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1.
潜在地震滑坡危险区区划方法   总被引:5,自引:0,他引:5       下载免费PDF全文
不同地区地震活动的强度和频率是不同的.基于地震危险性分析的地震滑坡危险研究在综合了地震烈度、位置、复发时间等因素的基础上,考虑了地震动峰值加速度时空分布的特点,可以有效地应用于潜在地震滑坡危险区区划.以汶川地震灾区为研究对象,根据研究区的地质构造、地震活动特点等划分出灾区的潜在震源区,对该区进行地震危险性分析,并在此基础上采用综合指标法做出基于地震危险性分析的地震滑坡危险性区划.所得地震滑坡危险性区划按照滑坡危险程度分为高危险、较高危险、较低危险和低危险四级,表示未来一段时间内研究区在遭受一定超越概率水平的地震动作用下,不同地区地震滑坡发生的可能程度. 本文给出的地震滑坡危险性区划结果中,汶川地震滑坡崩塌较发育的汶川、北川、茂县等部分区域均处于高危险或较高危险区域;在对具有较高DEM精度的北川擂鼓镇地区所作的地震滑坡危险性区划中,汶川地震中实际发生的地震滑坡灾害与地震滑坡危险区划结果表现出较好的一致性.对区域范围而言,基于地震危险性分析的地震滑坡区划,可为初期阶段的土地规划使用及重大工程选址提供参考.  相似文献   

2.
汶川地震滑坡危险性评价——以武都区和文县为例   总被引:1,自引:0,他引:1       下载免费PDF全文
利用GIS技术详细研究汶川地震在甘肃省陇南市武都区和文县触发的滑坡地质灾害的分布规律及其与地震烈度、地形坡度、断层、高程、地层岩性的相关关系,采用基于GIS的加权信息量模型的崩塌滑坡危险性评价方法,对研究区的地震滑坡危险性进行学科分析。结果表明:极高危险区在高程上主要分布在集水高程区,高度危险区主要沿白水江、白龙江等主干河流两侧极高易发区的边界向两侧扩展,轻度和极轻度危险区面积占比较小,主要分布在低烈度、活动断裂不发育、人类活动微弱的高海拔地区,另外国道G215沿极高危险性区域分布明显;利用危险性等级分区结果统计人口公里格网数据,得到武都区和文县潜在影响人口,发现研究区约78万人将受到地震滑坡灾害的潜在影响。  相似文献   

3.
采用第五代地震动参数区划图的潜在震源区划分方案并结合Newmark位移模型,基于陇县工程地质岩性特征及地形高程数据,考虑地震动地形放大效应以及Newmark模型参数的不确定性,得出陕西陇县地区的地震动发生率为50年10%水平下滑坡的失稳概率,根据所得结果将研究区的潜在地震滑坡危险程度分为四个等级:极低危险区、低危险区、中危险区、高危险区。中、高危险区主要集中于陇县地区的泥岩、粉砂岩以及黄土覆盖地且斜坡坡度大于40°的地区,其中千河及其通关河两岸部分地区的地震滑坡危险性较高。本文结果可为该地区的地震滑坡风险管理和土地规划提供参考。  相似文献   

4.
地震诱发滑坡的危险性分析与预测   总被引:1,自引:0,他引:1  
徐桂弘 《内陆地震》2008,22(2):188-192
结合地震滑坡的特点和相关文献研究,介绍了地震力的分析方法、地震滑坡的机理、地震危险性分析的方法、地震活动性参数的确定方法以及场点地震危险性概率计算原则。将两种地震诱发滑坡预测结果进行对比,分析结果表明,地震滑坡危险区主要集中在中国西部地区(川、滇、甘、陕、新疆等省区)及中国台湾地区,随预测年限的增加场地的地震滑坡危险性也随之增高,地震崩塌滑坡的危险区域明显加大。  相似文献   

