多频NMR测井系统中微弱信号检测问题研究
|
摘要
多频核磁共振测井仪器是目前最先进的测井技术手段之一。未来几十年中它在我国石油勘测行业有着较大需求。为突破国际大公司的技术封锁,研制开发具有我国自主知识产权的多频核磁共振测井仪具有非常重要意义。多频核磁共振测井系统工程实现中重要关键技术之一是微弱信号获取与处理问题。本文主要讨论核磁共振回波信号提取及其回波串衰变信号构建过程中的干扰噪声滤除方法和高效数据采集等相关问题。本论文的主要工作内容有如下几个方面：
(1)要求工作在高温环境中的微弱信号放大器的设计与实现,是多频核磁共振测井系统中关键的环节和难点。针对测井回波信号具有一定频带宽度,传统低噪声窄带点匹配方法已不能满足设计要求,本文提出一种适用于相对较宽频带的微弱信号噪声匹配方法,并设计研制出具有高信噪比高增益的前置放大器。测井实践结果表明,它不仅完全达到各项设计技术指标要求,而且为后续信号处理过程奠定良好的基础。
(2)噪声滤除的最大化与信号损失的最小化是一个矛盾体的两个方面。如何做到两者之间的平衡,是滤波器选择截止频率的常遇问题。文中基于信号损失和残余噪声功率和最小准则,讨论广义信噪比最大为前提的最佳滤波截止频率确定问题,根据核磁共振测井仪回波信号的特征,建立小波滤噪方法分解尺度选择的量化指标。其结果已应用于实际反演算法,并取得较好效果。
(3)针对在实际测井工作中发现测井环境温度会造成回波串衰减率测量偏差问题。在计算说明环境温度与跃迁能量之间关系的基础上,以正演所得的双峰孔隙度回波模型为对象,基于Bloch方程建立回波串温度误差模型,从理论上也得到此项误差的存在机理。论文给出一种消除温度偏差的近似方法。
(4)为提高核磁共振测井低孔渗储层测量的准确性,需用较小的回波间隔并增加测量时问,但由此造成数据处理时间的减少,强化了T2谱测量的实时性要求,基于压缩感知的检测方式,设计专用核磁共振回波信号检测系统,以可靠性较高的硬件电路结构和低的功耗满足了测量的实时性要求。理论计算和实验表明,该实时采集处理系统不仅适合于对测量结果有高质量要求的回波信号检测过程,而且同时满足核磁共振测井过程的实时性及低功耗要求。
Multi-frequency NMR logging method is one of the most advanced logging techniques nowadays. In following decades, it would still be wide used in oil exploration. To break through the technique blockade, it is of great importance to develop multi-frequency NMR logging instrument. In the NMR logging system development, the weak signal's capture and process is very crucial. In the dissertation, the echo's extraction and high efficient acquisition and noise filtering method in the echo string construction were discussed deeply. The main content of this paper was as below.
(1) The design and implementation of weak signal amplifier which is required to work in high temperature environment is a key process in multi-frequency logging system. Because echo signal has a wide bandwidth, traditional point noise match method could not meet design target. Due to this, a wide band noise match method was proposed and a high gain high SNR amplifier had been designed. Logging experiments showed, it not only met the design specification but also provide a good foundation for the follow-up signal process.
(2) Noise filtering maximum and signal loss minimum are the two aspects of a paradox. The tradeoff between the two issues is common problem to choose a filtering cutoff frequency. Based on the signal loss and residual noise power sum minimum principle, the choice of best filtering cutoff frequency was analyzed to get generalized SNR maximum. The quantitative standard to choose wavelet decomposition scale was established according to the echo's characteristics. The result had been applied in inversion algorithm and get good performance.
(3) The measurement deviation caused by the difference between calibration and logging temperature was analyzed. Based on the relation between environment temperature and transition energy, the temperature error mechanism was described. In addition, the model of temperature error was built through Bloch equation based on the twin-peak porosity model. Furthermore, a approximate temperature error suppression method was proposed according to the characteristic and mechanism of temperature deviation.
(4) To improve the accuracy in ultra-deep NMR logging, short echo interval and long acquisition time should be adopted. Because of this, the signal process time decreased. To meet the real time requirement of T2 spectra measurement, a detection method based on compressed sensing was proposed to design a capture system dedicated for NMR echo signal. Experiments showed that the detection system gained good performance in NMR logging system which requires high quality result and has rigid demand in real time and low cost.
(1)要求工作在高温环境中的微弱信号放大器的设计与实现,是多频核磁共振测井系统中关键的环节和难点。针对测井回波信号具有一定频带宽度,传统低噪声窄带点匹配方法已不能满足设计要求,本文提出一种适用于相对较宽频带的微弱信号噪声匹配方法,并设计研制出具有高信噪比高增益的前置放大器。测井实践结果表明,它不仅完全达到各项设计技术指标要求,而且为后续信号处理过程奠定良好的基础。
(2)噪声滤除的最大化与信号损失的最小化是一个矛盾体的两个方面。如何做到两者之间的平衡,是滤波器选择截止频率的常遇问题。文中基于信号损失和残余噪声功率和最小准则,讨论广义信噪比最大为前提的最佳滤波截止频率确定问题,根据核磁共振测井仪回波信号的特征,建立小波滤噪方法分解尺度选择的量化指标。其结果已应用于实际反演算法,并取得较好效果。
(3)针对在实际测井工作中发现测井环境温度会造成回波串衰减率测量偏差问题。在计算说明环境温度与跃迁能量之间关系的基础上,以正演所得的双峰孔隙度回波模型为对象,基于Bloch方程建立回波串温度误差模型,从理论上也得到此项误差的存在机理。论文给出一种消除温度偏差的近似方法。
(4)为提高核磁共振测井低孔渗储层测量的准确性,需用较小的回波间隔并增加测量时问,但由此造成数据处理时间的减少,强化了T2谱测量的实时性要求,基于压缩感知的检测方式,设计专用核磁共振回波信号检测系统,以可靠性较高的硬件电路结构和低的功耗满足了测量的实时性要求。理论计算和实验表明,该实时采集处理系统不仅适合于对测量结果有高质量要求的回波信号检测过程,而且同时满足核磁共振测井过程的实时性及低功耗要求。
Multi-frequency NMR logging method is one of the most advanced logging techniques nowadays. In following decades, it would still be wide used in oil exploration. To break through the technique blockade, it is of great importance to develop multi-frequency NMR logging instrument. In the NMR logging system development, the weak signal's capture and process is very crucial. In the dissertation, the echo's extraction and high efficient acquisition and noise filtering method in the echo string construction were discussed deeply. The main content of this paper was as below.
