改善地震资料品质的几种数字技术及应用
|
摘要
随着科技发展和社会的需要,人们对地震勘探资料品质的要求越来越高。本文所述三类方法通过提高地震资料的分辨率和信噪比,以改善地震资料质量。
本文共分五个部分。第一章主要阐述了地震资料的分辨率和信噪比的研究现状以及文章的研究内容和创新点。其中主要研究方法为:基于自然梯度的盲反褶积算法、自适应波束压制高密点地震中面波和数学形态学压制面波。第二章研究了一种提取混合相位地震子波方法。算法的核心思想是自适应地迭代调整滤波器系数,以提取最优混合相位子波。为提高算法收敛速度,本文采用了基于自然梯度的迭代算法,根据地震信号的次高斯分布特性,提出概率密度函数,通过求解此函数,提取了混合相位子波。第三章分析了高密点地震数据的特性,提出自适应波束压制面波方法,通过设计代价函数,优化了自适应波束。本方法在理论模型和实际资料处理中取得了一定效果。第四章借鉴于数学形态学在图像处理领域的发展,创新性地将应用在聚类中数学形态学的思想应用于地震勘探中的面波压制中。通过基本运算膨胀、腐蚀、开运算和闭运算组成的算法对原始剖面中的面波进行压制,取得了一定效果。为了进一步改善剖面质量,对原始剖面进行中值滤波,再用本方法进行面波压制,效果明显。文章的结论部分对全文内容进行了总结和展望。
The high signal-to-noise ratio (SNR) and resolution is the aim that people are pursuing for gaining good seismic profile and geologic interpretation. This paper tells us three methods in order to improve SNR and resolution of seismic data. Natural gradient blind deconvolution algorithm can extract true mixed-phase-wavelet. Adapt beamforming can suppressing single-sensor ground-roll, not only synthetic seismic data but also Yakenbei real seismic data, there is a obvious effect. Mathematical morphology also has a obvious effect on suppressing ground-roll. Natural gradient blind deconvolution algorithm extract true mixed-phase-wavelet, and improve the resolution.
Estimating seismic wavelet is one of the most important problems of geophysics. True wavelet’s estimating is a important task in seismology, it is the base of seismic deconvolution, migration, feature extracting, geophysics interpretation and so on. Convention deconvolution assumes statistics under seismic wavelet and reflex coefficient are unknown, this will low the resolution and reliability of seismic data. Natural gradient blind deconvolution algorithm hasn’t need any assumes, using natural gradient estimate wavelet, gain true mixed-phase-wavelet, and use in deconvolution to improve resolution. First of all, we introduced mathematic foundation of original data’s can be separated, then we introduced mathematical principal of seismic blind deconvolution, and inducted compensate function, derived the seismic blind deconvolution’s iteration function, at last, we use this method to verify synthetic seismic data and real seismic data, the result shows than natural gradient blind deconvolution algorithm can estimate true mixed-phase-wavelet better, and it can estimate reflex coefficient better.
Compare natural gradient blind deconvolution to convention deconvolution, it has many advantages: first, it hasn’t needed assuming seismic wavelet minimum phase; second, this algorithm used natural gradient method, and this algorithm seems stead, contraction fast, not sensible to noise, and those are very good to write software; third, this algorithm is adaptive, it can update filter coefficient; forth, in synthetic seismic data, though compare to convention deconvolution, it is obvious that natural gradient blind deconvolution can estimate seismic wavelet better. So, we uses this algorithm to real seismic data, we still have a good effect, it can improve seismic data’s resolution.
At the same time, this paper also study on improving seismic data’s SNR. On single-sensor seismic section, the energy of ground-roll is very strong, reflect wave is hidden, and the SNR is very low. In convention seismic processing, we usually use High-pass filter to suppress ground-roll, it has problems, ground-roll still lie or many effect wave is filtered. F-K method and wavelet transform method also have the same disadvantages. So, we propose adaptive beamforming method to suppress ground-roll. This method uses the different apparent velocity of reflect wave and ground-roll, design cost function, make the filter coefficient better and better, suppress energy of ground-roll area. It is used in Xinjiang Yakenbei single-sensor data, and the effect is very good, SNR is improved.
Adaptive beamforming mainly has those advantages. First, on single-sensor seismic section, the energy of ground-roll is very strong, and the SNR is very low. We transform the data to 2D Fourier area in order to make original data and ground-roll are in its self’s apparent velocity; Second, we need the beamforming can accept stand signals, at the same time, attenuate other noise, those noise and effect signals have same frequency in time area. We want to design useful filter to improve SNR; Third, when adaptive beamforming suppressing ground-roll, there exit space alias. Ground-roll is in low frequency area, effect wave and other noise are in wide frequency. Ground-roll and effect wave are mixed, especially when group interval is big, it is disadvantage to suppress noise. The group interval in single-sensor is 5m in most times, this decrease the space alias; Forth, the adaptive beamformging has contraction problems, it is up to step’s uncertainty. As adaptive beamforming is adaptive, it can be used in suppressing other linear noise; Fifth, in real seismic data processing, compare this method to convention band-pass filter, it is better than band-pass filter in seismic section and frequency section. So this method can be wide in seismic data processing, and has important theory and application value in improve SNR of seismic data.
This paper also study mathematical morphology to suppress ground-roll. Mathematical morphology is stand on strict math theory, it’s basic idea and method make a important influence on image processing. In fact, mathematical morphology has compromise a new theory, and is applied in many areas. In this paper, we use this method to suppress ground-roll, though its basic calculation, such as dilation, erosion, opens, close. The result shows this method very well, and can perfectly suppress ground-roll. After midian filtering to original data, then suppressing ground-roll with this method, we can see the data became much clean than before, at the same time, the ground-roll has been suppressed and concordant axis became continuiously.
