Spatial distribution analysis of landslides triggered by the 2013-04-20 Lushan earthquake,China

The 2013-04-20 Lushan earthquake(seismic magnitude Ms 7.0 according to the State Seismological Bureau)induced a large number of landslides.In this study,spatial characteristics of landslides are developed by interpreting digital aerial photography data.Seven towns near the epicenter,with an area of about 11.11 km2,were severely affected by the earthquake,and 703 landslides were identified from April 24,2013 aerial photography data over an area of 1.185 km2.About 55.56% of the landslide area was less than 1000 m2,whereas about 3.23 % was more than 10,000 m2.Rock falls and shallow landslides were the most commonly observed types in the study area,and were primarily located in the center of Lushan County.Most landslide areas were widely distributed near river channels and along roads.Five main factors were chosen to study the distribution characteristics of landslides:elevation,slope gradients,fault,geologic unit and river system.The spatial distribution of coseismal landslides is studied statistically using both landslide point density(LPD),defined as the number of landslides(LS Number)per square kilometer,and landslide area density(LAD),interpreted as the percentage of landslides area affected by earthquake.The results show that both LPD and LAD have strong positive correlations with five main factors.Most landslides occurred in the gradient range of 40°-50° and an elevation range of 1.0-1.5 km above sea level.Statistical results also indicate that landslides were mainly formed in soft rocks such as mudstone and sandstone,and concentrated in IX intensity areas.     

The Spatial Distribution and Attribute Parameter Statistics of Landslides Triggered by the May 12th, 2008, MW7.9 Wenchuan Earthquake

A complete landslide inventory and attribute database is the importantly fundamental for the study of the earthquake-induced landslide. Substantial landslides were triggered by the M_W7.9 Wenchuan earthquake on May 12^th, 2008. Google Earth images of pre- and post-earthquakes show that 52 194 co-seismic landslides were recognized and mapped, with a total landslides area of 1 021 km².Based on the statistics,we assigned all landslide parameters and established the co-seismic landslides database, which includes area, length, and width of landslides, elevation of the scarp top and foot edge, and the top and bottom elevations of each located slope. Finally, the spatial distribution and the above attribute parameters of landslides were analyzed. The results show that the spatial distribution of the co-seismic landslides is extremely uneven. The landslides that mainly occur in a rectangular area (a width of 30 km of the hanging wall of the Yingxiu-Beichuan fault and a length of 120 km between Yingxiu and Beichuan) are obviously controlled by surface rupture, terrain, and peak ground acceleration. Meanwhile, a large number of small landslides (individual landslide area less than 10 000 m²)contribute less to the total landslides area. The number of landslides larger than 10 000 m² accounts for 38.7% of the total number of co-seismic landslides, while the area of those landslides account for 88% of the total landslides area. The 52 194 co-seismic landslides are caused by bedrock collapse that usually consists of three parts:source area, transport area, and accumulation area. However, based on the area-volume power-law relationship, the resulting regional landslide volume may be much larger than the true landslide volume if the landslide volume is calculated using the influenced area from each landslide.     

Interpretation of the Spatial Distribution Characteristics of the Co-seismic Landslides Induced by the 1920 Haiyuan M8 1/2 Earthquake Using Remote Sensing Images

Analyzing the spatial distribution characteristics of earthquake-induced secondary disasters based on advanced techniques is significantly important, especially in understanding the process of strong earthquakes in the Loess Pateau. Using ArcGIS, this study interprets multi-temporal high-resolution satellite images, field investigation data, and historical seismic records. Major conclusions are obtained as follows:① Landslides induced by the Haiyuan earthquake are mainly distributed in the intersection area of the end of the Haiyuan fault and Liupanshan fault, as indicated by multiple dense distribution centers; ② The landslide distribution of the Haiyuan Earthquake is determined by the distance to the fault, topographic relief, slope, lithology, and other factors. In detail, the closer the distance to the fault, the greater the density of the landslide. The greater the slope and relief of the terrain, the greater the density and the smaller the average area of a landslide. Compared with tertiary strata, Quaternary strata has a larger average area, and the density of the landslides is smaller; ③ The density curve of the death toll in the Haiyuan earthquake can be used as a reference for the distribution of co-seismic landslides. Several Haiyuan co-seismic landslides are distributed in the Tongwei landslide area; however, the major landslides here are induced by the 1718 Tongwei earthquake rather than the 1920 Haiyuan earthquake; ④ The co-seismic landslides of the Haiyuan earthquake exhibits the "slope effect" in the south-west plate of Haiyuan fault, presenting the dominant sliding direction towards the fault and epicenter; however, the "slope effect" is not evident in the northeast plate of the fault.     

