壳粒磷块岩及其成矿规律

壳粒磷块岩被作为一种独立的重要类型划分出来。壳粒磷块岩的性质从极重要的方面揭示了海相磷块岩的本质特征。本文以生物成矿论为指导,研究了扬子地块晚震旦世陡山沱期磷块岩形成的古地理,并阐述了磷块岩成矿与富集规律。     

山西高凡银金矿床黄铁矿的微量元素标型特征及其应用

高凡银金矿床黄铁矿的微量元素研究发现富Au、Ag、Pb、Zn、As、Sb,贫Co、Ni、Se,且S/Se较大,S/As和Fe/(Pb+Zn)较小,As/(Co+Ni)、(Pb+Zn)/(Co+Ni)>10。同时高凡矿田中的滩上铜钼矿化点的黄铁矿则显示相反的特征。总结出一套区分两类矿化黄铁矿微量元素的标型特征,对高凡矿田和五台地区各矿化点的判别和评价中显示了一定的适用性,可作为本区寻找同类金银矿床的找矿标型特征。     

河南晚石炭世含煤地层中的风暴异地煤

河南省晚石炭世含煤地层中,存在着因风暴作用强烈侵蚀滨海泥炭沼泽,而后通过风暴回流把泥炭物质和植物遗体长距离搬运到浅海环境再沉积的风暴异地煤。这种异地煤的直接顶底板是具有风暴沉积特征的浅海碳酸盐岩.煤破碎成粉砂级—泥级,煤层原生结构已完全看不到,煤层下部为含有大量海相动物化石的滞积层,中部为具有类似丘状层理的波状层,上部为水平纹层,其层序特征几乎可与煤层直接顶底板浅海碳酸盐风暴岩类比。研究这种异地煤的沉积背景和形成机制以及这种煤层所反映出的煤质特征、煤岩特征、沉积特征、层序及层序类型特征,不仅可以开拓出一种新型异地成煤类型而扩大煤地质学的研究领域,而且有助于研究古气候、古水深和煤岩层对比等问题。     

中国与蒙古之地质

东昆仑中部缝合带清水泉一带发育石榴斜长紫苏麻粒岩、紫苏辉石黑云母石榴子石麻粒岩、石榴二辉斜长麻粒岩和石榴单斜辉石麻粒岩,它们与混合岩化黑云母石榴子石变粒岩、黑云母辉石变粒岩、石墨大理岩、含透辉石透闪石大理岩、透辉石大理岩、黑云斜长角闪岩和片麻岩等高级变质岩系以及纯橄岩、辉橄岩、橄长岩、辉长岩、辉绿岩和玄武岩等共同构成蛇绿混杂岩。麻粒岩相变质作用的温压条件为T=760~880℃,p=830~1200MPa,为高温中高压麻粒岩相变质作用,估算其形成深度为40~45km。麻粒岩相变质作用的SHRIMP锆石U-Pb年龄为(507·7±8·3)Ma。清水泉地区蛇绿岩形成于~520Ma,到~508Ma时俯冲至地下40~45km深处而发生中高压麻粒岩相变质作用,然后发生构造折返而剥露至地表。证实了清水泉高级变质岩和基性—超基性岩片是形成于早—中寒武世的蛇绿混杂岩,标志一个古生代早期的非常重要的板块汇聚边界,这对于进一步研究东昆仑造山带构造演化、乃至中国西部大地构造格局具有非常重要的意义。     

2D stabilised analytic signal method in DC pole-pole potential data interpretation

Using analytic signal method, interpretation of pole-pole secondary electric potentials due to 2D conductive/resistive prisms is presented. The estimated parameters are the location, lateral extent or width and depth to top surface of the prism. Forward modelling is attempted by 2D-Finite Difference method. The proposed stabilised analytic signal algorithm (RES2AS) uses Tikhonov’s regularization scheme and FFT routines. The algorithm is tested on three theoretical examples and field data from the campus of Roorkee University. The stability of RES2AS is also tested on synthetic error prone secondary pole-pole potential data.     

