An Application of Multivariate Simulation in the Cement Industry

The profitability of a cement plant depends largely on its capacity to produce homogeneous cement with chemical composition close to specified targets for the cement type produced. One crucial step is the mixing of limestone with other raw materials in proportions calculated to meet these targets. Major design and operation decisions depend on the efficiency of this homogenizing step. The adequate modeling of the mixing process requires simulation of representative cross-correlated time series of chemical compositions of the raw materials involved. The chemical composition signals are obtained by multivariate geostatistical simulation using an LU (Cholesky) decomposition of the covariance matrix. Modifications to the usual LU method are presented. First, the effect on the raw covariance matrix of the closure property of chemical analysis is imposed. Second, the problem of memory space limitations in the LU method is tackled by using overlapping sliding neighbourhoods. The simulation algorithm is applied to the Joppa cement plant owned by Lafarge North America. The simulated raw material input streams are fed into the quality mix control (QMC), a proprietary software that models and controls the mixing operation to produce an output stream with cement characteristics as close as possible to desired targets. Two signal series are studied, one autocorrelated with a moderate temporal range and one with no autocorrelation. The QMC produces C₃S output signals having comparable short scale periodic variograms except that the variance of the uncorrelated signal is four times greater than those of the autocorrelated signal and the real Joppa data. The raw material feeder variograms have the same sill for both the white noise and the autocorrelated signals. However, the autocorrelated signal feeder variogram presents lower short term dispersion variance, a characteristic feature of Joppa operations. Our results show the importance of simulating the right temporal structure of the raw materials to realistically forecast the behavior of the output signals. We also discuss some practical implications of these findings for the design and operation of a cement plant.     

基于多元统计的甘肃永靖黑方台滑坡强度预测模型

预测滑坡强度是滑坡风险分析与控制的基础和关键.以黑方台为研究区,在野外调查的基础上,针对研究区35处滑坡几何参数的数理统计,系统地分析了滑距与滑坡几何特征参数的相关关系,并按照黄土滑坡、黄土-基岩滑坡分别建立了滑坡空间预测的一元回归和多元回归统计模型.在统计模型中,分别以原始边坡坡度、塌落角、滑体宽度等因素为自变量,以滑坡延伸角为因变量,采用单因素和多因素拟合的方法,实现滑坡强度的简便预测.     

滇中荒田铅锌矿床Rb-Sr同位素年代学与C-O-S-Pb同位素地球化学特征

滇中荒田铅锌矿床赋存于下二叠统碳酸盐岩与上二叠统峨眉山玄武岩接触界面上,矿体主要呈似层状、透镜状产出。矿石矿物组合以闪锌矿、方铅矿为主,脉石矿物以石英、方解石、白云石为主。热液方解石C、O同位素组成表明荒田铅锌矿床成矿流体中CO_2的碳具有多元性,主要来源于幔源与海相碳酸盐岩的混合碳;硫化物硫同位素组成表明荒田铅锌矿床硫以岩浆硫为主,可能混有其他硫源(可能包括地层硫酸盐),铅同位素表明赋矿围岩、玄武岩和燕山期花岗岩均有可能为成矿提供了成矿物质,是多源混合后的产物;闪锌矿Rb-Sr同位素等时线年龄为(83.2±3.4)Ma,指示荒田铅锌矿床形成于晚燕山期,荒田铅锌矿床成矿动力学背景可能与右江褶皱带在中生代末期发生了大规模的岩石圈伸展有关。而晚二叠世海相喷发火山岩对矿区铅锌矿床的形成起了重要的盖层、赋矿层及矿化作用。综上,荒田铅锌矿床成矿流体中的不同组分来源不同,矿床类型为沉积-改造型矿床。     

