地面微地震资料弱信号提取方法研究
详细信息 本馆镜像全文    |  推荐本文 | | 获取馆网全文
摘要
针对地面微地震有效信号特点和资料采集方式,结合微地震信号高阶累积量统计特征分析,考虑到有效信号和噪声在时空方向的不同分布特征,研究了时间和空间两个方向地面微地震信号的四阶累积量估计方法;考虑到贝叶斯估计方法对于弱信号估计的优势,研究了基于贝叶斯框架的四阶累积量的自适应算法,把信号四阶累积量的联合概率密度函数作为原信号的概率密度函数进行最大后验概率估计,建立了地面微地震资料四阶累积量贝叶斯估计方法;提取弱信号的同时不可避免会提取到弱的无用相关信号,使得弱有效信号不易识别,根据区域相关噪声在时间方向具有区域均匀分布而有效信号具有局部分布的特点,提出进一步采用自适应减法剔除贝叶斯估计结果中的这种区域性相关噪声。通过系列方法的分析研究,形成了地面微地震有效信号的有效提取方法。利用该方法对实际资料进行处理,取得了较好的效果。
According to the surface microseismic effective signal characteristics and data acquisition methods,combined with microseismic signal higher-order cumulant statistical characteristic analysis,considering the different distribution characteristics of effective signal and noise in the time and space direction,we discuss the fourth-order cumulants estimation method of surface microseismic signal in time and space domain.Considering the advantages of Bayesian estimation method for week signal estimation,we study self-adaptive algorithm of fourth-order cumulant based on Bayes framework,take the joint probability density function of signal fourth-order cumulant as that of original signal to carry out maximum posterior estimation,to establish fourth-order cumulant Bayesian estimiation method for surface microseismic data.While extracting weak signals,useless relevant signals would be extracted,so the weak effective signals are difficult to recognize.As we all know,regional relevant noise is evenly distributed in time direction and effective signal is locally distributed,in terms of the characteristic,we propose adopting self-adaptive subtraction to remove the regional relevant noise in Bayesian estimation results.By analyzing the method series,effective extraction method for surface microseismic effective signals is formed.The method has obtained good result in the actual data application.
引文
[1]Liu X C,Liu X L.Weak signal detection researchbased on duffing oscillator used for downhole com-munication[J].Journal of Computers,2011,6(2):359-367
    [2]Refae A B,Khalil S,Vincent B,et al.Increasingbandwidth with single sensor seismic data—the Le-hib oilfield case study[J].First Break,2008,26(1):79-84
    [3]Strobbia C,Glushchenko A,Laake A,et al.Arcticnear surface challenges:the point receiver solution tocoherent noise and statics[J].First Break,2009,27(1):69-76
    [4]Mancini F,Fairhead S,King A,et al.Data qualityuplift from a dual-azimuth acquisition offshore Libya[J].Expanded Abstracts of 80th Annual InternatSEG Mtg,2010,17-22
    [5]Mougenot D,Cherepovskiy1 A,Liu J J.MEMS-based accelerometers:expectations and practical a-chievements[J].First Break,2011,29(2):85-90
    [6]Kendall R,Jin S D,Ronen S.An SVD-polarizationfilter for ground roll attenuation on multicomponentdata[J].Expanded Abstracts of 75th Annual InternatSEG Mtg,2005,928-932
    [7]Franco R,Musacchio G.Polarization filter with sin-gular value decomposition[J].Geophysics,2001,66(3):932-938
    [8]梁军利,杨树元.一种基于非周期随机共振的微弱信号检测方法[J].微计算机应用,2007,28(11):1121-1126Liang J L,Yang S Y.A method based on stochasticresonance weak signal detection method[J].MicroComputer Application,2007,28(11):1121-1126
    [9]何大海,赵文礼,梅晓俊.基于随机共振原理的微弱信号检测与应用[J].机电工程,2008,25(4):71-74He D H,Zhao W L,Mei X J.Based on the principleof stochastic resonance weak signal detection and itsapplication[J].Mechanical and Electrical Engineer-ing,2008,25(4):71-74
    [10]辛春雨,刘凤侠,张宇.结合数字滤波技术的随机共振弱信号检测[J].吉林大学学报(理学版),2009,47(2):358-361Xin C Y,Liu F X,Zhang Y.Combined with digitalfilter technology of stochastic resonance weak signaldetection[J].Journal of Jilin University(Natural Sci-ence Edition),2009,47(2):358-361
    [11]Harris D B,Jarpe S P,Harben P E.Seismic noisecancellation in a geothermal field[J].Geophysics,1991,56(10):1677-1680
    [12]Candy J,Followill F.Multichannel noisecancellation:a seismic application[J].Mechanical Systems andSignal Processing,1989,3(3):213-228
    [13]Vincent P D,Tsoflias G P,Steeples D W,et al.Fixed-source and fixed-receiver walkaway seismicnoise tests:a field comparison[J].Geophysics,2006,71(6):W41-W44
    [14]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].石油地球物理勘探,2005,40(1):103-107Yang Y S,Li Y Y,Liu T Y.Higher order statisticsin seismic signal and“magnetic bright spot”in theapplication of recognition[J].Oil Geophysical Pros-pecting,2005,40(1):103-107
    [15]王晶,张庆,梁霖,等.采用遗传算法的自适应随机共振系统弱信号检测方法研究[J].西安交通大学学报,2010,44(3):32-36Wang J,Zhang Q,Liang L,et al.The genetic algo-rithm of adaptive stochastic resonance weak signaldetection method[J].Journal of Xian Jiao Tong U-niversity,2010,44(3):32-36
    [16]高晋占.微弱信号检测[M].北京:清华大学出版社,2004:1-36Gao J Z.Weak signal detection[M].Beijing:Tsing-hua University Press,2004:1-36
    [17]张鑫,井西利.一种基于正态反高斯模型的贝叶斯图像去噪方法[J].光学学报,2010(1):71-74Zhang X,Jing X L.A method based on normal in-verse Gauss model Bayesian image denoising method[J].Journal of Optics,2010(1):71-74
    [18]詹海刚,施平,陈楚群.基于贝叶斯反演理论的海水固有光学特性准分析算法[J].科学通报,2006,51(2):204-210Zhan H G,Shi P,Chen C Q.Based on Bayesian in-version theory of inherent optical properties of sea-water analysis algorithm[J].Chinese Science Bulle-tin,2006,51(2):204-210
    [19]吴逍,纪国宜.基于谐波小波包理论检测微弱信号的研究[J].电子测量技术,2010,33(6):1-3Wu X,Ji G Y.Based on harmonic wavelet packettheory study of weak signal detection[J].ElectronicMeasurement Technology,2010,33(6):1-3
    [20]李舜酩,许庆余.微弱振动信号的谐波小波频域提取[J].西安交通大学学报,2004,38(1):51-55Li S M,Xu Q Y.Weak vibration signal extraction ofharmonic wavelet in frequency domain[J].Journal ofXian Jiao Tong University,2004,38(1):51-55

版权所有:© 2023 中国地质图书馆 中国地质调查局地学文献中心