5.
许冲  徐锡伟 《地球物理学报》2012,55(9):2994-3005
基于统计学习理论与地理信息系统(GIS)技术的地震滑坡灾害空间预测是一个重要的研究方向,其可以对相似地震条件下地震滑坡的发生区域进行预测.2010年4月14日07时49分(北京时间),青海省玉树县发生了Mw6.9级大地震,作者基于高分辨率遥感影像解译与现场调查验证的方法,圈定了2036处本次地震诱发滑坡,这些滑坡大概分布在一个面积为1455.3 km2的矩形区域内.本文以该矩形区域为研究区,以GIS与支持向量机(SVM)模型为基础,开展基于不同核函数的地震滑坡空间预测模型研究.应用GIS技术建立玉树地震滑坡灾害及相关滑坡影响因子空间数据库,选择高程、坡度、坡向、斜坡曲率、坡位、水系、地层岩性、断裂、公路、归一化植被指数(NDVI)、同震地表破裂、地震动峰值加速度(PGA)共12个因子作为地震滑坡预测因子.以SVM模型为基础,基于线性核函数、多项式核函数、径向基核函数、S形核函数等4类核函数开展地震滑坡空间预测研究,分别建立了玉树地震滑坡危险性指数图、危险性分级图、预测结果图.4类核函数对应的模型正确率分别为79.87%,83.45%,84.16%,64.62%.基于不同的训练样本开展模型训练与讨论工作,表明径向基核函数是最适用于该地区的地震滑坡空间预测模型.本文为地震滑坡空间预测模型中核函数的科学选择提供了依据,也为地震区的滑坡防灾减灾工作提供了参考.  相似文献   

6.
通过对伊犁地区地貌、地质构造、历史地震和地质灾害分布的研究,运用Logistic回归模型方法,分析伊犁地区地貌、地层岩性、地形坡度等地震滑坡影响因子,采用ArcGIS的空间分析特性和SPSS软件的统计功能,得到伊犁地区地震滑坡危险性模型和滑坡危险性分布图。认为伊犁地区地震滑坡危险性较高的地区主要位于特克斯县、尼勒克县、巩留县、新源县境内,较低的地区位于霍城县、昭苏县和察布查尔锡伯自治县境内。并且极高危险区面积占伊犁地区总面积的1%,高危险区面积占6%,中危险区面积占18%,低危险区面积占39%,极低危险区分区面积占37%。该成果可以为大震现场调查、灾后重建、规划选址等方面提供参考依据。  相似文献   

7.
地貌信息熵是判断地貌发育演化阶段的量化指标,常用以表示流域地貌面的受侵蚀程度,是地形地貌因子的反映。以GIS技术为操作平台,利用芦山地震滑坡体积作为泥石流物质来源数据,采用地貌信息熵方法,对55条泥石流沟进行了基于滑坡物源的泥石流危险性区划研究,期望能为即将来临的雨季做好泥石流危险区规划和防灾工程部署提供参考。研究结果表明:研究区泥石流沟谷流域地貌信息熵值变化范围为0.003 2~0.938 1,沟谷地貌演化从幼年期至老年期均有分布;泥石流危险区面积自极高危险区至极低危险区基本呈现递减趋势,80.77%研究区面积的泥石流沟谷比较活跃,处于幼年期—壮年期的泥石流沟谷增加了泥石流发生的危险性;泥石流沟谷流域斜坡物质响应率变化范围为0~133.24mm,低度和极低度物源敏感区面积共占研究区沟谷流域面积的72.93%,表明近的泥石流沟谷流域对滑坡物源不敏感;基于滑坡物源的泥石流危险性评价结果表明,以上的泥石流沟谷流域处于中度及以上危险区,泥石流活动较为活跃。  相似文献   

8.
山区强烈地震诱发的滑坡崩塌是一种致灾严重、发生范围较广的灾害,进行地震滑坡危险区划是降低损失的有效手段之一。以2013年4月20日MW7.0芦山地震为例,以芦山县、宝兴县及其周边受滑坡崩塌灾害影响较为严重的区域作为研究对象,选定地层岩性、坡度、地震烈度、距断层距离和距水系距离等5类与地震滑坡关系密切的影响因子,采用层次分析法确定每个影响因子的权重,继而采取综合指标法,将研究区划分为低危险、中度危险、较高危险和最高危险4个等级的危险区,用以表示该区域在遭受给定的地震烈度作用下发生地震滑坡的可能性的大小。实地勘察的滑坡点分布与预测的地震滑坡危险区的对比表明,两者吻合程度较高,约有77%的滑坡点落在较高危险和最高危险区。研究成果可为地震滑坡灾害应急、山区地震滑坡预测、滑坡灾害预防等工作提供参考依据。  相似文献   

