Using principal component analysis in the investigation of groundwater hydrochemistry of Upper Jezireh Basin,Syria

Principal component analysis (PCA) was applied to hydrochemical and isotopic data of 34 groundwater samples. This allowed the reduction of 20 variables to four significant PCs that explain 81.9% of the total variance; F1 (47.1%) explains the groundwater mineralization, whereas F2 (17%) shows isotopic enrichment and nitrate pollution. Based on an iso-factor scores map of F1, three water zones were delineated: Zone A (F1 < ?1), with fresh groundwater from the unconfined aquifer; Zone B (1 > F1 > ?1), with moderate mineralization from the confined–unconfined aquifer boundary; and Zone C (F1 > 1), with the most mineralized hot water from the confined aquifer. The iso-factor scores map of F2 delineates positive values representing samples from the unconfined aquifer, with freshwater and nitrate contamination associated with stable isotope enrichment, whereas negative values represent samples from the confined aquifer. The results clearly demonstrate the usefulness of PCA in groundwater hydrochemistry investigations.     

Comparison of LiDAR waveform processing methods for very shallow water bathymetry using Raman,near‐infrared and green signals

Airborne light detection and ranging (LiDAR) bathymetry appears to be a useful technology for bed topography mapping of non‐navigable areas, offering high data density and a high acquisition rate. However, few studies have focused on continental waters, in particular, on very shallow waters (<2 m) where it is difficult to extract the surface and bottom positions that are typically mixed in the green LiDAR signal. This paper proposes two new processing methods for depth extraction based on the use of different LiDAR signals [green, near‐infrared (NIR), Raman] of the SHOALS‐1000T sensor. They have been tested on a very shallow coastal area (Golfe du Morbihan, France) as an analogy to very shallow rivers. The first method is based on a combination of mathematical and heuristic methods using the green and the NIR LiDAR signals to cross validate the information delivered by each signal. The second method extracts water depths from the Raman signal using statistical methods such as principal components analysis (PCA) and classification and regression tree (CART) analysis. The obtained results are then compared to the reference depths, and the performances of the different methods, as well as their advantages/disadvantages are evaluated. The green/NIR method supplies 42% more points compared to the operator process, with an equivalent mean error (?4·2 cm verusu ?4·5 cm) and a smaller standard deviation (25·3 cm verusu 33·5 cm). The Raman processing method provides very scattered results (standard deviation of 40·3 cm) with the lowest mean error (?3·1 cm) and 40% more points. The minimum detectable depth is also improved by the two presented methods, being around 1 m for the green/NIR approach and 0·5 m for the statistical approach, compared to 1·5 m for the data processed by the operator. Despite its ability to measure other parameters like water temperature, the Raman method needed a large amount of reference data to provide reliable depth measurements, as opposed to the green/NIR method. Copyright © 2010 John Wiley & Sons, Ltd.     

基于SAPSO-ELM的瓦斯涌出量分源预测及应用

为了提高瓦斯涌出量预测精度,针对瓦斯涌出量影响因素的多重相关性、复杂性等问题,结合主成分分析法和分源预测理论,对开采层、邻近层、采空区的瓦斯涌出量数据分别进行主成分分析降维,得到预测指标。针对极限学习机(ELM)存在的输入权值矩阵与隐含层阈值随机生成的问题,利用模拟退火粒子群算法(SAPSO)对极限学习机的参数寻优,将新疆某煤矿回采工作面瓦斯涌出量及影响因素作为SAPSO-ELM模型的输入进行训练,再利用训练好的SAPSO-ELM模型对陕西某煤矿回采工作面的瓦斯涌出量进行验证预测,并对比原始ELM模型的预测结果。结果表明,SAPSO-ELM模型的平均相对误差为3.45%,ELM模型的平均相对误差为8.81%,与ELM模型相比,SAPSO-ELM模型预测精度及效率均优于原始ELM模型。分源预测理论和主成分分析法的结合有效解决了多因素间的多重相关性并降低了预测模型的复杂度,SAPSO-ELM预测模型实现了瓦斯涌出量的快速精准预测,对预防瓦斯事故发生和保障煤矿安全高效开采具有较好的指导作用。     

保持主分量变换结果信息的量化方法

从主分量变换后图像信息保持的角度出发，充分分析了变换结果存储导致信息丢失的原因；同时以直接线性变换为基础，提出了另外两种新的对主分量变换结果进行量化的方法；并对这3种方法进行了充分的比较，文中最后以信息熵为指标评价了3种方法的优劣。     