中国COVID-19疫情扩散的时空模式及影响因素

在2020年全球暴发新型冠状病毒肺炎（COVID-19）疫情的背景下,揭示中国疫情扩散时空模式及影响因素对于科学制定防疫策略具有重要作用。针对2020年1月24日—3月18日期间中国COVID-19疫情从快速扩散到逐步控制的完整过程,基于累计确诊病例数据,以317个地级市为对象,建立疫情扩散时空模式判别模型,结合峰位置、半峰间距、峰度、偏度等参数,解析时空模式的基本特征;基于交通可达性、城市关联程度和人口流动构建多元Logistic回归模型,揭示时空模式的关键影响因素。结果显示：① 距武汉市直线距离588 km为判别疫情扩散4种空间模式的有效边界,综合同一空间模式下的时间过程类别,得到13类疫情扩散时空模式。② 蛙跳型的疫情扩散相对严重;除近距离蛙跳型以外,其余空间模式的疫情扩散时间过程差异明显;各种时空模式的新增确诊病例峰值大多为2020年2月3日;所有普通类城市的平均半峰间距约为14 d,与COVID-19病毒的潜伏期一致。③ 与武汉市的人口关联度主要影响蔓延型和近距离蛙跳型空间模式,与武汉市的通航状况对远距离蛙跳型空间模式具有正向影响,迁出人口数量对蛙跳型空间模式有显著作用,综合型空间模式受初级和次级疫情暴发地的双重影响。不同城市应根据自身的疫情扩散时空模式,在疫情期间高度重视交通管控,从关键环节遏制疫情扩散。     

二维复合极值模型在暴雨多变量联合分布中的应用

为了更好地描述暴雨多变量特征,构建以GPD为边缘分布的泊松-二维Copula复合极值模型,并将其应用于成都温江站暴雨雨量和暴雨雨峰的联合概率分布计算,研究表明该模型:理论联合频率与经验联合频率吻合较好,能用于暴雨联合分布的计算;考虑了暴雨过程出现的频次,增加了概率模型的物理内涵,选取独立的暴雨过程极大值作为样本,克服了GPD阈值选取的随意性;可以推广应用于连续分布为其他的Copula函数的情形,具有广泛的应用前景。     

嵊泗列岛海域三种贻贝贝体框架特征的差异

以壳长SL、壳宽SW、壳高SH(BD)、OA(壳顶至韧带末端的直线距离)、OB(壳顶至壳背面最高点的直线距离)、OC(壳顶至壳后端最远点的直线距离)、OD(壳顶至壳高性状在腹缘的落点的直线距离)、AB(韧带末端至壳背缘最高点的直线距离)、BC(壳背缘最高点至壳后端最远点的直线距离)、CD(壳后端最远点至壳高性状在腹缘的落点的直线距离)为贝体框架变量,采用多元分析方法系统比较了嵊泗列岛海域厚壳贻贝、紫贻贝和"杂交贻贝"贝体框架特征的差异,结果表明:(1)在所涉9项贝体框架特征指标中,紫贻贝与厚壳贻贝间无显著差异的指标仅为L5(OC/SL)和L7(AB/SL)(P0.05),而"杂交贻贝"各项指标则均与厚壳贻贝和紫贻贝具显著差异(P0.05),厚壳贻贝和紫贻贝变异系数大于10%的指标均仅为L7(AB/SL),而"杂交贻贝"则仅为L3(OA/SL);(2)厚壳贻贝与紫贻贝间的欧氏距离最短(P0.05),仅为0.160;厚壳贻贝与"杂交贻贝"间和紫贻贝与"杂交贻贝"间的欧氏距离相近(P0.05),分别为0.452和0.418;(3)经主成分分析,提取到的3个特征值均大于1的主成分,累计贡献率达82.928%,其中第一主成分、第二主成分、第三主成分可依次归为与滤食功能区水平剖面占比相关的贝体框架因子,与消化功能区水平剖面占比相关的贝体框架因子,和与消化功能区垂直剖面占比相关的贝体框架因子,通过第一主成分仅能较清晰地区分厚壳贻贝和"杂交贻贝";(4)采用逐步判别法,以判别贡献率较大的L1(SW/SL)、L3(OA/SL)、L4(OB/SL)、L5(OC/SL)、L6(OD/SL)和L7(AB/SL)为自变量,所建Fisher分类函数方程组可较清晰区分厚壳贻贝、紫贻贝和"杂交贻贝",三者的判别准确率依次为94.6%、94.6%和100%,综合判别准确率为96.4%。     

Multivariate drought characteristics using trivariate Gaussian and Student t copulas

This study aims to investigate the changing properties of drought events in Weihe River basin, China, by modeling the multivariate joint distribution of drought duration, severity and peak using trivariate Gaussian and Student t copulas. Monthly precipitations of Xi'an gauge are used to illustrate the meta‐elliptical copula‐based methodology for a single‐station application. Gaussian and Student t copulas are found to produce a better fit comparing with other six symmetrical and asymmetrical Archimedean copulas, and, checked by the goodness‐of‐fit tests based on a modified bootstrap version of Rosenblatt's transformation, both of them are acceptable to model the multivariate joint distribution of drought variables. Gaussian copula, the best fitting, is employed to construct the dependence structures of positively associated drought variables so as to obtain the multivariate joint and conditional probabilities of droughts. A Kendall's return period (KRP) introduced by Salvadori and De Michele (2010) is then adopted to assess the multivariate recurrent properties of drought events, and its spatial distributions indicate that prolonged droughts are likely to break out with rather short recurrence intervals in the whole region, while drought status in the southeast seems to be severer than the northwest. The study is of some merits in terms of multivariate drought modeling using a preferable copula‐based method, the results of which could serve as a reference for regional drought defense and water resources management. Copyright © 2011 John Wiley & Sons, Ltd.     