本文共分五个部分。第一章主要阐述了地震资料的分辨率和信噪比的研究现状以及文章的研究内容和创新点。其中主要研究方法为:基于自然梯度的盲反褶积算法、自适应波束压制高密点地震中面波和数学形态学压制面波。第二章研究了一种提取混合相位地震子波方法。算法的核心思想是自适应地迭代调整滤波器系数,以提取最优混合相位子波。为提高算法收敛速度,本文采用了基于自然梯度的迭代算法,根据地震信号的次高斯分布特性,提出概率密度函数,通过求解此函数,提取了混合相位子波。第三章分析了高密点地震数据的特性,提出自适应波束压制面波方法,通过设计代价函数,优化了自适应波束。本方法在理论模型和实际资料处理中取得了一定效果。第四章借鉴于数学形态学在图像处理领域的发展,创新性地将应用在聚类中数学形态学的思想应用于地震勘探中的面波压制中。通过基本运算膨胀、腐蚀、开运算和闭运算组成的算法对原始剖面中的面波进行压制,取得了一定效果。为了进一步改善剖面质量,对原始剖面进行中值滤波,再用本方法进行面波压制,效果明显。文章的结论部分对全文内容进行了总结和展望。
The high signal-to-noise ratio (SNR) and resolution is the aim that people are pursuing for gaining good seismic profile and geologic interpretation. This paper tells us three methods in order to improve SNR and resolution of seismic data. Natural gradient blind deconvolution algorithm can extract true mixed-phase-wavelet. Adapt beamforming can suppressing single-sensor ground-roll, not only synthetic seismic data but also Yakenbei real seismic data, there is a obvious effect. Mathematical morphology also has a obvious effect on suppressing ground-roll. Natural gradient blind deconvolution algorithm extract true mixed-phase-wavelet, and improve the resolution.
Estimating seismic wavelet is one of the most important problems of geophysics. True wavelet’s estimating is a important task in seismology, it is the base of seismic deconvolution, migration, feature extracting, geophysics interpretation and so on. Convention deconvolution assumes statistics under seismic wavelet and reflex coefficient are unknown, this will low the resolution and reliability of seismic data. Natural gradient blind deconvolution algorithm hasn’t need any assumes, using natural gradient estimate wavelet, gain true mixed-phase-wavelet, and use in deconvolution to improve resolution. First of all, we introduced mathematic foundation of original data’s can be separated, then we introduced mathematical principal of seismic blind deconvolution, and inducted compensate function, derived the seismic blind deconvolution’s iteration function, at last, we use this method to verify synthetic seismic data and real seismic data, the result shows than natural gradient blind deconvolution algorithm can estimate true mixed-phase-wavelet better, and it can estimate reflex coefficient better.
Compare natural gradient blind deconvolution to convention deconvolution, it has many advantages: first, it hasn’t needed assuming seismic wavelet minimum phase; second, this algorithm used natural gradient method, and this algorithm seems stead, contraction fast, not sensible to noise, and those are very good to write software; third, this algorithm is adaptive, it can update filter coefficient; forth, in synthetic seismic data, though compare to convention deconvolution, it is obvious that natural gradient blind deconvolution can estimate seismic wavelet better. So, we uses this algorithm to real seismic data, we still have a good effect, it can improve seismic data’s resolution.
At the same time, this paper also study on improving seismic data’s SNR. On single-sensor seismic section, the energy of ground-roll is very strong, reflect wave is hidden, and the SNR is very low. In convention seismic processing, we usually use High-pass filter to suppress ground-roll, it has problems, ground-roll still lie or many effect wave is filtered. F-K method and wavelet transform method also have the same disadvantages. So, we propose adaptive beamforming method to suppress ground-roll. This method uses the different apparent velocity of reflect wave and ground-roll, design cost function, make the filter coefficient better and better, suppress energy of ground-roll area. It is used in Xinjiang Yakenbei single-sensor data, and the effect is very good, SNR is improved.
Adaptive beamforming mainly has those advantages. First, on single-sensor seismic section, the energy of ground-roll is very strong, and the SNR is very low. We transform the data to 2D Fourier area in order to make original data and ground-roll are in its self’s apparent velocity; Second, we need the beamforming can accept stand signals, at the same time, attenuate other noise, those noise and effect signals have same frequency in time area. We want to design useful filter to improve SNR; Third, when adaptive beamforming suppressing ground-roll, there exit space alias. Ground-roll is in low frequency area, effect wave and other noise are in wide frequency. Ground-roll and effect wave are mixed, especially when group interval is big, it is disadvantage to suppress noise. The group interval in single-sensor is 5m in most times, this decrease the space alias; Forth, the adaptive beamformging has contraction problems, it is up to step’s uncertainty. As adaptive beamforming is adaptive, it can be used in suppressing other linear noise; Fifth, in real seismic data processing, compare this method to convention band-pass filter, it is better than band-pass filter in seismic section and frequency section. So this method can be wide in seismic data processing, and has important theory and application value in improve SNR of seismic data.
This paper also study mathematical morphology to suppress ground-roll. Mathematical morphology is stand on strict math theory, it’s basic idea and method make a important influence on image processing. In fact, mathematical morphology has compromise a new theory, and is applied in many areas. In this paper, we use this method to suppress ground-roll, though its basic calculation, such as dilation, erosion, opens, close. The result shows this method very well, and can perfectly suppress ground-roll. After midian filtering to original data, then suppressing ground-roll with this method, we can see the data became much clean than before, at the same time, the ground-roll has been suppressed and concordant axis became continuiously.
引文
[1]AMARI S I,Cichocki A. Adaptive blind signal processing-neural network approaches [J]. Proceedings of the IEEE,Oct.1998,86(10):2026-2046
[2]AMARI S I. A new learning algorithm for blind signal separation. In :INPS’95’8.Cambridge. MA:MIT Press,1996
[3]BELLINI S. Bussgang Techniques for Blind Deconvolution and Blind Equalization [M],Englewood Cliffs,Prentice-Hall.1994
[4]BELLINI S. Blind equalization [J], Alta Freq.,1988, vol.57,pp.445- 450.