(1) The design and implementation of weak signal amplifier which is required to work in high temperature environment is a key process in multi-frequency logging system. Because echo signal has a wide bandwidth, traditional point noise match method could not meet design target. Due to this, a wide band noise match method was proposed and a high gain high SNR amplifier had been designed. Logging experiments showed, it not only met the design specification but also provide a good foundation for the follow-up signal process.
(2) Noise filtering maximum and signal loss minimum are the two aspects of a paradox. The tradeoff between the two issues is common problem to choose a filtering cutoff frequency. Based on the signal loss and residual noise power sum minimum principle, the choice of best filtering cutoff frequency was analyzed to get generalized SNR maximum. The quantitative standard to choose wavelet decomposition scale was established according to the echo's characteristics. The result had been applied in inversion algorithm and get good performance.
(3) The measurement deviation caused by the difference between calibration and logging temperature was analyzed. Based on the relation between environment temperature and transition energy, the temperature error mechanism was described. In addition, the model of temperature error was built through Bloch equation based on the twin-peak porosity model. Furthermore, a approximate temperature error suppression method was proposed according to the characteristic and mechanism of temperature deviation.
(4) To improve the accuracy in ultra-deep NMR logging, short echo interval and long acquisition time should be adopted. Because of this, the signal process time decreased. To meet the real time requirement of T2 spectra measurement, a detection method based on compressed sensing was proposed to design a capture system dedicated for NMR echo signal. Experiments showed that the detection system gained good performance in NMR logging system which requires high quality result and has rigid demand in real time and low cost.
引文
[1]张抗.1980年以来我国石油剩余可采储量变化分析.中国石油勘探,2002(04)：1-8.
[2]张抗.对中国石油可采资源量的讨论.石油与天然气地质,2003,24(01)：7-11.
[3]沈平平,赵文智,窦立荣.中国石油资源前景与未来10年储量增长趋势预测.石油学报,2000,21(04)：1-6.
[4]胡文海,陈冬晴.美国油气田分布规律和勘探经验.北京：石油工业出版社,2002.995-997.
[5]杜小弟,姚超.深层油气勘探势在必行.海相油气地质,2001,6(01)：1-5.
[6]朱光有,张水昌.中国深层油气成藏条件与勘探潜力.石油学报,2009,30(06)：793-802.
[7]傅强,武晓光,游瑜春.平湖油气田古近系花港组低阻油层成因与识别分析.海洋石油,2003,23(S1)：21-24.
[8]肖立志.核磁共振成像测井与岩石核磁共振及其应用.北京：科学出版社,1999.
[9]何雨丹,肖立志,毛志强,等.测井评价“三低”油气藏面临的挑战和发展方向.地球物理学进展,2005,20(02)：282-288.
[10]Hopgood J R, Rayner P J W. Single channel nonstationary stochastic signal separation using linear time-varying filters. IEEE Transactions on Signal Processing, 2003,51(7):1739-1752.
[11]Vaseghi S V. Advanced digital signal processing and noise reduction. John Wiley&Sons Ltd,2000.
[12]戴逸松.微弱信号检测技术.北京：电子工业出版社,2005.
[13]徐岩,罗冠炜.低信噪比弱信号提取算法研究.甘肃工业大学学报,2003(3)：88-91.
[14]刘建科,张海宁,马毅.红外测温中检测强噪声下微弱信号的新途径.物理学报,2000,49(1)：106-109.
[15]朱彤,黄华.低信噪比图像高斯噪声非线性模值滤波算法分析.中国科技信息,2009(12)：157-158.
[16]沈亚强.低信噪比语音信号的自适应滤波.浙江师大学报(自然科学版),1992(1)：55-59.
[17]吴慧娟,文玉梅,李平.低信噪比下的LMS自适应无偏时延估计.电子学报,2009(3)：500-505.
[18]魏瑾.低信噪比下的数字调制信号自动识别.信息安全与通信保密,2010(11)：36-38.
[19]刘柏森,卢志茂,申丽然,等.基于希尔伯特-黄变换的低信噪比语音端点检测.吉林大学学报(工学版),2011(3)：844-848.
[20]Zhou D, Debrunner V, Havlicek J. A spatially selective filter based on the undecimated wavelet transform that is robust to noise estimation error. in:6th IEEE Southwest Symposium on Image Analysis and Interpretation. NV,USA:2004. 162-166.
[21]Su L, Zhao G, Zhang R. Translation-invariant wavelet de-noising method with improved thresholding. in:IEEE International Symposium on Communications and Information Technology. Beijing, China:2005.619-622.
