The use of digital technologies for landslide disaster risk research and disaster risk management: progress and prospects

Landslide is a serious natural disaster next only to earthquake and flood, which will cause a great threat to people’s lives and property safety. The traditional research of landslide disaster based on experience-driven or statistical model and its assessment results are subjective , difficult to quantify, and no pertinence. As a new research method for landslide susceptibility assessment, machine learning can greatly improve the landslide susceptibility model’s accuracy by constructing statistical models. Taking Western Henan for example, the study selected 16 landslide influencing factors such as topography, geological environment, hydrological conditions, and human activities, and 11 landslide factors with the most significant influence on the landslide were selected by the recursive feature elimination (RFE) method. Five machine learning methods [Support Vector Machines (SVM), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Linear Discriminant Analysis (LDA)] were used to construct the spatial distribution model of landslide susceptibility. The models were evaluated by the receiver operating characteristic curve and statistical index. After analysis and comparison, the XGBoost model (AUC 0.8759) performed the best and was suitable for dealing with regression problems. The model had a high adaptability to landslide data. According to the landslide susceptibility map of the five models, the overall distribution can be observed. The extremely high and high susceptibility areas are distributed in the Funiu Mountain range in the southwest, the Xiaoshan Mountain range in the west, and the Yellow River Basin in the north. These areas have large terrain fluctuations, complicated geological structural environments and frequent human engineering activities. The extremely high and highly prone areas were 12043.3 km² and 3087.45 km², accounting for 47.61% and 12.20% of the total area of the study area, respectively. Our study reflects the distribution of landslide susceptibility in western Henan Province, which provides a scientific basis for regional disaster warning, prediction, and resource protection. The study has important practical significance for subsequent landslide disaster management.     

梅雨锋上的三类暴雨

利用常规气象观测资料、区域自动站资料、FY-2C云顶亮温（T_BB）资料及NCEP 1°×1°再分析资料,对2015年6月17-18日发生在黔东南地区的典型梅雨锋西段暴雨进行了诊断分析。结果表明（：1）在500 hPa两槽一脊单阻型梅雨形势下,冷空气沿贝加尔湖阻塞高压东侧南下与来自南海、孟加拉湾的暖湿气流在黔东南交汇,500 hPa短波槽东移促使低空切变线东移南压和地面梅雨锋发展,配合200 hPa南亚高压东部脊附近的\     

柴西开特米里克地区干柴沟组米氏旋回及湖平面变化特征

目前,柴达木盆地米兰科维奇旋回研究普遍基于山前浅水沉积相地层进行讨论,而深水相地层中是否有记录米兰科维奇旋回特征有待探讨。本次研究通过对开2井上干柴沟组（N₁）和下干柴沟组上段（E₃²）自然伽马测井曲线进行频谱分析和滤波分析,探讨了盆地西部开特米里克地区深湖相地层的米兰科维奇沉积旋回特征。结果显示,该地区上干柴沟组（N₁）和下干柴沟组（E₃²）地层旋回周期与米兰科维奇地球轨道周期参数具有良好的对应性,说明天文轨道周期旋回对该地区地层沉积具有显著影响。在此基础上,结合Fischer图解及地层岩性特征,确定了干柴沟组上、下界线附近（约38.1~32.8 Ma）湖平面经历了一次相对长周期的升降变化。下干柴沟组上段处于湖平面上升阶段,主要受米氏旋回中的偏心率周期控制,气候相对温暖湿润,对应湖平面高位期。上干柴沟组处于湖平面下降阶段,主要受米氏旋回中的轴斜率周期控制,气候相对寒冷干旱,对应湖平面低位期。

     

西藏狮泉河地区高钾-钾玄质火山岩的岩石学、地球化学及锆石U-Pb年龄

报道的高钾-钾玄质火山岩位于狮泉河镇南东方向约20km处,向东延伸。高钾-钾玄质火山岩SiO2变化于60.35%~68.68%之间,属中酸性岩范畴;具有高的K2O+Na2O含量（8.8%~10.66%）,K2O/Na2O值在1.92~2.49之间,MgO含量较低,介于0.88%~3.47%之间,Al2O3含量为14.02%~14.91%,属于高钾-钾玄质系列。岩石强烈富集大离子亲石元素（LILE）Rb、Ba、Th、U和轻稀土元素（LREE）,高场强元素（HFSE）Nb、Ta、Ti具有明显负异常,Cr、Ni、Co相容元素含量低于或接近地壳的平均含量,结合Th/Yb-Ta/Yb、（Th×100）/Zr-（Nb×100）/Zr判别图及La-La/Yb图解,暗示岩浆源区可能为下地壳。在左左乡南东约2km处和狮泉河水泥厂北东约1km处各采集1个高钾-钾玄质火山岩样品,对其中的锆石进行LA-ICP-MS U-Pb同位素测定,得到的206Pb/238U年龄加权平均值分别为22.04±0.42Ma和22.29±0.31Ma,此年龄被解释为狮泉河一带高钾-钾玄质火山岩的喷发时代,即中新世阿启塔期。由此表明,该火山岩是印度板片向北俯冲时在狮泉河一带俯冲板片断离,岩浆发生部分熔融的产物。     