[5]BELLOFIORE S, BALANIS C A. Smart-Antenna Systems for Mobile Comm Networks Part 1 :Overview and Antenna Design. IEEE Antennas and Porpagation Magazine. 2002a,44(3):145-154
[6]BELLOFIORE S. BALANIS J F. Smart-Antenna Systems for Mobile Comm Networks Part 2: Beamforming and Network Throughput. IEEE antennas and Propagation Magazine.2002b,44(4):106-114
[7]BENVENSTE A. Goursat M.Bling.Equalizers [J].IEEE Trans on commun. 1984,32(8):871-883.
[8]CANALES L L.Random noise reduction[C]. 54th Annual Internat. Mtg., Soc. Expl. Geophysics.,1984
[9]CHANAD B , MALAY K ,PADMAJA Y.A multi-scale morphologic edge detector. Pattern Recognition,1998,31(10):1469-1478.
[10]DONOHO D L. De-noising by soft-thresholding[J].IEEE Trans. Informat-ion Theory,1995,41(3):613-627
[11]FABIO L, RICCARDO L. High performance embedded Morphological wave-let coding[J]. IEEE Signal Processing Letters,2003, 10(10):293-295
[12]FARINA A. Antenna-based house signal processing techniques for radar systems [J]. Artech House. Norwood.1992: 62-156
[13]GABRIAL W F. Adaptive arrays-an introduction. Proc IEEE. 1976a, 64(2):239-272
[14]GABRIAL W F. Adaptive processing array systems. Proc IEEE. 1992,80(1):152-163
[15]GABRIAL W F. Special issue on adaptive processing antennas systems, IEEE Transon A& P,AP-34,1986
[16]GABRIAL W F.Special issue on adaptive antennas,IEEE Trans on A& P,AP-24,1976b
[17]GASTERATOS A, TSALIDES S . Fuzzy soft mathematical morphology [J]. Image Signal Processing,1998,145(1):41-49.
[18]GERSHMAN A B, NICKLE U, BOHME J F. Adaptive Beamforming Algorithms with Robustness a gainst Jammer Motion [J]. IEEE Trans. on Signal Processing. 1997,45(7):1878-1885
[19]GHASSAN R, ABDULAZIZ A F. Singel-sensor 3D land seismic acquisition in Kuwait.SEG,2006.
[20]GODARA L C ,GRAY D A. A structure gradient algorithm for adaptive beamforming [J]. Acoust.Soc.Amer.,1989,86:1040-1046.
[21]GODARA L C .Improved LMS algorithm for adaptive beamforming [J].IEEE Trans. Antennas Propagat,1990,38:1631一1635.
[22]GODARA L C, CANTONI A. Analysis of the performance of adaptive beamforming using perturbation sequences [J]. IEEE Trans. Antennas Propagat 268-279.1983, AP-31:
[23]GODARD D N.Self-recovering equalization and carrier tracking in two dimension data communication systems[J].IEEE Trans on Commun.1980, 28(11):1867-1875.
[24]GODFREY R, ROCCA F.Zero memory non-linear deconvolution, Geophysical Prospecting, 1981, 29(2): 189-228.
[25]HANSEN R C. Special issue on active and adaptive antennas,IEEE Trans on A&P,AP-12,1964
[26]HATZINAKOS D,NIKIAS C L. Blind equalization using atricepstrum based algorithm, IEEE Trans.Commun.,1991,vol COM-39:669-682.
[27]HATZINAKOS D,NIKIAS C L. Estimation of mutipath channel response in frequency selective channels, IEEE J.Sel.Areas Commun.1989,vol.7: 12-19.
[28]HOSHUYAMA O,SUGIYAMA A, HIRANO A.“A robust adaptive beamformer for microphone arrays with a blocking matrix using constrained adaptive filters”, IEEE Trans. Signal Processing, vol. 47, pp. 2677–2684, Oct. 1999.
[29]HUDSON J E. Adaptive array principles. London. Peter Peregrinus. 1981 :1-95
[30]ISABELLE B, OLIVIER C,OSCAR C. Fusion of spatial relationships for guiding recognition, example of brain structure recognition in 3D MRI [J]. Pattern Recognition Letters 2005,26(4):449-457
[31]JI Z, LI H H. A novel mathematical morphology filter and itsperformance analysis in noise reduction [J].Chinese Journal of Electronics,2005, 14(1):68-72
[32]JONES I F, LEVY S. Signal-to-noise ratio enhancement in multichannel seismic data via the Karhunen-Loéve transform[J].Geophysical Prospecting, 1987,35(1):12-32
[33]KNAPP C H, CARTER G C. The generalized correlation method for estimation of time delay. IEEE Trans. Acoust., Speech, Signal Processing, 1976,24:320-327
[34]KOSKINEN L, ASTOLA J, NEUVE Y. Soft morphological filters [A].Proc, SPIE Int.Society of Optical Engineering[C],1999,1568:262-270
[35]LI Y J, YANG B J. Application of higher order cumulants for seismic wavelet reconstruction[C].The 20th CODATA International Conference,Beijing,2006.
[36]LIU D,TONG L. An analysis of constant modulus algorithm for array signal processing[J]. Signal Processing.1999,73 (2):81-104
[37]MARAGOS P A. Morphological skeleton representation and coding of binary image. IEEE ASSP,1986,34:1228 1244.
[38]MARAGOS P. Pattern spectrum and multi-scale shape representation. PAMI, 1989, 34(11):701-716
[39]MONZINGO R, MILLER T. Introduction to adaptive arrays. New York: Wiley and Sons, 1980: 24-65
[40]MORLET J.Seismic tomorrow: Interferometry and quantum mechanics[C]. Presented at the 45th Annual International SEG Meeting,1972
[41]NIKIAS C L , LIU F. Bicepstrum computation based on second-andthird-order statistics with applications. Proc.ICASSP’90,2381-2386.