2-D deformation analysis of a half-space due to a long dip-slip fault at finite depth

Closed form analytical expressions of stresses and displacements at any field point due to a very long dip-slip fault of finite width buried in a homogeneous, isotropic elastic half-space, are presented. Airy stress function is used to derive the expressions of stresses and displacements which depend on the dip angle and depth of the upper edge of the fault. The effect of dip angle and depth of the upper edge of the fault on stresses and displacements is studied numerically and the results obtained are presented graphically. Contour maps for stresses and displacements are also presented. The results of Rani and Singh (1992b) and Freund and Barnett (1976) have been reproduced.     

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矩阵广义初等变换及其运用

提出了关于分块矩阵的广义初等变换这一概念,以此为工具简化了一些复杂矩阵问题的解决方法。     

Ensemble filter methods with perturbed observations applied to nonlinear problems

The ensemble Kalman filter (EnKF) has been shown repeatedly to be an effective method for data assimilation in large-scale problems, including those in petroleum engineering. Data assimilation for multiphase flow in porous media is particularly difficult, however, because the relationships between model variables (e.g., permeability and porosity) and observations (e.g., water cut and gas–oil ratio) are highly nonlinear. Because of the linear approximation in the update step and the use of a limited number of realizations in an ensemble, the EnKF has a tendency to systematically underestimate the variance of the model variables. Various approaches have been suggested to reduce the magnitude of this problem, including the application of ensemble filter methods that do not require perturbations to the observed data. On the other hand, iterative least-squares data assimilation methods with perturbations of the observations have been shown to be fairly robust to nonlinearity in the data relationship. In this paper, we present EnKF with perturbed observations as a square root filter in an enlarged state space. By imposing second-order-exact sampling of the observation errors and independence constraints to eliminate the cross-covariance with predicted observation perturbations, we show that it is possible in linear problems to obtain results from EnKF with observation perturbations that are equivalent to ensemble square-root filter results. Results from a standard EnKF, EnKF with second-order-exact sampling of measurement errors that satisfy independence constraints (EnKF (SIC)), and an ensemble square-root filter (ETKF) are compared on various test problems with varying degrees of nonlinearity and dimensions. The first test problem is a simple one-variable quadratic model in which the nonlinearity of the observation operator is varied over a wide range by adjusting the magnitude of the coefficient of the quadratic term. The second problem has increased observation and model dimensions to test the EnKF (SIC) algorithm. The third test problem is a two-dimensional, two-phase reservoir flow problem in which permeability and porosity of every grid cell (5,000 model parameters) are unknown. The EnKF (SIC) and the mean-preserving ETKF (SRF) give similar results when applied to linear problems, and both are better than the standard EnKF. Although the ensemble methods are expected to handle the forecast step well in nonlinear problems, the estimates of the mean and the variance from the analysis step for all variants of ensemble filters are also surprisingly good, with little difference between ensemble methods when applied to nonlinear problems.     

Impacts of systematic precipitation bias on simulations of water and energy balances in Northwest America

At high latitudes and in mountainous areas, evaluation and validation of water and energy flux simu-lations are greatly affected by systematic precipitation errors. These errors mainly come from topographic effects and undercatch of precipitation gauges. In this study, the Land Dynamics （LAD） land surface model is used to investigate impacts of systematic precipitation bias from topography and wind-blowing on water and energy flux simulation in Northwest America. The results show that topographic and wind adjustment reduced bias of streamflow simulations when compared with observed streamflow at 14 basins. These systematic biases resulted in a -50%-100% bias for runoff simulations, a -20%-20% bias for evapotranspiration, and a -40%-40% bias for sensible heat flux, subject to different locations and adjustments, when compared with the control run. Uncertain gauge adjustment leads to a 25% uncertainty for precipitation, a 20% 100% uncertainty for runoff simulation, a less-than-10% uncertainty for evapotranspiration, and a less-than-20% uncertainty for sensible heat flux.