[22]Mallat S. A Wavelet Tour of Signal Processing. ACADEMIC PRESS,2008.
[23]Luisier F, Blu T, Unser M. A New SURE Approach to Image Denoising:Interscale Orthonormal Wavelet Thresholding. IEEE Transactions on Image Processing, 2007,16(3):593-606.
[24]Gunthera U L, Ludwigb C, Ruterjans H. WAVEWAT—Improved Solvent Suppression in NMR Spectra Employing Wavelet Transforms. Journal of Magnetic Resonance,2002,156(1):19-25.
[25]Donoho D L, JOHNSTONE L M. adapting to unknown smoothness via wavelet shrinkage. Journal of the American Statistical Association,1995,90(432):1200-1224.
[26]Donoho D L. De-noising by soft-thresholding. IEEE Transactions on Information Theory,1995,41(3):613-627.
[27]Chen G Y, Bui T D, Krzyzak A. Image denoising using neighbouring wavelet coefficients. in:IEEE International Conference on Acoustics, Speech, and Signal Processing. QC, Canada:2004.917-920.
[28]Chang S G, Yu B, Vetterli M. Adaptive wavelet thresholding for image denoising and compression. IEEE Transactions on Image Processing,2000,9(9):1532-1546.
[29]Baracheb D, Antoinea J, Dereppeb J. The Continuous Wavelet Transform, an Analysis Tool for NMR Spectroscopy. Journal of Magnetic Resonance, 1997,128(1):1-11.
[30]Azzalinia A, Fargea M, Schneiderb K. Nonlinear wavelet thresholding:A recursive method to determine the optimal denoising threshold. Applied and Computational Harmonic Analysis,2005,18(2):177-185.
[31]Alsberg B K, Woodward A M, Winson M K, et al. Wavelet Denoising of Infrared Spectra. Analyst,1997(7):645-652.
[32]王东,丁宣浩,陈利霞.信号奇异性的Lipschitz意义.桂林电子工业学院学报,2003,23(5)：9-12.
[33]Mallat S, Hwang W L. Singularity detection and processing with wavelets. IEEE Transactions on Information Theory,1992,38(2):617-643.
[34]孙延奎.小波分析及其应用.北京：机械工业出版社,2005.
[35]谢庆明,肖立志,廖广志SURE算法在核磁共振信号去噪中的实现.地球物理学报,2010,53(11)：2776-2783.
[36]王希武,董光波,谢桂海.基于小波变换的核磁共振FID信号的去噪方法研究.核电子学与探测技术,2008,28(02)：365-370.
[37]Xueguang S, Zhengliang Y, Li S. Resolution of multicomponent NMR signals using wavelet compression and immune algorithm. Spectrochimica Acta Part A:Molecular and Biomolecular Spectroscopy,2003,59(5):1075-1082.
[38]Trbovica N, Danceaa F, Langerb T, et al. Using wavelet de-noised spectra in NMR screening. Journal of Magnetic Resonance,2005,173(2):280-287.
[39]Taswell C. The what, how, and why of wavelet shrinkage denoising. Computing in Science & Engineering,2002,2(3):12-19.
[40]Tahoces J C C P, Martin-Pastor M, Penedo M, et al. Wavelet-based ultra-high compression of multidimensional NMR data sets. Journal of Magnetic Resonance, 2004,168(2):288-295.
[41]Sendur L, Selesnick I W. Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency. IEEE Transactions on Signal Processing, 2002,50(11):2744-2756.
[42]Rahman S M M, Hasan M K. Wavelet-domain iterative center weighted median filter for image denoising. Signal Processing,2003,83(5):1001-1012.
[43]Portilla J, Strela V, Wainwright M J, et al. Image denoising using scale mixtures of Gaussians in the wavelet domain. IEEE Transactions on Image Processing, 2003,12(11):1338-1351.
[44]Neue G. Simplification of dynamic NMR spectroscopy by wavelet transforms. Solid State Nuclear Magnetic Resonance,1996,5(4):305-314.
[45]Kivanc Mihcak M, Kozintsev I, Ramchandran K, et al. Low-complexity image denoising based on statistical modeling of wavelet coefficients. IEEE Signal Processing Letters,1999,6(12):300-303.
[46]Kima S B, Wangb Z, Oraintarac S, et al. Feature selection and classification of high-resolution NMR spectra in the complex wavelet transform domain. Chemometrics and Intelligent Laboratory Systems,2008,90(2):161-168.
[47]Gunthera U L, Ludwigb C, Ruterjans H. WAVE WAT—Improved Solvent Suppression in NMR Spectra Employing Wavelet Transforms. Journal of Magnetic Resonance,2002,156(1):19-25.
[48]Gordon S, Edward K. Self-Diffusion Maps from Wavelet De-Noised NMR Images. Journal of Magnetic Resonance, Series B,1996,111(1):50-60.
[49]Farge M. Wavelet Transforms and their Applications to Turbulence. Fluid Mechanics, 1992,24:395-458.
[50]Dancea F, Gunther U. Automated Protein NMR Structure Determination Using Wavelet De-noised NOESY Spectra. Journal of Biomolecular NMR, 2005,33(3):139-152.
[51]Cobasa J C, Tahocesb P G, Martin-Pastora M, et al. Wavelet-based ultra-high compression of multidimensional NMR data sets. Journal of Magnetic Resonance, 2004,168(2):288-295.
[52]Cobasa J C, Bernsteinb M A, Martin-Pastorc M, et al. A new general-purpose fully automatic baseline-correction procedure for 1D and 2D NMR data. Journal of Magnetic Resonance,2006,183(1):145-151.