[42]OPPENHEIM A V, SCHAFER R W. Nonlinear filtering of multiplied and convolved signals[J]. IEEE Trans.Audio and Electroacoustics, 1968, 16(3):437-466
[43]PAN R, NIKIAS C L. The complex cepsrrum of higher order cumulants and nonminimum phase identification , IEEE Trans.Acoust.Speech Signal Process..1988,vol.ASSP-36:186-205.
[44]PAULRAJ A J, PAPADIAS C. Time-space processing for wireless communications [J]. IEEE Trans. On Signal Processing.1997,(11):49-83
[45]RUSSO F, LAZZARI A. Color edge detection in presence of Gaussian noise using nonlinear prefiltering [J]. IEEE Trans. On Instrumentation and Measurement, 2005,54(1):352-358
[46]SASAKI K, SATO T. Makamura Y. Holographic passive sonar, IEEE Trans. Sonics Ultrason, 1977,24:193-200
[47]SATO Y A. Method of self-recovering equalization for multilevel amplitude-modulation systems[J].IEEE Trans on Commun..1975,23(6): 679-682.
[48]SCARGLE J D.Absolute value optimization to estimate phase properties of stochastic time series[J].IEEE Trans.Information Theory, 1977, 23(1):140-143
[49]SHERIFF R E,GELDART L P.勘探地震学[M].初英,等译.石油工业出版社,1999,第2版
[50]SHINHA D,DOUGHERTY E R. Fuzzy mathematical morphology[J],J Vision , Communication and Imagine and Representation, 1992,3(3):286-302.
[51]THI H N, JUTTEN C. Blind source separation for convolutive mixtures[J].Signal Processing.1995,45(2):209-229.
[52]TOKKOLA K. Bling Separation of delayed sources based on information maximization [A].in Proc of ICASSP[C].Atlanta:ICASSP,1996:3059-3512.
[53]ULRYCH T J, VELIS D R.Wavelet estimation revisited[J].The Leading Edge,1995,14(11):1139-1142
[54]ULRYCH T J.Application of homomorphic deconvolution to seismology[J]. Geophysics,1971,36:650-660
[55]WALDEN A T, HOSKEN J W.The nature of the non-Gaussianity of primary reflection coefficients and its significance for deconvolution[J]. Geophysical Prospecting,1986, 34(7):1038-1066
[56]WALDEN A T. Non-Gaussianreflectivity,entropy and deconvolution [J]. Geophysics, 1985 ,50(12): 2862-2888
[57]WIGGINS R A. Minimum entropy deconvolution[J]. Geoexploration, 1978, 16:21-35
[58]WITKIN A P, BABAUD J.Uniqueness of the Gaussian kernel for scale-space filtering[J].IEEE Trans.Pattern Analysis and Machine Intelligence, 1986,8(1):26-33
[59]YELLIN D,WENSTEN E. Criteria for multichannel signal separation[J]. IEEE Trans Signal Process .1994,42(8):2158-2168.
[60]YU S J, LEE J H. The statistical performance of eigenspace-based adaptive array beamformers.IEEE Trans. Antennas and Propagation.1996, 44(5) :665-671
[61]柏子游,张勇,虞烈.基于模糊数学和神经网络的数学形态学方法[J].西安交通大学学报,1999,33(4):104-105.
[62]包乾宗,高静怀,陈文超.面波压制的Tidgelet域方法[J].地球物理学报,2007,50(4):1210-1215
[63]曹务祥.高空间密度采样资料分析[J].勘探地球物理进展,2006,29(2):178, 182
[64]陈爱萍,邹文.基于S-变换的面波压制技术[J] .世界地质,2005,24(1):82-86
[65]程冰洁,张玉芬.AVO简化方程的物理意义及其在尤其识别中的应用[J].物探化探计算技术,2003,25(1):26-30.
[66]程乾生.信号数字处理的数学原理[M],北京:石油工业出版社,1979:182
[67]邸凯昌,李德仁,李德毅.从空间数据库发现聚类:一种基于数学形态学的算法[J].中国图象图形学报,1998,3(3):173-179.
[68]段天友.小波变换衰减面波与受损信号的幅度补偿[J].江汉石油学院学报, 1997,19(4):33-38
[69]高春鸣.二值图象骨架的数学形态学描述及重建[J].长沙铁道学院学报,1996,14(4):89-93.
[70]高少武,赵波,周兴元.自适应相干噪声衰减技术[J].石油地球物理勘探,2003,38(3):242-246.
[71]龚耀寰.自适应滤波(第二版)—时域自适应滤波和智能天线[M],电子工业出版社.2003
[72]何樵登.地震勘探原理和方法[M].北京:地质出版社.1980.
[73]赫永峰.自适应网格滤波器在消除面波中的应用[J].世界地质,1998, 17(3):91-95
[74]侯志强,韩崇昭,左东广,等.基于局部多结构元素数学形态学的灰度图像边缘检测算法[J].西安交通大学学报,2003,37(4):389-393.
[75]蒋刚毅,郑义,郁梅.基于数学形态学的交通标志自动识别[J].汕头大学学报,1998,13(1):90-96.
[76]李庆忠.走向精确勘探的道路[M].石油工业出版社,1993
[77]李林,高政,黄卫平.一种新颖的基于截集分解的形态学算子[J].光学技术,2002,27(1):74-77
[78]李文杰,魏修成,宁俊瑞,等.基于频率衰减特性的面波压制方法[J] .石油物探,2008,47(3):225-227
[79]李亚俊.地震勘探子波估计、面波消减新技术研究[D].长春:吉林大学,地球探测科学与技术学院,2007
[80]梁笃国,黄学明,吴敏金.基于形态学与神经网络相结合的手写体汉字识别试验系统[J].华北师范大学学报(自然科学版),1996,(3):40-45.
[81]梁虹,周纪勤.梁洁基于数学形态学的商标图象边缘提取[J].计算机应用研究,2001,(3):94-96.
