探地雷达技术在铁路隧道监测中的应用

周边工程施工威胁着铁路隧道运营安全,为了实时了解铁路隧道结构变化情况,需要进行隧道监测.探地雷达具有较好的探测和显示功能,常用于建筑结构物体的无损探测.为了弥补测绘技术不能探测隧道内部结构体的不足,将探地雷达技术应用于隧道结构监测.着重介绍了隧道探测的技术特点和探测参数设置,根据隧道介质反射图像特征,确定隧道内部结构信息.结果证明了隧道探测技术的有效性,研究内容可为相关工程技术应用提供参考.     

内蒙古中西部宝音图群石榴角闪岩的地球化学、变质作用及年代学特征

宝音图群分布于内蒙古中西部狼山、图古日格和达茂旗一带，主要由云母片岩、石英片岩、石英岩、角闪岩和大理岩组成。在狼山格尔敖包沟和图古日格西南地区发育十分典型的石榴角闪岩，均以似层状或透镜状产自云母片岩中，但两地具有不同的矿物组合，前者主要以石榴石+角闪石+斜长石+含钛矿物（金红石和钛铁矿）为特征，后者主要以石榴石+角闪石+绿帘石+含钛矿物（金红石和钛铁矿）为特征。地球化学研究显示两个地区的石榴角闪岩原岩具有相似的化学组成，均为亚碱性玄武岩的拉斑系列。宝音图群的石榴角闪岩具有右倾的稀土配分模式，REE总量为83.31×10^-6~125.9×10^-6，（La/Yb）_N比值为2.17~6.48，δEu=0.87~0.98。Ta、Nb、Ti没有明显的负异常，其配分型式类似E-MORB特点。微量元素构造判别图解表明这些石榴角闪岩的原岩产于板内拉张环境中。本文通过模拟格尔敖包沟样品LS01和图古日格西南地区样品LS35的P-T视剖面图研究其变质作用及矿物演化过程，结果显示这两个样品均经历了早期进变质，峰期以及峰后近等温减压的顺时针型P-T轨迹。样品LS01的峰期温压条件为~11kbar/~735℃，模拟得到的峰期矿物组合可能为石榴石+角闪石+斜长石+黑云母+透辉石+金红石+熔体，P-M（H₂O）视剖面图显示，由于峰后演化过程中存在饱和流体渗透，导致峰期透辉石无法保留。样品LS35的峰期温度压力条件为~8kbar/675℃，其峰期组合为石榴石+角闪石+绿帘石+金红石。两个地区的石榴角闪岩的温度压力条件明显不同，推测宝音图群中呈透镜体或似层状产出的变质基性岩，存在着递增型的变质作用。锆石LA-ICP-MS U-Pb年代学的研究结果显示石榴角闪岩样品LS01的变质锆石年龄为394±8Ma。由此推测这些石榴角闪岩的原岩可能形成于华北北缘在新元古代发育的裂陷盆地，在泥盆纪中期，宝音图群作为华北克拉通的一部分，被卷入到与古亚洲洋闭合有关的造山过程，发育中压型递增变质作用。     

利用主成分分析改善土地利用变化的遥感监测精度——以珠江三角洲城市用地扩张为例

近年来,珠江三角洲由于经济的快速发展,城市用地急剧增加,利用多时相的遥感图,可以定量地监测这种城市化的现象。但理,由一般的遥感动态监测方法所得的结果往往夸大变化的程度,以及获得一些不合理的结论．该文提出主成分分析的方法来改善遥感动态监测的精度。将该方法应用应用于珠江三角洲发展最快的东莞市,获得了较满意的结果。     

北京云蒙山地区花岗岩穹隆及伸展构造的探讨

北京云蒙山花岗岩为一中生代侵入的花岗岩穹隆,花岗岩穹隆的叶理普遍发育,叶理轨迹基本平行于穹隆的外部边界,并显示出从核部到边部逐渐增强,东南侧明显强于西北侧的特点。变形构造研究显示,花岗岩穹隆的边部及围岩中普遍存在不同层次及不同运动方向的伸展构造。东南侧以具河防口－水峪伸展型韧性剪切带为特征,剪切运动标志显示为从ＮＷ－ＳＥ的正剪切运动,有限应变分析估算其剪切位移量在１０ｋｍ以上,剪切带上部被河防口正     

