蕴含地理事件微博客消息的自动识别方法

微博客文本蕴含类型丰富的地理事件信息,能够弥补传统定点监测手段的不足,提高事件应急响应质量。然而,由于大规模标注语料的普遍匮乏,无法利用监督学习过程识别蕴含地理事件信息的微博客文本。为此,本文提出一种蕴含地理事件微博客消息的自动识别方法,通过快速获取的语料资源增强识别效果。该方法利用主题模型具有提取文档中主题集合的优势,通过主题过滤候选语料文本,实现地理事件语料的自动提取。同时,将分布式表达词向量模型引入事件相关性计算过程,借助词向量隐含的语义信息丰富微博客短文本的上下文内容,进一步增强事件消息的识别效果。通过以新浪微博为数据源开展的实验分析表明,本文提出的蕴含地理事件信息微博客消息识别方法,识别来自事件微博话题的消息文本的F-1值可达到71.41%,比经典的基于SVM模型的监督学习方法提高了10.79%。在模拟真实微博环境的500万微博客数据集上的识别准确率达到60%。     

藏北羌塘盆地晚三叠世地层特征与对比

羌塘盆地上三叠统日干配错组、土门各拉组和肖茶卡组、那底岗日组分别分布于南、北羌塘凹陷。在以往研究和笔者在中国地质调查局青藏重点沉积盆地油气资源潜力分析项目工作基础上,通过对各地层单元的详细研究和区域对比,从古生物、岩石组合、沉积系列、地层单元的接触关系、年代地层进行了综合分析。结果显示北羌塘凹陷那底岗日组与南羌塘凹陷日干配错组和土门各拉组的时代主要为晚三叠世诺利期—瑞替期,三者为羌塘晚三叠世诺利期—瑞替期裂陷盆地形成过程中的同期异相的火山-沉积系列。北羌塘凹陷肖茶卡组的是残留陆表海沉积产物,其时代主要为晚三叠世卡尼期-诺利期早期,与那底岗日组、日干配错组和土门各拉组均为上下对比关系。     

Propagation of electromagnetic waves from an induction source in media with time-dependent conductivity and permittivity

Electromagnetic wave propagation from an induction source (a transmitter coil) in media with time-dependent conductivity and permittivity is simulated using the vector finite-element method. A special algorithm is suggested for source approximation.     

GIS数据自动更新技术的研究

保持GIS数据的现势性是GIS应用和发展的“瓶颈”。本文探讨了GIS数据自动更新的一般过程,将自动变化检测归纳为三种基本实现方法,详细讨论了三种方法的具体实现过程,并对各个方法的特点进行了分析,最后指出了需要进一步研究的问题。     

High-performance three-horizon composition algorithm for large-scale terrains

This work presents a high-performance algorithm to compute the horizon in very large high-resolution DEMs. We used Stewart's algorithm as the core of our implementation and considered that the horizon has three components: the ground, near, and far horizons. To eliminate the edge-effect, we introduced a multi-resolution halo method. Moreover, we used a new data partition approach, to substantially increase the parallelism in the algorithm. In addition, several optimizations have been applied to considerably reduce the number of arithmetical operations in the core of the algorithm. The experimental results have demonstrated that by applying the above-described contributions, the proposed algorithm is more than twice faster than Stewart's algorithm while maintaining the same accuracy.     

A general-purpose parallel raster processing programming library test application using a geographic cellular automata model