[82]梁勇,李天牧.多方位形态学结构元素在图像边缘检测中的应用[J].云南大学学报(自然科学版).1999,1(5):392-394.
[83]梁运基,立桂林.陆上高分辨率地震勘探检波器性能及参数选择分析[J].石油物探,2005,44(6):640-644
[84]刘财,王典,杨宝俊.二维小波变换在地震勘探面波消除中的应用[J] .地球物理学进展,2003,18(4):711-714
[85]刘财,谢金娥,郭全仕,等.一种基于自然梯度的地震盲反褶积方法[J] .石油物探,2008,47(5):439-443
[86]刘慧敏,舒宁,林卉.图象边缘信息分析中数学形态学的应用方法[J].武汉大学报(信息科学版).2001,26(4):325-330.
[87]刘喜武,刘洪.地震盲反褶积综述[J].地球物理学进展,2003,18(2):203-209
[88]刘洋,刘财,王典,等.时变中值滤波技术在地震随机噪声衰减中的应用[J].石油地球物理勘探,2008,43(3):327-332
[89]柳楣,林盛.一种消除煤田地震勘探中相干干扰的新方法[J].中国煤田地质,1998,10(4):77-80
[90]陆基孟.地震勘探原理[M].石油大学出版社,1993
[91]吕铁英,彭嘉雄.一种基于数学形态学的图象多尺度分析方法的研究[J].数据采集与处理,1998,13(2):107-111.
[92]罗福龙,易碧金.地震检波器技术及应用[J].物探装备,2005,15(1):6-14
[93]罗省贤,李录明.几种叠前去噪方法[J].石油地球物理勘探,1997,32(3):411-417.
[94]马建仓,牛奕龙,陈海洋.盲信号处理[M].北京:国防工业出版社.2006.
[95][美] R.A.蒙津戈,自适应阵导论[M],国防工业出版社,1988
[96][美] B.维德罗,[美] S.D.史蒂恩斯,自适应信号处理[M],四川:四川大学出版社.1989
[97]孟小红.盲源反褶积方法与应用研究[J].石油地球物理勘探,2005,40(6):642-645
[98]强琳,刘贵忠.利用小波变换多尺度传播特性实现地震信号去噪[J].西安交通大学学报,1998,32(1):29-32
[99]秦政.石油地球物理勘探—地震勘探原理和解释[M].北京:石油工业出版社.1987.
[100]沈福民.自适应信号处理[M],西安:西安电子科技大学出版社.2001
[101]宋骥,郑义,陈际.一种基于多结构元素彩色图像的形态滤波器[J].汕头大学学报(自然科学版),2003,18(1):7781.
[102]唐常青,吕宏伯,黄铮,等.数学形态学方法及其应用[M].北京:科学出版社.1990
[103]王德民.基于数学形态学的纹理特征及其有效性的实验比较[J].电子学报,1995,23(1):103-105.
[104]王俊平,铁满霞,郝跃.基于数学形态学的集成电路真实缺陷图象滤波[J].西安电子科技大学学报,2001,28(2):211-214.
[105]王思远,张增祥,文沃根,等.基于色彩理论与数学形态学的彩色地图自动分层研究[J].测绘通报,2001,(10):7-11.
[106]魏巍刘学伟.自适应噪声抵消技术在地震资料处理中的应用[J].石油天然气学报,2008,30(4),79~83
[107]吴正国,唐劲松,章林柯,等.自适应盲信号与图像处理[M].北京:电子工业出版社.2005.
[108]谢金娥,郭全仕,刘财,等.压制高密点地震中面波干扰的自适应波束方法[J] .石油物探,2009,48(2) :110-114
[109]熊翥.地震数据数字处理技术[M].石油工业出版社,1984
[110]徐伯勋,白旭滨,等.信号处理及应用[M].地质出版社,1997
[111]杨行峻,郑君里.人工神经网络与盲信号处理[M].北京:清华大学出版社,2002:18-19,333.
[112]杨云,张子敬.数学形态学在地图模式识别中的应用[J].解放军测绘学院学报,1998,15(1):36-39.
[113]殷泽兴,钱浙滨,李介谷.基于数学形态学和神经网络的货币识别方法[J].上海交通大学学报,1999,33(9):1142-1145.
[114]印兴耀.一种基于负熵的Bussgang地震盲反褶积方法[J],石油地球物理勘探,2007,42(5),500-505
[115]俞寿朋.频率加强滤波[J].石油地球物理勘探.1984,19(3):200-209
[116]岳蔚,刘沛.基于数学形态学消噪的电能质量扰动检测方法[J].电力系统自动化. 2002,26(7):1317
[117]张华,潘冬明,红兴岩.二维小波变换在去除面波干扰中应用[J] .石油物探,2007,46(2):147-150
[118]张俊.一种新的基于模糊推理的灰值形态学和它的光学实现[J].光子学报,1997.26(7):583-589.
[119]张宇,张关泉.小波变换用于去除高频随机噪声[J].石油地球物理勘探,1997,32(3):327-337
[120]章珂,李衍达,刘贵忠,等.多分辨率地震信号反褶积[J].地球物理学报,1999,42(4):529-535
[121]周南.数学形态学骨架及重建[J].中国图象图形学报,1997,2(10):713- 715.
[122]周维忠,赵海洋,孙国基,等.基于多尺度数学形态学的边缘检测[J].数据采集与处理,2000,15(3) :316-319
[123]周煦渔,施鹏飞.三角范逻辑的模糊形态学分析上海交通大学学报[J].1998, 32(9):20-24
[124]朱振宇,刘洪,李幼铭,等.基于预条件共轭梯度法的盲反褶积方法[J] .勘探地球物理进展,2004,27(3):174-176
[2]AMARI S I. A new learning algorithm for blind signal separation. In :INPS’95’8.Cambridge. MA:MIT Press,1996
[3]BELLINI S. Bussgang Techniques for Blind Deconvolution and Blind Equalization [M],Englewood Cliffs,Prentice-Hall.1994
[4]BELLINI S. Blind equalization [J], Alta Freq.,1988, vol.57,pp.445- 450.
