基于雷达海冰图像互相关的冰漂流场测量

我国的渤海和黄海北部在每年冬季都出现不同程度的冰情,它直接影响结冰海区的石油平台、船舶以及港口等设施的正常作业,对海冰的观测与预报随着上述海区的开发利用越发显得重要,利用航海雷达连续准确地跟踪海冰漂移运动轨迹,是当今观测、调查、研究冰漂流移动规律的有效方法之一,我国的科研人员利用雷达成像技术对渤海的冰情进行了长期的雷达海冰观测、研究,在由雷达海冰图像对海冰的物理特征的识别、分类以及冰漂流场测量方面取得了一些进展[1-4]。     

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.     

基于时序NDVI的昭觉植被覆盖度变化研究

归一化植被指数(NDVI)能精确地反映植被绿度、光合作用强度,在一定程度上反映着植被的演化信息,是评价生态环境状况的重要指标之一。本文利用Landsat TM数据分别对昭觉地区2009年和2014年的NDVI进行计算,并分别利用均值法、像元二分模型及NDVI差值植被指数对研究区域植被覆盖变化进行定量分析,结果表明:昭觉县NDVI均值上升了11.6%,植被覆盖度中极度改善的面积比例约占38%,昭觉县整体NDVI植被覆盖度显著提高,并对其变化原因进行简要分析,为生态环境建设提供决策依据。     

大孔径静态干涉成像光谱仪图像的条纹消除与配准

大孔径静态干涉成像光谱仪获取的图像是叠加了干涉信息的二维图像,点干涉图的获取需要经过全视场的推扫,平台姿态误差会导致提取的干涉图存在误差,进而影响复原光谱准确性,因此,图像配准是干涉图提取的关键。由于图像受到干涉调制作用,导致传统的图像配准方法在应用于LASIS图像配准时配准精度有限。为了减弱干涉条纹对图像配准的影响,本文提出一种利用条纹模板消除LASIS图像条纹,再结合图像频域配准的方法实现LASIS图像的高精度配准。配准结果表明,本方法很好地消除了干涉条纹对配准精度的影响,配准精度可以达到0.029 4个像素,相对于已开展的LASIS图像配准方法,沿轨方向配准精度提高了45.48%,跨轨方向配准精度提高了52.22%,图像旋转精度提高了39.13%。     

确定砂土动剪切模量和阻尼比的方法对比

确定土体动剪切模量的常用方法有规范法、Kumar法和自相关函数法,确定相应阻尼比的方法有规范法、Das and Luo法、Kokusho法、Kumar法和互相关函数法,为了分析不同方法所产生差异,实现定量化对比分析,笔者以福建标准砂（粒径为0.5~1.0 mm）为研究对象,采用不排水的应力控制动三轴试验,探讨不同的确定土体动剪切模量和阻尼比方法的差异性,并给出了不同土体条件建议选用的方法。结果表明：1）3种方法确定动剪切模量的结果有一定的差异,随剪应变的增大结果的差异逐渐增大,有效围压对结果的差异性有所影响,当剪应变为4×10^-3,有效围压为100 kPa时,3种方法差异显著,相对误差最大接近20%;2）而5种方法确定阻尼比的结果差异显著,随着剪应变的增大,5种方法确定的阻尼比相对误差大体上均在迅速减小,只有规范法在有效围压为100 kPa时,其相对误差有较小的增大趋势;5种方法中,Kumar法确定的阻尼比最接近平均阻尼比,互相关函数法远高于平均阻尼比,Das and Luo法和Kokusho法确定的阻尼比基本一致但低于平均阻尼比。建议以后的工程应用中,加载方式为应力控制时,可采用自相关函数法确定动剪切模量,采用Kumar法确定阻尼比,二者确定的动剪切模量和阻尼比均最接近平均值。     

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     

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M_L≥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。     

2003年云南大姚6.2级、6.1级地震序列S波分裂研究c

在利用双差定位法对2003年7月21日、 10月16日云南大姚先后发生的6.2级、6.1级地震序列进行精定位的基础上，采用互相关系数法测量了两个地震序列的快波偏振方向和快慢波的延迟时间. 结果表明:① 两个地震序列的S波分裂现象较明显，除三台乡台以外，大部分台站记录到的地震快波平均偏振方向与区域最大水平主压应力方向基本一致，三台乡台的偏振方向出现两个峰值，平均偏振方向是N80deg;E，表现出与区域最大水平主压应力方向不一致的现象；② 序列延迟时间与震源深度之间没有明显规律性关系，但不同延迟时间范围内偏振方向表现出不同的规律；③ 两次地震序列S波分裂结果对比发现，6.2级地震序列快波的偏振方向明显较分散，快波平均偏振方向比6.1级地震序列大20deg;，而延迟时间方面二者相差不大；④S波分裂快波偏振方向所表现出的空间差异性，可能是由于在区域背景应力场的基础上叠加了两次6级主震产生的应力扰动引起的.      

利用背景噪声进行地震成像的新方法

利用背景地震噪声提取格林函数，即通过一对台站记录的地震噪声进行互相关计算，来近似台站间的格林函数，并进一步通过地震成像获取地下结构的认识，成为最近跨学科研究的热点问题之一.本文首先叙述了其发展背景及过程，然后分别从四个角度，简要介绍了目前对其物理原理的解释.如果该方法能得到进一步的发展，将不再依赖于天然地震以及人工地震，而仅仅利用地震台站记录的地震噪声便可获取高分辨率的地下成像.该方法将会大大促进地震学的发展.     

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。