渤海莱州湾沉积物REE与重金属污染特征及物源判别

基于莱州湾32个典型站位的表层沉积物样品,系统分析了稀土元素(REE)组成和分布特征以及重金属污染程度和污染来源;探讨了影响重金属和稀土元素分布的环境因素,并从重金属潜在源区、化学蚀变指数(CIA)和REE特征3个角度分析了莱州湾表层沉积物来源。研究表明,稀土元素总量自莱州湾东南部向北部递增,部分稀土元素（Eu、Gd和Tb）含量主要与细粒级的黏土矿物和有机碳含量相关,受化学风化的影响很小。研究区部分站位受到了Cd和Hg的污染,且主要是人类活动富集的;Cu、Pb、Zn可能源于地壳自然风化产物,其中Cu和Zn还与人类活动密切相关;Cr则受自然风化和人类活动的双重影响而富集;大部分重金属含量的累积还受湾内水动力的影响。沉积物源的判别结果显示,研究区CIA平均值为50.83,接近黄河沉积物的CIA值（50.9～59.7）,低于中国黄土;研究区稀土元素配分模式和特征参数也与黄河十分接近,表明黄河是莱州湾表层沉积物的重要来源;广利河与潍河-弥河三角洲仅对南部和西南部海域沉积物有贡献,而黄土和山东省土壤对莱州湾沉积物的贡献量相对更少。     

长江中下游6—7月降水的降尺度模型

基于偏相关的强迫因子选取方法,以长江中下游6—7月降水为例,进行了降水变率的归因分析,并建立了相应的统计降尺度模型。结果表明,影响长江中下游6—7月降水的强迫因子主要有两个:西太平洋850 h Pa的位势高度(W_(PH8))和黑潮延伸区的海表温度(K_(SST))。W_(PH8)反映的是西太平洋副热带高压对长江中下游降水的影响;K_(SST)反映了黑潮延伸区的变率。基于这两个因子的线性降尺度模型能较好地拟合长江中下游6—7月的降水,在独立检验和模式检验阶段,模型体现出了可靠性,因而可用于长江中下游降水的季节预测。     

逻辑回归与支持向量机模型在滑坡敏感性评价中的应用

白龙江流域是我国滑坡泥石流灾害四大高发区之一,进行该区域滑坡敏感性评价,能够为决策者在灾害管理和设施建设规划方面提供帮助,对区域防灾减灾具有重要指导意义。本研究采用边坡单元为基本研究单元,在野外调查及前人研究基础上,选择控制该区域滑坡发育的19个要素作为影响因子; 经过主成分分析和独立性检验得到该区域对滑坡形成贡献最大的6个因子：高程、坡度、坡向、岩性、断裂距离和人口密度; 分别使用二元逻辑回归模型（LR）和支持向量机模型（SVM）对该区域进行滑坡敏感性评价; 最后,采用ROC曲线对模型精度进行验证。研究结果表明,两模型各能将38.76%、14.48%、9.40%、11.28%、26.07%和13.49%、21.61%、8.17%、26.70%、30.04%的边坡单元分别预测为极高危险区、高危险区、中度危险区、低危险区和极低危险区; 精度验证结果表明两种模型均能有效地进行该区域滑坡敏感性评价,并且支持向量机模型具有更好的分类能力、预测精度和稳定性。     

沿海滩涂生态化开发适宜性评价及其在江苏大丰市的应用

基于我国沿海滩涂生态化开发的需求,构建了沿海滩涂开发评价指标体系,建立了沿海滩涂生态化开发适宜性评价方法。基于压力—状态—响应模型,选取体现滩涂自然生态特征、滩涂开发环境影响特征、滩涂生态保护特征的指标,形成沿海滩涂生态化开发适宜性评价指标体系;运用德尔菲法确定各评价指标的权重,在评价指标量化与标准化处理的基础上,计算综合评价指数,从而确定滩涂生态化开发的适宜程度。以江苏盐城大丰市沿海滩涂为例,对其进行生态化开发适宜性评价,结果表明,在滩涂开发中生态保护措施有力的前提下适宜开发。     