A general-purpose parallel raster processing programming library (pRPL) was developed and applied to speed up a commonly used cellular automaton model with known tractability limitations. The library is suitable for use by geographic information scientists with basic programming skills, but who lack knowledge and experience of parallel computing and programming. pRPL is a general-purpose programming library that provides generic support for raster processing, including local-scope, neighborhood-scope, regional-scope, and global-scope algorithms as long as they are parallelizable. The library also supports multilayer algorithms. Besides the standard data domain decomposition methods, pRPL provides a spatially adaptive quad-tree-based decomposition to produce more evenly distributed workloads among processors. Data parallelism and task parallelism are supported, with both static and dynamic load-balancing. By grouping processors, pRPL also supports data–task hybrid parallelism, i.e., data parallelism within a processor group and task parallelism among processor groups. pSLEUTH, a parallel version of a well-known cellular automata model for simulating urban land-use change (SLEUTH), was developed to demonstrate full utilization of the advanced features of pRPL. Experiments with real-world data sets were conducted and the performance of pSLEUTH measured. We conclude not only that pRPL greatly reduces the development complexity of implementing a parallel raster-processing algorithm, it also greatly reduces the computing time of computationally intensive raster-processing algorithms, as demonstrated with pSLEUTH.     

Massively parallel spatial point pattern analysis: Ripley’s K function accelerated using graphics processing units

This study presents a massively parallel spatial computing approach that uses general-purpose graphics processing units (GPUs) to accelerate Ripley’s K function for univariate spatial point pattern analysis. Ripley’s K function is a representative spatial point pattern analysis approach that allows for quantitatively evaluating the spatial dispersion characteristics of point patterns. However, considerable computation is often required when analyzing large spatial data using Ripley’s K function. In this study, we developed a massively parallel approach of Ripley’s K function for accelerating spatial point pattern analysis. GPUs serve as a massively parallel platform that is built on many-core architecture for speeding up Ripley’s K function. Variable-grained domain decomposition and thread-level synchronization based on shared memory are parallel strategies designed to exploit concurrency in the spatial algorithm of Ripley’s K function for efficient parallelization. Experimental results demonstrate that substantial acceleration is obtained for Ripley’s K function parallelized within GPU environments.     

求解模型对欧拉矢量的影响

利用GNSS水平运动场求解欧拉矢量时,待估参数的个数会对欧拉矢量结果产生影响,针对该问题本文分别从理论推导和具体算例角度进行了分析.首先从理论上推导出不同的求解模型得到的欧拉矢量的差异;然后以2004 ～2007年中国大陆GNSS水平运动场为基础,选用两种常用的求解模型(块体整体旋转模型和块体的整体旋转与均匀应变模型),讨论了求解模型对欧拉矢量及后续研究的影响.结果表明,两种模型下块体整体旋转的差异最大可达2.60mm·a-1,是不能忽略的.因此认为选用不同的块体运动模型会得到不同的地壳水平运动图像,在地壳水平运动分析中对此需加以重视.     

Comparison of direct statistical and indirect statistical-deterministic frameworks in downscaling river low-flow indices

ABSTRACT

This work explores the ability of two methodologies in downscaling hydrological indices characterizing the low flow regime of three salmon rivers in Eastern Canada: Moisie, Romaine and Ouelle. The selected indices describe four aspects of the low flow regime of these rivers: amplitude, frequency, variability and timing. The first methodology (direct downscaling) ascertains a direct link between large-scale atmospheric variables (the predictors) and low flow indices (the predictands). The second (indirect downscaling) involves downscaling precipitation and air temperature (local climate variables) that are introduced into a hydrological model to simulate flows. Synthetic flow time series are subsequently used to calculate the low flow indices. The statistical models used for downscaling low flow hydrological indices and local climate variables are: Sparse Bayesian Learning and Multiple Linear Regression. The results showed that direct downscaling using Sparse Bayesian Learning surpassed the other approaches with respect to goodness of fit and generalization ability.

Editor D. Koutsoyiannis; Associate editor K. Hamed     

全国前兆台网“九五”系统台站接入的设计与实施

介绍了“全国前兆台网‘九五’系统台站接入改造”项目的技术方案,即采用接入地震前兆通信协议转换器或更换“九五”前兆公用数据采集器方式将“九五”前兆仪器接入“十五”前兆系统,并详述了该方案的技术特点;介绍了方案的部署实施情况,测试平台运行情况以及通过对测试平台的监控而发现并解决的问题.并行运行结果显示,接入改造系统运行稳定,产出数据可靠.