[53]Zheng C, Zhang Y. Low-Field Pulsed NMR Signal Denoising Based on Wavelet Transform. in:IEEE 15th Signal Processing and Communications Applications. Eskisehir:2007.1-4.
[54]郑传行,张一鸣.基于小波变换的低场脉冲核磁共振信号滤波.核电子学与探测技术,2008,28(1)：30-34.
[55]马鹏阁,柳毅,齐林,等.低信噪比下脉冲激光雷达回波信号小波域滤波算法.电光与控制,2011(4)：26-29.
[56]魏小薇,曹志刚.低信噪比下数字幅度调制的调制进制快速识别.清华大学学报(自然科学版),2006(1)：35-38.
[57]柳刚,李术才,薛翊国,等.基于小波变换的雷达低信噪比信号处理技术及应用研究.工程勘察,2009(9)：85-90.
[58]李辉,崔琛,余剑.低信噪比下模拟调制方式自动识别.电子对抗,2007(1)：21-25.
[59]吴强,张福泰,何竹青,等.低信噪比信号去噪的研究.微计算机信息,2009(10)：310-311.
[60]刘世奇,刘江平,张恒磊,等.低信噪比地震记录干扰波压制方法研究.工程地球物理学报,2009(1)：48-52.
[61]张松华.低信噪比下RFID调制识别测试的研究[硕士论文].湖南大学,2010.
[62]牛有田,陈建峰,陈会峰,等.强噪声下小波包自适应算法的研究.河南师范大学学报(自然科学版),2011(1)：80-82.
[63]刘东红,张永顺.小波包变换及其在低信噪比雷达信号检测中的应用.空军工程大学学报(自然科学版),2004(6)：30-33.
[64]甄蜀春,张善文,赵兴录.低信噪比下的一种信号检测方法.现代雷达,2001(5)：32-33.
[65]李合生,毛剑琴,李世玲.基于小波变换的低信噪比雷达回波快速检测法.控制与决策,2001(3)：307-310.
[66]高俊楠,丁建江,王鹏.低信噪比下雷达信号脉内特征提取新方法.空军雷达学院学报,2008(1)：15-17.
[67]Li B X, Haykin S. Chaotic detection of small target in sea clutter. in:IEEE International Conference on Acoustics, Speech, and Signal Processing. Minneapolis, MN, USA:1993.237-240.
[68]Wang G, Chen D, Lin J, et al. The application of chaotic oscillators to weak signal detection. IEEE Transactions on Industrial Electronics,1999,46(2):440-444.
[69]Wang G, Zhenga W, He S. Estimation of amplitude and phase of a weak signal by using the property of sensitive dependence on initial conditions of a nonlinear oscillator. Signal Processing,2002,82(1):103-115.
[70]李月,杨宝俊,石要武,等.用混沌振子检测淹没在强背景噪声中的方波信号.吉林大学自然科学学报,2001(2)：65-68.
[71]聂春燕,徐振忠.混沌系统在弱信号检测中的应用.传感器技术2003,22(1)：55-57.
[72]Li Y, Yang B. Chaotic system for the detection of periodic signals under the background of strong noise. Chinese Science Bulletin,2003,48(5):508-510.
[73]李月,杨宝俊.混沌振子检测引论.北京：电子工业出版社,2004.49-75.
[74]路鹏,李月.微弱正弦信号幅值混沌检测的一种改进方案.电子学报,2005,33(3)：527-529.
[75]史建锋,王可人.基于混沌振子的低信噪比条件下信号检测研究.探测与控制学报,2006(5)：39-42.
[76]王娜,郑德忠,张淑清.基于混沌振子的低信噪比语音端点检测新方法.仪器仪表学报,2009(7)：1432-1435.
[77]Donoho D L. Compressed sensing. IEEE Transactions on Information Theory, 2006,52(4):1289-1306.
[78]Candes E J, Romberg J, Tao T. Robust uncertainty principles:exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory,2006,52(2):489-509.
[79]Candes E J, Romberg J. Quantitative robust uncertainty principles and optimally sparse decompositions. Foundations Of Computational Mathematics, 2006,6(2):227-254.
[80]Candes E J, Romberg J. Practical signal recovery from random projection. IEEE Transactions on Signal Processing,2005.
[81]Donoho D L, Tsaig Y. Extensions of compressed sensing. Signal Processing, 2006,86(3):549-571.
[82]Baraniuk R G. Compressive Sensing. IEEE Signal Processing Magazine, 2007,24(4):118-121.
[83]Kashin B. The widths of certain finite dimensional sets and classes of smooth functions. Izv. Akad. Nauk SSSR,1977,41(2):334-351.
[84]Candes E J, Tao T. Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies. IEEE Transactions on Information Theory, 2006,52(12):5406-5425.
[85]Donoho D L. For most large underdetermined systems of linear equations the minimal 11-norm solution is also the sparsest solution. Communications on Pure and Applied Mathematics,2006,59(6):797-829.
[86]魏丹,李树涛.压缩传感综述.自动化学报,2009,35(11)：1369-1377.
[87]韦穗,杨海蓉,张成.压缩传感理论与重构算法.电子学报,2011,36(1)：142-148.
[88]Baraniuk R, Steeghs P. Compressive Radar Imaging. in:Radar Conference. Boston, MA:IEEE,2007.128-133.
[89]Bhattachrya S, Blumensath T, Mulgrew B. Fast encoding of synthetic aperture radar raw data using compressed sensing. in:IEEE/SP 14th Workshop on Statistical Signal Processing. Madison, WI, USA:IEEE,2007.448-452.
[90]王良君,石光明,刘丹华.压缩感知理论及其研究进展.电子学报,2009,5(5)：1070-1081.
