基于GIS的岷江上游地貌形态初步分析

近些年来,地理信息系统(GIS)在理论及技术方法等方面日臻完善,同时GIS技术被广泛地应用于地质工作的各个领域。以地理信息系统(GIS)为平台,利用矢量化地形高程数据,构建岷江上游地区数字高程模型(DEM),并对数字图像进行了几何校正,镶嵌配准,实现了岷江上游地区地形地貌的三维模拟显示;利用DEM分析探讨地貌形态,生成一系列地貌分析专题图如地貌高程图、地形坡度图等,并进行了初步的讨论分析,得出岷江上游地区主体属于中高山陡崖地貌,地形地貌总体呈现由NW向SE倾斜的分布趋势。而岷江源头地区东侧则由于新构造活动强烈,具有隆升幅度较西侧剧烈的特征。通过对岷江上游地区地貌形态的分析研究,为中国东西部地貌边界带的新构造研究做了有益的补充。     

江西武功山花岗岩地貌特征与成因

江西武功山花岗岩穹窿构造地貌景观类型具有多样性、典型性,具有极高的美学价值、科学价值和旅游开发价值。武功山花岗岩地貌类型可划分为花岗岩风化壳高山草甸、花岗岩侵蚀构造地貌、花岗岩流水侵蚀地貌和花岗岩崩塌堆积地貌等,而且每一类型又有多种微地貌景观,具有显著的集聚性特征,主要集中在金顶、明月山和羊狮幕地区,空间上可分为4个梯度,分别为中心高山草甸带、中心边缘构造侵蚀地貌区、外围峡谷、孤峰、瀑布、温泉区、武功山主山体前缘区。花岗岩穹窿构造核部及外缘区域的花岗岩地貌发育模式和发育阶段具有明显差异,反映了武功山不同山体单元多期差异性隆升后,在亚热带气候环境下,流水溯源侵蚀、构造、岩体性质等内外营力对原始地貌面的差异性作用过程。本研究不仅可为花岗岩穹窿构造地貌景观空间展布规律和地貌特征的研究提供有效借鉴,而且可为武功山地区地学科普、地质遗迹保护等提供支撑。     

江西武功山花岗岩地貌特征与成因

江西武功山花岗岩穹窿构造地貌景观类型具有多样性、典型性,具有极高的美学价值、科学价值和旅游开发价值。武功山花岗岩地貌类型可划分为花岗岩风化壳高山草甸、花岗岩侵蚀构造地貌、花岗岩流水侵蚀地貌和花岗岩崩塌堆积地貌等,而且每一类型又有多种微地貌景观,具有显著的集聚性特征,主要集中在金顶、明月山和羊狮幕地区,空间上可分为4个梯度,分别为中心高山草甸带、中心边缘构造侵蚀地貌区、外围峡谷、孤峰、瀑布、温泉区、武功山主山体前缘区。花岗岩穹窿构造核部及外缘区域的花岗岩地貌发育模式和发育阶段具有明显差异,反映了武功山不同山体单元多期差异性隆升后,在亚热带气候环境下,流水溯源侵蚀、构造、岩体性质等内外营力对原始地貌面的差异性作用过程。本研究不仅可为花岗岩穹窿构造地貌景观空间展布规律和地貌特征的研究提供有效借鉴,而且可为武功山地区地学科普、地质遗迹保护等提供支撑。     

大巴山地区构造地貌特征及演化——基于DEM数据处理与应用

上扬子北缘的大巴山地区是秦岭造山带和扬子地块长期相互作用的地区,构造变形和地貌特征记录了印支期、燕山期,尤其是喜山期构造运动和地貌响应过程。本文以地理信息系统（GIS）为平台,通过对数字高程数据（DEM）的处理,结合地质图、构造水系图等各种数据,对大巴山及其前缘盆地的构造地貌特征进行综合分析研究,以坡度、地形起伏度、高程图和剖面图的统计为主,分析其构造地貌特征。研究表明,大巴山自晚燕山—喜山期前陆褶皱冲断形成以来,经历了多次隆升过程,特别是自喜山期以来,大巴山共经历了至少三次大规模的隆升。     