Wide-band multi-spectral space for color representation

This paper develops a wide-band multi-spectral space for color representation with Aitken PCA algorithm. This novel mathematical space using the broad-band spectra matching method aims at improving the accuracy of color representation as well as reducing costs for processing and storing multi-spectral images. The results show that the space can present our experimental original spectral spaces (i. e. Munsell color matt and DIN-6164 color chips) with high efficiency, and that the spanning space with three eigenvectors can present the original space at more than 98% CSCR, and when 5 eigenvectors are used it can cover almost the whole original spaces.     

利用漳州深井4分量钻孔应变求测区应变场

利用漳州深井形变综合观测中的4分量钻孔应变观测数据,计算了测区的主应变大小和方向。计算得到的上下两个端面的主应变方位角都在170°附近变化,近东西向性质明显。与利用多个地震求解综合节面解推断出的构造应力场一致。     

拉林铁路板块缝合带隧道地应力分析

青藏高原板块缝合带为印度板块和欧亚板块两大陆块的缝合区域,带区地质条件复杂,构造运动强烈。川藏线拉林铁路几乎沿雅鲁藏布江缝合带展布,高地应力问题十分突出,但目前针对板块缝合带隧道的地应力研究相对较少。本文采用空心包体法对拉林铁路沿线隧道进行了原位地应力测量,并与成兰、兰渝和锦屏等几个典型工程的地应力进行对比分析。研究表明：拉林铁路沿线隧道埋深大,构造应力突出,总体表现为最大水平主应力 > 垂直主应力 > 最小水平主应力;平均侧压系数（1.0~1.5）分布较为集中且处于较高水平;最大主应力量值大多在20~50 MPa之间,最大主应力与埋深的梯度为0.033 7 MPa/m,方向以北北西-北北东向为主。建议采用仰拱结构减小隧道墙脚处的应力集中现象。     

A protocol for classifying ecologically relevant marine zones,a statistical approach

Mapping ecologically relevant zones in the marine environment has become increasingly important. Biological data are however often scarce and alternatives are being sought in optimal classifications of abiotic variables. The concept of ‘marine landscapes’ is based on a hierarchical classification of geological, hydrographic and other physical data. This approach is however subject to many assumptions and subjective decisions.     

Determination of thermal maturity and organic matter type by principal components analysis of the distributions of polycyclic aromatic compounds

The thermal maturity and organofacies sensitivity of polycyclic aromatic compound (PAC) distributions was explored by examination of the aromatic fractions of solvent extracts from a diverse set of 53 shales, coals and kerogen macerals which have undergone either natural or artificial maturation and which represent all three principal sedimentary organic matter (OM) types. Systematic changes with maturation were observed in the following groups of isomers: tri- and tetramethylnaphthalenes, methyl- and dimethylphenanthrenes, methyl- and dimethyldibenzothiophenes, methylpyrenes, and methylchrysenes. The maturity differences were quantified by mathematical ratios of the relative concentrations of the more thermally stable isomers to the less stable, on the basis of theoretical considerations and empirical observations. The PAC maturity parameters, unlike those derived from saturated biomarker stereoisomers, are typically effective across the entire oil generation window. To compensate for the effects of OM type on the maturity parameters, they were combined using principal components analysis. The resulting first principal component was in good agreement with independent indicators of maturity. The relative distributions of C₀–C₃ alkylphenanthrenes, dibenzothiophene, methyldibenzothiophenes and methyldibenzofurans were evaluated by a separate principal components analysis. The results permitted an independent grouping of the samples by OM type and suggested additional, simple molecular ratios that allow graphical recognition of OM type, including the ratio of dibenzothiophenes to dibenzofurans and a ratio using C₂-alkylphenanthrene isomers.     

基于主成分分析和贝叶斯正则化方法的神经网络年最大洪峰流量预测模型探讨

针时水文预测建模中输入因子过多而导致神经网络结构规模过大,泛化能力差的问题,利用主成分分析和贝叶斯正则化方法对神经网络进行改进,优化网络结构,从而提高泛化能力。以洮儿河流域镇西站年最大洪峰流量预测为例,研究结果表明,改进的神经网络预测方法与传统的神经网络方法相比,泛化能力有显著提高,而且网络的收敛也比较稳定,实际预测中效果良好。