2008年MS8.0汶川地震诱发滑坡灾害在映秀地区的演化特征

强烈的地震不仅能够在山岳地区触发大量的同震滑坡,对震后灾区地质灾害的发育水平也存在重要影响。因此,研究地震滑坡的演化特征对于强震区地质灾害防治具有重要作用。文中以2008年M_S8. 0汶川地震震中附近受到强震扰动的映秀为研究区,通过对该区域(面积约66km²)震前1期(2005年4月)、震后5期(2008年6月、2011年4月、2013年4月、2015年5月和2017年5月)的高分辨率影像进行滑坡解译和编录,借助GIS平台,获取了高程、坡度、坡向、曲率、地层岩性、距最近水系的距离和距发震断裂的距离这7个主要因子的数据,研究地震滑坡的长期演化特征;同时,应用相关分析方法对比了不同时期的滑坡活动性强度,对该区域内汶川地震诱发的滑坡灾害的演化规律进行了研究。结果表明,2008—2017年,研究区内的滑坡总面积急剧减少,同震滑坡面积从21. 41km²降低到1. 33km²,表明震后滑坡的灾害活动程度已经恢复或接近震前水平。整体而言,研究区内滑坡的规模不断减小,滑坡活动性随着时间减弱,再活动滑坡和新增滑坡数...     

川滇地区强震孕育过程中的中等地震动态演化特征

引用有关地震活动的频度面积S值方法^[1],对川滇地区1970年以来6．7级以上强震前后的中等地震活动进行研究,发现强震的S值在震前和震后的异常变化特征既有共性又有个性。共性主要表现为：在震前S值异常为高值或低值异常,震后下降或上升;对特定区域研究发现,滇西南地区强震的孕震区中等地震活动的S值震前异常主要为高值,而滇西地区强震孕震区中等地震活动的S值震前异常主要表现为低值。表明对川滇地区强震孕震区中等地震活动的频度面积S值方法的动态追踪,利用其变化规律可以为强震的预测预报提供一定的判定指标。     

地震崩塌滑坡危险性应急评估模型效果对比——以2022年6月1日MW5.8芦山地震为例

震后快速准确获取同震崩塌滑坡的分布范围和评估可能的灾害损失对地震灾害应急救援和安置规划至关重要,2022年M_W5.8芦山地震为开展不同评价模型在区域地震崩塌滑坡的快速评估研究提供了宝贵窗口。文中选用基于机器学习方法建立的新一代中国地震滑坡危险性模型(下文称Xu₂₀₁₉模型)和简易Newmark模型进行了2022年M_W5.8芦山地震崩塌滑坡快速应急评估研究,基于本次事件的地震滑坡数据库(包括2 352处崩塌滑坡,面积为5.51km²),探讨2种模型的准确性和适用性。结果表明,基于Xu₂₀₁₉模型计算得到的崩塌滑坡面积为5.07km²,与实际崩塌滑坡面积十分吻合,而基于Newmark模型计算预测的崩塌滑坡面积达21.3km²。从评估结果的空间分布上来看,2种模型预测的高危险区域基本一致,高危险区域基本位于发震断层的上盘。但Xu₂₀₁₉模型对于西北区域(崩塌滑坡集中发育区)的危险性预测明显偏低,而Newmark模型对西...     

澜沧江大朝山水库蓄水前的地震活动本底

分析了大朝山水库蓄水前，水库区域历史中强、强地震背景与1992年来的地震活动格局，及蓄水前9年中库坝区天然地震活动的时、空、强分布，结果表明：大朝山水库区为地震活动水平较低的区域，库坝附近30km以2、3级地震活动为主，库首方向距大坝40km左右的景谷与库尾方向距大坝50km的昌宁、凤庆一带为5级地震活动区，外围100km左右的耿马、澜沧、普洱等地区为6、7级强震活动区；库坝区的地震活动基本符合G—R关系式，b值为0．69，累积应变曲线呈稳定增长趋势，时间分布上出现活跃与平静交替的分布格局。     

