模糊地理实体不确定性综合描述研究

从二型模糊的角度讨论了模糊地理实体的不确定性,基于空间数据的位置误差,利用模糊集的λ-截集和圆误差带模型推导了模糊地理实体隶属度的误差模型,并给出了隶属度不确定性的度量方法,提出了用隶属度误差立方体来综合描述模糊点的不确定性。     

Development of Geological Data Warehouse

Data warehouse (DW), a new technology invented in 1990s, is more useful for integrating and analyzing massive data than traditional database. Its application in geology field can be divided into 3 phrases: 1992-1996, commercial data warehouse (CDW) appeared; 1996-1999, geological data warehouse (GDW) appeared and the geologists or geographers realized the importance of DW and began the studies on it, but the practical DW still followed the framework of DB; 2000 to present, geological data warehouse grows, and the theory of geo-spatial data warehouse (GSDW) has been developed but the research in geological area is still deficient except that in geography. Although some developments of GDW have been made, its core still follows the CDW-organizing data by time and brings about 3 problems: difficult to integrate the geological data, for the data feature more space than time; hard to store the massive data in fifferent levels due to the same reason; hardly support the spatial analysis if the data are organized by time as CDW does. So the GDW should be redesigned by organizing data by scale in order to store mass data in different levels and synthesize the data in different granularities, and choosing space control points to replace the former time control points so as to integrate different types of data by the method of storing one type data as one layer and then to superpose the layers. In addition, data cube, a wide used technology in CDW, will be no use in GDW, for the causality among the geological data is not so obvious as commercial data, as the data are the mixed result of many complex rules, and their analysis needs the special geological methods and software; on the other hand, data cube for mass and complex geo-data will devour too much store space to be practical. On this point, the main purpose of GDW may be fit for data integration unlike CDW for data analysis.     

Assessing relevance of tweets for risk communication

ABSTRACT

Although Twitter is used for emergency management activities, the relevance of tweets during a hazard event is still open to debate. In this study, six different computational (i.e. Natural Language Processing) and spatiotemporal analytical approaches were implemented to assess the relevance of risk information extracted from tweets obtained during the 2013 Colorado flood event. Primarily, tweets containing information about the flooding events and its impacts were analysed. Examination of the relationships between tweet volume and its content with precipitation amount, damage extent, and official reports revealed that relevant tweets provided information about the event and its impacts rather than any other risk information that public expects to receive via alert messages. However, only 14% of the geo-tagged tweets and only 0.06% of the total fire hose tweets were found to be relevant to the event. By providing insight into the quality of social media data and its usefulness to emergency management activities, this study contributes to the literature on quality of big data. Future research in this area would focus on assessing the reliability of relevant tweets for disaster related situational awareness.     

Complex behaviour of a coal sample in a large scalein situ compression test

Summary A large scalein situ compression test of a laminated coal cube is described. The cube was uniformly loaded by a system of hydraulic jacks supplied simultaneously from one pump. At the same time both load and deformations were controlled. Deformations were measured by 60 longitudinal gauges located at the side surface of the cube in the regular net. Due to the heterogeneity of the rock the readings showed scatter. As a first approximation, the scattered data were fitted to the plane using the least square method. Orientations of the planes were analysed and show permanent relative movement between adjacent lamina indicating complex behaviour of the cube under loading. Detailed analysis of this behaviour indicated that the cube was subjected to both bending and twisting.     

红外卫星云图和相关向量机的有眼热带气旋客观定强模型

热带气旋TC(Tropical Cyclone)是全球影响最严重的自然灾害之一。TC强度和路径的准确预报,对于减轻其带来的灾害影响至关重要。本文基于静止红外卫星云图和相关向量机RVM(Relevance Vector Machine)构建有眼TC客观定强模型。首先,利用高斯平滑对红外卫星云图进行去噪;然后,利用基于测地活动轮廓GAC(Geodesic Active Contour)模型的偏微分方程PDE(Partial Differential Equation)法对有眼TC的眼壁进行分割,提取眼壁的亮温梯度信息,计算眼壁亮温梯度的最大值及梯度数据不同概率时的均值,从而构造与TC强度密切相关的特征因子;最后,利用RVM构建单特征因子、多特征因子与近地面最大中心风速的客观定强模型,研究不同特征维度对TC客观定强误差的影响。实验结果表明,在单特征因子的模型定强中,95%概率眼壁亮温梯度均值的定强误差最小,相比利用单特征因子所构建的定强模型,多特征因子的模型定强误差更小,即多特征因子中包含更多与TC强度相关的特征信息。在多特征因子的模型定强中,二特征因子优于三特征因子模型,说明应当合理选择特征因子维数,并非越多越好。本文所用RVM模型具有良好的高维非线性处理能力,能对TC强度进行有效估计。     

