导纳函数的中国南海海底地形模型

针对目前大面积海域依然存在海深测量数据空白的问题,该文选取中国南海4°×4°(12~16°N,115~119°E)海域范围为研究区域,通过频率域上的相关性分析,得到重力异常在30~130km波长范围与研究海域海底地形的相关程度较高。以ETOPO1海深模型作为先验模型,应用导纳函数实现了在无船测数据情况下对海深的反演,最终得到1′×1′的海深模型。将反演结果与检核点进行比较发现,模型1在2 500m以上海域相对误差较小,反演精度较高;2 500m以下海域相对误差变化大,反演精度较低。无船测数据环境下,适当加入一定数量船测海深值作为控制点得到的海深模型2,相较于未加控制点的模型1,在1 000m以下水深处的标准差明显优于模型1,与检核点的差值精度最大提高了45%左右。     

校正地基摄影获取树枝倾角

针对传统树枝倾角测量方法的复杂性和精度低的问题,该文提出了通过校正地基摄影获取树枝倾角的方法。基于投影变换和小孔成像原理,通过自制树枝模型的方法,分析了直接图上量取树枝倾角与真实值之间误差的成因,提出了基于投影变化的树枝倾角计算公式。结果表明:该研究获取树枝倾角的相对误差2.72%明显优于传统测量的31.33%,同时随拍摄距离变化,树枝倾角估算结果具有稳定性。该研究方法简单、有效,非常适合复杂环境下树枝倾角的大量提取,并提出了一种基于摄影成像原理获取树枝倾角的方法,发展了树枝倾角测量系统软件,提高测量精度并降低了测量难度。     

Mapping hourly dynamics of urban population using trajectories reconstructed from mobile phone records

Understanding the spatiotemporal dynamics of urban population is crucial for addressing a wide range of urban planning and management issues. Aggregated geospatial big data have been widely used to quantitatively estimate population distribution at fine spatial scales over a given time period. However, it is still a challenge to estimate population density at a fine temporal resolution over a large geographical space, mainly due to the temporal asynchrony of population movement and the challenges to acquiring a complete individual movement record. In this article, we propose a method to estimate hourly population density by examining the time‐series individual trajectories, which were reconstructed from call detail records using BP neural networks. We first used BP neural networks to predict the positions of mobile phone users at an hourly interval and then estimated the hourly population density using log‐linear regression at the cell tower level. The estimated population density is linearly correlated with population census data at the sub‐district level. Trajectory clustering results show five distinct diurnal dynamic patterns of population movement in the study area, revealing spatially explicit characteristics of the diurnal commuting flows, though the driving forces of the flows need further investigation.     

针对星敏感器非线性畸变的非参数化在轨检校方法

为了对星敏感器的非线性畸变进行在轨检校,本文提出了一种非参数化的检校方法,该方法以B样条格网的形式表达非线性畸变,不需要预先估计一个参数化的畸变模型,从而可以处理任何形式的畸变。针对星敏感器不同模式畸变的仿真实验表明,本文提出的方法有效、可靠并且具有鲁棒性。     

基于小波变换与ARMA模型的建筑物沉降预报

对建筑物进行沉降监测并预报其变化趋势,能有效保障建筑物的安全性。本文提出一种基于小波变换的ARMA模型用于建筑物沉降预报。利用小波多尺度分析将沉降监测数据分解为高频信号和低频信号,并分别采用ARMA模型进行预测,然后将各序列预测结果进行合成,得到最终预测结果。并以青岛市某高层建筑物监测数据为例,分别采用传统ARMA模型以及基于小波变换的ARMA模型进行预报对比分析,结果表明基于小波变换的ARMA模型取得了较高的预报精度。     

渤南洼陷沙四下亚段沉积时期古气候演化

正全球于古近纪发育PETM、ETM2、EECO、和MECO多个短期热气候事件(雷华蕊等,2018)。受热气候事件影响,渤南洼陷沙四下亚段沉积时期红层沉积发育并记录着热气候事件控制下古气候演化过程。立足于渤南洼陷沙四下亚段红层,恢复古气候演化过程,一方面可以点带面,揭示全球古近纪气候演化;另一方面为研究极端气候条件对湖盆沉积体系的控制作用奠定基础。     

东营凹陷沙河街组碳酸盐胶结物成因机制

正碳酸盐矿物是砂岩储层中主要的成岩产物之一。碳酸盐胶结物较为活跃,其溶蚀及沉淀广泛发育于砂岩储层内。碳酸盐的溶蚀及沉淀是砂岩储层改造的重要成因之一。明确碳酸盐胶结物的成因机制对预测深部储层具有重要的意义。本次研究,采用铸体薄片及扫描电镜观察,明确碳酸盐胶结物的产状。对不同产状碳酸盐胶结物的分布、     

塔里木盆地周缘新元古代冰期事件与古老烃源岩发育

正四川盆地震旦系含油气系统已被证实为我国新元古界第一个具有商业价值的原生型气藏,而与扬子板块在同时期具有亲缘性的塔里木盆地新元古界油气资源一直未取得突破。塔里木盆地新元古界埋藏较深,钻遇南华系-震旦系的井位非常少,盆地深部层系的成像品质较差,同时新元古界本身缺乏可进行有效对比的古生物化石。诸多因素造成塔里木盆地新元古界划分与对比关系不清,地层格架模糊,直接导致后续深入研究无法进行。然而,     

Study on Deformation Failure Mechanism and Control Technology of Surrounding Rock in Soft Rock Roadway

Geotechnical and Geological Engineering - In order to reveal the mechanism of surrounding rock deformation and instability of soft rock roadway in 12# working face of Puhe coal mine, and...     

Identifying urban–rural differences in social vulnerability to natural hazards: a case study of China

Identifying and analyzing the urban–rural differences of social vulnerability to natural hazards is imperative to ensure that urbanization develops in a way that lessens the impacts of disasters and generate building resilient livelihoods in China. Using data from the 2000 and 2010 population censuses, this study conducted an assessment of the social vulnerability index (SVI) by applying the projection pursuit cluster model. The temporal and spatial changes of social vulnerability in urban and rural areas were then examined during China’s rapid urbanization period. An index of urban–rural differences in social vulnerability (SVI_D) was derived, and the global and local Moran’s I of the SVI_D were calculated to assess the spatial variation and association between the urban and rural SVI. In order to fully determine the impacts of urbanization in relation to social vulnerability, a spatial autoregressive model and Bivariate Moran’s I between urbanization and SVI were both calculated. The urban and rural SVI both displayed a steadily decreasing trend from 2000 to 2010, although the urban SVI was always larger than the rural SVI in the same year. In 17.5% of the prefectures, the rural SVI was larger than the urban SVI in 2000, but was smaller than the urban SVI in 2010. About 12.6% of the urban areas in the prefectures became less vulnerable than rural areas over the study period, while in more than 51.73% of the prefectures the urban–rural SVI gap decreased over the same period. The SVI_D values in all prefectures had a significantly positive spatial autocorrelation and spatial clusters were apparent. Over time, social vulnerability to natural hazards at the prefecture-level displayed a gathering–scattering pattern across China. Though a regional variation of social vulnerability developed during China’s rapid urbanization, the overall trend was for a steady reduction in social vulnerability in both urban and rural areas.