9.
陈帅  苗则朗  吴立新 《地震学报》2022,44(3):512-527
地震滑坡危险性评估可为震后应急响应等提供科学的决策依据。纽马克位移法可不依赖同震滑坡编目快速评估同震滑坡危险性。工程岩体物理力学参数是该方法的核心参数之一,但其赋值过于单一,难以反映复杂地质背景下岩体强度的空间差异性。针对上述问题,本文在分析地震滑坡影响因子的基础上,选择距断层距离、高程和距水系距离作为影响岩体强度的评价指标并建立岩体强度评价模型,获得区域岩体强度修正系数,进而修正传统方法的临界加速度。结合震后的即时地震动峰值加速度,采用简化纽马克位移法计算边坡累积位移,开展地震滑坡危险性快速评估,并以汶川MW7.9地震的地震滑坡危险性评估为例验证本文方法。结果表明,相对于传统方法,本文方法划分的地震滑坡危险区与同震滑坡分布更加一致。   相似文献   

10.
构建基于GIS的地震危险性分区评价模型,选取地震震级、地震频次、断层长度、断裂时代及断裂性质5个评价要素,依据各要素特征提出相应的定量化处理方法,最后依据各评价要素权重,通过叠加分析获得评价结果;利用这一方法对西藏日喀则地区进行地震危险性分区评价,结果显示:日喀则地区的地震高危险区主要分布于谢通门—拉孜一带及南木林东北,地震危险区主要分布于高危险区外缘、日喀则市区及南木林县的小部分地区,其他地区属次较安全区、较安全区或安全区。其结果可为日喀则地区的国土规划提供参考依据。  相似文献   

11.
An MW6.6 earthquake occurred in eastern Hokkaido, Japan on September 6th, 2018. Based on the pre-earthquake image from Google Earth and the post-earthquake image from high resolution (3 m) planet satellite, we manually interpret 9 293 coseismic landslides and select 7 influencing factors of seismic landslide, such as elevation, slope, slope direction, road distance, flow distance, peak ground acceleration (PGA) and lithology. Then, 9 293 landslide points are randomly divided into training samples and validation samples with a proportion of 7:3. In detail, the training sample has 6 505 landslide points and the validation sample has 2 788 landslide points. The hazard risk assessment of seismic landslide is conducted by using the information value method and the study area is further divided into five risk grades, including very low risk area, low risk area, moderate risk area high risk area and very high risk area. The results show that there are 7 576 landslides in high risk area and very high risk area, accounting for 81.52% of the total landslide number, and the landslide area is 22.93 km2, accounting for 74.35% of the total area. The hazard zoning is in high accordance with the actual situation. The evaluation results are tested by using the curve of cumulative percentage of hazardous area and cumulative percentage of landslides number. The results show that the success rate of the information value method is 78.50% and the prediction rate is 78.43%. The evaluation results are satisfactory, indicating that the hazard risk assessment results based on information value method may provide scientific reference for landslide hazard risk assessment as well as the disaster prevention and mitigation in the study area.  相似文献   

