全球地学断面(GGT)地理信息系统的编码技术

依据全球地学断面的特点 ,以及《数字化地质图图层及属性文件格式》和《GGT数字化指南》等标准 ,研制一套全球地学断面地理信息系统的编码标准 ,为建立中国乃至全球地学断面地理信息系统设置主题、分层标准和命名规则 ,以及实体编码标准 ,并把它们应用于中国地学大断面三维地理信息系统的建立中。该编码标准为地学断面信息系统的建立 ,为深入研究大陆岩石圈结构构造、地球动力学、物质成分及不同地学断面的对比 ,进而建立大陆岩石圈的三维结构模型具有重要意义     

上饶地区遥感地质调查基础数据库及其应用

结合１∶５万遥感地质填图，从数据采集、数据库管理、分析运算、数据输出等方面系统介绍了基于遥感的区调基础数据库的建立过程；重点论述了属性库管理中的数据分类和编码问题，它是空间查询和属性检索及地学专题制图的基础；从遥感数据录入、对数据库的更新等方面论述了遥感（ＲＳ）和地理信息系统（ＧＩＳ）的技术整合及其在区调中的应用前景；探讨了地学数据标准化及专家系统（ＥＳ）和ＧＩＳ结合建立地学模型等问题。     

月球地质图空间数据库设计

我国即将完成1: 2 500 000全月球数字地质图编制,为便于成果数据存储管理和集成共享,需要建立完备合理的空间数据库。本文基于本次编图工作实践,对月球地质图空间数据库要素内容进行了全面整理和归类。月球地质图空间数据库要素包括物质要素、构造要素、其他特殊符号、注记共4个大类,在大类基础上细分为13个中类及40个小类,并据此设计了具有可扩展性的要素分类代码。空间要素采用分层的方式进行组织管理,图层名前4位为所属图幅号,第5位为比例尺代码,其后为图层英文名称的缩写。每个要素图层与一张属性表关联,对属性表所有字段的字段名称、别名、类型、能否为空、长度、小数位及单位进行了定义和规范。本文选取月表撞击坑坑物质、月海岩石、撞击断裂及高程点要素详细阐述了其属性表结构。属性表主键为“要素标识号”,用于唯一标识某一个图元,其由图幅号、比例尺代码、要素分类代码、图元顺序码四层14位层次码构成。数据库设计遵循可扩展原则,特定区域或其他比例尺的月球地质图空间数据库建设也可参照执行,本文以月球冯·卡门撞击坑及邻区地质图为例进行了空间数据库设计。科学合理的数据库设计是数据库建设的前提,将为编图成果存储管理、集成共享与国际合作提供重要支撑。     

基于MapObjects的数据组织方式改进的研究的探讨

分析了ESRI的组件式GIS产品MapObjects组织和管理空间数据的方法，对其在多图层，多专题，分布式存储的空间数据的管理中所遇到的问题和不足进行了探讨，提出了一种解决思路。同时建立了一种改进的模型并给出了相应的算法，在实际的应用中取得了较好的效果。     

基于面向服务体系结构（SOA）的钾盐资源潜力系统

为了对中国钾盐矿产资源潜力进行全面预测,建立了全国性的钾盐矿产资源潜力预测系统。对收集的钾盐信息进行系统地整理,在Oracle空间数据库中建立面向对象的空间数据模型,基于面向服务体系结构进行系统的框架设计,将属性数据和空间数据的操作发布为Web服务,客户端通过网络调用服务实现空间与属性数据的一体化管理及分析,采用面向对象语言C#和ArcObjects二次开发具体实现本系统。系统可通过Web服务与其他地学系统有机集成,对地学数据基于服务集成与共享及一站式服务具有重要参考价值。     

基于GeoSciML的地学数据互操作服务研究

地球科学数据依托于WebGIS进行数据共享与服务是全球地质行业发展的必然趋势,但是,由于地质数据存储格式不同、数据模型与数据结构差异及地质数据的敏感性等原因导致多源异构数据的产生,给数据共享与综合利用带来了困难。研究传统地质数据模型和GeoSciML模型,对比分析两者图层和要素属性方面的异同,从语义融合角度提出了采用传统模型的地质空间数据到GeoSciML模型的数据映射方法。结果表明,在数据格式转换和整合过程中保证了信息的完整准确,地质空间数据的互操作性强且符合数据映射要求,为我国地质数据面向国际深度共享与服务提供了科学方法。     

