露天采煤矿区土地的复垦：土壤透水性实验

露天采煤矿区土地的复垦：土壤透水性实验党志，万国江（中国科学院地球化学研究所，贵阳５５０００２）关键词露天采煤矿区，复垦，土壤透水性实验煤矿区土地复垦常用的方法是在矿渣表面覆盖一层约５～４０ｃｍ厚的土壤，种草植树以恢复植被。但在一些化学风化强烈的地区...     

SH型智能测厚仪

本文介绍了ＳＨ型智能测厚仪的应用范围、特点、工作原理、整体设计、各种校正及性能指标。     

全球地震台网（GSN）数字地震仪的传递函数

数字化记录不但提高了地震观测的动态范围和分辨率，也促进了资料利用的自动化，本文介绍了全球地震台网（ＧＳＮ）宽频带地震计、低通滤波器以及数字地震仪的幅频特性、相频特性的计算方法。     

常规电容传感器的改进及在盐湖卤水中的应用

本文采用一种疏水性材料，改变了电容传感器探头的表面特性，使其适用于察尔汗盐湖高浓度卤水的水位测量，对改进后的电容传感器性能，进行了较全面的系统测试，取得了令人满意的结果。     

西鄂尔多斯荒漠灌丛生态系统碳密度

为了估算西鄂尔多斯天然荒漠灌丛生态系统碳密度并揭示碳储量在不同层片（灌丛植株、草本层、枯落物层及土壤层）、器官间的分配规律,以该区5种优势荒漠灌丛（沙冬青Ammopiptanthus mogolicus、霸王Zygophyllum xanthoxylum、四合木Tetraena mongolica、半日花Helianthemum songaricum和红砂Reaumuria songarica）群落为对象,测定了5种灌丛生态系统碳密度。结果表明：西鄂尔多斯5种荒漠灌丛生态系统碳密度40.28~55.51 t·hm^-2,其中土壤层碳密度占绝对优势（97.15%~98.51%）,为39.40~54.48 t·hm^-2,且在0~50 cm随着土层深度的增加而增加;植被层生物量密度垂直分布格局表现为灌丛层 > 草本层 > 枯落物层,灌丛层碳密度空间上表现为距离黄河越近碳密度越大（沙冬青和半日花灌丛生物量碳分别占各自植被层生物量密度的92.16%和62.42%）,而草本层碳密度表现出与之相反的规律;草本层根系生物量碳也是灌丛生态系统碳重要组成部分,碳密度8.41~38.29 g·m^-2,占植被层碳密度的5.36%~45.18%;除红砂灌丛外,灌丛草本层地下部分碳密度显著高于地上部分（P<0.05）;灌丛个体碳储量分布表现为枝条 > 根系 > 叶片,粗枝和粗根是单株灌丛碳储量的主要贡献者,且在灌丛种间差异显著（P<0.05）,根系生物量碳占植被层碳储量的20.00%~33.53%,叶片生物量碳占总植被层碳储量的2.02%~24.54%。     

Characteristic Features of REE and Pb–Zn–Ag Mineralizations in the Na Son Deposit,Northeastern Vietnam

The Na Son deposit is a small‐scale Pb–ZnPb–Zn–Ag deposit in northeast Vietnam and consists of biotite–chlorite schist, reddish altered rocks, quartz veins and syenite. The biotite–chlorite schist is intruded by syenite. Reddish altered rocks occur as an alteration halo between the biotite–allanite‐bearing quartz veins and the biotite–chlorite schist. Allanite occurs in the biotite–allanite‐bearing quartz veins and in the proximal reddish altered rocks. Rare earth element (REE) fluorocarbonate minerals occur along fractures or at rim of allanite crystals. The later horizontal aggregates of sulfide veins and veinlets cut the earlier reddish altered rocks. The earlier Pb–Zn veins consist of a large amount of galena and lesser amounts of sphalerite, pyrite and molybdenite. The later Cu veins cutting the Pb–Zn veins include chalcopyrite and lesser amounts of tetrahedrite and pyrite. The occurrences of two‐phase H₂O–CO₂ fluid inclusions in quartz from biotite–allanite‐bearing quartz veins and REE‐bearing fluorocarbonate minerals in allanite suggest the presence of CO₂ and F in the hydrothermal fluid. The oxygen isotopic ratios of the reddish altered rocks, biotite–chlorite schist, and syenite range from +13.9 to +14.9 ‰, +11.5 to +13.3 ‰, and +10.1 to +11.6 ‰, respectively. Assuming an isotopic equilibrium between quartz (+14.6 to +15.8 ‰) and biotite (+8.6 ‰) in the biotite–allanite‐bearing quartz vein, formation temperature was estimated to be 400°C. At 400°C, δ¹⁸O values of the hydrothermal fluid in equilibrium with quartz and biotite range from +10.5 to +11.7 ‰. These δ¹⁸O values are consistent with fluid that is derived from metamorphism. Assuming an isotopic equilibrium between galena (+1.5 to +1.7 ‰) and chalcopyrite (+3.4 ‰), the formation temperature was estimated to be approximately 300°C. The formation temperature of the Na Son deposit decreased with the progress of mineralization. Based on the geological data, occurrence of REE‐bearing minerals and oxygen isotopic ratios, the REE mineralization is thought to result from interaction between biotite–chlorite schist and REE‐, CO₂‐ and F‐bearing metamorphic fluid at 400°C under a rock‐dominant condition.     

Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines,artificial neural networks,kernel logistic regression,and logistic model tree

Preparation of landslide susceptibility maps is considered as the first important step in landslide risk assessments, but these maps are accepted as an end product that can be used for land use planning. The main objective of this study is to explore some new state-of-the-art sophisticated machine learning techniques and introduce a framework for training and validation of shallow landslide susceptibility models by using the latest statistical methods. The Son La hydropower basin (Vietnam) was selected as a case study. First, a landslide inventory map was constructed using the historical landslide locations from two national projects in Vietnam. A total of 12 landslide conditioning factors were then constructed from various data sources. Landslide locations were randomly split into a ratio of 70:30 for training and validating the models. To choose the best subset of conditioning factors, predictive ability of the factors were assessed using the Information Gain Ratio with 10-fold cross-validation technique. Factors with null predictive ability were removed to optimize the models. Subsequently, five landslide models were built using support vector machines (SVM), multi-layer perceptron neural networks (MLP Neural Nets), radial basis function neural networks (RBF Neural Nets), kernel logistic regression (KLR), and logistic model trees (LMT). The resulting models were validated and compared using the receive operating characteristic (ROC), Kappa index, and several statistical evaluation measures. Additionally, Friedman and Wilcoxon signed-rank tests were applied to confirm significant statistical differences among the five machine learning models employed in this study. Overall, the MLP Neural Nets model has the highest prediction capability (90.2 %), followed by the SVM model (88.7 %) and the KLR model (87.9 %), the RBF Neural Nets model (87.1 %), and the LMT model (86.1 %). Results revealed that both the KLR and the LMT models showed promising methods for shallow landslide susceptibility mapping. The result from this study demonstrates the benefit of selecting the optimal machine learning techniques with proper conditioning selection method in shallow landslide susceptibility mapping.     

Mechanism of two rapid and long-runout landslides in the 16 April 2016 Kumamoto earthquake using a ring-shear apparatus and computer simulation (LS-RAPID)

Around hundred landslides were triggered by the Kumamoto earthquakes in April 2016, causing fatalities and serious damage to properties in Minamiaso village, Kumamoto Prefecture, Japan. The landslides included many rapid and long-runout landslides which were responsible for much of the damage. To understand the mechanism of these earthquake-triggered landslides, we carried out field investigations with an unmanned aerial vehicle to obtain DSM and took samples from two major landslides (Takanodai landslide and Aso-ohashi landslide) to measure parameters of the initiation and the motion of landslides. A series of ring-shear tests and computer simulations were conducted using a measured Kumamoto earthquake acceleration record from KNet station KMM005, 10 km west of Aso-ohashi landslide. The research results supported our assumed mechanism of sliding-surface liquefaction for the rapid and long-runout motion of these landslides.     

北京居民生活满意度的多层级定序因变量模型分析

传统经济学视角出发的个体生活满意度研究往往忽略了数据的嵌套性,多层级模型更适用于地理学中分析不同层级地理单元特征对个体生活满意度的影响。采用多层级定序因变量模型,以北京为例,研究居民生活满意度在城市内部街道和居住小区尺度内的异质性,并研究了高层级地理空间单元的属性特征对居民生活满意度的影响。结果表明,生活满意度在居住小区层级的异质性远高于街道层级,个体自身的社会经济属性对个体的生活满意度有显著影响,总的来说,高收入、老年人、北京户口居民的生活满意度更高。除个体属性特征以外,居住小区的类型是影响生活满意度的重要因素,商品房小区居民的满意度显著高于保障房和单位房小区居民。     

Assessment of the impacts of climate change and brackish irrigation water on rice productivity and evaluation of adaptation measures in Ca Mau province,Vietnam

This paper presents applications of the peaks-over-threshold methodology for both the univariate and the recently introduced bivariate case, combined with a novel bootstrap approach. We compare the proposed bootstrap methods to the more traditional profile likelihood. We have investigated 63 years of the European Climate Assessment daily precipitation data for five Hungarian grid points, first separately for the summer and winter months, then aiming at the detection of possible changes by investigating 20 years moving windows. We show that significant changes can be observed both in the univariate and the bivariate cases, the most recent period being the most dangerous in several cases, as some return values have increased substantially. We illustrate these effects by bivariate coverage regions.