重庆地区重复地震识别及在数字地震台网定位评价中的应用

利用重庆数字地震台网2010年1月至2017年12月的地震波形资料和观测报告,选出5个研究区1 251个ML≥1.5地震进行波形互相关计算,识别出358对同时被2个地震台站记录且各台波形互相关系数(cc)不小于0.8的重复地震对,涉及342个地震事件,约占地震总数的27%。将筛选出的重复地震对用于定量判断地震目录中震相拾取误差及评估台网定位精度,结果显示:重庆数字地震台网的垂直定位误差约为3 km,水平定位误差约为5 km,Pg、Sg震相拾取误差分别为0.5 s和0.7 s;其中武隆区地震定位和震相拾取精度最高,綦江区最低。     

S盒的互相关测试算法设计

S盒是构成分组密码算法重要的非线性部件,其密码学性质直接影响整个密码算法的安全性,因此对S盒的安全性检测十分重要。以往对S盒的安全性评估大多数集中在检测分量函数的安全性上,本文考虑了分量函数之间的关系,并利用Shannon在对称密码系统设计中所提出的混淆和扩散的思想,提出了S盒的互相关测试指标,设计了测试算法,更好地评价了S盒的安全性能。对DES和AES的S盒分别进行了实验,给出了测试结果。     

Spatial and multivariate analysis of geochemical data from metavolcanic rocks in the Ben Nevis area, Ontario

A study of the lithogeochemistry of metavolcanics in the Ben Nevis area of Ontario, Canada has shown that factor analysis methods can distinguish lithogeochemical trends related to different geological processes, most notably, the principal compositional variation related to the volcanic stratigraphy and zones of carbonate alteration associated with the presence of sulphides and gold. Auto- and cross-correlation functions have been estimated for the two-dimensional distribution of various elements in the area. These functions allow computation of spatial factors in which patterns of multivariate relationships are dependent upon the spatial auto- and cross-correlation of the components. Because of the anisotropy of primary compositions of the volcanics, some spatial factor patterns are difficult to interpret. Isotropically distributed variables such as CO ₂ are delineated clearly in spatial factor maps. For anisotropically distributed variables (SiO ₂ ), as the neighborhood becomes smaller, the spacial factor maps becomes better. Interpretation of spatial factors requires computation of the corresponding amplitude vectors from the eigenvalue solution. This vector reflects relative amplitudes by which the variables follow the spatial factors. Instability of some eigenvalue solutions requires that caution be used in interpreting the resulting factor patterns. A measure of the predictive power of the spatial factors can be determined from autocorrelation coefficients and squared multiple correlation coefficients that indicate which variables are significant in any given factor. The spatial factor approach utilizes spatial relationships of variables in conjunction with systematic variation of variables representing geological processes. This approach can yield potential exploration targets based on the spatial continuity of alteration haloes that reflect mineralization.List of symbols c _i Scalar factor that minimizes the discrepancy between andU _i - D Radius of circular neighborhood used for estimating auto- and cross-correlation coefficients - d Distance for which transition matrixU is estimated - d _ij Distance between observed valuesi andj - E Expected value - E _i Row vector of residuals in the standardized model - F(d _ij) Quadratic function of distanced _ij F(d _ij)=a+bd _ij+cd _ij ² - L Diagonal matrix of the eigenvalues ofU - _i Eigenvalue of the matrixU;ith diagonal element ofL - N Number of observations - p Number of variables - Q Total predictive power ofU - R Correlation matrix of the variables - R _0j Variance-covariance signal matrix of the standardized variables at origin;j is the index related tod andD (e.g.,j=1 ford=500 m,D=1000 m) - R _1j Matrix of auto- and cross-correlation coefficients evaluated at a given distance within the neighborhood - R _m ² Multiple correlation coefficient squared for themth variable - S _i Column vectori of the signal values - s _k ² Residual variance for variablek - T _i Amplitude vector corresponding toV _i;ith row ofT=V ^–1 - T Total variation in the system - U Nonsymmetric transition matrix formed by post-multiplyingR ₀₁ ^–1 byR _ij - U _i Componenti of the matrixU, corresponding to theith eigenvectorV _i;U _i= _iV_iT_i - U* _i ComponentU _i multiplied byc _i - U _ij Sum of componentsU _i+U _j - V _i Eigenvector of the matrixU;ith column ofV withUV=VL - w Weighting factor; equal to the ratio of two eigenvalues - X _i Random variable at pointi - x _i Value of random variable at pointi - y _i Residual ofx _i - Z _i Row vectori for the standardized variables - z _i Standardized value of variable     

Cross-Correlation Detection of Point Sources in the WMAP First Year Data

We apply a Cross-Correlation (CC) method developed previously for detecting gamma-ray point sources to the WMAP first year data by using the Point-Spread Function of WMAP and obtain a full sky CC coefficient map. We find that the CC method is a pow- erful tool to examine the WMAP foreground residuals which can be further cleaned accord- ingly. Evident foreground signals are found in the WMAP foreground cleaned maps and the Tegmark cleaned map. In this process 101 point sources are detected, and 26 of them are new sources additional to the originally listed WMAP 208 sources. We estimate the ?ux of these new sources and verify them by another method. As a result, a revised mask file based on the WMAP first year data is produced by including these new sources.     