Early Identification of the Jiangdingya Landslide of Zhouqu Based on SBAS-InSAR Technology

SBAS-InSAR technology is characterized by the advantages of reducing the influence of terrain-simulation error, time-space decorrelation, atmospheric error, thereby improving the reliability of surface-deformation monitoring. This paper studies the early landslide identification method based on SBAS-InSAR technology. Selecting the Jiangdingya landslide area in Zhouqu County, Gansu Province as the research area, 84 ascending-orbit Sentinel-1A SAR images from 2015 to 2019 are collected. In addition, using SBAS-InSAR technology, the rate and time-series results of surface deformation of the landslide area in Jiangdingya during this period are extracted, and potential landslides are identified. The results show that the early landslide identification method based on SBAS-InSAR technology is highly feasible and is a better tool for identifying potential landslides in large areas.     

Study of the Non-Uniform Distribution of Earthquake Risk Within Potential Sources

Potential sources are aggregates of probable future epicenters.In this area,for source models currently,in common use for seismic risk analysis in China,the mean area of each potential source is about 3000-4000 km2.It is assumed that seismic risk has a uniform distribution within the range of each potential source,but studies have shown that the uniform distribution model to a large extent may give an underestimation of the seismic risk.In this paper,the relative distribution of historical epicenters in space within potential sources is discussed,a method is proposed to quantitatively describe the non-uniform distribution of strong earthquakes within potential sources,and some preliminary results are given.By using the results of this paper,seismic risk analysis and seismic zonation can be made more scientific and more reasonable.     

Spatial Distribution of Seismic Landslides in the Areas of 1927 Gulang M8.0 Earthquake

The 1927 Gulang M8.0 earthquake has triggered a huge number of landslides, resulting in massive loss of people''s life and property. However, integrated investigations and results regarding the landslides triggered by this earthquake are rare; such situation hinders the deep understanding of these landslides such as scale, extent, and distribution. With the support of Google Earth software, this study intends to finish the seismic landslides interpretation work in the areas of Gulang earthquake (VIII-XI degree) using the artificial visual interpretation method, and further analyze the spatial distribution and impact factors of these landslides. The results show that the earthquake has triggered at least 936 landslides in the VIII-XI degree zone, with a total landslide area of 58.6 km². The dense area of seismic landslides is located in the middle and southern parts of the X intensity circle. Statistical analysis shows that seismic landslides is mainly controlled by factors such as elevation, slope gradient, slope direction, strata, seismic intensity, faults and rivers. The elevation of 2 000-2 800 m is the high-incidence interval of the landslide. The landslide density is larger with a higher slope gradient. East and west directions are the dominant sliding directions. The areas with Cretaceous and Quaternary strata are the main areas of the Gulang seismic landslides. The X intensity zone triggered the most landslides. In addition, landslides often occur in regions near rivers and faults. This paper provides a scientific reference for exploring the development regularities of landslides triggered by the 1927 Gulang earthquake and effectively mitigating the landslide disasters of the earthquake.     

Discussion on the Relation Between the Probabilistic Distribution of Magnitude in a Seismic Province and That in an Area with Potential Seismic Source

According to the joint probabilistic distribution model of magnitude and space,the author discusses the relationship between the probabilistic distribution of magnitude in a seismic province and that in an area with potential seismic sources.The results show that if the magnitude probabilistic distribution follows the truncated exponential form in a seismic province,there must be some potential source in which the magnitude probabilistic distribution does not conform to that form.The result is consistent with the concept of "characteristic earthquake" derived from the study of actual records of seismicity and the study of geology.The author suggests that the relationship between the probabilistic distribution of magnitude in a seismic province and that in a seismic potential area must be considered in the study of the analysis of seismicity,seismic zonation and engineering seismology,for the purpose of the evaluation of the probabilistic distribution of magnitude correctly in every area with potential s     