[91]苏袁超,朱晓明.短波极低信噪比下抑制强单音干扰算法改进.电子科技,2006(2)：30-32.
[92]张爱民,胡艳龙,韩方景.低信噪比下基于自适应门限的窄带干扰抑制研究.电子信息对抗技术,2009(1)：51-54.
[93]蔡权伟,魏平,肖先赐.一种低信噪比信号的调制盲识别方法.电子科技大学学报,2006(2)：196-199.
[94]江宝安,万群.基于Gabor-Radon变换的低信噪比雷达LFM信号检测与估计.重 庆邮电大学学报(自然科学版),2010(1)：27-32.
[95]高阳,李言俊,张科.多尺度方向性Hermite变换在低信噪比图像边缘检测中的应用.武汉大学学报(工学版),2006(2)：47-50.
[96]沈庭芝,方子文,张梅英.用改进的模拟退火法(SA)对低信噪比图像的恢复.北京理工大学学报,1997(1)：86-90.
[97]胡法龙,肖立志.一种核磁共振测井信号强度的确定方法.核电子学与探测技术,2006,26(5)：557-560.
[98]Motchenbacher C D, Connelly J A. Low-Noise Electronic System Design. Wiley-Interscience; 1 edition,1993.
[99]周凯波,李丹,汤天知,等.核磁共振测井仪前置放大电路的低噪声分析.华中科技大学学报(自然科学版),2008,36(1)：91-94.
[100]张文权,翁爱华.地面核磁共振正则化反演方法研究.吉林大学学报(地球科学版),2007,37(4)：809-813.
[101]姜国.井下仪器高温电路设计方法研究.石油仪器,2005,19(3)：58-60.
[102]宋湘云.高温电路的潜力及问题.微电子学,1992(5)：62-67.
[103]谢然红,肖立志,刘天定.原油的核磁共振弛豫特性.西南石油大学学报,2007,29(5)：21-24.
[104]谢然红,肖立志.储层流体及其在岩石孔隙中的核磁共振弛豫温度特性.地质学报,2007,81(2)：284-288.
[105]谢然红,肖立志,傅少庆.饱和水岩石核磁共振表面弛豫温度特性.中国石油大学学报(自然科学版),2008,32(2)：44-46.
[106]孟繁莹,肖立志,仵岳奇,等.新疆塔河特低孔低渗油气藏核磁共振测井应用基础实验.江汉石油学院学报,2003,25(3)：61-62.
[107]王忠东,汪浩,李能根,等.核磁共振岩心基础实验分析.测井技术,2001,25(3)：170-174.
[108]Foley I, Farooqui S A, Kleinberg R L. Effect of Paramagnetic Ions on NMR Relaxation of Fluids at Solid Surfaces. Journal of Magnetic Resonance, Series A, 1996,123(1):95-104.
[109]Latour L L, Kleinberg R L, Szginer A. Nuclear magnetic resonance properties of rocks at elevated temperatures. Journal of Colloid and Interface Science, 1992,150(2):535-548.
[110]Tissot B P, Welte D H. Petroleum formation and occurrence:a new approach to oil and gas exploration. Springer-Verlag,New York, NY,1978.
[111]朱起悦FPGA器件的可靠性预计.环境技术,2008(03)：11-14.
[112]Olshausen B A, Field D J. Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature,1996,381(6583):607-609.
[113]赵航芳,贾艳云.压缩传感-稀疏信号的采样与重构.声学与电子工程,2010(04)：13-17.
[114]杨震,叶蕾,郭海燕.基于压缩感知重构信号的说话人识别系统抗噪方法研究.信号处理,2010,26(3)：321-326.
[115]Davenport M A, Wakin M B, Baraniuk R G. Detection and estimation with compressive measurementsfunctions. in:Houston TX.USA:Rice University,Department of Electrical and Computer Engineering,2006.3-13.
[116]Haupt J, Nowak R. Compressive sampling for signal detection. in:IEEE International Conference on Acoustics, Speech and Signal Processing. Madison, WI: 2007.1509-1512.
[117]季向阳,戴琼海,付长军.压缩感知研究.计算机学报,2011,34(3)：425-433.
[2]张抗.对中国石油可采资源量的讨论.石油与天然气地质,2003,24(01)：7-11.
[3]沈平平,赵文智,窦立荣.中国石油资源前景与未来10年储量增长趋势预测.石油学报,2000,21(04)：1-6.
[4]胡文海,陈冬晴.美国油气田分布规律和勘探经验.北京：石油工业出版社,2002.995-997.
[5]杜小弟,姚超.深层油气勘探势在必行.海相油气地质,2001,6(01)：1-5.
[6]朱光有,张水昌.中国深层油气成藏条件与勘探潜力.石油学报,2009,30(06)：793-802.
[7]傅强,武晓光,游瑜春.平湖油气田古近系花港组低阻油层成因与识别分析.海洋石油,2003,23(S1)：21-24.
[8]肖立志.核磁共振成像测井与岩石核磁共振及其应用.北京：科学出版社,1999.
[9]何雨丹,肖立志,毛志强,等.测井评价“三低”油气藏面临的挑战和发展方向.地球物理学进展,2005,20(02)：282-288.
[10]Hopgood J R, Rayner P J W. Single channel nonstationary stochastic signal separation using linear time-varying filters. IEEE Transactions on Signal Processing, 2003,51(7):1739-1752.
[11]Vaseghi S V. Advanced digital signal processing and noise reduction. John Wiley&Sons Ltd,2000.
[12]戴逸松.微弱信号检测技术.北京：电子工业出版社,2005.
