基于地球化学组合求异的津巴布韦奇马尼马尼地区成矿预测

如何有效地解释评价异常是化探找矿取得成功的关键,传统的地球化学异常处理方法一般是以单一元素的含量值与背景值的差异来圈定异常,因子-泛克立格求异模型则建立在矿床主成矿期元素共生组合与地球化学异常之间的对应关系。采用因子-泛克立格模型对津巴布韦东部奇马尼马尼地区进行成矿预测,结果表明:奇马尼马尼地区存在7种元素组合,分别对应于不同的岩性组合,F4和F6元素组合异常可作为区内热液成矿指示标志,与东部塔卡金矿套合程度高。利用该模型圈定了5处找矿远景区,Ⅰ号远景区为铜镍金钛铁矿成矿有利区,Ⅱ号远景区具有与塔卡金矿相似的地球化学组合背景,Ⅲ号、Ⅳ号和Ⅴ号远景区则具寻找热液型金铜矿及多金属矿的潜力。     

滑动窗口的普通克立格方法在降水量插值中的应用

利用2009年全国2200个观测站降水量资料,使用滑动窗口的普通克立格方法对降水量资料进行格点化估计。针对滑动窗口的普通克立格方法在降水量格点化应用中存在的问题,设计了3种技术处理的试验方案。比较了全局搜索与方位邻近方法的误差,讨论了最大影响半径及屏蔽效应对插值效果的作用。提出了方位邻近法的样本点选择策略,结果表明,相对传统滑动窗口的普通克立格方法较常使用的全局搜索法而言,方位邻近法显著降低了计算资源的耗用,同时又具有较高的插值精度,特别是在站点密集地区有突出的优势;试验结果也表明:变程为4°～5°的经(纬)线弧长时,在方位邻近法下,我国大部分区域有较好的插值效果;屏蔽效应弱,ε取值为0.1时,降水量插值准确率较高,随着ε增大,插值误差也逐渐增大。     

中国美丽乡村空间格局演变及其影响因素

以2014年、2017年、2020年对应的140、560、1216个美丽乡村为研究对象,运用GIS空间分析方法研究美丽乡村空间格局演变特征,采用地理探测器和多尺度地理加权回归探究美丽乡村空间格局的影响因素及其在空间上的分异。研究表明：① 美丽乡村空间分布的集聚性增强,主要集聚区呈现“带状-网状-面状”分布的演变趋势,热点区由沿海地带向内扩散。② 美丽乡村的空间分异是多因素共同作用的结果,但各影响因素对美丽乡村空间分异的解释度呈显著差异,其中非物质文化遗产、5A级景区、人口密度对美丽乡村空间分布的解释力最大。③ 主要影响因素对美丽乡村空间分布的影响作用存在空间差异,且局部不平衡性显著,正相关和负相关分析单元具有呈块状与带状的聚集特征。     

空间数据插值的自动化方法研究

克里金插值是空间数据插值的常用方法,其关键在于变异函数的正确拟合。目前一些空间统计分析工具及开发包对变异函数的拟合主要以一定的人工经验为先导,通过直观的方法来确定变异函数的一些参数并反复调整,这种人工干预的操作步骤不仅妨碍了克里金插值的自动化,参数设置和模型选择也没有科学理论依据,因此预测的结果往往不精确。针对这一问题,围绕着自动化目标,基于R语言中的变异函数的自动拟合函数实现了自动克里金插值,并对自动拟合模型进行了自动交叉验证。实验结果表明该空间数据插值自动化方法可行。     

利用分形和多尺度分析的中低分辨率SAR图像变化检测

对于合成孔径雷达(synthetic aperture radar,SAR)图像像素级变化检测,常见的对数比、交叉熵差异图在提取建筑物等人造目标的变化时不能保持其结构特征。本文将分形维数引入到差异图构造中,定义了分形-对数比(fractal dimension-log ratio, FD-LR)融合差异图,在有效提取不同地物类型变化的同时,能够保持其轮廓结构。为克服斑噪干扰,对FD-LR进行多尺度分析,通过贝叶斯分割和决策级融合提取变化信息。实验结果表明,该方法模型简单,能够有效检测不同地物类型的变化,在中低分辨率复杂场景的SAR图像变化检测中具有优势。     

Simulation of normal distributed smooth fields by Karhunen-Loéve expansion in combination with kriging

