开放式地理实体关系抽取的Bootstrapping方法

从网络文本中抽取地理实体间空间关系和语义关系要求高时效性和强鲁棒性。本文提出一种开放式地理实体关系的自动抽取方法,通过bootstrapping技术统计词语的词性、位置和距离特征来计算语境中词语权值,据此确定描述地理实体关系的关键词,最终组织成结构化实例,并使用百度百科和Stanford CoreNLP开展了试验。研究结果表明,本文方法能自动挖掘自然语言的部分词法特征,无须领域专家知识和大规模标注语料,适用于未知关系类型的信息抽取任务;较之经典的Frequency、TFIDF和PPMI频率统计方法,精度和召回率分别提升约5%和23%。     

一种快速二维 Delaunay 三角网点定位算法

在构建二维Delaunay三角网的逐点插入法中,定位待插点所在三角形的快慢是影响整个算法构网速度的关键因素。针对目前已有算法存在的搜索路径长、搜索路径求解计算量大等问题,结合三角形重心的几何性质,对点定位算法进行改进,避免求三角形重心和相交边的过程。实验结果表明,文中算法较目前其他点定位算法能够有效地缩短搜索路径,减少点定位的计算时间,提高Delaunay三角网构网过程中点定位的效率。     

低空大比例尺地形图航测生产关键技术

针对传统航空摄影测量生产工艺在生产过程中遇到的影响大比例尺测图精度的问题,该文探讨了低空航测生产大比例尺地形图的关键技术。通过选用宽角相机、低航高飞行、强化影像匹配、野外布设标志控制点、优选平差模型、精化测图操作等改进方式,以提高大比例尺航测精度;组建无人飞艇低空航测系统,对提出的技术方法予以实现;最后给出了山西境内近30个县市建成区1∶500测绘生产实践的部分验证成果。     

变形监测技术在库岸防治工程中的应用

针对库岸稳定性检测的需求,该文将现代变形监测技术应用于乌江彭水县水电站鹿角镇库岸防治工程,对地表进行抗滑桩顶和岸坡表层水平位移和垂直位移监测,地下进行岸坡深层水平位移监测。文章对边坡变形监测进行分析,主要包括岸坡表层变形情况、岸坡深层位移情况、重要建筑物基础不均匀沉降情况及抗滑桩顶变形初步分析。对库安防治工程进行预警分析,可保证彭水县水电站的安全。     

Grid pattern recognition in road networks using the C4.5 algorithm

Pattern recognition in road networks can be used for different applications, including spatiotemporal data mining, automated map generalization, data matching of different levels of detail, and other important research topics. Grid patterns are a common pattern type. This paper proposes and implements a method for grid pattern recognition based on the idea of mesh classification through a supervised learning process. To train the classifier, training datasets are selected from worldwide city samples with different cultural, historical, and geographical environments. Meshes are subsequently labeled as composing or noncomposing grids by participants in an experiment, and the mesh measures are defined while accounting for the mesh’s individual characteristics and spatial context. The classifier is generated using the C4.5 algorithm. The accuracy of the classifier is evaluated using Kappa statistics and the overall rate of correctness. The average Kappa value is approximately 0.74, which corresponds to a total accuracy of 87.5%. Additionally, the rationality of the classifier is evaluated in an interpretation step. Two other existing grid pattern recognition methods were also tested on the datasets, and comparison results indicate that our approach is effective in identifying grid patterns in road networks.     

Curve matching approaches to waveform classification: a case study using ICESat data

Light Detection and Ranging (LiDAR) waveforms are being increasingly used in many forest and urban applications, especially for ground feature classification. However, most studies relied on either discretizing waveforms to multiple returns or extracting shape metrics from waveforms. The direct use of the full waveform, which contains the most comprehensive and accurate information has been scarcely explored. We proposed to utilize the complete waveform to test its ability to differentiate between objects having distinct vertical structures using curve matching approaches. Two groups of curve matching approaches were developed by extending methods originally designed for pixel-based hyperspectral image classification and object-based high spatial image classification. The first group is based on measuring the curve similarity between an unknown waveform and a reference waveform, including curve root sum squared differential area (CRSSDA), curve angle mapper (CAM), and Kullback–Leibler (KL) divergence. The second group assesses the curve similarity between an unknown and reference cumulative distribution functions (CDFs) of their waveforms, including cumulative curve root sum squared differential area (CCRSSDA), cumulative curve angle mapper (CCAM), and Kolmogorov–Smirnov (KS) distance. When employed to classify open space, trees, and buildings using ICESat waveform data, KL provided the highest average classification accuracy (87%), closely followed by CCRSSDA and CCAM, and they all significantly outperformed KS, CRSSDA, and CAM based on 15 randomized sample sets.     

姚家岭锌金多金属矿床围岩蚀变三维空间定量分析研究

姚家岭锌金多金属矿床是近年来在长江中下游成矿带铜陵矿集区新发现的大型多金属矿床。姚家岭锌金多金属矿床的成矿作用具有多阶段性的特点,矿床范围内围岩蚀变强烈,蚀变类型复杂多样。三维地质信息技术及三维空间分析方法为定量化分析围岩蚀变的空间分布以及与矿化之间的相关关系提供了有利工具。本文基于上述方法,有效地对姚家岭锌金多金属矿床的围岩蚀变的分带性及其与不同矿化之间的关联性行了定量分析。较之于传统研究方法,不仅能快速地对蚀变与矿化的分布特征进行分析,还能从定量的角度获取不同信息之间的规律性和相关性。本文采用的相关方法不但有助于更准确的判定蚀变分带及规律,还可为矿床的成矿模式及找矿模型的建立提供定量化的数据支持。     

Shale Gas Formation and Occurrence in China: An Overview of the Current Status and Future Potential

Shale gas is one of the most promising unconventional resources both in China and abroad. It is known as a form of self-contained source-reservoir system with large and continuous dimensions. Through years of considerable exploration efforts, China has identified three large shale gas fields in the Fuling, Changning and Weiyuan areas of the Sichuan Basin, and has announced more than 540 billion m~3 of proven shale gas reserves in marine shale systems. The geological theories for shale gas development have progressed rapidly in China as well. For example, the new depositional patterns have been introduced for deciphering the paleogeography and sedimentary systems of the Wufeng shale and Longmaxi shale in the Sichuan Basin. The shale gas storage mechanism has been widely accepted as differing from conventional natural gas in that it is adsorbed on organic matter or a mineral surface or occurs as free gas trapped in pores and fractures of the shale. Significant advances in the techniques of microstructural characterization have provided new insights on how gas molecules are stored in micro- and nano-scale porous shales. Furthermore, newly-developed concepts and practices in the petroleum industry, such as hydraulic fracturing, microseismic monitoring and multiwell horizontal drilling, have made the production of this unevenly distributed but promising unconventional natural gas a reality. China has 10–36 trillion m~3 of promising shale gas among the world's whole predicted technically recoverable reserves of 206.6 trillion m~3. China is on the way to achieving its goal of an annual yield of 30–50 billion m~3 by launching more trials within shale gas projects.