12.
The MS7.0 Jiuzhaigou earthquake in Sichuan Province of 8 August 2017 triggered a large number of landslides. A comprehensive and objective panorama of these landslides is of great significance for understanding the mechanism, intensity, spatial pattern and law of these coseismic landslides, recovery and reconstruction of earthquake affected area, as well as prevention and mitigation of landslide hazard. The main aim of this paper is to present the use of remote sensing images, GIS technology and Logistic Regression(LR)model for earthquake triggered landslide hazard mapping related to the 2017 Jiuzhaigou earthquake. On the basis of a scene post-earthquake Geoeye-1 satellite image(0.5m resolution), we delineated 4834 co-seismic landslides with an area of 9.63km2. The ten factors were selected as the influencing factors for earthquake triggered landslide hazard mapping of Jiuzhaigou earthquake, including elevation, slope angle, aspect, horizontal distance to fault, vertical distance to fault, distance to epicenter, distance to roads, distance to rivers, TPI index, and lithology. Both landsliding and non-landsliding samples were needed for LR model. Centroids of the 4834 initial landslide polygons were extracted for landslide samples and the 4832 non-landslide points were randomly selected from the landslide-free area. All samples(4834 landslide sites and 4832 non-landslide sites)were randomly divided into the training set(6767 samples)and validation set(2899 samples). The logistic regression model was used to carry out the landslide hazard assessment of the Jiuzhaigou earthquake and the results show that the landslide hazard assessment map based on LR model is very consistent with the actual landslide distribution. The areas of Wuhuahai-Xiamo, Huohuahai and Inter Continental Hotel of Jiuzhai-Ruyiba are high hazard areas. In order to quantitatively evaluate the prediction results, the trained model calculated with the training set was evaluated by training set and validation set as the input of the model to get the output results of the two sets. The ROC curve was used to evaluate the accuracy of the model. The ROC curve for LR model was drawn and the AUC values were calculated. The evaluation result shows good prediction accuracy. The AUC values for the training and validation data set are 0.91 and 0.89, respectively. On the whole, more than 78.5% of the landslides in the study area are concentrated in the high and extremely high hazard zones. Landslide point density and landslide area density increase very rapidly as the level of hazard increases. This paper provides a scientific reference for earthquake landslides, disaster prevention and mitigation in the earthquake area.  相似文献   

13.
Forests play a significant role in protecting people, settlements in mountainous terrains from hydrogeomorphic hazards, including shallow landslides. Although several studies have investigated the interactions between forests and slope instabilities, a full understanding of them has not yet been obtained. Additionally, models that incorporate forest stand properties into slope failure probability analyses have not been developed. In principle, physical‐based models, which are powerful tools for landslide hazard analyses, represent an appropriate approach to linking stand properties and slope stability. However, the reliability of these models depends on numerous parameters that describe highly complex geotechnical and hydrological processes (e.g. potential failure depth, saturation ratio, root reinforcement, etc.) that are difficult to measure and model. In particular, the spatial heterogeneity of root reinforcement remains a problem, and the use of physically based models from a forest management perspective has been limited. This paper presents a procedure for assessing slope stability in terms of the Factor of Safety that accounts for forest stand characteristics such as tree density, average diameter at breast height and minimum distance between trees. The procedure combines a three‐dimensional (3D) slope stability model with an evaluation of the variability of root reinforcement in terms of a probability distribution, according to forest characteristics. Monte Carlo simulation is used to account for the residual uncertainties in both stand characteristics and 3D stability model parameters. The proposed method was applied in a subalpine catchment in the Italian Alps, mainly covered by coniferous forest and characterized by steep slopes and high landslide risk. The results suggest that the procedure is highly reliable, according to landslide inventory maps [area under the ROC curve (AUC) is 0.82 and modified success rate (MSR) is 0.70]. Thus, it represents a promising tool for studying the role of root reinforcement in landslide hazard mapping and guiding forest management from a slope stability perspective. Copyright © 2017 John Wiley & Sons, Ltd.  相似文献   

14.
Landslides threaten lives and property throughout the United States, causing in excess of $2 billion in damages and 25–50 deaths annually. In regions subjected to urban expansion caused by population growth and/or increased storm intensities caused by changing climate patterns, the economic and society costs of landslides will continue to rise. Using a geographic information system (GIS), this paper develops and implements a multivariate statistical approach for mapping landslide susceptibility. The presented susceptibility maps are intended to help in the design of hazard mitigation and land development policies at regional scales. The paper presents (a) a GIS‐based multivariate statistical approach for mapping landslide susceptibility, (b) several dimensionless landslide susceptibility indexes developed to quantify and weight the influence of individual categories for given potential risk factors on landslides and (c) a case study in southern California, which uses 11 111 seismic landslide scars collected from previous efforts and 5389 landslide scars newly digitized from local geologic maps. In the case study, seven potential risk factors were selected to map landslide susceptibility. Ground slope and event precipitation were the most important factors, followed by land cover, surface curvature, proximity to fault, elevation and proximity to coastline. The developed landslide susceptibility maps show that areas classified as having high or very high susceptibilities contained 71% of the digitized landslide scars and 90% of the seismic landslide scars while only occupying 26% of the total study area. These areas mostly have ground slopes higher than 46% and 2‐year, 6‐hour precipitation greater than 51 mm. Only 12% of digitized landslides and less than 1% of recorded seismic landslides were located in areas classified as low or very low susceptibility, while occupying 42% of the total study region. These areas mostly have slopes less than 27% and 2‐year, 6‐hour precipitation less than 41 mm. Copyright © 2007 John Wiley & Sons, Ltd.  相似文献   

