生活垃圾卫生填埋地质环境(LFGE)效应的系统分析方法研究——以上海浦东新区为例

在系统分析总结国内外本领域的研究发展现状的基础上,以上海浦东新区城市生活垃圾卫生填埋处理为研究背景,首次提出ＬＦＧＥ效应系统分析方法的完整理论和应用体系,体系主要包括：ＬＦＧＥ效应的主控因素特征分析;在垃圾淋滤试验、有关土的工程性能试验和反映运移转化特征的土柱试验基础上的作用机制研究;具单元、组合、整体模拟及压实效应模拟功能的模拟计算研究;在不确定性条件存在下,随机模拟研究和以贝叶斯决策分析理论为基础的ＬＦＧＥ效应决策分析技术经济转换研究     

紫阳县城滑坡监测及变形规律分析

本文介绍了紫阳县城滑坡监测工作的技术方案及数据处理方法和结果。通过各种方案的对比分析．对监测网的各种技术方案的选取提供了依据．文中所提出的依据稳健估计原理、利用Ｓ变换解决参考基准的定位问题．是基准网非稳定情况下进行监测数据处理的成功方法，颇有借鉴意义。     

On the joint robust estimation of parameters and variance

A method of joint estimation of the parameters and the variance σ² in the linear model is presented in this paper. The M-estimators with finite rejection points are adopted for estimating the parameters. In order to estimate the variance σ², the Grubbs statistics and the Kurtosis test statistics are adopted to test the residual sequence {;r_i};. And the sample variance of {;r_i}; after discarding the outliers is taken as the estimation of σ². This method of estimating σ² is less computational demanding and more accurate, compared with the well-known method which takes 1.483 med_i |r_i| as the estimation of σ. The breakdown point of the above estimation of σ² is more than 20%, while the brekdown point is less than 10%, if only the Grubbs statistics is used.     

三种统计预报模型在江苏省道路低温预警中的应用

为了更好地开展道路交通低温灾害的预警,减轻道路结冰给车辆行驶造成的危害,本文利用2012—2016年江苏省高速公路网AWMS系统交通气象观测数据,在对路面低温发生规律进行分析的基础上,结合多元线性回归、朴素贝叶斯以及支持向量机3种统计预报方法,开展了路面低温预警的统计建模与预报试验。结果表明:(1)江苏全省高速公路网路面温度出现0℃以下、-2℃以下、-5℃以下的低温频率均呈"北高南低"分布。(2)全省高速公路网路面温度出现0℃以下的低温时次大多在15:00到次日06:00之间。(3)在对京沪高速M9308站的单站建模与预报试验中发现,路面低温预报因子组合中以13:00气温、13:00—18:00气温变温、13:00路面温度、13:00—18:00路面变温、13:00路基温度、13:00—18:00路基变温、18:00相对湿度和18:00风速U分量为自变量组合的预报方程效果最好,3种方法中以朴素贝叶斯模型的预报准确率最高;(4)就全省高速公路网而言,3种统计预报模型的路面低温预报准确率均超过75%,通过对全路网路面低温预报的试验结果对比发现,多元线性回归方法对江苏省北部路网的预报效果最好,预报准确率大多在85%以上;而支持向量机模型对江苏省南部路网的预报效果最好,大部分站点的低温预报准确率达95%以上。     

基于多源时空大数据的区际迁徙人群多层次空间分布估算模型——以COVID-19疫情期间自武汉迁出人群为例

已有研究很少关注区际迁徙人群在不同尺度上空间分布的动态估算问题。COVID-19疫情爆发以来,坚决防止疫情扩散成为社会最紧迫的事情。在2020年1月23日武汉“封城”前夕,已有500多 万人离开了武汉,快速准确地推算这部分人群的去向,可以为防止疫情扩散和制定防疫决策提供科学依据。本文以此为例,基于开源腾讯位置请求大数据、百度迁徙大数据、土地覆盖数据等多源地理时空大数据,提出一种区际迁徙人群多层次空间分布动态估算模型,用于推算2020年除夕 （2020年1月24日）之前从武汉流入湖北省内各地的人群数量及其分布特征。结果显示：① 春节时段湖北省各地级市农村地 区人群增加数量占人群变化总量的比例平均达124.7%,从武汉市迁入各地级市的人群中至少51.3%流入农村地区;② 区县尺 度人群变化总量的空间分布呈现3个圈层结构：第一圈层为疫情核心区,包括武汉及其周边地区,以人群流出为主;第二圈层为 重点关注区,包括黄冈、黄石、仙桃、天门、潜江、随州、襄阳,以及孝感、荆门、荆州和咸宁的部分地区,以人群总量和农村地区人 群数量大幅增加为主;第三圈层为次级关注区,包括湖北西部宜昌、恩施、神农架和荆门部分地区,以人群小幅流入为主。最后,建议湖北省内,尤其是位于第二圈层内的区县,应高度关注农村地区人群的疫情防控。此研究成果在2~3天完成,显示大数据是可以快速地响应重大公共安全事件,为决策的制定提供一定支持的。     

Origin, weathering, and geochemical composition of loess in southwestern Hungary

Loess geochemistry generally reflects paleo-weathering conditions and it can be used to determine the average composition of the upper continental crust (UCC). In this study, major and trace element concentrations were analyzed on loess samples from southwestern Hungary to determine the factors influencing their chemical compositions and to propose new average loess compositions. All studied loess samples had nearly uniform chemical composition, suggesting similar alteration history of these deposits. Chemical Index of Alteration values (58–69) suggested a weak to moderate degree of weathering in a felsic source area. Typical non-steady state weathering conditions were shown on the Al₂O₃–CaO + Na₂O–K₂O patterns, indicating active tectonism of the Alpine–Carpathian system during the Pleistocene. Whole-rock element budgets were controlled by heavy minerals derived from a felsic magmatic or reworked sedimentary provenance. Geochemical parameters indicated that dust particles must have been recycled and well homogenized during fluvial and eolian transport processes.     

