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     

When do we need a trend model in kriging?

Under usual estimation practice with local search windows for data and for interpolation situations, universal kriging and ordinary kriging yield the same estimates, using a data set with apparent trend, for both the unknown attribute and its trend component. Modeling the trend matters only in extrapolation situations. Because conditions of the case study presented arise most frequently in practice, the simpler ordinary kriging is the preferred option.     

钻孔走向偏斜的初步探讨

在急倾斜构造部位大力使用硬合金回转钻进时,常常会产生弯曲度很大的走向偏斜情况。这是因为不同岩层的阻力差异使钻头受到一种平行于岩层走向的偏斜力,迫使钻头向侧方研磨井壁;在不同井径变换部位所产生的偏斜效果是不同的。这样造成钻孔沿地层走向发生有规律的定向偏斜。     

塔里木盆地绿洲形成与演变

塔里木盆地的绿洲按形成历史可划分为古绿洲、旧绿洲和新绿洲,它们分别代表绿洲发展演变的三个阶段。本文对绿洲未来演变趋势作了预测,探讨了盆地古绿洲衰亡原因、认为除了风沙、盐碱及河流改道等自然原因外,人类活动引起的地表水资源时空分配发生变化是主要原因。     

二红洼镁铁-超镁铁杂岩体特征及成因

新疆哈密二红洼镁铁-超镁铁杂岩体地表分南北两个岩体,深部可能连为一体,可划分为两个侵入期、四个岩相.第一侵入期构成岩体的主体部分,分异良好,由重力结晶分异形成自橄揽岩相、橄榄辉长岩相向含石英苏长辉长岩相的分异趋势,基性程度依次降低.岩浆多次贯入使得纵向上橄榄岩相和橄榄辉长岩相重复出现.第二侵入期辉长苏长岩相分异程度低,岩性稳定,为岩浆快速冷凝的产物.岩浆属于拉斑玄武岩系列,发生过金属硫化物熔离,有利于铜镍硫化物成矿.     

北京平原区基岩井水位的年动态特征及其成因分析

系统阐述了北京平原区１０口地震地下水位动态观测井及其水位年动态特征，主要分析了降雨与开采对年动态特征的影响，讨论了大同－阳高地震前后某些井水位长期异常的信度     

中国东部的降水区划及备区旱涝变化的特征

为了研究我国旱涝发生的规律、成因和预测，事先掌握降水的气候型区和各区降水变化的气候特征是十分必要的。本文利用１９５１—１９８６年中国东部１４０个站的月降水资料，分析了下半年降水相对系数、月际和年际标准差等参量时空变化的特征，并综合应用逐级归并法和成批调整法，对中国东部地区进行了降水气候区的划分。在此基础上，进一步探讨了各区旱涝的频数和长期变化的趋势。     

川滇交界地区构造应力场及强震趋势的统计预测

本文对川滇地区中、强震震源机制解和中小地震平均节面解分析，指出川滇地区现今构造应力场在北西－北西西向压应力作用下，以水平剪切错动为主。同时，采用极值理论、最大信息熵、线性预测和灰色理论等方法综合分析，预测川滇地区未来强震的趋势。     

则木河与小江断裂交汇区地震危险性的概率预测

本文针对则木河与小江断裂交汇区的强震趋势问题，给出了地震危险性的分析方法，结果及问题讨论。