基于信息量和逻辑回归耦合模型的黄土地震滑坡危险性分析

黄土地震滑坡危险性分析对黄土地区城镇化、工程建设的规划和地震灾害预防具有重要意义。以甘肃省定西市岷县—漳县交界处为研究区域,通过统计分析该区历史地震滑坡灾害数据,归纳并建立包含地震、坡度、坡高、坡向、地层岩性、年平均降雨量、河流流域和地貌类型等8个影响因子的评价指标体系,采用信息量模型、逻辑回归模型和信息量-逻辑回归耦合模型分别分析该区域黄土地震滑坡危险性。结果表明：(1)地震、河流和降雨是诱发黄土滑坡灾害发生的主要因素,其中地震因子贡献率最大;(2)研究区可划分为高、较高、中、低和极低危险区五个等级,其中高危险区主要集中于岷县、漳县与陇西县等地;(3)根据受试者工作特性(ROC)曲线精度检验结果,三种模型的AUC值分别为0.889、0.617和0.898,信息量-逻辑回归耦合模型结果的精确性相比其他两个模型更高。     

基于GIS的地震滑坡危险性分析研究——以伊犁地区为例

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。     

地震滑坡危险性评估模型及初步应用

统计分析了汶川地震滑坡在不同影响因子下数量和密度,然后采用归一化方法确定影响地震滑坡的关键因子。基于关键因子建立了地震滑坡密度数学模型。结果表明:从滑坡数量看,滑坡主要集中在(10°~20°)至(40°~50°)的坡度(90 m栅格)区间,主体集中在35°附近,在3°~7°之间有一个滑坡分布的小峰值;滑坡密度随着坡度的增加而增加,在3°~7°的低坡地带有一个滑坡密度小峰值,在坡度一定的情况下,除了烈度外,其他影响因子下滑坡密度并没有表现出足够明显的规律性变化;归一化计算结果表明:坡度和烈度是地震滑坡的关键因子;根据逻辑斯蒂模型计算的结果,将地震滑坡危险性分为几无(≤0.01)、轻微(0.01~0.03)、中等(0.03~0.09)、严重(0.09~0.27)、特重(≥0.27)5个等级。地震滑坡危险性预测数据在鲁甸地震等地震应急中发挥了较好作用。     

滇西南地震活动特征分析

对滇西南1970年以来所有M≥4.6级地震前的空间活动图像进行研究,用地震频度作为参数描述地震前区域的地震活动增强与平静现象,用空区、条带描述地震的孕震过程,得到以下认识:①地震频度增加是滇西南地区5级以上地震前的共性特征;②滇西南地区M≥4.6级地震前出现空区的比例随着地震震级的增加而有所增加,4.6~4.9级地震前只有60%的地震出现空区,5.0~5.9级地震前有90.9%的地震出现空区,而6.0级以上地震前全部出现空区;③空区平均持续时间也随着震级的加大而增加,空区平均持续时间为13.58个月,最短为3个月,最长为37个月;④主震位置(27/33)多发生在空区边缘(含长轴端12次、短轴端8次)。震前出现条带的规律则不明显。     

不同高度和不同地磁扰动期间热层大气密度模式值与探测值的显著差异

热层大气密度是空间大气环境的重要参数,经过多年的研究已开发了多种大气模式,但其误差普遍较大,尤其在磁暴期间偏差值甚至超过100%.本文利用中国星载大气密度探测器和CHAMP卫星加速度计在轨获得的连续探测数据,针对近10年(2003—2014)中多次强磁暴事件和多次中等强度扰动事件,即2003年11月、2004年7月和2005年8~9月多次强磁暴事件(Kp值均达到9),2006年4月、2012年4月的两次中等强度磁暴事件(Kp值分别达到7和6),分析和比对不同强度磁扰事件期间不同高度全球大气密度就位探测值与模式值(NRLMSISE00)之间的差别.在2005年8月24日强磁扰事件中,560 km高度中国卫星就位探测值上涨幅度约2~3倍,扰动区中的增变比高达5.7倍,375 km高度CHAMP卫星就位探测值上涨幅度约0.8倍,扰动区中增变比达4.0倍,期间大气密度模式值不仅没有出现明显的涨落,更没有出现强烈的区域扰动;在2003年11月和2004年7月的强磁扰事件中,CHAMP卫星就位探测值均有显著涨变和强烈扰动变化,而模式值无明显扰动变化;在中等强度磁扰事件中,高度560 km附近就位探测值在北、南半球高纬地区显著上涨,远高于模式值,高度350 km附近就位探测值在地球阴影区域显著上涨,上涨幅度也大于模式值.分析结果表明现有大气模式对地磁扰动(尤其是强磁暴事件)期间全球热层大气密度的响应并不明显,需要进一步改善.     