[5]BELLOFIORE S, BALANIS C A. Smart-Antenna Systems for Mobile Comm Networks Part 1 :Overview and Antenna Design. IEEE Antennas and Porpagation Magazine. 2002a,44(3):145-154
[6]BELLOFIORE S. BALANIS J F. Smart-Antenna Systems for Mobile Comm Networks Part 2: Beamforming and Network Throughput. IEEE antennas and Propagation Magazine.2002b,44(4):106-114
[7]BENVENSTE A. Goursat M.Bling.Equalizers [J].IEEE Trans on commun. 1984,32(8):871-883.
[8]CANALES L L.Random noise reduction[C]. 54th Annual Internat. Mtg., Soc. Expl. Geophysics.,1984
[9]CHANAD B , MALAY K ,PADMAJA Y.A multi-scale morphologic edge detector. Pattern Recognition,1998,31(10):1469-1478.
[10]DONOHO D L. De-noising by soft-thresholding[J].IEEE Trans. Informat-ion Theory,1995,41(3):613-627
[11]FABIO L, RICCARDO L. High performance embedded Morphological wave-let coding[J]. IEEE Signal Processing Letters,2003, 10(10):293-295
[12]FARINA A. Antenna-based house signal processing techniques for radar systems [J]. Artech House. Norwood.1992: 62-156
[13]GABRIAL W F. Adaptive arrays-an introduction. Proc IEEE. 1976a, 64(2):239-272
[14]GABRIAL W F. Adaptive processing array systems. Proc IEEE. 1992,80(1):152-163
[15]GABRIAL W F. Special issue on adaptive processing antennas systems, IEEE Transon A& P,AP-34,1986
[16]GABRIAL W F.Special issue on adaptive antennas,IEEE Trans on A& P,AP-24,1976b
[17]GASTERATOS A, TSALIDES S . Fuzzy soft mathematical morphology [J]. Image Signal Processing,1998,145(1):41-49.
[18]GERSHMAN A B, NICKLE U, BOHME J F. Adaptive Beamforming Algorithms with Robustness a gainst Jammer Motion [J]. IEEE Trans. on Signal Processing. 1997,45(7):1878-1885
[19]GHASSAN R, ABDULAZIZ A F. Singel-sensor 3D land seismic acquisition in Kuwait.SEG,2006.
[20]GODARA L C ,GRAY D A. A structure gradient algorithm for adaptive beamforming [J]. Acoust.Soc.Amer.,1989,86:1040-1046.
[21]GODARA L C .Improved LMS algorithm for adaptive beamforming [J].IEEE Trans. Antennas Propagat,1990,38:1631一1635.
[22]GODARA L C, CANTONI A. Analysis of the performance of adaptive beamforming using perturbation sequences [J]. IEEE Trans. Antennas Propagat 268-279.1983, AP-31:
[23]GODARD D N.Self-recovering equalization and carrier tracking in two dimension data communication systems[J].IEEE Trans on Commun.1980, 28(11):1867-1875.
[24]GODFREY R, ROCCA F.Zero memory non-linear deconvolution, Geophysical Prospecting, 1981, 29(2): 189-228.
[25]HANSEN R C. Special issue on active and adaptive antennas,IEEE Trans on A&P,AP-12,1964
[26]HATZINAKOS D,NIKIAS C L. Blind equalization using atricepstrum based algorithm, IEEE Trans.Commun.,1991,vol COM-39:669-682.
[27]HATZINAKOS D,NIKIAS C L. Estimation of mutipath channel response in frequency selective channels, IEEE J.Sel.Areas Commun.1989,vol.7: 12-19.
[28]HOSHUYAMA O,SUGIYAMA A, HIRANO A.“A robust adaptive beamformer for microphone arrays with a blocking matrix using constrained adaptive filters”, IEEE Trans. Signal Processing, vol. 47, pp. 2677–2684, Oct. 1999.
[29]HUDSON J E. Adaptive array principles. London. Peter Peregrinus. 1981 :1-95
[30]ISABELLE B, OLIVIER C,OSCAR C. Fusion of spatial relationships for guiding recognition, example of brain structure recognition in 3D MRI [J]. Pattern Recognition Letters 2005,26(4):449-457
[31]JI Z, LI H H. A novel mathematical morphology filter and itsperformance analysis in noise reduction [J].Chinese Journal of Electronics,2005, 14(1):68-72
[32]JONES I F, LEVY S. Signal-to-noise ratio enhancement in multichannel seismic data via the Karhunen-Loéve transform[J].Geophysical Prospecting, 1987,35(1):12-32
[33]KNAPP C H, CARTER G C. The generalized correlation method for estimation of time delay. IEEE Trans. Acoust., Speech, Signal Processing, 1976,24:320-327
[34]KOSKINEN L, ASTOLA J, NEUVE Y. Soft morphological filters [A].Proc, SPIE Int.Society of Optical Engineering[C],1999,1568:262-270
[35]LI Y J, YANG B J. Application of higher order cumulants for seismic wavelet reconstruction[C].The 20th CODATA International Conference,Beijing,2006.
[36]LIU D,TONG L. An analysis of constant modulus algorithm for array signal processing[J]. Signal Processing.1999,73 (2):81-104
[37]MARAGOS P A. Morphological skeleton representation and coding of binary image. IEEE ASSP,1986,34:1228 1244.
[38]MARAGOS P. Pattern spectrum and multi-scale shape representation. PAMI, 1989, 34(11):701-716
[39]MONZINGO R, MILLER T. Introduction to adaptive arrays. New York: Wiley and Sons, 1980: 24-65
[40]MORLET J.Seismic tomorrow: Interferometry and quantum mechanics[C]. Presented at the 45th Annual International SEG Meeting,1972
[41]NIKIAS C L , LIU F. Bicepstrum computation based on second-andthird-order statistics with applications. Proc.ICASSP’90,2381-2386.