中国大型、超大型铜矿床密集区综合信息预测的方法研究

大型、超大型矿床的研究，是自20世纪70年代以来世界地质行业关注的重大课题。本文结合近几年来国家和部委的多项应用研究重大项目的科研成果，选择大型、超大型铜矿床密集区作为研究对象，在综合信息理论与方法体系的指导下，提出了把GIS用于矿产资源评价并真正达到决策分析目的关键是建立知识库，建立因变量图层、自变量图层和控制图层以及编制相应的属性表，系统地总结了一套操作性较强的综合信息预测大型、超大型铜矿床密集区的思路和方法。     

阿尔泰成矿区金矿预测图形库及属性库的建立方法

详细介绍阿尔泰成矿区金矿预测图形库及属性库的建立原则及方法 ,尤其是图层划分的一般原则、属性结构建立及属性分类方法。图层划分应依据研究中所形成的认识和矿产预测信息处理的需要 ,便于输入调用和分析 ,同一类图元、属性结构一致的图元尽可能放到同一图层。最后给出了阿尔泰成矿区图形库和属性库的具体内容。     

地理信息系统及图形数据库的形成

地理信息系统(GIS)是由计算机系统、地理数据和用户组成，为多学科提供服务的新兴学科。GIS的应用系统由硬件、软件、数据、人员和方法五个部分构成，其软件系统应具备数据输入、数据编辑、数据存贮与管理、空间查询与空间分析、可视化表达与输出等五项基本功能。图形数据库的形成主要是通过光栅扫描矢量化方法，对地质矿产和地物化遥资料及其它地质资料进行数字化，按照图层划分方案建立关系属性表，属性库建立的目的主要是从矿产预测角度出发，对有预测意义的属性信息进行有效管理。     

基于GDW的空间数据融合

空间数据融合是目前地学信息领域面临的难题。通过介绍Oracle数据仓库系统架构和其在地学信息存储和分析方面的优点,深入分析ArcInfo中的Geodatabase的空间数据组织模型,重点研究了Oracle Spatial的空间数据存储模型、空间数据和空间索引组织模式,并给出了空间数据融合的具体实现过程。针对具体项目,结合GIS二次开发,能非常高效地实现空间数据的融合。     

基于BERT的金矿地质实体关系抽取模型研究

金矿实体关系的智能识别是提高金矿文献分析挖掘和知识提取的重要方法和途径。此次研究针对目前金矿实体关系抽取涉及到的核心问题,如金矿实体关系复杂、人工标注信息少等特点,提出了基于BERT(Bidirectional Encoder Representations from Transformer)的远程监督关系抽取模型。并通过金矿地质数据编码、金矿分类和金矿地质实体过滤等模块的优化改进,提高了金矿地质实体关系抽取的准确率。最后通过对金矿文献数据的实体关系抽取实验,验证了该方法的有效性。      

Application of Dempster-Shafer theory of evidence to GIS-based landslide susceptibility analysis

GIS-based spatial data integration tasks for predictive geological applications, such as landslide susceptibility analysis, have been regarded as one of the primary geological application issues of GIS. An efficient framework for proper representation and integration is required for this kind of application. This paper presents a data integration framework based on the Dempster-Shafer theory of evidence for landslide susceptibility mapping with multiple geospatial data. A data-driven information representation approach based on spatial association between known landslide occurrences and input geospatial data layers is used to assign mass functions. After defining mass functions for multiple geospatial data layers, Dempster’s rule of combination is applied to obtain a series of combined mass functions. Landslide susceptibility mapping using multiple geospatial data sets from Jangheung in Korea was conducted to illustrate the application of this methodology. The results of the case study indicated that the proposed methodology efficiently represented and integrated multiple data sets and showed better prediction capability than that of a traditional logistic regression model.     