[13]徐岩,罗冠炜.低信噪比弱信号提取算法研究.甘肃工业大学学报,2003(3)：88-91.
[14]刘建科,张海宁,马毅.红外测温中检测强噪声下微弱信号的新途径.物理学报,2000,49(1)：106-109.
[15]朱彤,黄华.低信噪比图像高斯噪声非线性模值滤波算法分析.中国科技信息,2009(12)：157-158.
[16]沈亚强.低信噪比语音信号的自适应滤波.浙江师大学报(自然科学版),1992(1)：55-59.
[17]吴慧娟,文玉梅,李平.低信噪比下的LMS自适应无偏时延估计.电子学报,2009(3)：500-505.
[18]魏瑾.低信噪比下的数字调制信号自动识别.信息安全与通信保密,2010(11)：36-38.
[19]刘柏森,卢志茂,申丽然,等.基于希尔伯特-黄变换的低信噪比语音端点检测.吉林大学学报(工学版),2011(3)：844-848.
[20]Zhou D, Debrunner V, Havlicek J. A spatially selective filter based on the undecimated wavelet transform that is robust to noise estimation error. in:6th IEEE Southwest Symposium on Image Analysis and Interpretation. NV,USA:2004. 162-166.
[21]Su L, Zhao G, Zhang R. Translation-invariant wavelet de-noising method with improved thresholding. in:IEEE International Symposium on Communications and Information Technology. Beijing, China:2005.619-622.
[22]Mallat S. A Wavelet Tour of Signal Processing. ACADEMIC PRESS,2008.
[23]Luisier F, Blu T, Unser M. A New SURE Approach to Image Denoising:Interscale Orthonormal Wavelet Thresholding. IEEE Transactions on Image Processing, 2007,16(3):593-606.
[24]Gunthera U L, Ludwigb C, Ruterjans H. WAVEWAT—Improved Solvent Suppression in NMR Spectra Employing Wavelet Transforms. Journal of Magnetic Resonance,2002,156(1):19-25.
[25]Donoho D L, JOHNSTONE L M. adapting to unknown smoothness via wavelet shrinkage. Journal of the American Statistical Association,1995,90(432):1200-1224.
[26]Donoho D L. De-noising by soft-thresholding. IEEE Transactions on Information Theory,1995,41(3):613-627.
[27]Chen G Y, Bui T D, Krzyzak A. Image denoising using neighbouring wavelet coefficients. in:IEEE International Conference on Acoustics, Speech, and Signal Processing. QC, Canada:2004.917-920.
[28]Chang S G, Yu B, Vetterli M. Adaptive wavelet thresholding for image denoising and compression. IEEE Transactions on Image Processing,2000,9(9):1532-1546.
[29]Baracheb D, Antoinea J, Dereppeb J. The Continuous Wavelet Transform, an Analysis Tool for NMR Spectroscopy. Journal of Magnetic Resonance, 1997,128(1):1-11.
[30]Azzalinia A, Fargea M, Schneiderb K. Nonlinear wavelet thresholding:A recursive method to determine the optimal denoising threshold. Applied and Computational Harmonic Analysis,2005,18(2):177-185.
[31]Alsberg B K, Woodward A M, Winson M K, et al. Wavelet Denoising of Infrared Spectra. Analyst,1997(7):645-652.
[32]王东,丁宣浩,陈利霞.信号奇异性的Lipschitz意义.桂林电子工业学院学报,2003,23(5)：9-12.
[33]Mallat S, Hwang W L. Singularity detection and processing with wavelets. IEEE Transactions on Information Theory,1992,38(2):617-643.
[34]孙延奎.小波分析及其应用.北京：机械工业出版社,2005.
[35]谢庆明,肖立志,廖广志SURE算法在核磁共振信号去噪中的实现.地球物理学报,2010,53(11)：2776-2783.
[36]王希武,董光波,谢桂海.基于小波变换的核磁共振FID信号的去噪方法研究.核电子学与探测技术,2008,28(02)：365-370.
[37]Xueguang S, Zhengliang Y, Li S. Resolution of multicomponent NMR signals using wavelet compression and immune algorithm. Spectrochimica Acta Part A:Molecular and Biomolecular Spectroscopy,2003,59(5):1075-1082.
[38]Trbovica N, Danceaa F, Langerb T, et al. Using wavelet de-noised spectra in NMR screening. Journal of Magnetic Resonance,2005,173(2):280-287.
[39]Taswell C. The what, how, and why of wavelet shrinkage denoising. Computing in Science & Engineering,2002,2(3):12-19.
[40]Tahoces J C C P, Martin-Pastor M, Penedo M, et al. Wavelet-based ultra-high compression of multidimensional NMR data sets. Journal of Magnetic Resonance, 2004,168(2):288-295.
[41]Sendur L, Selesnick I W. Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency. IEEE Transactions on Signal Processing, 2002,50(11):2744-2756.
[42]Rahman S M M, Hasan M K. Wavelet-domain iterative center weighted median filter for image denoising. Signal Processing,2003,83(5):1001-1012.
[43]Portilla J, Strela V, Wainwright M J, et al. Image denoising using scale mixtures of Gaussians in the wavelet domain. IEEE Transactions on Image Processing, 2003,12(11):1338-1351.
[44]Neue G. Simplification of dynamic NMR spectroscopy by wavelet transforms. Solid State Nuclear Magnetic Resonance,1996,5(4):305-314.
[45]Kivanc Mihcak M, Kozintsev I, Ramchandran K, et al. Low-complexity image denoising based on statistical modeling of wavelet coefficients. IEEE Signal Processing Letters,1999,6(12):300-303.
