长江流域沿江镉异常源追踪与定量评估的方法技术研究:以长江流域安徽段为例

长江全流域性的Cd异常是被中国正在进行的多目标地球化学调查发现的重大生态环境问题。以长江流域安徽段为研究对象,对沿江镉异常源追踪与定量评估的方法技术进行了系统研究。通过对安徽段长江干流及其主要支流悬浮物中元素含量的测量查明:悬浮物是流域内重金属元素大跨度迁移的主要载体;Cd在悬浮物中的富集程度远远大于其他重金属元素,这或许正是Cd可以形成沿江流域性异常的主要原因;长江干流悬浮物中重金属元素含量的变化明显受到沿江支流的影响。安徽段长江干流及其主要支流重金属元素输出通量定量计算结果表明:秋蒲河是安徽段重金属元素输出通量最大的支流,每年在安徽段长江两岸土壤中沉积下约4.45t的Cd。利用悬浮物加密测量、1∶5万土壤测量和岩石测量对顺安河流域Cd异常源追踪结果显示:内生金属矿床特别是铅锌矿是悬浮物中Cd的最大的供应源。采用河漫滩沉积物分层采样技术和同位素测年技术,初步恢复了研究区Cd等重金属元素沉积和污染的地球化学历史,对研究区Cd等重金属元素异常的未来演变趋势进行了预警预测。     

附加约束条件的序贯平差

从观测量分组的参数平差出发,详细推导了附加约束条件的序贯平差及其精度估计,讨论了附加约束条件的抗差序贯平差的计算过程.算例表明,推导的公式正确有效,简单实用,附加约束条件的序贯平差抗差估计能有效地抵制粗差的影响.     

抗差最小二乘配置在数字化地图几何纠正中的应用讨论

抗差估计具有较好的抗拒异常观测值及粗差的能力,而最小二乘配置又能较好地处理系统误差,本文结合两者的优点,利用抗差最小二乘配置对数字化地图进行几何纠正,其中对协方差函数采用抗差拟合,得到了较好的结果。实验证明在GIS数据处理的扫描数字化地图几何纠正中,抗差最小二乘配置在抗拒异常值和处理系统误差方面优于单纯的最小二乘估计和单纯的最小二乘配置方法。     

建筑物沉降监测专家分析系统设计与开发

建筑物沉降监测数据分析与处理是一项较复杂的系统工程,本文从自动化处理观点出发,介绍了设计沉降监测专家分析系统的咨询新理念和主体思想,探讨了开发选用的数学模型与开发过程,以期为相关变形监测与分析提供帮助和启示.     

对一种振冲碎石桩复合地基沉降计算方法——Priebe法的改进

通过对经典的Priebe沉降计算法分析,以及面积置换率的变化对桩体压缩性假设作了改进;对桩土重度差异及承受荷载时应力和变形与深度的关系作了分析,归纳出相应的数学计算公式,用工程实例进行了验证。得到的结果表明改进是合理、适用的,可为今后的设计和施工提供参考。     

基于粉喷桩复合地基沉降计算的探讨

在对粉喷桩复合地基现有的沉降计算理论分析的基础上,结合粉喷桩的荷载传递规律,提出符合工程实际的沉降计算方法。     

基于岭估计的有理多项式参数求解方法

在使用最小二乘法解算卫星遥感影像的RPC参数时,如果控制点非均匀分布或模型过度参数化,其法方程系数矩阵很容易产生病态,获得的解将偏离真值,甚至得到错误的解.使用岭估计可改善法方程的状态,保证解稳定.采用岭估计方法,通过所获取的不同岭参数对SPOT和QuickBird影像进行实验,证实L曲线法是一种稳定的、有效的岭参数确定方法,可显著提高RPC参数的解算精度.     

应用探地雷达探测活动断层

在浅覆盖区采用探地雷达探测地震活动断层。通过实验确定出最佳的采集参数和数据处理流程,在雷达剖面上,能够清晰地显示出断层上部的形态特征、上断点埋深和岩土分层。结合钻孔资料,利用雷达剖面上对第四系覆盖的分层结果和上断点所在的层位可以分析和评价断层的活动性。     

Bayesian estimation of seismic hazard for two sites in Switzerland

Seismic hazard analysis is based on data and models, which both are imprecise and uncertain. Especially the interpretation of historical information into earthquake parameters, e.g. earthquake size and location, yields ambiguous and imprecise data. Models based on probability distributions have been developed in order to quantify and represent these uncertainties. Nevertheless, the majority of the procedures applied in seismic hazard assessment do not take into account these uncertainties, nor do they show the variance of the results. Therefore, a procedure based on Bayesian statistics was developed to estimate return periods for different ground motion intensities (MSK scale).Bayesian techniques provide a mathematical model to estimate the distribution of random variables in presence of uncertainties. The developed method estimates the probability distribution of the number of occurrences in a Poisson process described by the parameter . The input data are the historical occurrences of intensities for a particular site, represented by a discrete probability distribution for each earthquake. The calculation of these historical occurrences requires a careful preparation of all input parameters, i.e. a modelling of their uncertainties. The obtained results show that the variance of the recurrence rate is smaller in regions with higher seismic activity than in less active regions. It can also be demonstrated that long return periods cannot be estimated with confidence, because the time period of observation is too short. This indicates that the long return periods obtained by seismic source methods only reflects the delineated seismic sources and the chosen earthquake size distribution law.     

Estimation of covariance matrix from geochemical data with observations below detection limits

Multivariate statistical analyses have been extensively applied to geochemical measurements to analyze and aid interpretation of the data. Estimation of the covariance matrix of multivariate observations is the first task in multivariate analysis. However, geochemical data for the rare elements, especially Ag, Au, and platinum-group elements, usually contain observations the below detection limits. In particular, Instrumental Neutron Activation Analysis (INAA) for the rare elements produces multilevel and possibly extremely high detection limits depending on the sample weight. Traditionally, in applying multivariate analysis to such incomplete data, the observations below detection limits are first substituted, for example, each observation below the detection limit is replaced by a certain percentage of that limit, and then the standard statistical computer packages or techniques are used to obtain the analysis of the data. If a number of samples with observations below detection limits is small, or the detection limits are relatively near zero, the results may be reasonable and most geological interpretations or conclusions are probably valid. In this paper, a new method is proposed to estimate the covariance matrix from a dataset containing observations below multilevel detection limits by using the marginal maximum likelihood estimation (MMLE) method. For each pair of variables, sayY andZ whose observations containing below detection limits, the proposed method consists of three steps: (i) for each variable separately obtaining the marginal MLE for the means and the variances, , , , and forY andZ: (ii) defining new variables by and and lettingA=C+D andB=C–D, and obtaining MLE for variances, and forA andB; (iii) estimating the correlation coefficient _YZ by and the covariance _YZ by . The procedure is illustrated by using a precious metal geochemical data set from the Fox River Sill, Manitoba, Canada.