玉树地震滑坡分布调查及其特征与形成机制

On April 14, 2010 at 07:49 (Beijing time), a catastrophic earthquake with MS7.1 occurred at the central Qinghai-Tibetan Plateau. The epicenter was located at Yushu county, Qinghai Province, China. A total of 2036 landslides were determined from visual interpretation of aerial photographs and high resolution remote sensing images, and verified by selected field investigations. These landslides covered a total area of about 1.194km2. Characteristics and failure mechanisms of these landslides are listed in this paper, including the fact that the spatial distribution of these landslides is controlled by co-seismic main surface fault ruptures. Most of the landslides were small scale, causing rather less hazards, and often occurring close to each other. The landslides were of various types, including mainly disrupted landslides and rock falls in shallows and also deep-seated landslides, liquefaction induced landslides, and compound landslides. In addition to strong ground shaking, which is the direct landslide triggering factor, geological, topographical, and human activity also have impact on the occurrence of earthquake triggered landslides. In this paper, five types of failure mechanisms related to the landslides are presented, namely, the excavated toes of slopes accompanied by strong ground shaking; surface water infiltration accompanied by strong ground shaking; co-seismic fault slipping accompanied by strong ground shaking; only strong ground shaking; and delayed occurrence of landslides due to snow melt or rainfall infiltration at sites where slopes were weakened by co-seismic ground shaking. Besides the main co-seismic surface ruptures, slope fissures were also delineated from visual interpretation of aerial photographs in high resolution. A total of 4814 slope fissures, with a total length up to 77.1km, were finally mapped. These slope fissures are mainly distributed on the slopes located at the southeastern end of the main co-seismic surface rupture zone, an area subject to strong compression during the earthquake.     

A case study of spatial heterogeneity of soil moisture in the Loess Plateau, western China： A geostatistical approach

Soil moisture distribution shows highly variation both spatially and temporally. This study assesses the spatial heterogeneity of soil moisture on a hill-slope scale in the Loess Plateau in West China by using a geostatistical approach. Soil moisture was measured by time-domain reflectometry （TDR） in 313 samples. Two kinds of sampling scales were used （2 × 2 m and 20 ×20 m） at two soil layers （0-30 cm and 30-60 cm）. The general characteristics of soil moisture were analyzed by a classical statistics method, and the spatial heterogeneity of soil moisture was analyzed using a geostatistical approach. The results showed that the spherical model is the best-fit model to simulate soil moisture on the experimental hill-slope. The parameters of this model indicated that the spatial dependence of soil moisture in the selected hill-slope was moderate. Even the 2 × 2 m sampling scale was too coarse to show the detailed spatial variances of soil moisture in this area. The dependent distance increased from 27.4 m to 494.16 m as the sampling scale became coarse （from 2× 2 m to 20 ×20 m）. A map of soil moisture was generated by using original soil moisture data and interpolated values determined by the Kriging method. The average soil moisture （area weighted） in the different layers of soil was calculated on the basis of this map （10.94% for the 0-30 cm soil layer, 11.88% for the 30-60 cm soil layer）. This average soil moisture is lower than the corresponding average effective soil moisture, which suggests that the soil moisture is not sufficient to support vegetation in this area.     

Earthquake-induced landslide displacement attenuation models and application in probabilistic seismic landslide displacement analysis

A landslide displacement (DLL) attenuation model has been developed using spectral intensity and a ratio of critical acceleration coefficient to ground acceleration coefficient. In the development of the model,a New Zealand earthquake record data set with magnitudes ranging from 5.0 to 7.2 within a source distance of 175 km is used. The model can be used to carry out deterministic landslide displacement analysis,and readily extended to carry out probabilistic seismic landslide displacement analysis. DLL attenuation models have also been developed by using earthquake source terms,such as magnitude and source distance,that account for the effects of earthquake faulttype,source type,and site conditions. Sensitivity analyses show that the predicted DLL values from the new models are close to those from the Romeo model that was developed from an Italian earthquake record data set. The proposed models are also applied to an analysis of landslide displacements in the Wenchuan earthquake,and a comparison between the predicted and the observed results shows that the proposed models are reliable,and can be confidently used in mapping landslide potential.     