[46]Kima S B, Wangb Z, Oraintarac S, et al. Feature selection and classification of high-resolution NMR spectra in the complex wavelet transform domain. Chemometrics and Intelligent Laboratory Systems,2008,90(2):161-168.
[47]Gunthera U L, Ludwigb C, Ruterjans H. WAVE WAT—Improved Solvent Suppression in NMR Spectra Employing Wavelet Transforms. Journal of Magnetic Resonance,2002,156(1):19-25.
[48]Gordon S, Edward K. Self-Diffusion Maps from Wavelet De-Noised NMR Images. Journal of Magnetic Resonance, Series B,1996,111(1):50-60.
[49]Farge M. Wavelet Transforms and their Applications to Turbulence. Fluid Mechanics, 1992,24:395-458.
[50]Dancea F, Gunther U. Automated Protein NMR Structure Determination Using Wavelet De-noised NOESY Spectra. Journal of Biomolecular NMR, 2005,33(3):139-152.
[51]Cobasa J C, Tahocesb P G, Martin-Pastora M, et al. Wavelet-based ultra-high compression of multidimensional NMR data sets. Journal of Magnetic Resonance, 2004,168(2):288-295.
[52]Cobasa J C, Bernsteinb M A, Martin-Pastorc M, et al. A new general-purpose fully automatic baseline-correction procedure for 1D and 2D NMR data. Journal of Magnetic Resonance,2006,183(1):145-151.
[53]Zheng C, Zhang Y. Low-Field Pulsed NMR Signal Denoising Based on Wavelet Transform. in:IEEE 15th Signal Processing and Communications Applications. Eskisehir:2007.1-4.
[54]郑传行,张一鸣.基于小波变换的低场脉冲核磁共振信号滤波.核电子学与探测技术,2008,28(1)：30-34.
[55]马鹏阁,柳毅,齐林,等.低信噪比下脉冲激光雷达回波信号小波域滤波算法.电光与控制,2011(4)：26-29.
[56]魏小薇,曹志刚.低信噪比下数字幅度调制的调制进制快速识别.清华大学学报(自然科学版),2006(1)：35-38.
[57]柳刚,李术才,薛翊国,等.基于小波变换的雷达低信噪比信号处理技术及应用研究.工程勘察,2009(9)：85-90.
[58]李辉,崔琛,余剑.低信噪比下模拟调制方式自动识别.电子对抗,2007(1)：21-25.
[59]吴强,张福泰,何竹青,等.低信噪比信号去噪的研究.微计算机信息,2009(10)：310-311.
[60]刘世奇,刘江平,张恒磊,等.低信噪比地震记录干扰波压制方法研究.工程地球物理学报,2009(1)：48-52.
[61]张松华.低信噪比下RFID调制识别测试的研究[硕士论文].湖南大学,2010.
[62]牛有田,陈建峰,陈会峰,等.强噪声下小波包自适应算法的研究.河南师范大学学报(自然科学版),2011(1)：80-82.
[63]刘东红,张永顺.小波包变换及其在低信噪比雷达信号检测中的应用.空军工程大学学报(自然科学版),2004(6)：30-33.
[64]甄蜀春,张善文,赵兴录.低信噪比下的一种信号检测方法.现代雷达,2001(5)：32-33.
[65]李合生,毛剑琴,李世玲.基于小波变换的低信噪比雷达回波快速检测法.控制与决策,2001(3)：307-310.
[66]高俊楠,丁建江,王鹏.低信噪比下雷达信号脉内特征提取新方法.空军雷达学院学报,2008(1)：15-17.
[67]Li B X, Haykin S. Chaotic detection of small target in sea clutter. in:IEEE International Conference on Acoustics, Speech, and Signal Processing. Minneapolis, MN, USA:1993.237-240.
[68]Wang G, Chen D, Lin J, et al. The application of chaotic oscillators to weak signal detection. IEEE Transactions on Industrial Electronics,1999,46(2):440-444.
[69]Wang G, Zhenga W, He S. Estimation of amplitude and phase of a weak signal by using the property of sensitive dependence on initial conditions of a nonlinear oscillator. Signal Processing,2002,82(1):103-115.
[70]李月,杨宝俊,石要武,等.用混沌振子检测淹没在强背景噪声中的方波信号.吉林大学自然科学学报,2001(2)：65-68.
[71]聂春燕,徐振忠.混沌系统在弱信号检测中的应用.传感器技术2003,22(1)：55-57.
[72]Li Y, Yang B. Chaotic system for the detection of periodic signals under the background of strong noise. Chinese Science Bulletin,2003,48(5):508-510.
[73]李月,杨宝俊.混沌振子检测引论.北京：电子工业出版社,2004.49-75.
[74]路鹏,李月.微弱正弦信号幅值混沌检测的一种改进方案.电子学报,2005,33(3)：527-529.
[75]史建锋,王可人.基于混沌振子的低信噪比条件下信号检测研究.探测与控制学报,2006(5)：39-42.
[76]王娜,郑德忠,张淑清.基于混沌振子的低信噪比语音端点检测新方法.仪器仪表学报,2009(7)：1432-1435.
[77]Donoho D L. Compressed sensing. IEEE Transactions on Information Theory, 2006,52(4):1289-1306.
[78]Candes E J, Romberg J, Tao T. Robust uncertainty principles:exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory,2006,52(2):489-509.
[79]Candes E J, Romberg J. Quantitative robust uncertainty principles and optimally sparse decompositions. Foundations Of Computational Mathematics, 2006,6(2):227-254.
[80]Candes E J, Romberg J. Practical signal recovery from random projection. IEEE Transactions on Signal Processing,2005.