北祁连山银灿铜矿矿床地质特征及成因

银灿铜矿地处于北祁连走廊南山北坡—冷龙岭岛弧带中,矿区出露一套具双峰式火山岩特征的火山沉积建造,矿体主要赋存在石英角斑岩中并存在明显分带,上部为铜矿体向下部变为锌矿体,矿石类型属黄铁矿型多金属矿石,成矿作用过程直接或间接与火山活动有关,是北祁连地区早古生代典型的海相火山岩型块状硫化物矿床。     

广东科学中心E区隔震支座的优化布置分析

广东科学中心E区结构体型复杂,刚度和质量在竖向分布不均匀,导致结构扭转效应比较明显.本文结合结构设计提出了五种隔震控制方案.利用SAP2000有限元分析软件,对采用不同控制方案的结构进行了模态分析和时程分析,对比分析了不同方案的控制效果.分析结果表明,采用隔震技术不仅大大降低结构的地震反应,也使结构的扭转效应得到有效的控制.依据分析结果对该工程隔震支座的布置提出建议.     

非差导航卫星实时/事后精密钟差估计

研究了导航卫星精密钟差的估计算法,实现了基于非差载波相位观测值的实时和事后精密卫星钟差的解算,并与IGS分析中心提供的精密钟差产品进行了比较。结果表明,采用自编软件解算的事后精密卫星钟差与IGS最终精密钟差产品具有较好的一致性,其互差仅为0.05ns左右;实时估计结果与CODE提供的事后钟差产品符合较好,二者差异为0.1ns左右。     

THE EFFECT OF DRAINAGE ON CHEMICALELEMENTS CONTENT OF MARSH

I INTRODUCTIONWetland has very important effect on retainingwater and against drought, regUlating climate, controlling soil erosion and so on ac. Today men often attachimportance to economic effect of wetland reclamation,but neglect and destroy other functions and effectswhich result in blind wetland development, a lot ofwetland degeneration. The area of freshwater wetland inthe 1950s was 134 x 10'ha, but in the 1970s 70% hadchanged into farmland (MITSCH, 1993). The area ofwetland in d…     

Distributed estimation and analysis of precipitation recharge coefficient in strongly-exploited Beijing plain area,China

The precipitation recharge coefficient(PRC), representing the amount of groundwater recharge from precipitation, is an important parameter for groundwater resources evaluation and numerical simulation. It was usually obtained from empirical knowledge and site experiments in the 1980 s. However, the environmental settings have been greatly modified from that time due to land use change and groundwater over-pumping, especially in the Beijing plain area(BPA). This paper aims to estimate and analyze PRC of BPA with the distributed hydrological model and GIS for the year 2011 with similar annual precipitation as long-term mean. It is found that the recharge from vertical(precipitation + irrigation) and precipitation is 291.0 mm/yr and 233.7 mm/yr, respectively, which accounts for 38.6% and 36.6% of corresponding input water. The regional mean PRC is 0.366, which is a little different from the traditional map. However, it has a spatial variation ranging from –7.0% to 17.5% for various sub-regions. Since the vadose zone is now much thicker than the evaporation extinction depth, the land cover is regarded as the major dynamic factor that causes the variation of PRC in this area due to the difference of evapotranspiration rates. It is suggested that the negative impact of reforestation on groundwater quantity within BPA should be well investigated, because the PRC beneath forestland is the smallest among all land cover types.     

基于GNSS和SLR观测数据的北斗卫星(GEO/IGSO)精密定轨

研究了联合GNSS和卫星激光测距（satellite laser ranging,SLR）观测数据对北斗卫星定轨精度的影响。仅利用GNSS观测值对北斗卫星进行定轨时,C01、C08和C10卫星的SLR检核结果显示,这3颗卫星的SLR残差均存在一定的偏差且不相同。加入SLR观测值联合定轨时,分别对估计和不估计系统偏差的卫星轨道结果进行分析。结果显示,对C01（GEO）卫星加入SLR观测值时必须引入系统偏差,否则会使轨道偏离;C08和C10（IGSO）卫星估计和不估计系统偏差时对预报轨道的精度均有一定的改善。最后统计了这3颗卫星的系统偏差,C01卫星的系统偏差达到50 cm,C08和C10卫星的系统偏差在5 cm以内。