[42]OPPENHEIM A V, SCHAFER R W. Nonlinear filtering of multiplied and convolved signals[J]. IEEE Trans.Audio and Electroacoustics, 1968, 16(3):437-466
[43]PAN R, NIKIAS C L. The complex cepsrrum of higher order cumulants and nonminimum phase identification , IEEE Trans.Acoust.Speech Signal Process..1988,vol.ASSP-36:186-205.
[44]PAULRAJ A J, PAPADIAS C. Time-space processing for wireless communications [J]. IEEE Trans. On Signal Processing.1997,(11):49-83
[45]RUSSO F, LAZZARI A. Color edge detection in presence of Gaussian noise using nonlinear prefiltering [J]. IEEE Trans. On Instrumentation and Measurement, 2005,54(1):352-358
[46]SASAKI K, SATO T. Makamura Y. Holographic passive sonar, IEEE Trans. Sonics Ultrason, 1977,24:193-200
[47]SATO Y A. Method of self-recovering equalization for multilevel amplitude-modulation systems[J].IEEE Trans on Commun..1975,23(6): 679-682.
[48]SCARGLE J D.Absolute value optimization to estimate phase properties of stochastic time series[J].IEEE Trans.Information Theory, 1977, 23(1):140-143
[49]SHERIFF R E,GELDART L P.勘探地震学[M].初英,等译.石油工业出版社,1999,第2版
[50]SHINHA D,DOUGHERTY E R. Fuzzy mathematical morphology[J],J Vision , Communication and Imagine and Representation, 1992,3(3):286-302.
[51]THI H N, JUTTEN C. Blind source separation for convolutive mixtures[J].Signal Processing.1995,45(2):209-229.
[52]TOKKOLA K. Bling Separation of delayed sources based on information maximization [A].in Proc of ICASSP[C].Atlanta:ICASSP,1996:3059-3512.
[53]ULRYCH T J, VELIS D R.Wavelet estimation revisited[J].The Leading Edge,1995,14(11):1139-1142
[54]ULRYCH T J.Application of homomorphic deconvolution to seismology[J]. Geophysics,1971,36:650-660
[55]WALDEN A T, HOSKEN J W.The nature of the non-Gaussianity of primary reflection coefficients and its significance for deconvolution[J]. Geophysical Prospecting,1986, 34(7):1038-1066
[56]WALDEN A T. Non-Gaussianreflectivity,entropy and deconvolution [J]. Geophysics, 1985 ,50(12): 2862-2888
[57]WIGGINS R A. Minimum entropy deconvolution[J]. Geoexploration, 1978, 16:21-35
[58]WITKIN A P, BABAUD J.Uniqueness of the Gaussian kernel for scale-space filtering[J].IEEE Trans.Pattern Analysis and Machine Intelligence, 1986,8(1):26-33
[59]YELLIN D,WENSTEN E. Criteria for multichannel signal separation[J]. IEEE Trans Signal Process .1994,42(8):2158-2168.
[60]YU S J, LEE J H. The statistical performance of eigenspace-based adaptive array beamformers.IEEE Trans. Antennas and Propagation.1996, 44(5) :665-671
[61]柏子游,张勇,虞烈.基于模糊数学和神经网络的数学形态学方法[J].西安交通大学学报,1999,33(4):104-105.
[62]包乾宗,高静怀,陈文超.面波压制的Tidgelet域方法[J].地球物理学报,2007,50(4):1210-1215
[63]曹务祥.高空间密度采样资料分析[J].勘探地球物理进展,2006,29(2):178, 182
[64]陈爱萍,邹文.基于S-变换的面波压制技术[J] .世界地质,2005,24(1):82-86
[65]程冰洁,张玉芬.AVO简化方程的物理意义及其在尤其识别中的应用[J].物探化探计算技术,2003,25(1):26-30.
[66]程乾生.信号数字处理的数学原理[M],北京:石油工业出版社,1979:182
[67]邸凯昌,李德仁,李德毅.从空间数据库发现聚类:一种基于数学形态学的算法[J].中国图象图形学报,1998,3(3):173-179.
[68]段天友.小波变换衰减面波与受损信号的幅度补偿[J].江汉石油学院学报, 1997,19(4):33-38
[69]高春鸣.二值图象骨架的数学形态学描述及重建[J].长沙铁道学院学报,1996,14(4):89-93.
[70]高少武,赵波,周兴元.自适应相干噪声衰减技术[J].石油地球物理勘探,2003,38(3):242-246.
[71]龚耀寰.自适应滤波(第二版)—时域自适应滤波和智能天线[M],电子工业出版社.2003
[72]何樵登.地震勘探原理和方法[M].北京:地质出版社.1980.
[73]赫永峰.自适应网格滤波器在消除面波中的应用[J].世界地质,1998, 17(3):91-95
[74]侯志强,韩崇昭,左东广,等.基于局部多结构元素数学形态学的灰度图像边缘检测算法[J].西安交通大学学报,2003,37(4):389-393.
[75]蒋刚毅,郑义,郁梅.基于数学形态学的交通标志自动识别[J].汕头大学学报,1998,13(1):90-96.
[76]李庆忠.走向精确勘探的道路[M].石油工业出版社,1993
[77]李林,高政,黄卫平.一种新颖的基于截集分解的形态学算子[J].光学技术,2002,27(1):74-77
[78]李文杰,魏修成,宁俊瑞,等.基于频率衰减特性的面波压制方法[J] .石油物探,2008,47(3):225-227
[79]李亚俊.地震勘探子波估计、面波消减新技术研究[D].长春:吉林大学,地球探测科学与技术学院,2007
[80]梁笃国,黄学明,吴敏金.基于形态学与神经网络相结合的手写体汉字识别试验系统[J].华北师范大学学报(自然科学版),1996,(3):40-45.