基于反褶积算法的地震干涉技术被动源成像

地震干涉技术可以将任意2个检波器接收到的数据合成为在若干检波器之间传播的波,就好像其中的一个检波器作为一个虚拟震源来发挥作用。它可以从混沌无序的地震信号中发现有用信息,从地震噪声中提取有用信号以此推断地震波穿过介质的地质构造。基于反褶积算法,对其理论公式进行了较详细的推导,实现被动源地震干涉成像,证明了反褶积算法的可行性;并将其结果与互相关算法的结果进行对比,分析了2种方法在信噪比和分辨率方面的差异。数值计算表明,反褶积算法的纵向分辨率比互相关算法的高。对其进行的加噪试算表明,震源叠加后的反褶积算法呈现出高信噪比的特点。     

地震干涉技术被动源地震成像

被动源地震成像是基于地下反射波响应和透射波响应之间的关系,通过在地表接收由地下非人工震源发出的透射波响应,利用互相关运算来合成反射波响应的方法.合成的反射波响应,又称虚炮集记录等价于地表地震剖面记录.本文研究了由地下随机分布的被动源发出的透射波记录来合成虚炮集记录的基本原理,推导了反射波记录和透射波记录的关系公式;并通过数值模拟方法,分析了被动源成像的可行性.结果表明:利用被动源地震信息进行成像是可行的.     

Morlet 小波用于环境激励下的模态参数识别研究

本文分别从卷积和Parseval定理的角度推导了非正交小波变换系数的实用计算方法。在环境激励下以互相关函数代替系统的自由响应数据,给出了基于Morlet小波变换的频率、阻尼比的参数识别方法,重点介绍了基于最小二乘法的振型识别技术。采用2层楼仿真算例和潮白河桥应用实例验证本算法,识别结果表明基于Morlet小波变换的模态参数识别技术能够有效地识别出环境激励下系统的模态参数。     

Estimating the orientation error of the Dahan Downhole Accelerometer using the maximum cross-correlation coefficient between the observed and synthetic waves

A new algorithm to correct the orientation error of the accelerometerat the Dahan Downhole Array, Hualien, Taiwan is developed. This algorithmconsists of three stages: (1) rotating two horizontal ground motions on thefree surface to the SH-SVdirection and SH axis offers a reference direction.(2) computing the synthetic downhole SH waves at a downhole station and (3)searching a rotation angle for downhole observation that yields a best waveformmatch between the synthetic and observed downhole seismograms. At this point, the rotated angle corresponding to the best waveform match can be considered as the orientation error. We selected five earthquakes with good data qualityfor analysis. Results show that this algorithm gives a more stable estimationthan a conventional method because it allows the selection of data from a wider time window for analyses. The estimated orientation error of the accelerometers at the Dahan Downhole Array after the 1999 reinstallation are40°, 114° and 285° at depths of 50, 100 and 200 m, respectively.     

大别-苏鲁及其邻近地区基于背景 噪声的勒夫波群速度成像

大别-苏鲁造山带是中国大陆东部地区最重要的构造之一. 为了研究该地区的地壳上地幔速度结构,本文收集了国家数字地震台网和中国区域地震台网的山东、河南、安徽、江苏和湖北等省的144个宽频带地震台连续两年(2009年5月—2011年5月)的水平向地震记录(E分量和N分量)数据,首先对台站对之间E-E,E-N,N-N和N-E分量进行互相关,然后分别对这4个互相关分量采用相位权重叠加法进行叠加,最后旋转到横向分量(T-T)获得勒夫波经验格林函数(EGF);用频时分析(FTAN)方法获得4000余条勒夫波群速度频散曲线,并进一步反演得到了周期为6—40s的勒夫波群速度分布图.结果表明,周期为6—10s的勒夫波群速度分布与地表构造特征相吻合.大别造山带、苏鲁造山带、湖北西部隆起均表现为高速;华北盆地发育,表现为大面积的低速;江汉盆地、南襄盆地、合肥盆地等因其规模不同而显示不同程度的低速.在周期为6—30s的勒夫波群速度分布图上,大别和苏鲁地区均显示高速,已有的研究结果中地壳的低速并没有得到反映.其原因一方面可能与勒夫波群速度纵向分辨不高有关,另一方面高压变质岩深度分布可能比已有研究结果给出的要深. 6—30 s的分布图上郯庐断裂带及其邻近地区表现为不同程度的高速,可能与该地区白垩纪以来处于拉张构造体制,地幔物质受到扰动,造成物质上涌有关.     

微震研究与应用进展

随着地震观测台站密度的不断增加以及地震检测技术的快速发展,微震研究受到了地震学界的广泛关注.与发震周期较长的大地震相比,微震复发周期短、发生频次高,可以获得更高分辨率的地壳内部介质物性和应力状态等变化信息.微震在许多领域具有广泛的应用,如研究断层几何形态、前震与地震成核的关系、余震时空演化特征及余震触发机理、远程动态触...