滇西南地区龙陵—澜沧断裂带地震危险性评价

龙陵—澜沧新生地震断裂带位于地震活动强烈的滇西南地区, 该地区历史上曾发生多次M_S≥7.0大地震, 已知的历史地震破裂几乎覆盖了整条断裂带． 本文首先对滇西南地区主要断裂的最新构造活动特征进行分析; 然后通过该区域地震活动b值图像的空间扫描计算, 圈定出高应力集中区, 并结合历史地震和现今小震的分布情况以及晚第四纪以来断裂的活动强度、 古地震最晚离逝时间等定量参数, 综合分析龙陵—澜沧断裂带的未来大震危险性; 最后基于地震空区理论, 识别该断裂带存在的地震空区． 研究结果表明, 龙陵—澜沧断裂带内的龙陵、 永德、 沧源、 澜沧及孟连断裂中东段在未来10年内均存在发生中强以上地震的危险性, 应引起关注．      

GIS‐based regional landslide susceptibility mapping: a case study in southern California

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.     

Emergency assessment of seismic landslide susceptibility： a case study of the 2008 Wenchuan earthquake affected area

The 8.0 Mw Wenchuan earthquake triggered widespread and large scale landslides in mountainous regions.An approach was used to map and assess landslide susceptibility in a given area. A numerical rating system was applied to five factors that contribute to slope instability. Factors such as lithology, topography, streams and faults have an important influence as event-controlling factors for landslide susceptibility assessment. A final map is provided to show areas of low,medium, and high landslide susceptibility. Areas identified as having high landslide susceptibility were located in the central,northeastern, and far south regions of the study area. The assessment results will help decision makers to select safe sites for emergency placement of refuges and plan for future reconstruction. The maps may also be used as a basis for landslide risk management in the study area.     

基于长时间尺度下滑坡易发性评价研究———以甘肃省永靖县为例

利用决策树模型,基于五期土地利用评价因子,对甘肃省永靖县进行近40年的长时间尺度下的滑坡易发性评价,五期评价结果均显示研究区内滑坡灾害的极高和高易发性区域主要集中在中部黄河流域(盐锅峡镇至刘家峡水电站段)周边、西南部川城村—红泉镇—王台乡周边区域以及中部偏东的三条岘乡,该区域人口密集,人类活动较多.研究结果与前人研究结果类似,且通过受试者工作特征曲线的精度检验,说明五期评价结果均具有较高的可靠性.另外,研究区内的自然植被和裸土地与滑坡易发性指标之间具有负相关关系,而旱地、水域和城乡建设用地等人类活动频繁的区域则更容易导致滑坡灾害的发生.从时间尺度上来看,极高和高易发性分区面积逐年下降,但自 2000年,极高和高易发性分区面积减少速度出现显著减缓,同期,该区域内的土地利用变化为城乡建设用地面积增加而植被面积减少,这使得区域内边坡稳定性下降,使部分防灾工程措施的减灾能力下降.本研究为该地区的灾害预防、预测和城乡土地规划提供了参考.     

联合DBSCAN聚类采样和SVM分类的滑坡易发性评价

针对基于机器学习的滑坡易发性评价中非滑坡样本选取不规范导致的分类精度较低问题,本文提出联合基于密度的噪声应用空间聚类(Density-Based Spatial Clustering of Applications with Noise,DBSCAN)采样策略和支持向量机(Support Vector Machine,...     

DATABASE OF LANDSLIDES TRIGGERED BY 2015 GORKHA(NEPAL) MW7.8 EARTHQUAKE

In this study, a detailed database of landslides triggered by the 25 April 2015 Gorkha (Nepal)M_W7.8 earthquake is constructed based on visual interpretation of pre- and post-earthquake high-resolution satellite images and field reconnaissance. Results show the earthquake triggered at least 47 200 landslides, which have a NWW direction spatial distribution, similar with the location and strike of the seismogenic fault. The landslides are of a total area about 110km² and an oval distribution area about 35 700km². On the basis of a scale relationship between landslide area (A)and volume (V), V=1.314 7×A^{1.208 5}, the total volume of the coseismic landslides is estimated to be about 9.64×10⁸m³. In the oval landslide distribution area, the landslide number density, area density, and volume density were calculated and the results are 1.32km^-2, 0.31%, and 0.027m, respectively. This study provides a detailed and objective inventory of landslides triggered by the Gorkha earthquake, which provides very important and essential basic data for study of mechanics of coseismic landslides, spatial pattern, distribution law, and hazard assessment. In addition, the landslide database related to an individual earthquake also provides an important earthquake case in a subduction zone for studying landslides related to multiple earthquakes from a global perspective.     

APPLICATION OF LOGISTIC REGRESSION MODEL FOR HAZARD ASSESSMENT OF EARTHQUAKE-TRIGGERED LANDSLIDES: A CASE STUDY OF 2017 JIUZHAIGOU(CHINA)MS7.0 EVENT

The M_S7.0 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.     