15.
以四川省地理环境条件为研究背景,根据相关单位提供的资料,对四川省滑坡危险性等级进行了区划研究。将地形地貌、地层岩性、断裂构造、河流水系、降雨量、地震烈度等6项作为滑坡的主要影响因素,采用模式识别方法进行滑坡危险性等级区划,对算法中所涉及到的危险系数计算公式、因子权重分析、阈值选取等进行了一系列控制试验,验证了算法的可行性以及降低算法中存在的不确定性。采用“识别率”和“改变率”2个准则来判断“分类结果作为新的训练集”,即RTS试验的收敛性,从而给出识别率高、改变率稳定的分类结果,以及能合理反映识别结果的最佳参数。通过3次逐级识别分类,将四川省滑坡危险度划分为7个等级,区划结果与实际滑坡发生情况吻合。本文方法同样适用于其它地区的滑坡危险性等级区划。  相似文献   

16.
刘杰  武震 《地震工程学报》2020,42(6):1723-1734
本研究以围绕着白龙江流域的甘肃省南部的宕昌县、舟曲县和武都区部分地区为研究区,根据全国滑坡编目中得到的272个历史滑坡数据以及选取的高程、坡度、坡向、平面曲率、剖面曲率、归一化植被指数(NDVI)、降雨、岩性、距道路距离和距河流距离10种影响因子,利用三种具有代表性的定量方法:信息量模型、以及基于频率比模型的逻辑回归模型和人工神经网络模型对研究区内滑坡灾害危险性进行评价。三种评价结果均显示研究区内滑坡灾害的极高和高危险区主要沿白龙江河谷地区呈带状分布。从危险性分区图可看出,人工神经网络模型得到的分区图较为合理,既表现出沿河谷地区集中分布的趋势,也呈现出对滑坡历史数据较为独立的特征,这一研究结果与前人研究结果一致。根据受试者工作特征曲线(ROC曲线)对三种模型的精度进行检验,检验得到的AUC值分别为0.818、0.829和0.837,说明三种评价结果均具有较高的可靠性,基于频率比模型的人工神经网络模型相比其他两个模型具有更好的评价精度,能更好地进行滑坡危险性的预测和评价,其中高程、降雨、岩性以及距道路距离对评价结果影响更大,这四种影响因子重要性值占比为52.1%。为该地区的城市扩建与灾害预防预测提供了参考。  相似文献   

17.
GIS支持下的地震诱发滑坡危险区预测研究   总被引:24,自引:0,他引:24  
唐川  朱静  张翔瑞 《地震研究》2001,24(1):73-81
为了满足对地震诱发滑坡危险区预测的不断增长的迫切要求,灾害评价成为帮助决策过程重要的基础工具之一。即使地震滑坡危险性各组份的评价很困难,但地理信息可辅助提出这种灾害制图的有关方法。描述了用于地理信息系统识别和定量计算不同地震滑坡危险区的技术方法,确定了地震烈度、地形坡度、岩土体类型和现存滑坡密度共4个因子参与的地震诱发滑坡危险性分析。在ARC/INFO DRID支持下,进行叠合分析,由此编制了云南省地震诱发滑坡危险区预测图。由地貌学家提出的地震诱发滑坡预测为规划和工程师提供了对区域规划和建筑工程有价值的技术方法。  相似文献   

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