[81]梁虹,周纪勤.梁洁基于数学形态学的商标图象边缘提取[J].计算机应用研究,2001,(3):94-96.
[82]梁勇,李天牧.多方位形态学结构元素在图像边缘检测中的应用[J].云南大学学报(自然科学版).1999,1(5):392-394.
[83]梁运基,立桂林.陆上高分辨率地震勘探检波器性能及参数选择分析[J].石油物探,2005,44(6):640-644
[84]刘财,王典,杨宝俊.二维小波变换在地震勘探面波消除中的应用[J] .地球物理学进展,2003,18(4):711-714
[85]刘财,谢金娥,郭全仕,等.一种基于自然梯度的地震盲反褶积方法[J] .石油物探,2008,47(5):439-443
[86]刘慧敏,舒宁,林卉.图象边缘信息分析中数学形态学的应用方法[J].武汉大学报(信息科学版).2001,26(4):325-330.
[87]刘喜武,刘洪.地震盲反褶积综述[J].地球物理学进展,2003,18(2):203-209
[88]刘洋,刘财,王典,等.时变中值滤波技术在地震随机噪声衰减中的应用[J].石油地球物理勘探,2008,43(3):327-332
[89]柳楣,林盛.一种消除煤田地震勘探中相干干扰的新方法[J].中国煤田地质,1998,10(4):77-80
[90]陆基孟.地震勘探原理[M].石油大学出版社,1993
[91]吕铁英,彭嘉雄.一种基于数学形态学的图象多尺度分析方法的研究[J].数据采集与处理,1998,13(2):107-111.
[92]罗福龙,易碧金.地震检波器技术及应用[J].物探装备,2005,15(1):6-14
[93]罗省贤,李录明.几种叠前去噪方法[J].石油地球物理勘探,1997,32(3):411-417.
[94]马建仓,牛奕龙,陈海洋.盲信号处理[M].北京:国防工业出版社.2006.
[95][美] R.A.蒙津戈,自适应阵导论[M],国防工业出版社,1988
[96][美] B.维德罗,[美] S.D.史蒂恩斯,自适应信号处理[M],四川:四川大学出版社.1989
[97]孟小红.盲源反褶积方法与应用研究[J].石油地球物理勘探,2005,40(6):642-645
[98]强琳,刘贵忠.利用小波变换多尺度传播特性实现地震信号去噪[J].西安交通大学学报,1998,32(1):29-32
[99]秦政.石油地球物理勘探—地震勘探原理和解释[M].北京:石油工业出版社.1987.
[100]沈福民.自适应信号处理[M],西安:西安电子科技大学出版社.2001
[101]宋骥,郑义,陈际.一种基于多结构元素彩色图像的形态滤波器[J].汕头大学学报(自然科学版),2003,18(1):7781.
[102]唐常青,吕宏伯,黄铮,等.数学形态学方法及其应用[M].北京:科学出版社.1990
[103]王德民.基于数学形态学的纹理特征及其有效性的实验比较[J].电子学报,1995,23(1):103-105.
[104]王俊平,铁满霞,郝跃.基于数学形态学的集成电路真实缺陷图象滤波[J].西安电子科技大学学报,2001,28(2):211-214.
[105]王思远,张增祥,文沃根,等.基于色彩理论与数学形态学的彩色地图自动分层研究[J].测绘通报,2001,(10):7-11.
[106]魏巍刘学伟.自适应噪声抵消技术在地震资料处理中的应用[J].石油天然气学报,2008,30(4),79~83
[107]吴正国,唐劲松,章林柯,等.自适应盲信号与图像处理[M].北京:电子工业出版社.2005.
[108]谢金娥,郭全仕,刘财,等.压制高密点地震中面波干扰的自适应波束方法[J] .石油物探,2009,48(2) :110-114
[109]熊翥.地震数据数字处理技术[M].石油工业出版社,1984
[110]徐伯勋,白旭滨,等.信号处理及应用[M].地质出版社,1997
[111]杨行峻,郑君里.人工神经网络与盲信号处理[M].北京:清华大学出版社,2002:18-19,333.
[112]杨云,张子敬.数学形态学在地图模式识别中的应用[J].解放军测绘学院学报,1998,15(1):36-39.
[113]殷泽兴,钱浙滨,李介谷.基于数学形态学和神经网络的货币识别方法[J].上海交通大学学报,1999,33(9):1142-1145.
[114]印兴耀.一种基于负熵的Bussgang地震盲反褶积方法[J],石油地球物理勘探,2007,42(5),500-505
[115]俞寿朋.频率加强滤波[J].石油地球物理勘探.1984,19(3):200-209
[116]岳蔚,刘沛.基于数学形态学消噪的电能质量扰动检测方法[J].电力系统自动化. 2002,26(7):1317
[117]张华,潘冬明,红兴岩.二维小波变换在去除面波干扰中应用[J] .石油物探,2007,46(2):147-150
[118]张俊.一种新的基于模糊推理的灰值形态学和它的光学实现[J].光子学报,1997.26(7):583-589.
[119]张宇,张关泉.小波变换用于去除高频随机噪声[J].石油地球物理勘探,1997,32(3):327-337
[120]章珂,李衍达,刘贵忠,等.多分辨率地震信号反褶积[J].地球物理学报,1999,42(4):529-535
[121]周南.数学形态学骨架及重建[J].中国图象图形学报,1997,2(10):713- 715.
[122]周维忠,赵海洋,孙国基,等.基于多尺度数学形态学的边缘检测[J].数据采集与处理,2000,15(3) :316-319
[123]周煦渔,施鹏飞.三角范逻辑的模糊形态学分析上海交通大学学报[J].1998, 32(9):20-24
[124]朱振宇,刘洪,李幼铭,等.基于预条件共轭梯度法的盲反褶积方法[J] .勘探地球物理进展,2004,27(3):174-176
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |