西藏加查拉岗村巨型古滑坡发育特征与形成机理研究

青藏高原地质构造活跃,内外动力作用强烈,加之气候异常变化,区内大型滑坡发育。以雅鲁藏布江断裂附近新发现的拉岗村古滑坡为研究对象,在现场调查、槽探揭露、地质测年和工程地质分析等基础上,对其发育特征及成因机制进行了分析研究。研究表明,(1)拉岗村滑坡属巨型岩质滑坡,体积达3.6×107 m3,最大水平滑动距离约3050m,滑坡后壁与堆积体前缘高差达965m,最大运动速率达78.1m/s,具明显高速远程特征;(2)受冷冻风化和冰体"楔劈"作用影响,滑坡后部岩体崩裂,全新世以来气候变化冰川逐渐消退,融雪降水入渗加剧劣化岩体结构,降低岩体强度;(3)根据14 C和10Be测年结果,拉岗村古滑坡形成于距今4140~9675a,沿雅鲁藏布江断裂发生的强震可能是该滑坡的直接诱因,岩体受到地震抛掷力作用,原有节理裂隙和新生破裂面发生张剪-拉裂破坏迅速贯通,首先沿断裂附近碎裂结构岩体发生破坏,上部岩体随之失稳并高速下滑。该研究可为认识青藏高原断裂带内大型古滑坡的形成机理提供借鉴。     

Revisiting the death of geography in the era of Big Data: the friction of distance in cyberspace and real space

Many scholars have argued that the importance of geographic proximity in human interactions has been diminished by the use of the Internet, while others disagree with this argument. Studies have noted the distance decay effect in both cyberspace and real space, showing that interactions occur with an inverse relationship between the number of interactions and the distance between the locations of the interactors. However, these studies rarely provide strong evidence to show the influence of distance on interactions in cyberspace, nor do they quantify the differences in the amount of friction of distance between cyberspace and real space. To fill this gap, this study used massive amounts of social media data (Twitter) to compare the influence of distance decay on human interactions between cyberspace and real space in a quantitative manner. To estimate the distance decay effect in both cyberspace and real space, the distance decay function of interactions in each space was modeled. Estimating the distance decay in cyberspace in this study can help predict the degree of information flow across space through social media. Measuring how far ideas can be diffused through social media is useful for users of location-based services, policy advocates, public health officials, and political campaigners.     

A model for integrated spatial land use characteristics linking to surface nutrient concentration

Quantifying land use heterogeneity helps better understand how it influences biophysical systems. Land use area proportions have been used conventionally to predict water quality variables. Lacking an insight into the combined effect of various spatial characteristics could lead to the statistical bias and confused understanding in previous studies. In this study, using spatial techniques and mathematical models, a diagnostic model was developed and applied for quantifying and incorporating three spatial components, namely, slope, distance to sampling spots, and arrangement. The upper catchment of Miyun Reservoir was studied as the test area. Total nitrogen, total phosphorus, and chemical oxygen demand of water samples from field measurements were used to characterize the surface water quality in 52 sub-watersheds. Using parameter calibrations and determinations, combined spatial characteristics were explored and detected. Adjusted land use proportions were calculated by spatial weights of discriminating the relative contribution of each location to water quality and used to build the integrated models. Compared with traditional methods only using area proportions, our model increased the explanatory power of land use and quantified the effects of spatial information on water quality. This can guide the optimization of land use configuration to control water eutrophication.     

TRANSIT: a GIS toolbox for estimating the duration of ancient sail-powered navigation

Cost-surface analysis in Geographic Information System (GIS) environment has been less frequently used in the study of ancient sail navigation than in other studies of the human past. Navigation cost-surface analysis entails the use of GIS tools that are versatile but not very easy to grasp and to put to work. This article describes an ArcGIS toolbox built to facilitate cost-surface analysis of ancient sail navigation. It estimates the navigation time from a start location, considering parameters relevant for the generation of an accumulated anisotropic cost-surface, automating the complex workflow required to meaningfully pre- and post-process the data. Acknowledging the limitations inherent to the tool, and to the modeling of a complex matter such as sail navigation, the toolbox is first described and then used in a worked example. Historically recorded voyages in the Mediterranean during classical antiquity are compared to estimated durations generated by the toolbox. In spite of structural and expected limitations, the results indicate that the proposed toolbox may produce reasonable estimates. These should be thought of as values gravitating around, not matching, likely past durations. The estimated values may prove useful as an indication of the order of magnitude of past voyages’ duration, and as frame of reference in measuring ancient maritime space through time.     

Supervised classification of electric power transmission line nominal voltage from high-resolution aerial imagery

For many researchers, government agencies, and emergency responders, access to the geospatial data of US electric power infrastructure is invaluable for analysis, planning, and disaster recovery. Historically, however, access to high quality geospatial energy data has been limited to few agencies because of commercial licenses restrictions, and those resources which are widely accessible have been of poor quality, particularly with respect to reliability. Recent efforts to develop a highly reliable and publicly accessible alternative to the existing datasets were met with numerous challenges – not the least of which was filling the gaps in power transmission line voltage ratings. To address the line voltage rating problem, we developed and tested a basic methodology that fuses knowledge and techniques from power systems, geography, and machine learning domains. Specifically, we identified predictors of nominal voltage that could be extracted from aerial imagery and developed a tree-based classifier to classify nominal line voltage ratings. Overall, we found that line support height, support span, and conductor spacing are the best predictors of voltage ratings, and that the classifier built with these predictors had a reliable predictive accuracy (that is, within one voltage class for four out of the five classes sampled). We applied our approach to a study area in Minnesota.     

基于北斗的自动气象站报文传输方案设计

针对北斗卫星下行通信受4G和WIFI信号带内干扰导致传输成功率低的情况,设计了基于增加传输次数来降低误码率的自动气象站报文北斗传输方案。首次提出并建成用于传输自动气象站报文的双北斗指挥机用户群,根据北斗用户机的传输能力设计专用的气象报文编码格式,利用冗余频度开展报文重发,实现单个时次报文的4次重复接收,稳定地保证了较高的传输成功率。方案同时实现了观测报文1min内到达预报员桌面,在稳定性和时效性上均取得了突破。     

热带气旋西行造成的远距离降雨特征分析

2018年7月27~28日凉山州地区出现了持续性的强降水天气过程,本文运用水平分辨率为1°×1°的NECP6小时再分析资料、新型监测资料及常规观测资料进行分析。发现此次过程不同于常规的降水过程,环流背景上受副高外围的东南气流和热带气旋形成的东风波倒槽共同作用形成的急流轴影响,为降水地提供充足的水汽和能量补充,从而形势上形成了热带气旋远距离降水,同时副热带高压的稳定维持也阻挡了高原上的低值系统快速东移,使得降水机制长时间停滞,对降水地造成影响。     

基于小波长短期记忆网络的风电功率超短期概率预测

随着大规模的风电并网,风电所具有的间歇性与随机性对电力系统的稳定性产生了很大的影响,风电功率预测成为当前解决该问题重要的方式之一.本文利用长短期记忆（LSTM）网络良好的时序记忆特性,将小波分解技术与LSTM深度网络结合,提出基于小波长短期记忆网络的风电功率超短期概率预测模型.首先通过小波分解技术将原始时间序列进行平稳化处理,再建立各子序列样本的LSTM网络预测模型,借助最大似然估计法估计预测误差的高斯分布函数,最终实现对未来4 h时刻的风电功率概率区间预测.最后,采用中国东北某风电场数据对所提方法进行算例分析,结果表明,将小波分解与深度学习方法结合可以较好地提高预测的精度,提高概率预测的区间可靠性.     

The irregular galaxy Cassiopeia 1 – a new member of the Local group

A photometry in the V, R, I (Cousins) system has been performed for stars in the galaxy Cassiopeia 1. The resulting colour-agnitude (CM) diagram of Cas 1 reveals the existence of blue stars and the absence of red ones. From an analysis of the CM diagram of the galaxy it is concluded that A_v = 4.0 mag, and the distance modulus (m – M)_o = 24.5 mag, corresponding to a distance of 790 kpc. The visual magnitude of the galaxy is V = 14^m.62 (M_v = −13^m.8) and the colour index (V – R) = 0.89 mag. From the distance determination the galaxy Cassiopeia 1 is a member of the Local group.     

地下流体数字化改造中几个技术性问题的探讨

总结了地下流体前兆台站数字化改造中遇到的问题, 对出现的问题进行分析研究并探索解决方法。 介绍了井口、 脱气、 集气装置的原理和制作方法。 对比分析了数据传输中几种不同方法的优缺点, 为“十五”地下流体前兆台网数字化改造和未来地下流体台网的建设提供了科学依据。