基于ASTER-GDEM数据的羊卓雍错地区构造地貌分析

基于ASTER-GDEM数据, 运用GIS空间分析统计技术, 通过对数字地形高程、地表起伏度、地表坡度等地貌参数的统计和条带剖面及面积-高程积分等数字地貌分析, 结合前人的区域地质资料及野外实地调查和验证, 对藏南羊卓雍错地区的构造地貌特征进行了初步分析, 旨在为研究区的盆地地貌格局演化分析提供依据。研究结果表明, 研究区构造地貌总体为一近东西向盆地, 最高海拔7515 m, 最低海拔2581 m, 属于高海拔-极高海拔区域; 流域内以平原和丘陵地貌为主, 平均起伏度为314 m, 平均坡度19°, 地势起伏较小, 发育有三级层状地貌; 盆地边缘受断裂的控制, 构造现象发育, 线性影像特征明显。其东、西部分别受桑日-错那断陷带和亚东-露谷断陷带所围限, 具有多期构造活动特征。盆地南北两侧受绒布-哲古断裂和邛多江断裂控制较弱。受构造活动和地表水流综合作用的影响, 盆地内部地貌已进入老龄化阶段。      

喜马拉雅造山带中段北坡构造地貌初步研究

研究区位于喜马拉雅造山带中段北坡, 构造地貌丰富、典型.本文在野外调查的基础上, 将区内构造地貌分为断裂构造地貌、花岗岩构造地貌、夷平面构造地貌、冰川构造地貌、水系及阶地构造地貌和土林构造地貌等类型, 并对这些构造地貌特征进行初步分析描述, 为喜马拉雅造山带的构造隆升研究提供了地表构造地貌上的有力信息.      

渤海蓬莱9-1油藏花岗岩储层特征与成储化条件分析

渤海蓬莱9-1潜山油藏是以中生代花岗岩风化壳为储层的大型油藏。在花岗岩潜山这个新领域的勘探实践中,储层特征及成储化条件是困扰勘探的关键。通过薄片、岩心、测井、地震以及野外露头等资料综合分析了花岗岩储层特征与形成条件。研究表明,花岗岩风化壳上部主要为孔隙型储层,风化壳底部主要为裂缝型储层,平面上花岗岩风化壳储层主要分布在花岗岩体东侧。花岗岩大型节理发育、风化淋滤作用、构造改造、古地貌和浅埋藏是花岗岩规模化成储的重要条件,其中花岗岩大型节理发育是规模化成储的内在基础,风化淋滤作用和构造改造是规模化成储的主导因素,古地貌和浅埋藏是规模化储层得以保存的关键,构造改造与古地貌的耦合关系决定了储层发育程度,构造改造强烈且处于古地貌斜坡区的花岗岩体东侧是有利储层发育区,构造改造较弱并处于古地貌斜坡区的花岗岩体西侧储层发育次之,构造改造较弱且位于古地貌高点的花岗岩体轴部储层最不发育。该研究成果对于蓬莱9-1花岗岩油藏的后续开发及类似花岗岩潜山的勘探具有参考意义。     

皖南地区离子吸附型稀土矿床成矿条件及成矿特征

安徽省皖南地区岩浆岩分布广泛,地貌、气候条件适宜,有利于花岗岩风化壳的形成、保存,且基岩的稀土丰度较高,有利于离子吸附型稀土矿床的形成。通过在皖南地区的工作,分析了离子吸附型稀土矿床的内生成矿条件(基岩中稀土丰度、稀土元素赋存状态、矿物组合及岩石结构、构造),表生成矿条件(气候条件、风化壳发育程度、地貌特征),总结了离子吸附型稀土矿床的成矿特征、找矿标志。     

新疆蒙库地区华力西中期花岗岩及其构造环境意义的研究

新疆蒙库地区位于阿尔泰山南麓,南接准噶尔盆地。该地区花岗岩分布广泛,主要为华力西中、晚期花岗岩。对蒙库地区华力西中期（石炭纪）花岗岩进行分析和研究认为,该区花岗岩处于华力西期大陆边缘造山带环境中,具有汇聚、造山期花岗岩的特征。岩浆沿断裂带上侵就位,为板块俯冲作用、陆—陆碰撞构造环境下形成的活动大陆边缘岩浆弧。深入研究蒙库地区花岗岩有助于我们了解这一时期该地区区域构造、岩浆演化的规律。     

罗平地区断裂构造的遥感研究

罗平地区地质构造背景复杂,对该区构造格架的研究有利于揭示弥勒—师宗断裂北缘的构造演化。本文利用遥感技术可以宏观上对断裂特征进行全面解译和识别的特点,结合野外调查,分析提取断裂的影像特征,在对断裂构造进行密度分析的基础上,详细分析了断裂在地质地貌上的特征。研究发现,罗平地区北东向断裂带是师宗—弥勒断裂北沿部分,对罗平地区三叠系以来的地层沉积和构造演化有着直接的控制作用,形成了断裂两侧不同的地貌特征。     