Comparison of landslide susceptibility maps generated through multiple logistic regression for three test areas in Korea

The aim of this study was to apply, verify and compare a multiple logistic regression model for landslide susceptibility analysis in three Korean study areas using a geographic information system (GIS). Landslide locations were identified by interpreting aerial photographs, satellite images and a field survey. Maps of the topography, soil type, forest cover, lineaments and land cover were constructed from the spatial data sets. The 14 factors that influence landslide occurrence were extracted from the database and the logistic regression coefficient of each factor was computed. Landslide susceptibility maps were drawn for these three areas using logistic regression coefficients derived not only from the data for that area but also using those for each of the other two areas (nine maps in all) as a cross‐check of method validity. For verification, the results of the analyses were compared with actual landslide locations. Among the nine cases, the Janghung exercise using the logistic formula and the coefficient for Janghung had the greatest accuracy (88·44%), whereas Janghung results, when considered by the logistic formula and the coefficient for Boeun, had the least accuracy (74·16%). Copyright © 2007 John Wiley & Sons, Ltd.     

Spatio-temporal analysis and simulation on shallow rainfall-induced landslides in China using landslide susceptibility dynamics and rainfall I-D thresholds

An empirical simulation method to simulate the possible position of shallow rainfall-induced landslides in China has been developed. This study shows that such a simulation may be operated in real-time to highlight those areas that are highly prone to rainfall-induced landslides on the basis of the landslide susceptibility index and the rainfall intensity-duration (I-D) thresholds. First, the study on landslide susceptibility in China is introduced. The entire territory has been classified into five categories, among which high-susceptibility regions (Zone 4- ‘High’ and 5-‘Very high’) account for 4.15% of the total extension of China. Second, rainfall is considered as an external triggering factor that may induce landslide initiation. Real-time satellite-based TMPA 3B42 products may provide real rainfall spatial and temporal patterns, which may be used to derive rainfall duration time and intensity. By using a historical record of 60 significant past landslides, the rainfall I-D equation has been calibrated. The rainfall duration time that may trigger a landslide has resulted between 3 hours and 45 hours. The combination of these two aspects can be exploited to simulate the spatiotemporal distribution of rainfall-induced landslide hazards when rainfall events exceed the rainfall I-D thresholds, where the susceptibility category is ‘high’ or ‘very high’. This study shows a useful tool to be part of a systematic landslide simulation methodology, potentially providing useful information for a theoretical basis and practical guide for landslide prediction and mitigation throughout China.     

Landslide intelligent prediction using object-oriented method

Landslides are one of the most serious geological disasters in the world and happen quite frequently in the Three Gorges. Landslide prediction is a very important measure of landslide prevention and cure in the Three Gorges. Traditional methods lack in sufficiently mining the various complex information from a landslide system. They often need much manual intervention and possess poor intelligence and accuracy. An intelligent method proposed in this paper for landslide prediction based on an object-oriented method and knowledge driving is hopeful to solve the above problem. The method adopted Landsat ETM+ images, 1:50,000 geological map and 1:10,000 relief map in the Three Gorges as the data origins. It firstly produced the key factors influencing landslide development and used multi-resolution segmentation algorithm to segment the image objects based on the key landslide factors of engineering rock group, reservoir water fluctuation, slope structure and slope level. Secondly, the method chose some sample objects and adopted the decision tree algorithm C5.0 to mine the landslide forecast criteria according to the factor values of each sample object. Finally, under knowledge driving the method classified the image objects and realized landslide susceptibility analysis and intelligent prediction in the Three Gorges. The method proposed in this paper is object-oriented. Results of a real-world example show that: (1) the object-oriented method possesses much more compact knowledge representation, higher efficiency, more continuous classifying result and higher prediction accuracy compared with the pixel-oriented method; (2) it possesses the overall accuracy of 87.64% and kappa coefficient of 0.8305 and is more accurate than the other seven methods (such as the pixel-oriented methods of Parallelpiped, Minimum Distance, Maximum Likelihood, Mahalanobis Distance, K-means and Isodata and the object-oriented method of Nearest Neighbor); (3) about 46.97% landslides lie in the high susceptibility region, 24.24% landslides lie in the moderate susceptibility region, 27.27% landslides lie in the low susceptibility region and 1.52% landslides lie in the very low susceptibility region. Therefore the method can effectively realize landslide susceptibility analysis and provides a new idea for landslide intelligent and accurate prediction.