An open-accessed inventory of landslides triggered by the MS 6.8 Luding earthquake,China on September 5, 2022

This study constructs a preliminary inventory of landslides triggered by the M_S 6.8 Luding earthquake based on field investigation and human-computer interaction visual interpretation on optical satellite images. The results show that this earthquake triggered at least 5 007 landslides, with a total landslide area of 17.36 ?km², of which the smallest landslide area is 65 ?m² and the largest landslide area reaches 120 747 ?m², with an average landslide area of about 3 500 ?m². The obtained landslides are concentrated in the IX intensity zone and the northeast side of the seismogenic fault, and the area density and point density of landslides are 13.8%, and 35.73 ?km^?2 peaks with 2 ?km as the search radius. It should be noted that the number of landslides obtained in this paper will be lower than the actual situation because some areas are covered by clouds and there are no available post-earthquake remote sensing images. Based on the available post-earthquake remote sensing images, the number of landslides triggered by this earthquake is roughly estimated to be up to 10 000. This study can be used to support further research on the distribution pattern and risk evaluation of the coseismic landslides in the region, and the prevention and control of landslide hazards in the seismic area.     

基于分形的巴谢河流域滑坡空间分布格局及其影响因素分析

定量研究区域滑坡空间分布规律,揭示不同类型滑坡的分布格局,对预测和评价滑坡危险性有重要指导意义。基于ArcGIS空间分析功能及分形理论的关联维数和盒计维数,分析了巴谢河流域黄土滑坡及黄土-泥岩滑坡的空间分布格局及其影响因素。结果表明：区域滑坡个体关联具有多尺度分形,黄土滑坡与黄土-泥岩滑坡分别在8 km、12 km尺度上存在阈值,滑坡个体在该阈值尺度前后呈现不同的相关程度,且黄土滑坡个体空间的关联程度和聚集程度均高于黄土-泥岩滑坡;黄土-泥岩滑坡分布范围广、形态复杂,其面积展布盒计维数大于黄土滑坡;地层岩性及坡度对两类滑坡分布格局的影响较大,沟壑密度次之,起伏度影响较小。     

Characteristics of landslides in forests and grasslands triggered by the 2016 Kumamoto earthquake

We examined the characteristics of landslides triggered by the 2016 Kumamoto earthquake (Mw = 7.0: focal depth=10.0 km) in forests and grasslands within two affected watersheds (Tokosegawa: 6.9 km² and Nigorigawa: 6.1 km²) in southwestern Japan. We identified 190 landslides using aerial photographs and analyzed their sizes by geographic information system (GIS). Field investigations were conducted to obtain landslide depth, volume and residual sediment for 38 selected landslides (21 in forests and 17 in grasslands). The minimum area of detected landslides in grasslands (400 m²) was smaller than in forests (1000 m²), probably because of reduced detectability of landslides under tree cover. The ratio of total area occupied by landslides for a given range of slope gradient in the watersheds increased from 3.2% on gentle grassland slopes (10–15°) to 15.5% on steep (>45°) slopes, whereas the maximum landslide-area ratio in forest sites (7.4%) occurred on relatively gentle slopes (25–30°). Estimated landslide volume ranged from 27 to 9622 m³, based on mean depth of each landslide measured around individual landslide scars. Moreover, the volumetric ratio of landslide deposit volume to total landslide volume exceeded 100% for 48% of the landslides within forests and 35% of the landslides within grasslands. Our findings show that land cover had extensive and recognizable effects on the characteristics of landslides and resulting in-channel sediment accumulations. Resetting sediment dynamics after earthquakes associated with different land cover distributions needs to be considered within watersheds. © 2019 John Wiley & Sons, Ltd.     

Establishing a threshold for rainfall-induced landslides by a kinetic energy–duration relationship

Many investigators have attempted to define the threshold of landslide failure, that is, the level of the selected climatic variable above which a rainfall-induced landslide occurs. Intensity–duration (I–d) relationships are the most common type of empirical thresholds proposed in the literature for predicting landslide occurrence induced by rainfall. Recent studies propose the use of the kinetic power per unit volume of rainfall (J m⁻² mm⁻¹) to quantify the threshold of landslides induced by rainfall. In this paper, the relationship between rainfall duration and kinetic power corresponding to landslides triggered by rain was used to propose a new approach to define the threshold for predicting landslide occurrence. In particular, for the first time, a kinetic power per unit volume of rainfall–duration relationship is proposed for defining the minimum threshold needed for landslide failure. This new method can be applied using commonly used relationship for estimating the kinetic power per unit volume of rainfall and a new equation based on the measured raindrop size distribution. The applicability of this last method was tested using the data of rainfall intensity, duration and median volume diameter for 51 landslides in Taiwan. For the 51 landslides, the comparison between the measured pairs' kinetic power–duration and all selected relationships demonstrated that the equation based on the measured raindrop size distribution is the best method to define the landslide occurrence threshold, as it is both a process-oriented approach and is characterized by the best statistical performance. This last method has also the advantage to allow the forecasting of landslide hazard before the end of the rainfall event, since the rainfall kinetic power threshold value can be exceeded for a time interval less than the event duration.     