[81]Donoho D L, Tsaig Y. Extensions of compressed sensing. Signal Processing, 2006,86(3):549-571.
[82]Baraniuk R G. Compressive Sensing. IEEE Signal Processing Magazine, 2007,24(4):118-121.
[83]Kashin B. The widths of certain finite dimensional sets and classes of smooth functions. Izv. Akad. Nauk SSSR,1977,41(2):334-351.
[84]Candes E J, Tao T. Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies. IEEE Transactions on Information Theory, 2006,52(12):5406-5425.
[85]Donoho D L. For most large underdetermined systems of linear equations the minimal 11-norm solution is also the sparsest solution. Communications on Pure and Applied Mathematics,2006,59(6):797-829.
[86]魏丹,李树涛.压缩传感综述.自动化学报,2009,35(11)：1369-1377.
[87]韦穗,杨海蓉,张成.压缩传感理论与重构算法.电子学报,2011,36(1)：142-148.
[88]Baraniuk R, Steeghs P. Compressive Radar Imaging. in:Radar Conference. Boston, MA:IEEE,2007.128-133.
[89]Bhattachrya S, Blumensath T, Mulgrew B. Fast encoding of synthetic aperture radar raw data using compressed sensing. in:IEEE/SP 14th Workshop on Statistical Signal Processing. Madison, WI, USA:IEEE,2007.448-452.
[90]王良君,石光明,刘丹华.压缩感知理论及其研究进展.电子学报,2009,5(5)：1070-1081.
[91]苏袁超,朱晓明.短波极低信噪比下抑制强单音干扰算法改进.电子科技,2006(2)：30-32.
[92]张爱民,胡艳龙,韩方景.低信噪比下基于自适应门限的窄带干扰抑制研究.电子信息对抗技术,2009(1)：51-54.
[93]蔡权伟,魏平,肖先赐.一种低信噪比信号的调制盲识别方法.电子科技大学学报,2006(2)：196-199.
[94]江宝安,万群.基于Gabor-Radon变换的低信噪比雷达LFM信号检测与估计.重 庆邮电大学学报(自然科学版),2010(1)：27-32.
[95]高阳,李言俊,张科.多尺度方向性Hermite变换在低信噪比图像边缘检测中的应用.武汉大学学报(工学版),2006(2)：47-50.
[96]沈庭芝,方子文,张梅英.用改进的模拟退火法(SA)对低信噪比图像的恢复.北京理工大学学报,1997(1)：86-90.
[97]胡法龙,肖立志.一种核磁共振测井信号强度的确定方法.核电子学与探测技术,2006,26(5)：557-560.
[98]Motchenbacher C D, Connelly J A. Low-Noise Electronic System Design. Wiley-Interscience; 1 edition,1993.
[99]周凯波,李丹,汤天知,等.核磁共振测井仪前置放大电路的低噪声分析.华中科技大学学报(自然科学版),2008,36(1)：91-94.
[100]张文权,翁爱华.地面核磁共振正则化反演方法研究.吉林大学学报(地球科学版),2007,37(4)：809-813.
[101]姜国.井下仪器高温电路设计方法研究.石油仪器,2005,19(3)：58-60.
[102]宋湘云.高温电路的潜力及问题.微电子学,1992(5)：62-67.
[103]谢然红,肖立志,刘天定.原油的核磁共振弛豫特性.西南石油大学学报,2007,29(5)：21-24.
[104]谢然红,肖立志.储层流体及其在岩石孔隙中的核磁共振弛豫温度特性.地质学报,2007,81(2)：284-288.
[105]谢然红,肖立志,傅少庆.饱和水岩石核磁共振表面弛豫温度特性.中国石油大学学报(自然科学版),2008,32(2)：44-46.
[106]孟繁莹,肖立志,仵岳奇,等.新疆塔河特低孔低渗油气藏核磁共振测井应用基础实验.江汉石油学院学报,2003,25(3)：61-62.
[107]王忠东,汪浩,李能根,等.核磁共振岩心基础实验分析.测井技术,2001,25(3)：170-174.
[108]Foley I, Farooqui S A, Kleinberg R L. Effect of Paramagnetic Ions on NMR Relaxation of Fluids at Solid Surfaces. Journal of Magnetic Resonance, Series A, 1996,123(1):95-104.
[109]Latour L L, Kleinberg R L, Szginer A. Nuclear magnetic resonance properties of rocks at elevated temperatures. Journal of Colloid and Interface Science, 1992,150(2):535-548.
[110]Tissot B P, Welte D H. Petroleum formation and occurrence:a new approach to oil and gas exploration. Springer-Verlag,New York, NY,1978.
[111]朱起悦FPGA器件的可靠性预计.环境技术,2008(03)：11-14.
[112]Olshausen B A, Field D J. Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature,1996,381(6583):607-609.
[113]赵航芳,贾艳云.压缩传感-稀疏信号的采样与重构.声学与电子工程,2010(04)：13-17.
[114]杨震,叶蕾,郭海燕.基于压缩感知重构信号的说话人识别系统抗噪方法研究.信号处理,2010,26(3)：321-326.
[115]Davenport M A, Wakin M B, Baraniuk R G. Detection and estimation with compressive measurementsfunctions. in:Houston TX.USA:Rice University,Department of Electrical and Computer Engineering,2006.3-13.
[116]Haupt J, Nowak R. Compressive sampling for signal detection. in:IEEE International Conference on Acoustics, Speech and Signal Processing. Madison, WI: 2007.1509-1512.
[117]季向阳,戴琼海,付长军.压缩感知研究.计算机学报,2011,34(3)：425-433.