Availability and coverage of hydrologic data in the US geological survey National Water Information System (NWIS) and US Environmental Protection Agency Storage and Retrieval System (STORET)

The need for a unified and improved data access system for the nation’s vast hydrologic data holdings has increased over the past few years as researchers strive for better understanding the human impact on the nation’s water cycle. Large mission oriented data repositories such as the USGS’ National Water Information System (NWIS) and EPA’s Storage and Retrieval System (EPA STORET) play a crucial role in providing a substantial amount of the nationwide coverage, however they do differ regionally in terms of coverage (parameters) and geospatial data density. Besides the differences in geographic distribution, repositories tend to undergo changes in mission statements and as such have different foci in their data collection activities that change as time progresses. This paper places the two water information systems next to each in an attempt to work out the differences in terms of coverage and content and how they complement each other when overlaid. This is done through the use of a number the CUAHSI Hydrologic Information Systems components, namely a web-service suite called WaterOneFlow that permits interrogation of the available data content of a national water metadata catalogue into which these two information systems have been integrated.     

Earthquake hazard assessment through geospatial model and development of EaHaAsTo tool for visualization: an integrated geological and geoinformatics approach

An earthquake is a natural phenomenon which is very frequent in Himalayan region in India. In southern peninsula India, the spatial occurrence of earthquake is irregular, whereas the northeastern, the north and the northwestern Himalayan parts of India are subjected to regular occurrences of earthquakes as they mark the boundary of the Eurasian and the Indian Plate. Hence, it is important to study and develop spatial model and information tool to understand the seismic phenomenon. The geoinformatic technique plays a significant role in the analysis of geodatabase to study the natural disaster and hazard assessment. The main aim of the present study is to develop geospatial model based on earthquake hazard assessment tool (EaHaAsTo) through integrated geological and geoinformatic techniques to better understand the earthquake occurrences zones. The spatial and non-spatial data were collected and integrated in a GIS to prepare geospatial databases. The thematic and quantitative databases were generated, and analysis was carried out to understand the seismic characteristics of the study area. The geospatial model was developed by integrating thematic databases and geospatial analyzed using weighted linear combination method. Finally, the GIS based on customized EaHaAsTo was developed to visualize the output of the model in qualitative and quantitative forms.     

Harvesting ambient geospatial information from social media feeds

Social media generated from many individuals is playing a greater role in our daily lives and provides a unique opportunity to gain valuable insight on information flow and social networking within a society. Through data collection and analysis of its content, it supports a greater mapping and understanding of the evolving human landscape. The information disseminated through such media represents a deviation from volunteered geography, in the sense that it is not geographic information per se. Nevertheless, the message often has geographic footprints, for example, in the form of locations from where the tweets originate, or references in their content to geographic entities. We argue that such data conveys ambient geospatial information, capturing for example, people’s references to locations that represent momentary social hotspots. In this paper we address a framework to harvest such ambient geospatial information, and resulting hybrid capabilities to analyze it to support situational awareness as it relates to human activities. We argue that this emergence of ambient geospatial analysis represents a second step in the evolution of geospatial data availability, following on the heels of volunteered geographical information.     

Landslide investigations in Russia and the former USSR

The impact of natural hazards on mankind has increased dramatically over the past decades. Global urbanization processes and increasing spatial concentrations of exposed elements induce natural hazard risk at a uniquely high level. To mitigate affiliated perils requires detailed knowledge about elements at risk. Considering a high spatiotemporal variability of elements at risk, detailed information is costly in terms of both time and economic resources and therefore often incomplete, aggregated, or outdated. To alleviate these restrictions, the availability of very-high-resolution satellite images promotes accurate and detailed analysis of exposure over various spatial scales with large-area coverage. In the past, valuable approaches were proposed; however, the design of information extraction procedures with a high level of automatization remains challenging. In this paper, we uniquely combine remote sensing data and volunteered geographic information from the OpenStreetMap project (OSM) (i.e., freely accessible geospatial information compiled by volunteers) for a highly automated estimation of crucial exposure components (i.e., number of buildings and population) with a high level of spatial detail. To this purpose, we first obtain labeled training segments from the OSM data in conjunction with the satellite imagery. This allows for learning a supervised algorithmic model (i.e., rotation forest) in order to extract relevant thematic classes of land use/land cover (LULC) from the satellite imagery. Extracted information is jointly deployed with information from the OSM data to estimate the number of buildings with regression techniques (i.e., a multi-linear model from ordinary least-square optimization and a nonlinear support vector regression model are considered). Analogously, urban LULC information is used in conjunction with OSM data to spatially disaggregate population information. Experimental results were obtained for the city of Valparaíso in Chile. Thereby, we demonstrate the relevance of the approaches by estimating number of affected buildings and population referring to a historical tsunami event.