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.     

Landslide inventories and their statistical properties

Landslides are generally associated with a trigger, such as an earthquake, a rapid snowmelt or a large storm. The landslide event can include a single landslide or many thousands. The frequency–area (or volume) distribution of a landslide event quanti?es the number of landslides that occur at different sizes. We examine three well‐documented landslide events, from Italy, Guatemala and the USA, each with a different triggering mechanism, and ?nd that the landslide areas for all three are well approximated by the same three‐parameter inverse‐gamma distribution. For small landslide areas this distribution has an exponential ‘roll‐over’ and for medium and large landslide areas decays as a power‐law with exponent ‐2·40. One implication of this landslide distribution is that the mean area of landslides in the distribution is independent of the size of the event. We also introduce a landslide‐event magnitude scale m_L = log(N_LT), with N_LT the total number of landslides associated with a trigger. If a landslide‐event inventory is incomplete (i.e. smaller landslides are not included), the partial inventory can be compared with our landslide probability distribution, and the corresponding landslide‐event magnitude inferred. This technique can be applied to inventories of historical landslides, inferring the total number of landslides that occurred over geologic time, and how many of these have been erased by erosion, vegetation, and human activity. We have also considered three rockfall‐dominated inventories, and ?nd that the frequency–size distributions differ substantially from those associated with other landslide types. We suggest that our proposed frequency–size distribution for landslides (excluding rockfalls) will be useful in quantifying the severity of landslide events and the contribution of landslides to erosion. Copyright © 2004 John Wiley & Sons, Ltd.     

APPLICATION OF TWO SIMPLIFIED NEWMARK MODELS TO THE ASSESSMENT OF LANDSLIDES TRIGGERED BY THE 2008 WENCHUAN EARTHQUAKE

At present, with the wide application of the Newmark method, various Newmark empirical formulas with different ground motion parameters have been fitted by many researchers based on global strong-motion records. However, the existing study about the Wenchuan earthquake does not quantitatively evaluate the applicability of different Newmark models based on the actual landslides distribution. The aim of this paper is to present a comparison between observed landslides from the 2008 Wenchuan earthquake and predicted landslides using Newmark displacement method based on different ground motion parameters. The factor-of-safety map and critical acceleration(a_c)map in the study area are obtained by using the terrain data and geological data. The distribution of Arias intensity(I_a)and PGA in the study area is obtained by using the attenuation formulas of Arias intensity(I_a)and PGA, which is regressed by Wenchuan ground motion records. Based on the distribution of Arias intensity(I_a)and PGA parameters, we obtained the predicted locations of landslide using Newmark regression equations which are generated using global strong-motion records. The results shows that the assessment results can better reflect the macroscopic distribution characteristics of co-seismic landslides, most predicted landslide cells are distributed on the two sides of the Beichuan-Yingxiu Fault, especially the Pengguan complex rock mass in the hanging wall. The abilities to predict landslide occurrence of the two Newmark simplified models are different. On the whole, the evaluated result of simplified model based on parameter I_a is better than that based on PGA parameter. The GFC values obtained by the Newmark model of I_a and PGA parameters are 65.7% and 34.9%respectively. The evaluated result based on I_a can better reflect the macro distribution of coseismic landslides. The Ls_Pred value based on the Newmark model of parameter I_a is 26.5%, and the Ls_Pred value based on the Newmark model of PGA parameter is 10.3%. However the total area of predicted landslides accounts for 2.4% of the study area, which indicates that the predicted landslide cells are greater than the observed landslide cells. This reminds us that depending on the current input of shear strength and ground-motion parameters, we can only conduct landslide hazard assessment in macro areas, the ability to predict landslide can be improved using more accurate topographic data and input parameters.