江,等.基于短时能量比的语音端点检测算法的研[J].通信技术,2009,42(2)：181-183
[108]林玉野,高晓颖,曹炬.基于局部能量比的空中红外弱小目标检测方法[J].航天技术,2007,25(5)：48-50
[1()9]左国平,王彦春,隋荣亮.利用能量比法拾取地震初至的一种改进方法[J].石油物探,2004,43(4)：345-347
[110]胡茑庆,邱忠,谢光军,等.涡轮泵故障检测的频段能量比SOM算法[J].国防科技大学学报,2005,27(6)：93-96
[111]张军华,赵勇,赵爱国.用小波变换与能量比方法联合拾取初至波[J].物探化探计算技术,2002,24(4)：309-312
[112]卢毅,韩志锟,陈瑜.基于能量比预处理的FFT窄带滤波方法[J].东南大学学报,2010,40(5)：948-951
[113]张白林,潘树林,尹成.地震资料数字处理方法[M].北京：石油工业出版社,2008
[114]谢金娥,刘志成,吕秋玲.一种优化预测滤波压制随机噪声的方法[J].地球物理学进展,2012,27(3)：1213-1218
[115]赵德斌,黄真萍,王春梅.f-x域奇异值分解预测滤波法随机噪声衰减[J].石油物探,1998,37(3)：29-33
[116]彭真明,肖慈殉,王斌.改进的f-x域预测滤波及其应用[J].物探化探计算技术,1999,21(2)：145-150
[117]刘婉莹,孟凡顺,刘喜武.地震数据分数阶域空间预测滤波方法研究[J].中国海洋大学学报,2008,38(6)：1008-1012
[118]冯兴强,郭建平,杨长春.三维地震资料空变f-x-y域预测滤波[J].信号处理,2007,23(4)：593-597
[119]盛明兰,蓝章礼,周建庭.梯度均值预测滤波算法及其应用[J].计算机科学,2009,36(7)：300-302
[120]董劲男,秦贵和,张晋东,等.基于多项式预测滤波理论的实时信号传输[J].吉林大学学报,2008,38(4)：890-896
[121]Matheron G. Random Sets and Integral Geometry [M]. New York:Wiley Press,1975
[122]Serra J. Image Analysis and Mathematical Morphology [M]. New York:Academic Press,1982
[123]张建成,吴新杰.形态滤波在实时信号处理中应用的研究[J].传感技术学报,2007,20(4)：828-831
[124]陈辉,郭科,胡英.数学形态学在地震信号处理中的应用研究[J].地球物理学进展,2009,24(6)：1995-2002
[125]李春枝,何荣建,田光明.数学形态滤波在振动信号分析中的应用研究[J].计算机工程与科学,2008,30(9)：126-128
[126]汤井田,李晋,肖晓,等.数学形态滤波与大地电磁噪声压制[J].地球物理学报,2012,55(5)：1784-1792
[127]李海山,吴国忱,印兴耀.形态分量分析在地震数据重建中的应用[J].石油地球物理勘探,2012,47(2)：236-243
[128]王润秋,郑贵娟,付洪洲,等.地震资料处理中的形态滤波去噪方法[J].石油地球物理勘探,2005,40(3)：277-282
[129]舒泓,王毅.基于数学形态滤波和Hilbert变换的电压闪变测量[J].中国电机工程学报,2008,28(1)：111-114
[130]程扬军,黄纯,何朝晖,等.基于自适应顺序形态滤波的电能质量去噪方法[J].计算机仿真,2009,26(12)：218-220
[131]Maragos P, Schafer R W. Morphological filters-Part I:Their set theoretic analysis and relations to linear shift-invariant filters[J]. IEEE Trans. On ASSP,1987,35(8):1153-1169
[132]Maragos P, Schafer R W. Morphological filters-Part II:Their relations to median, order-statistic, and stack filters[J]. IEEE Trans. On ASSP,1987, 35(8):1170-1184
[133]刁成嘉.面向对象技术导论[M].北京：机械工业出版社,2006
[134]李宋琛.Linux面向对象窗口高级编程[M].北京：科学出版社,2001
[135]王自强,刘海燕,李媛洲.Linux下图形用户界面程序的开发与实现[J].计算机应用与软件,2005,22(6)：81-83
[136]王浩南,刘益成.基于嵌入式Linux系统下的Qt开发[J].电脑开发与应用,2010,23(1)：11-13
[137]孙一帆,郭唐永,邹彤,等.使用Qt库开发地震数据处理系统的分析与设计[J].大地测量与地球动力学,2011,31(增刊)：138-141
[138]万长胜,金革,王坚,等.用Qt进行面向对象软件的设计与开发[J].核电子学与探测技术,2004,24(3)：311-313
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