     

The Arc-view application in the field of hydrology and water resources

Through system design and analysis from the aspects of entity categories, hierarchical design, attribute design, and the final function design and realization of the system, this paper develops a software application based on Arc-view platform with a view to solving the deficiencies of conventional groundwater dynamic management in managing the spatial graphics data. Using this application into the supporting automatic monitoring system of groundwater level helps to achieve the automatic analysis of attribute data, and offer a new, visual and efficient automated management tool. Moreover, the computer geospatial modeling can realize the quick query and spatial analysis of geographic information, and thus facilitating dynamic simulation and prediction of the research object. However, a multi-functional, multi-level groundwater level information management system is a complex system engineering, which requires continuous improvement of the structure, development of functions, and supplement of user models.     

Effects of Local Shoreline and Subestuary Watershed Condition on Waterbird Community Integrity: Influences of Geospatial Scale and Season in the Chesapeake Bay

In many coastal regions throughout the world, there is increasing pressure to harden shorelines to protect human infrastructures against sea level rise, storm surge, and erosion. This study examines waterbird community integrity in relation to shoreline hardening and land use characteristics at three geospatial scales: (1) the shoreline scale characterized by seven shoreline types: bulkhead, riprap, developed, natural marsh, Phragmites-dominated marsh, sandy beach, and forest; (2) the local subestuary landscape scale including land up to 500 m inland of the shoreline; and (3) the watershed scale >500 m from the shoreline. From 2010 to 2014, we conducted waterbird surveys along the shoreline and open water within 21 subestuaries throughout the Chesapeake Bay during two seasons to encompass post-breeding shorebirds and colonial waterbirds in late summer and migrating and wintering waterfowl in late fall. We employed an Index of Waterbird Community Integrity (IWCI) derived from mean abundance of individual waterbird species and scores of six key species attributes describing each species’ sensitivity to human disturbance, and then used this index to characterize communities in each subestuary and season. IWCI scores ranged from 14.3 to 19.7. Multivariate regression model selection showed that the local shoreline scale had the strongest influence on IWCI scores. At this scale, percent coverage of bulkhead and Phragmites along shorelines were the strongest predictors of IWCI, both with negative relationships. Recursive partitioning revealed that when subestuary shoreline coverage exceeded thresholds of approximately 5% Phragmites or 8% bulkhead, IWCI scores decreased. Our results indicate that development at the shoreline scale has an important effect on waterbird community integrity, and that shoreline hardening and invasive Phragmites each have a negative effect on waterbirds using subestuarine systems.     

Reconceptualizing the role of the user of spatial data infrastructure

Spatial data infrastructures, which are Internet-based mechanisms for the coordinated production, discovery, and use of geospatial information in the digital environment, have diffused worldwide in the last two decades. Currently, there are about one hundred spatial data infrastructures at the national level and many other at supra- and sub-national levels. These contemporary spatial data infrastructures operate with two main assumptions: formal organizations are the producers and suppliers of geospatial information; users are the passive recipients of information. The recent phenomenon of volunteered geographic information departs from these assumptions. In this paper, we argue that reconceptualizing the user of a spatial data infrastructure can accommodate this new phenomenon. Such a reconceptualization creates a middle ground between spatial data infrastructure and volunteered geographic information, which has important implications for future research.     

Sediment yield assessment at basin scale using geospatial technique

Soil erosion and sediment yield from catchments are key limitations to achieving sustainable land use and maintaining water quality in nature. One of the important aspects in protecting the watershed is evaluation of sediment produced by statistical methods. Controlling sediment loading in protecting the watershed requires knowledge of soil erosion and sedimentation. Sediment yield is usually not available as a direct measurement but is estimated using geospatial models. One of the geospatial models for estimating sediment yield at the basin scale is sediment delivery ratio (SDR). The present study investigates the spatial SDR model in determining the sediment yield rate considering climate and physical factors of basin in geographic information system environment. This new approach was developed and tested on the Amammeh catchments in Iran. The validation of the model was evaluated using the Nash Sutcliffe efficiency coefficient. The developed model is not only conceptually easy and well suited to the local data needs but also requires less parameter, which offers less uncertainty in its application while meeting the intended purpose. The model is developed based on local data. The results predict strong variations in SDR from 0 in to 70 % in the uplands of the Basin.