太行山地貌计量指标空间特征及其构造地貌意义

系统的地貌计量指标分析有助于理解造山带新构造活动特征与地貌演化.太行山地处中国第二、三地形阶梯的边界,具有重要的构造地貌意义.基于ASTER GDEM地形数据,对太行山按流域进行了面积高程积分、河长坡降指标(SL)和Hack剖面等地貌计量指标的分析,结合地层、构造等资料,探讨了太行山构造地貌演化特征.结果表明,在分析的...     

遥感与地震技术探测两淮地区新构造——兼析其与天然地震的关系

新构造的发育情况对于人类生产生活具有重要意义。本文利用遥感及物探（地震勘探）资料对两淮地区的新构造断层进行了解译。对比分析了两者的特点,初步总结了两淮新构造发育的特征,搜集了两淮地区历史上的地震记录,通过对新构造断层与地震记录的空间位置对比,初步分析了新构造活动易致地震的地段及地质原因。本文对于综合采用遥感及地震技术探测新构造具有参考价值,细化后可以为矿山地质灾害防治、巷道布置、城乡规划、地震灾害防治等提供直接的、详实可靠的有关新构造断层的地质资料。     

MAPGIS地理信息系统在化探工作中的应用——以洛古河普查区面积性土壤化探测量为例

在洛古河普查区面积性土壤化探测量中,将GPS（全球卫星定位系统）与MAPGIS（地理信息系统）结合在一起,对化探数据进行处理及成图.该方法技术的应用,具有成图快速准确、精度高、可操作性强的特点.绘制的异常图与地形地质图套合后,有利于对异常的解释和推断,指导探矿工程的布置.     

Landslides susceptibility mapping based on geographical information system,GuiZhou, south-west China

The purpose of this study is to assess the susceptibility of landslides around the area of Guizhou province, in south-west of China, using a geographical information system (GIS). The base map is prepared by visiting the field area and mapping individual landslide at a scale of 1:500,000 topographic maps. In the study, slope, lithology, landslide inventory, tectonic activity, drainage distribution and annual precipitation were taken as independent causal factors. Therefore, six causal factors maps are prepared by collecting information from various authorized sources and converting them in to GIS maps. The susceptibility assessment is based on the qualitative map combination model and trapezoidal fuzzy number weighting (TFNW) approach. Using a predicted map of probability, the study area was classified into four categories of landslide susceptibility: low, moderate, high and very high. In addition, the weighting procedure showed that the TFNW is an efficient method for landslide causal factors weighting.     

构造的第三副面孔─—地貌的构造分析

地貌构造分析或叫新构造分析，是大地构造研究中最重要的领域。新构造学探讨地貌及相关的沉积特征，以阐明岩石圈近地表部分的新构造作用。由于Ｓ．Ｓ．Ｓｃｈｕｌｔｚ、Ｖ．Ａ．Ｏ－ｂｒｕｃｈｅｖ、Ｎ．Ｉ．Ｎｉｋｏｌａｅｖ、Ｎ．Ａ．Ｆｌｏｒｅｎｓｏｖ等人的工作，新构造学已作为一门独立的学科出现在苏联地学界。新构造学理论的基础是年轻内生作用的速率和特征与地球表面地貌的相关性。区域和应用新构造学研究的目的在于依据地貌资料编汇新构造图及进行各类地质预测。本文以中亚、东亚及整个地球为例进行了探讨。     

构造的第三副面孔——地貌的构造分析

地貌构造分析或叫新构造分析,是大地构造研究中最重要的领域。新构造学探讨地貌及相关的沉积特征,以阐明岩石圈近地表部分的新构造作用。由于Ｓ．Ｓ．Ｓｃｈｕｌｔｚ、Ｖ．Ａ．Ｏ－ｂｒｕｃｈｅｖ、Ｎ．Ｉ．Ｎｉｋｏｌａｅｖ、Ｎ．Ａ．Ｆｌｏｒｅｎｓｏｖ等人的工作,新构造学已作为一门独立的学科出现在苏联地学界。新构造学理论的基础是年轻内生作用的速率和特征与地球表面地貌的相关性。区域和应用新构造学研究的目的在于依据地貌资料编汇新构造图及进行各类地质预测。本文以中亚、东亚及整个地球为例进行了探讨。     