Research on temporal and spatial distribution, evolutionary character and mechanism of crustal deformation field before and after the Tangshan earthquake

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On the spatial characteristic of the short term and imminent anomalies of underground water behaviors before strong earthquake

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Efficient generation of conditional simulations by chebyshev matrix polynomial approximations to the symmetric square root of the covariance matrix

Consider the problem of generating a realization y₁ of a Gaussian random field on a dense grid of points ₁ conditioned on field observations y₂ collected on a sparse grid of points ₂. An approach to this is to generate first an unconditional realization y over the grid =_{1 2}, and then to produce y₁ by conditioning y on the data y₂. As standard methods for generating y, such as the turning bands, spectral or Cholesky approaches can have various limitations, it has been proposed by M. W. Davis to generate realizations from a matrix polynomial approximations to the square root of the covariance matrix. In this paper we describe how to generate a direct approximation to the conditional realization y₁, on ₁ using a variant of Davis' approach based on approximation by Chebyshev polynomials. The resulting algorithm is simple to implement, numerically stable, and bounds on the approximation error are readily available. Furthermore we show that the conditional realization y₁ can be generated directly with a lower order polynomial than the unconditional realization y, and that further reductions can be achieved by exploiting a nugget effect if one is present. A pseudocode version of the algorithm is provided that can be implemented using the fast Fourier transform if the field is stationary and the grid ₁ is rectangular. Finally, numerical illustrations are given of the algorithm's performance in generating various 2-D realizations of conditional processes on large sampling grids.     

Characterizing heterogeneous permeable media with spatial statistics and tracer data using sequential simulated annealing

Characterizing heterogeneous permeable media using flow and transport data typically requires solution of an inverse problem. Such inverse problems are intensive computationally and may involve iterative procedures requiring many forward simulations of the flow and transport problem. Previous attempts have been limited mostly to flow data such as pressure transient (interference) tests using multiple observation wells. This paper discusses an approach to generating stochastic permeability fields conditioned to geologic data in the form of a vertical variogram derived from cores and logs as well as fluid flow and transport data, such as tracer concentration history, by sequential application of simulated annealing (SA). Thus, the method incorporates elements of geostatistics within the framework of inverse modeling. For tracer-transport calculations, we have used a semianalytic transit-time algorithm which is fast, accurate, and free of numerical dispersion. For steady velocity fields, we introduce a transit-time function which demonstrates the relative importance of data from different sources. The approach is illustrated by application to a set of spatial permeability measurements and tracer data from an experiment in the Antolini Sandstone, an eolian outcrop from northern Arizona. The results clearly reveal the importance of tracer data in reproducing the correlated features (channels) of the permeability field and the scale effects of heterogeneity.     

Regional Seas and Sustainable Development Results of an International Workshop

Reports

Regional Seas and Sustainable Development Results of an International Workshop     

Estimation for partially observed Markov processes

Many stochastic process models for environmental data sets assume a process of relatively simple structure which is in some sense partially observed. That is, there is an underlying process (X_n, n 0) or (X_t, t 0) for which the parameters are of interest and physically meaningful, and an observable process (Y_n, n 0) or (Y_t, t 0) which depends on the X process but not otherwise on those parameters. Examples are wide ranging: the Y process may be the X process with missing observations; the Y process may be the X process observed with a noise component; the X process might constitute a random environment for the Y process, as with hidden Markov models; the Y process might be a lower dimensional function or reduction of the X process. In principle, maximum likelihood estimation for the X process parameters can be carried out by some form of the EM algorithm applied to the Y process data. In the paper we review some current methods for exact and approximate maximum likelihood estimation. We illustrate some of the issues by considering how to estimate the parameters of a stochastic Nash cascade model for runoff. In the case of k reservoirs, the outputs of these reservoirs form a k dimensional vector Markov process, of which only the kth coordinate process is observed, usually at a discrete sample of time points.