Middle to early Late Ordovician hydrothermal veining in the Molong Volcanic Belt,northeastern Lachlan Fold Belt: Sedimentological evidence

Detrital volcanic and vein quartz, accompanied by felsic volcanic debris, occur as minor constituents in the Ordovician subduction‐related mafic volcanics of the Molong Volcanic Belt. In the western province of the Molong Volcanic Belt, detrital quartz is present in the three episodes of the mafic Volcanics. Volcanic quartz occurs in allochthonous limestone blocks in the Bendigonian Hensleigh Siltstone overlying the Mitchell Formation. The second volcanic episode (the Fairbridge Volcanics) commenced after a hiatus of approximately 20 million years and lasted around 10 million years from Darriwilian to Gisbornian time. Locally derived vein quartz, volcanic quartz and felsic detritus are concentrated at the bases of autochthonous Wahringa and Yuranigh Limestone Members of the volcanics and are extensive and abundant in basal beds of the regional Eastonian limestone body that transgressed over an eroded volcanic centre at Cargo. This early Eastonian debris, deposited early in an 8 million‐year volcanic hiatus preceding the final Ordovician Bolindian volcanism, establishes a pre‐Eastonian age for mineralisation at Cargo. It is inferred that the pauses in volcanism were preceded by magmatic fractionation, intrusion and hydrothermal activity and followed by erosion, subsidence and deposition of autochthonous limestones. Minor occurrences of vein and volcanic quartz are found in Bolindian volcanogenic sediments of the third volcanic phase. It is concluded that hydrothermal vein formation (and mineralisation by inference) was associated with pauses in volcanic activity throughout the Middle to early Late Ordovician over a wide area in the western province, culminating in the mineralisation at Cargo and Copper Hill near Molong. Volcanism in the eastern province of the Molong Volcanic Belt was continuous from at least Darriwilian to latest Ordovician time. Here, detrital hydrothermal vein quartz and volcanic quartz and felsic detritus are distributed through late Middle and early Late Ordovician turbidites of the Weemalla Formation. The possible existence of cycles in the source area like those of the Fairbridge Volcanics is masked by the distal nature of these deposits. Vein formation occurred in both provinces from late Middle Ordovician to early Late Ordovician, long before the formation of the world‐class mineral deposit at Cadia associated with the latest Ordovician Cadia Monzonite.     

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     

Generation of correlated properties in heterogeneous porous media

The spatial distribution of rock properties in porous media, such as permeability and porosity, often is strongly variable. Therefore, these properties usefully may be considered as a random field. However, this variability is correlated frequently on length scales comparable to geological lengths (for example, scales of sand bodies or facies). To solve various engineering problems (for example, in the oil recovery process) numerical models of a porous medium often are used. A need exists then to understand correlated random fields and to generate them over discretized numerical grids. The paper describes the general mathematical methods required to do this, with one particular method (the nearest neighbor model) described in detail. How parameters of the mathematical model may be related to rock property statistics for the nearest neighbor model is shown. The method is described in detail in one, two, and three dimensions. Examples are given of how model parameters may be determined from real data.     

Assessment of seismic hazard for the Sannio-Matese area of Southern Italy — A summary

The working group on Test Regions for Evaluation of Methods for Seismic Hazard Assessment in Europe (TERESA), consisted of 15 members from 10 different European countries. Methods and experience gathered in these countries have been compared and discussed for two test areas in Europe: the Sannio-Matese region, Southern Italy, with high seismic activity, and the border region between Belgium, The Netherlands, and Federal Republic of Germany, with low activity.This paper summarizes the results for one of the test areas, Sannio-Matese. Most of the participants used statistical procedures to assess earthquake hazard, receiving results in terms of probability of occurrence for intensity as the ground-motion parameter. It was found that careful preparation of input data and parameters is the major influencing factor, therefore most of the efforts of the working group was devoted to this task.The scatter of the obtained results of the group is considerable, mainly because of the uncertainties in the data and the subjectiveness involved in the procedures. For better control of both factors, more objective methods have to be developed.     

Seismic hazard computations for regions with low earthquake activity — A case study for the Belgium,The Netherlands and NW Germany area

The algorithms to evaluate seismic hazard, used and/or developed by five teams participating in the TERESA project, applied to the low seismicity area Belgium, The Netherlands and NW Germany are compared. The main differences in the results can be explained by the majority criterion of Egozcue et al. (1989), the differences in the upper bound and zonification and, in some cases, by a higher attenuation.     

渤海陆缘地震的力学特征和成因

渤海陆缘是华北强震活动区,如1966年的邢台震群,1967年的河间地震,1969年的渤海地震,1975年的海城地震,1976年的唐山震群和1983年的菏泽地震,都是在该区发生的。显然,深入研究该地区的构造运动和地震成因是有重要科学意义的。本文拟从应力场角度进行研究。     

未来地震震级的定量计算

将某一地震带在强震前某一时期内发生的地震,按其面波震级大小自大到小排列,并以N=2、3、4、……来累计频度,采用公式logN=a-bM计算a、b,从而计算出第一个地震的震级M_1,这就是未来可能发生地震的震级。通过对川滇地区和华北地区的九次近期强震进行计算,结果表明,在震级测定误差范围(±0.3级)内,上述的M和logN之间具有很好的线性关系,这就为地震预报和地震区划中定量计算未来地震震级提出了一个新的方法。     

1988年四川八美5级地震参数和前兆的研究

本文对1988年6月四川省道孚八美(即乾宁)4.5、5.0、4.0级地震前后地震活动进行了研究,给出了地震参数、P波初动解、余震序列和前兆变化的分析及未来地震危险性的讨论。     

输水管网模糊地震可靠性的 Monte Carlo 法分析程序

本文详细介绍了笔者所建议的运用模糊图理论和方法进行城市输水管网的地震可靠性分析途径和计算程序。对于输水管网中每段管道的震害预测,建立了相应的专家系统。在此基础上,定义了管网系统的模糊可靠性,更好地反映了人们对于管道损坏状态估计的模糊性和对可靠性要求上的模糊性。以模糊矩阵来描述输水管网的状态是十分方便的。通过计算模糊矩阵的传递闭包,极易找出最短路、关键段等。由于地震作用和管道抗震能力都是随机的,因此整个管网系统的可靠性分析是藉助Monle Carlo法进行的。以Monle Carlo法对模糊图进行分析,从而得到管网的地震可靠性,这还是首次。用PASCAL和PROLOG编制了在微机上实用的计算机程序,特别是以智能语言PROLOG编制的程序,更有许多特色。由于采用了一些技巧,使在微机上可以分析相当大的系统,为城市抗震防灾工作提供了一个有力的工具。     

西秦岭北缘断裂带地震活动特征及近期发展趋势

本文讨论了西秦岭北缘断裂带在青藏高原东北部三次地震活动高潮幕和两次地震活动低潮幕中的作用和特点,以及地震活动的时空强特征和近期发展趋势。