水平距离和海拔高度对雷达估测降水影响及订正

以石家庄2006—2008年77次降水过程为例,分析了海拔高度和距雷达站水平距离对定量估测降水的影响,给出了估测降水时反射率因子取值的最佳高度以及距离高度订正值。对比了经验公式法和最优化法分别在有无距离高度订正4种情况下的估测效果,依据估测效果,设计了雷达定量估测降水最佳方案:若反射率因子小于30 dBz,则不进行距离高度订正,仅采用最优化法估测降水;若反射率因子大于30 dBz,则首先对反射率因子进行距离高度订正,然后采用最优化法估测降水。实例检验表明,该方案对单站10 mm·h~(-1)以上的强降水、过程降水量和区域降水量估测效果较好。     

Classification of mineral deposits into types using mineralogy with a probabilistic neural network

In order to determine whether it is desirable to quantify mineral-deposit models further, a test of the ability of a probabilistic neural network to classify deposits into types based on mineralogy was conducted. Presence or absence of ore and alteration mineralogy in well-typed deposits were used to train the network. To reduce the number of minerals considered, the analyzed data were restricted to minerals present in at least 20% of at least one deposit type. An advantage of this restriction is that single or rare occurrences of minerals did not dominate the results. Probabilistic neural networks can provide mathematically sound confidence measures based on Bayes theorem and are relatively insensitive to outliers. Founded on Parzen density estimation, they require no assumptions about distributions of random variables used for classification, even handling multimodal distributions. They train quickly and work as well as, or better than, multiple-layer feedforward networks. Tests were performed with a probabilistic neural network employing a Gaussian kernel and separate sigma weights for each class and each variable. The training set was reduced to the presence or absence of 58 reported minerals in eight deposit types. The training set included: 49 Cyprus massive sulfide deposits; 200 kuroko massive sulfide deposits; 59 Comstock epithermal vein gold districts; 17 quartzalunite epithermal gold deposits; 25 Creede epithermal gold deposits; 28 sedimentary-exhalative zinc-lead deposits; 28 Sado epithermal vein gold deposits; and 100 porphyry copper deposits. The most common training problem was the error of classifying about 27% of Cyprus-type deposits in the training set as kuroko. In independent tests with deposits not used in the training set, 88% of 224 kuroko massive sulfide deposits were classed correctly, 92% of 25 porphyry copper deposits, 78% of 9 Comstock epithermal gold-silver districts, and 83% of six quartzalunite epithermal gold deposits were classed correctly. Across all deposit types, 88% of deposits in the validation dataset were correctly classed. Misclassifications were most common if a deposit was characterized by only a few minerals, e.g., pyrite, chalcopyrite,and sphalerite. The success rate jumped to 98% correctly classed deposits when just two rock types were added. Such a high success rate of the probabilistic neural network suggests that not only should this preliminary test be expanded to include other deposit types, but that other deposit features should be added     

On the use of the autocorrelation method for estimating the depth to the magnetic basement

The depth to the top of magnetic dykes can be estimated from total field aeromagnetic data using the relation between the depth to magnetic sources and the autocorrelation function of magnetic data. By using synthetic anomalies we show that in the ideal case, depth can be determined to an accuracy of 10% or better, when the anomaly sources are two-dimensional dykes. However, the estimated depths depend on the width of the dykes. The estimated depth is about 0.6 times the actual depth to the top of thin dykes, and around the true depth for thick dykes having width-to-depth ratio around 3. The depth is considerably overestimated for very thick dykes (e.g., contacts, which is a special case of the thick dyke). Thus, the autocorrelation method requires that the width-to-depth ratio of the dyke is estimated independently to correctly estimate the depths. Alternatively, it must be assumed that the width-to-depth ratio for the two-dimensional source body is between 1.5 and 4.     

矿床三维地质建模及储量估算——以丰村铅锌矿径口矿段为例

钦杭成矿带是我国最重要的成矿带之一,丰村铅锌矿位于钦杭成矿带南段庞西垌地区,是区内典型的沉积热液改造型矿床。依据现代成矿预测理论,结合已有的地形地质图、勘探线剖面图、钻孔数据等资料,运用三维地质建模技术建立了丰村铅锌矿径口矿段的地表模型、断裂模型、地层实体模型、矿体模型。在此基础上分别应用地质块段法和块体模型统计法进行了矿体的资源储量估算,并将两种估算结果进行了对比分析。结果表明,块体模型统计法比地质块段法应用范围更广,效果更精准,可以清晰表达矿体品位值变化特征,进而加深对矿体、矿床的空间分布规律的认识。同时,利用多元信息进行三维地质建模,可靠性高,可视化效果好。将传统的二维地质拓展到三维空间进行定位与定量化,有助于分析矿区地质构造特点与矿床成因,是现代寻找矿床的一种有效手段。