综放工作面上覆老空积水探放技术

杨村煤矿D13171综放工作面上覆的中13201工作面采空区存有大量的老空水,给工作面的回采带来了极大的危害。分析认为,老空水的主要来源一是13201工作面上部的新近系及古近系泥灰含水层的下渗补给,二是工作面回采后,为防止采空区煤层自燃,向老空区注入的灭火灌浆水;估算老空水的水量约为16000m3。利用ZYG-650型矿用钻机对采空区积水进行了探放,探放水结果显示,所施工的4个钻孔的累计放水量为14800m3,工作面回采期间两个验证孔已探到老空区积水的最低位置,钻孔无流水,说明老空水已彻底排干,从而解除了对工作面的威胁,保证了矿井的安全高效生产。     

完善上海市海洋倾废管理的对策研究

文章分析上海市海洋倾废管理在法制建设、资源化利用、倾倒活动控制、采样检测和评价标准、审批效率以及倾废监管机制等方面存在的问题,针对问题提出加快推进海洋倾废管理法制建设、引导促进疏浚泥的资源化利用、科学调控海洋倾废活动、制定新的疏浚物采样和成分检测评价标准、通过审批标准化提高审批效率、建立海洋倾废综合监管机制6个方面的对策措施,从管理、审批和执法3个环节对海洋倾废活动实施全方位、全覆盖和全过程监管,为上海市海洋环境综合管理提供保障。     

菲律宾海盆磁条带特征及61 Ma以来的海底扩张过程重建

菲律宾海盆是西太平洋最大的边缘海盆地,作为地球上最壮观的“沟-弧-盆”体系的重要组成部分,其成因及构造演化可有效约束西太平洋洋陆过渡带的复杂动力学过程。海底磁异常条带是海底扩张的重要依据,其几何形态能够反映海底扩张的时间、方向及速率,为研究海盆的海底扩张过程提供重要信息。菲律宾海盆的次级海盆—西菲律宾海盆、四国海盆地磁异常表现为明显的条带状异常特征,帕里西维拉海盆内的磁条带特征虽不明显,但仍能看出南北向的分带现象。对海盆内的磁异常条带进行系统的分析、对比与解释,将菲律宾海盆划分为7个扩张阶段,构建了菲律宾海盆61 Ma以来的阶段性扩张模型。揭示了边缘海盆构造演化的一般规律及扩张过程为岛弧裂解、高速弧后扩张、慢速弧后扩张和扩张后作用4个阶段。     

水系结构遥感影像特征对自然灾害的指示意义研究：以陕西省为例

以ＭＳＳ影像、ＴＭ 影像和部分航片为基本信息源, 并结合一些相关资料, 以陕西省为例, 对水系结构进行分析研究, 据其影像特征, 将陕西省水系分为陕北型、秦岭型和关中型。并进一步探讨了水系结构的影像特征对自然灾害判读、解译的重要指示意义, 并且, 利用水系结构的遥感影像特征, 有助于找出自然灾害发生发展的规律, 采取合理治理措施, 以达到防灾减灾的目的。     

云南普朗斑岩型铜多金属矿床气液隐爆角砾岩的发现及其找矿意义

云南普朗铜多金属矿是格咱岛弧内一个典型的斑岩型矿床,尽管前人对该矿床开展了若干研究,但对于该矿床是否存在叠加成矿作用仍存在争议。研究团队在普朗典型矿床调研过程中,在普朗I号探矿平硐中首次发现了气液隐爆角砾岩。详细的岩相学及矿相学研究表明,隐爆角砾岩角砾为含矿石英二长斑岩,胶结物为细粒石英,且细粒石英中含有黄铜矿连晶。根据气液隐爆角砾岩产出位置及特征推断,隐爆作用系石英二长斑岩就位之后的岩浆活动引起,并伴随成矿作用发生。普朗斑岩型铜多金属矿成矿作用由卡尼阶与诺利阶两个成矿阶段构成,其中铜成矿作用跨越了卡尼阶和诺利阶,而钼成矿始于诺利阶。综合分析指出,普朗矿区深部具有斑岩型钼(铜)多金属矿的找矿潜力。     

基于DEM 的数字降水径流模型在黄河小花间的应用

DEM是目前用于流域地形分析的主要数据，在流域地形分析及水质构建等方面形成了比较成熟的算法，基于DEM的水文模拟技术的应用给传统的水文模拟方法带来了根本性的变化，基于栅格型DEM，应用最新引进的WMS专业水文处理软件，结合Arc/info、Areview地理信息系统工具，以黄河小花间（小浪底-花园口区间）卢氏以上流域作为研究区，进行了数字降水径流模型的应用研究。     

Vegetation dynamics in Qinling-Daba Mountains in relation to climate factors between 2000 and 2014

Using the Moderate Resolution Imaging Spectroradiometer-normalized difference vegetation index (NDVI) dataset, we investigated the patterns of spatiotemporal variation in vegetation coverage and its associated driving forces in the Qinling-Daba (Qinba) Mountains in 2000–2014. The Sen and Mann–Kendall models and partial correlation analysis were used to analyze the data, followed by calculation of the Hurst index to analyze future trends in vegetation coverage. The results of the study showed that (1) NDVI of the study area exhibited a significant increase in 2000–2014 (linear tendency, 2.8%/10a). During this period, a stable increase was detected before 2010 (linear tendency, 4.32%/10a), followed by a sharp decline after 2010 (linear tendency,–6.59%/10a). (2) Spatially, vegetation cover showed a “high in the middle and a low in the surroundings” pattern. High values of vegetation coverage were mainly found in the Qinba Mountains of Shaanxi Province. (3) The area with improved vegetation coverage was larger than the degraded area, being 81.32% and 18.68%, respectively, during the study period. Piecewise analysis revealed that 71.61% of the total study area showed a decreasing trend in vegetation coverage in 2010–2014. (4) Reverse characteristics of vegetation coverage change were stronger than the same characteristics on the Qinba Mountains. About 46.89% of the entire study area is predicted to decrease in the future, while 34.44% of the total area will follow a continuously increasing trend. (5) The change of vegetation coverage was mainly attributed to the deficit in precipitation. Moreover, vegetation coverage during La Nina years was higher than that during El Nino years. (6) Human activities can induce ambiguous effects on vegetation coverage: both positive effects (through implementation of ecological restoration projects) and negative effects (through urbanization) were observed.     

Detecting the dynamics of urban structure through spatial network analysis

Urban spatial structure in large cities is becoming ever more complex as populations grow in size, engage in more travel, and have increasing amounts of disposable income that enable them to live more diverse lifestyles. These trends have prominent and visible effects on urban activity, and cities are becoming more polycentric in their structure as new clusters and hotspots emerge and coalesce in a wider sea of urban development. Here, we apply recent methods in network science and their generalization to spatial analysis to identify the spatial structure of city hubs, centers, and borders, which are essential elements in understanding urban interactions. We use a ‘big’ data set for Singapore from the automatic smart card fare collection system, which is available for sample periods in 2010, 2011, and 2012 to show how the changing roles and influences of local areas in the overall spatial structure of urban movement can be efficiently monitored from daily transportation.

In essence, we first construct a weighted directed graph from these travel records. Each node in the graph denotes an urban area, edges denote the possibility of travel between any two areas, and the weight of edges denotes the volume of travel, which is the number of trips made. We then make use of (a) the graph properties to obtain an overall view of travel demand, (b) graph centralities for detecting urban centers and hubs, and (c) graph community structures for uncovering socioeconomic clusters defined as neighborhoods and their borders. Finally, results of this network analysis are projected back onto geographical space to reveal the spatial structure of urban movements. The revealed community structure shows a clear subdivision into different areas that separate the population’s activity space into smaller neighborhoods. The generated borders are different from existing administrative ones. By comparing the results from 3 years of data, we find that Singapore, even from such a short time series, is developing rapidly towards a polycentric urban form, where new subcenters and communities are emerging largely in line with the city’s master plan.

To summarize, our approach yields important insights into urban phenomena generated by human movements. It represents a quantitative approach to urban analysis, which explicitly identifies ongoing urban transformations.     

1961—2020年青海高原日照时数时空变化特征

太阳辐射是地球系统的主要能源,与人类的生活密切相关。通过选取青海高原50个气象观测站点1961—2020年逐月日照时数数据,分析了青海高原整体、不同纬度地区、不同海拔高度地区日照时数时空变化特征。结果表明：（1）1961—2020年青海高原年日照时数呈显著降低趋势,且在2004年发生突变降低。从空间分布来看,除南部个别站点日照时数持平或略微增加外,其余地区年日照时数均呈显著降低趋势,其中柴达木地区和东部农业区降低趋势最显著。（2）青海高原高纬度地区年日照时数降低趋势显著大于低纬度地区。春季不同纬度地区日照时数变化趋势均较小,夏季和冬季纬度相对较高地区日照时数减少趋势显著大于纬度较低地区,秋季纬度较低和纬度较高地区日照时数减少趋势显著大于中纬度地区。（3）青海高原海拔相对较低地区年日照时数降低趋势显著大于海拔相对较高地区。春季不同海拔高度地区日照时数变化趋势均较小,基本持平或略微减少,夏季和冬季海拔相对较低地区日照时数减少趋势显著大于海拔较高地区,秋季不同海拔高度日照时数均呈减少趋势,但是减少趋势显著性状况差异较大。     

1970-2015年秦岭南北气温时空变化及其气候分界意义

基于秦岭南北70个气象站点观测资料,辅以极点对称模态分解方法（ESMD）,对秦岭南北近期气温时空变化特征进行分析,进而以日平均温≥ 10 ℃积温天数为主要指标,以1月0 ℃等温线变化为辅助指标,探讨秦岭山脉的气候分界意义。结果表明：① 1970-2015年秦岭南北气温变化具有同步性,呈现出“非平稳、非线性、阶梯状”的增暖过程,变化阶段可分为：1970-1993年为低位波动期、1994-2002年为快速上升期、2003-2015年为增温停滞期;② ESMD信息分解结果表明,秦岭南北气温变化以年际波动为主导,并未呈现出明显的线性增暖趋势;③ 在空间上,秦岭南北气温趋势呈现“同步增温,南北分异”的响应特征,即秦岭以北地区空间增温具有一致性,秦岭以南地区则呈现“西乡—安康盆地交界”、“商丹盆地”两个低值中心;④ 在气候变暖背景下,秦岭作为气候分界线的作用依然明显,但是南北响应方式存在差异。其中,秦岭以南,北亚热带北界沿山地“垂直上升”,汉江谷地热量资源逐年增加;秦岭以北,尽管以城市带为中心的增温区不断延展,但是冷月气温偏低的格局并未改变。