多元回归分析方法在火山碎屑岩储层孔隙度计算中应用

火山碎屑岩储层具有岩性复杂、测井数据种类繁多以及区域大、层位多等特点,导致火山碎屑岩储层测井参数难于确定。以海拉尔盆地乌尔逊--贝尔凹陷的火山碎屑岩储层为研究主体,在准确识别岩性的基础上,对测井资料进行预处理,根据岩心分析资料计算其理论骨架参数值。依据孔隙度与中子、密度、声波三者之间的关系,将多元回归方法应用在孔隙度计算中,实现利用多元回归方法计算火山碎屑岩储层孔隙度。其结果与岩心分析孔隙度比较接近,绝对误差仅为1. 6%,能满足储量计算要求。     

长岛周边海域浮游植物分布及其环境影响因素

2013年春季和夏季对长岛周边海域进行了环境与生物综合调查,利用多元统计分析对长岛周边海域浮游植物分布及其影响因素进行分析。根据水环境分析结果对调查海域进行聚类分析,结果表明,春季(M1、M2)与夏季(A1、A2)均可分为两大类。M1与A2为高温高营养盐区,M2与A1为低温低营养盐区。调查期间,共发现69种浮游植物。硅藻为两季主要浮游植物,春季浮游植物细胞丰度远大于夏季。春季共发现1种明显的优势种,为柔弱几内亚藻(Guinardia delicatula(Cleve)Hasle)。夏季共发现5种优势种,分别为具槽帕拉藻(Paralia sulcata)、裸甲藻(Gymnodinium sp.)、圆筛藻(Coscinodiscus sp.)、离心列海链藻(Thalassiosira eccentrica)与具齿原甲藻(Prorocentrum dentatum)。影响春季浮游植物分布的最佳解释变量组合为温度(WT)、透明度(SD)、风速(WS)和溶解总碳(DTC)组合,其中,温度影响最显著(极显著负相关);夏季则为盐度(S)、活性磷酸盐(SRP)和N/Si组合,其中活性磷酸盐影响最大(显著负相关)。     

Sampling effort required to recover the main gradients in marine benthic species composition

Marine benthic surveys and monitoring programs may have several goals. When the aim of such studies is to reveal and describe gradients in species composition, the gain in ecological insight achieved by the study depends on how well the spatial variability of species’ occurrence and abundance within the study area is covered by the sampling design. The common practice in such programs is to collect four or five grab samples from each visited site to capture the site's species composition. The rationale for this practice is based on the aim of capturing a large proportion of species at a specific site (i.e. point). We argue that this practice has been established without taking Iterative approaches into account, such as decreasing the sampling effort at each site and instead increase the number of sites visited. The collection and processing of data from marine sediment ecosystems are time‐consuming and, thus, expensive. Therefore, finding the optimal trade‐off between the number of sites visited and the number of samples per site is important. We examined this trade‐off by exploring how reduction of sampling effort per site affects the stability and recovery of gradients in species composition of marine benthic macrofauna. Our full data set consisted of four grab samples from each of 28 sites from the inner part of the Oslofjord, SE Norway. Procrustes analysis and calculation of Kendall's rank correlation coefficients showed that the main gradient in species composition extracted by detrended correspondence analysis of data sets with reduced per‐site sampling effort closely resembled the main gradient extracted for the full data set. Our results therefore suggest that marine benthic surveys that aim to identify the main gradient structure of species composition and relate this structure to main environmental complex gradients are likely to benefit from reducing the sampling effort at each site and, instead, increasing the number of visited sites. We argue that coverage of both the variation in species composition and the environment in the study area will then be improved. Monitoring programs with other aims than to identify the main gradients in species composition may also benefit from the adoption of a many‐sites, one‐sample sampling strategy. This may be particularly true if additional sampled sites are based on a stratified sampling strategy that ensures that the spatial variability in the study area is accounted for.