中国南方网纹红土元素地球化学特征及其对网纹化过程的指示意义*

中国南方网纹红土是在季风气候条件下形成的一种特殊地层,对重建南方第四纪气候环境具有特殊重要的意义,但长期以来对网纹化过程的物化特点缺乏足够的认识。对江西九江、彭泽与湖南岳阳3个剖面的黏土质网纹红土中白色网纹和红色基质分离后进行了地球化学元素、游离铁含量等测试,结果表明:(1)白色网纹中,发生了Fe、Mn、Th、Ga、Cu、Zn、Co、Ni、Cr、 V等元素的显著亏损,SiO₂、Zr、Hf及轻稀土元素的显著富集,游离铁含量较低;红色基质中则完全相反;(2)网纹中Fe²⁺含量较高,基质中含量较低,揭示了网纹倾向于还原环境,基质倾向于氧化环境。研究认为网纹和基质这种氧化还原差异,是Fe、Mn、Co、Ni、Cr、V、Zn、Th、Ga、Cu等元素在网纹中游离淋失,并在基质中的沉淀富集,造成Zr、SiO₂及轻稀土等在网纹中的相对富集、在基质中“稀释”而呈现亏损的主要原因。     

大岗山水库地震监测系统建设及监测能力概估算和验证

本文阐述了大岗山水电站水库诱发地震监测系统建设和监测能力概况,并利用运行后的监测地震目录,分析了近年来库区附近地震活动的时空分布特征。     

Snow avalanche susceptibility evaluation in the central Shaluli Mountains of Tibetan Plateau based on machine learning method北大核心CSCD

Snow avalanches,which are widely and frequently developed at high elevations,seriously threatens the built traffic corridors in the Tibetan Plateau. Susceptibility evaluation of snow avalanche via machine learning model with a high forecast accuracy can be appled to quickly and effectively assess the regional avalanche risk. This paper took the central Shaluli Mountain region as the study area,in which the snow avalanche inventory was established through remote sensing interpretation and field investigation verification. We quantitatively extracted 17 evaluation factors via GIS-based analysis,and these factors were selected through the variance expansion factor（VIF）. Four machine learning models containing SVM,DT,MLP and KNN were used to compile the susceptibility index map of snow avalanches,and kappa coefficient and ROC curve were used to verify the accuracy. The results suggested that the susceptibility indexes obtained from SVM,DT,MLP and KNN were in the range of［0,0. 964］,［0,815］,［0,0. 995］and［0,1］,respectively. The accuracy test results show that these four models all have good prediction accuracy. Among them,the SVM model is the best. The results also indicated that the areas with the high snow avalanche susceptibility mainly distributed in Genie Mountain and Rigong Mountain,most of which were above the planation surface of the Tibetan Plateau. The average altitude of the extremely high snow-avalanche-prone areas is 4 939 m,while the average altitude of the high snow avalanche-prone areas is 4 859 m. The snow avalanche has low perniciousness on the Sichuan-Tibet Highway and the Sichuan-Tibet Railway in the study area. This study can provide theoretical basis and method reference for disaster prevention and mitigation of snow avalanche along Sichuan-Tibet Railway and other major projects across Shaluli Mountains region. © 2022 Science Press (China).     

川藏铁路西藏昌都段生态保护重要性评价

生态系统服务功能与生态系统敏感性是衡量生态系统质量及实现高质量生态文明建设的重要依据。川藏铁路沿途跨越多个自然地理单元,生态环境保护是铁路规划、建设及运行过程中面临的关键问题。以川藏铁路西藏昌都段为研究对象,采取资料分析与野外调查结合的手段,运用生态评价模型对研究区的生态系统服务和生态脆弱性进行分析,并基于研究结果对铁路途经区域的生态保护重要性进行评价。结果表明,当前昌都境内川藏铁路涉及的生态保护极重要区、重要区和一般重要区占比分别为42.19%、52.69% 和5.12%。评价结果有助于在铁路规划、建设及运营过程中对工程活动进行合理布局,有针对性地降低对生态系统的负面影响,达到社会经济效益和生态效益双赢的目的。     

Tomographic imaging of high seismic activities in underground island longwall face

Anomalous information identification is a key issue for seismic hazard prevention in underground mining. Velocity tomograms can image the stress redistribution around coal face and provide better understanding of strata failure mechanisms. In this paper, based on microseismic events recorded during mining operation, passive tomographic imagings have been presented to assess strong tremor hazard and locate high seismic activity zones around an island coal face under super-thick strata. The zones of high velocity or velocity gradient anomalies have been found to correlate well with the distribution of strong tremors, indicating that velocity tomography is feasible for seismic hazard assessment and risk region division in underground mining.