Site characterization model using least‐square support vector machine and relevance vector machine based on corrected SPT data (Nc)

Statistical learning algorithms provide a viable framework for geotechnical engineering modeling. This paper describes two statistical learning algorithms applied for site characterization modeling based on standard penetration test (SPT) data. More than 2700 field SPT values (N) have been collected from 766 boreholes spread over an area of 220 sqkm area in Bangalore. To get N corrected value (N_c), N values have been corrected (N_c) for different parameters such as overburden stress, size of borehole, type of sampler, length of connecting rod, etc. In three‐dimensional site characterization model, the function N_c=N_c (X, Y, Z), where X, Y and Z are the coordinates of a point corresponding to N_c value, is to be approximated in which N_c value at any half‐space point in Bangalore can be determined. The first algorithm uses least‐square support vector machine (LSSVM), which is related to a ridge regression type of support vector machine. The second algorithm uses relevance vector machine (RVM), which combines the strengths of kernel‐based methods and Bayesian theory to establish the relationships between a set of input vectors and a desired output. The paper also presents the comparative study between the developed LSSVM and RVM model for site characterization. Copyright © 2009 John Wiley & Sons, Ltd.     

Criteria of geographic relevance: an experimental study

The relevance of geographic information has become an emerging problem in geographic information science due to an enormous increase in volumes of data at high spatial, temporal, and semantic resolution, because of ever faster rates of new data capturing. At the same time, it is not clear whether the concept of relevance developed in information science and implemented for document-based information retrieval can be directly applied to this new, highly dynamic setting. In this study, we analyze the criteria users apply when judging the relevance of geographic entities in a given mobile usage context. Two different experiments have been set up in order to gather users' opinions on a set of possible criteria, and their relevance judgements in a given scenario. The importance ascribed to the criteria in both experiments clearly implies that a new concept of relevance is required when dealing with geographic entities instead of digital documents. This new concept of ‘Geographic Relevance’ is highly dependent on personal mobility and user's activity, whose understanding may in turn be refined by the assimilation of ‘Geographic Relevance’ itself.     

预警地震在地震预测中的意义

对新疆1970-2002年地震资料以不同范围、不同震级进行全时空扫描。结果表明，新疆台网监测能力较好地区绝大多数6级以上地震前地震活动关联图较为清晰。提出了预警地震的概念，认为预警地震在短临预报方面有一定的作用。     

方差体技术在地震勘探中的应用

三维地震数据体反映了地下一个规则网格的反射情况。当遇到地下有断层或某个局部区域地层不连续变化时，一些地震道的反射特征就会与附近地震道出现差异而导致地震首局部的不连续性。三维方差体技术就是求取三维数据体所有样点的方差值来反映这种不连续性信息。通过该技术在济宁2号井某采区的实际应用，可看出，方差体技术在三维地震信息的自动拾取以及提高地震资料解释成果精度等方面有明显优势，对断层、陷落柱有良好的自动识别能力。     

新型珊瑚海水混凝土力学性能试验研究

为了解决全珊瑚海水混凝土(coral aggregate seawater concrete,简称CASC)的脆性问题,采用碱式硫酸镁水泥(basic magnesium sulfate cement,简称BMSC)为胶凝材料和剑麻纤维为增韧材料制备了一种新型全珊瑚海水混凝土(NCASC),系统测定了基本力学性能,并与普通硅酸盐水泥制备的CASC、碱式硫酸镁水泥砂石混凝土(basic magnesium sulfate cement concrete,简称BMSCC)、普通砂石混凝土(ordinary portland cement concrete,简称OPC)以及普通轻骨料混凝土(lightweight aggregate concrete,简称LAC)的力学性能进行对比。结果表明:立方体抗压强度(f_(cu),m)在15~35 MPa范围内的NCASC的轴心抗压强度(f_c,m)、劈裂抗拉强度(f_(sp),m)与f_(cu),m之间具有较显著的线性关系;对于f_(cu),m为15~35 MPa的混凝土,相同强度NCASC的f_c,m高于OPC、LAC及BMSCC,其f_c,m与f_(cu),m比值最大,大致在0.85~0.96之间,NCASC的f_(sp),m比OPC和LAC高,但比BMSCC低,且随着强度的提高,OPC与NCASC的f_(sp),m间的差距在增大,而LAC和BMSCC与NCASC的f_(sp),m间的差距在缩小;在强度等级C15~C55的范围中,NCASC和CASC的f_(sp),m、f_c,m与f_(cu),m之间的关系基本可以采用相同的线性回归方程来描述,利用这两个回归线性方程得到对应于标准强度等级的CASC和NCASC的f_(sp),m和f_c,m数据,为CASC力学性能研究与结构设计提供参考依据。