Probabilistic landslide hazards and risk mapping on Penang Island, Malaysia

This paper deals with landslide hazards and risk analysis of Penang Island, Malaysia using Geographic Information System (GIS) and remote sensing data. Landslide locations in the study area were identified from interpretations of aerial photographs and field surveys. Topographical/geological data and satellite images were collected and processed using GIS and image processing tools. There are ten landslide inducing parameters which are considered for landslide hazard analysis. These parameters are topographic slope, aspect, curvature and distance from drainage, all derived from the topographic database; geology and distance from lineament, derived from the geologic database; landuse from Landsat satellite images; soil from the soil database; precipitation amount, derived from the rainfall database; and the vegetation index value from SPOT satellite images. Landslide susceptibility was analyzed using landslide-occurrence factors employing the probability-frequency ratio model. The results of the analysis were verified using the landslide location data and compared with the probabilistic model. The accuracy observed was 80.03%. The qualitative landslide hazard analysis was carried out using the frequency ratio model through the map overlay analysis in GIS environment. The accuracy of hazard map was 86.41%. Further, risk analysis was done by studying the landslide hazard map and damageable objects at risk. This information could be used to estimate the risk to population, property and existing infrastructure like transportation network.     

Probabilistic landslide susceptibility and factor effect analysis

The susceptibility of landslides and the effect of landslide-related factors at Penang in Malaysia using the geographic information system (GIS) and remote sensing data have been evaluated. Landslide locations were identified in the study area from interpretation of aerial photographs and from field surveys. Topographical and geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. The factors chosen that influence landslide occurrence were: topographic slope, topographic aspect, topographic curvature and distance from drainage, all from the topographic database; lithology and distance from lineament, taken from the geologic database; land use from Landsat Thermatic Mapper (TM) satellite images; and the vegetation index value from SPOT HRV (High-Resolution Visible) satellite images. Landslide hazardous areas were analyzed and mapped using the landslide-occurrence factors employing the probability–frequency ratio method using the all factors. To assess the effect of these factors, each factor was excluded from the analysis, and its effect verified using the landslide location data. As a result, all factors had relatively positive effects, except lithology, on the landslide susceptibility maps in the study area.     

Quantitative landslide susceptibility mapping at Pemalang area,Indonesia

For quantitative landslide susceptibility mapping, this study applied and verified a frequency ratio, logistic regression, and artificial neural network models to Pemalang area, Indonesia, using a Geographic Information System (GIS). Landslide locations were identified in the study area from interpretation of aerial photographs, satellite imagery, and field surveys; a spatial database was constructed from topographic and geological maps. The factors that influence landslide occurrence, such as slope gradient, slope aspect, curvature of topography, and distance from stream, were calculated from the topographic database. Lithology was extracted and calculated from geologic database. Using these factors, landslide susceptibility indexes were calculated by frequency ratio, logistic regression, and artificial neural network models. Then the landslide susceptibility maps were verified and compared with known landslide locations. The logistic regression model (accuracy 87.36%) had higher prediction accuracy than the frequency ratio (85.60%) and artificial neural network (81.70%) models. The models can be used to reduce hazards associated with landslides and to land-use planning.     

Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland,Malaysia

This paper presents landslide susceptibility analysis around the Cameron Highlands area, Malaysia using a geographic information system (GIS) and remote sensing techniques. Landslide locations were identified in the study area from interpretation of aerial photographs and field surveys. Topographical, geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing. Ten landslide occurrence factors were selected as: topographic slope, topographic aspect, topographic curvature and distance from drainage, lithology and distance from lineament, soil type, rainfall, land cover from SPOT 5 satellite images, and the vegetation index value from SPOT 5 satellite image. These factors were analyzed using an advanced artificial neural network model to generate the landslide susceptibility map. Each factor’s weight was determined by the back-propagation training method. Then, the landslide susceptibility indices were calculated using the trained back-propagation weights, and finally, the landslide susceptibility map was generated using GIS tools. The results of the neural network model suggest that the effect of topographic slope has the highest weight value (0.205) which has more than two times among the other factors, followed by the distance from drainage (0.141) and then lithology (0.117). Landslide locations were used to validate the results of the landslide susceptibility map, and the verification results showed 83% accuracy. The validation results showed sufficient agreement between the computed susceptibility map and the existing data on landslide areas.