Simulation of multigaussian stochastic fields can be made after a Karhunen-Loéve expansion of a given covariance function. This method is also called simulation by Empirical Orthogonal Functions. The simulations are made by drawing stochastic coefficients from a random generator. These numbers are multiplied with eigenfunctions and eigenvalues derived from the predefined covariance model. The number of eigenfunctions necessary to reproduce the stochastic process within a predefined variance error, turns out to be a cardinal question. Some ordinary analytical covariance functions are used to evaluate how quickly the series of eigenfunctions can be truncated. This analysis demonstrates extremely quick convergence to 99.5% of total variance for the 2nd order exponential (‘gaussian’) covariance function, while the opposite is true for the 1st order exponential covariance function. Due to these convergence characteristics, the Karhunen-Loéve method is most suitable for simulating smooth fields with ‘gaussian’ shaped covariance functions. Practical applications of Karhunen-Loéve simulations can be improved by spatial interpolation of the eigenfunctions. In this paper, we suggest interpolation by kriging and limits for reproduction of the predefined covariance functions are evaluated.     

Kriging with imprecise (fuzzy) variograms. I: Theory

Imprecise variogram parameters are modeled with fuzzy set theory. The fit of a variogram model to experimental variograms is often subjective. The accuracy of the fit is modeled with imprecise variogram parameters. Measurement data often are insufficient to create good experimental variograms. In this case, prior knowledge and experience can contribute to determination of the variogram model parameters. A methodology for kriging with imprecise variogram parameters is developed. Both kriged values and estimation variances are calculated as fuzzy numbers and characterized by their membership functions. Besides estimation variance, the membership functions are used to create another uncertainty measure. This measure depends on both homogeneity and configuration of the data.     

改进的边缘角度直方图在遥感图像检索中的应用

针对基于形状的遥感图像检索中区域分割困难这个问题,本文提出利用边缘角度直方图作为形状特征对遥感图像进行检索;针对遥感影像分辨率和颜色反差对检索性能的影响,本文提出采用多尺度分析的思想,提取图像在多个尺度上的边缘角度直方图特征进行检索。通过单一尺度边缘角度直方图特征和多尺度边缘角度直方图特征检索的对比实验,揭示了本文所提出的改进方案使遥感图像的检索精度提高了约31个百分点。     

模拟三维裂纹问题的多尺度扩展有限元法

扩展有限元法模拟裂纹时独立于网格,因此该方法是目前求解裂纹问题最有效的数值方法。为了在计算代价不大的情况,实现大型结构分析中考虑小裂纹或提高裂纹附近精度,在裂纹附近一般采用小尺度单元,其他区域采用大尺度单元。提出了分析三维裂纹问题的多尺度扩展有限元法,在需要的地方采用小尺度单元。基于点插值构造了六面体任意节点单元。所有尺度单元都采用8节点六面体单元,这样六面体任意节点单元可方便有效地连接不同尺度单元。采用互作用积分法计算三维应力强度因子。边裂纹和中心圆裂纹算例分析结果表明,该方法是正确和有效的。     

Geostatistical analysis and conditional simulation for estimating the spatial variability of hydraulic conductivity in the Choushui River alluvial fan,Taiwan

This work evaluated the spatial variability and distribution of heterogeneous hydraulic conductivity (K) in the Choushui River alluvial fan in Taiwan, using ordinary kriging (OK) and mean and individual sequential Gaussian simulations (SGS). A baseline flow model constructed by upscaling parameters was inversely calibrated to determine the pumping and recharge rates. Simulated heads using different K realizations were then compared with historically measured heads. A global/local simulated error between simulated and measured heads was analysed to assess the different spatial variabilities of various estimated K distributions. The results of a MODFLOW simulation indicate that the OK realization had the smallest sum of absolute mean simulation errors (SAMSE) and the SGS realizations preserved the spatial variability of the measured K fields. Moreover, the SAMSE increases as the spatial variability of the K field increases. The OK realization yields small local simulation errors in the measured K field of moderate magnitude, whereas the SGS realizations have small local simulation errors in the measured K fields, with high and low values. The OK realization of K can be applied to perform a deterministic inverse calibration. The mean SGS method is suggested for constructing a K field when the application focuses on extreme values of estimated parameters and small calibration errors, such as in a simulation of contaminant transport in heterogeneous aquifers. The individual SGS realization is useful in stochastically assessing the spatial uncertainty of highly heterogeneous aquifers. Copyright © 2004 John Wiley & Sons, Ltd.