Earthquake-induced landslides, as an important secondary geological disaster, typically occurring during or shortly after an earthquake, have the characteristics of large quantity and scale, wide distribution, complex mechanism, serious casualties and economic losses, and long-duration post-earthquake effect. Rapidly and accurately obtaining the spatial distribution and potential hazard assessment of coseismic landslide following an earthquake is critical for emergency rescue and resettlement planning. Currently, the most commonly-used coseismic landslide hazard assessment methods include the data-driven machine learning methods and the Newmark method based on mechanics mechanism. The 2022 M_W5.8 Lushan earthquake provides a valuable window for us to carry out rapid emergence assessment of earthquake-induced landslides with different evaluation models. In this study, a new generation of China's earthquake landslide hazard model(hereinafter referred to as Xu₂₀₁₉ model)and a simplified Newmark model are used to carry out the rapid landslide assessment of Lushan event. The Xu₂₀₁₉ model selects 9 earthquake-induced landslide inventories around China as training samples and uses a total of 13 influencing factors such as elevation, relative elevation, slope angle, and aspect, and etc. to generate a near real-time evaluation model for coseismic landslides based on the LR method. The model can rapidly assess coseismic landslides towards a single earthquake event according to the actual PGA distribution. For Newmark model, the cumulative displacement(Dn)is calculated by the critical acceleration(a_c)and PGA maps. For the landslide inventory of this earthquake event, we completed the landslide inventory covering the entire affected area based on high-resolution optical satellite images(Planet)with 3m resolution acquired on 6 July 2022. Based on the coseismic landslide inventory including 2 352 landslides with an area of 5.51km², the accuracy and applicability of the two models are estimated. The results show that the landslide area calculated based on Xu₂₀₁₉ model is 5.07km², which is very close to the actual landslide area, and the predicted area calculated based on Newmark model reaches 21.3km². From the perspective of the spatial distribution of the prediction results, the distribution of the predicted high failure probabilities of the two models is roughly same, with the high probability values mainly located on the left side of the seismogenic fault. However, the difference lies in the low probability predictions of the northwest region of Baoxing county by the Xu₂₀₁₉ model. A zoomed-in view of a specific area comparing the spatial distribution of predicted landslide probabilities with the landslide abundance area shows that most actual landslide are concentrated in the medium to high failure probability areas predicted by the Xu₂₀₁₉ model, with only a few sporadic events occurring in the low probability zone. On the other hand, the Newmark model primarily identifies high instability probability regions in steep slope areas, which correspond closely to the actual landslide and collapse occurrences. However, the predicted hazard level of the northwest region i.e. the landslide highly developed area is obviously low by Xu₂₀₁₉ model, while the prediction result based on Newmark model for the southwest region is obviously overestimated. In terms of the LR model, the prediction results are very close to the actual landslide distribution, and the majority of the landslides are essentially located in areas with a high failure probability, indicating that the model has a relatively high prediction accuracy. The ROC curve is used to assess the model's accuracy. The results suggest that the model based on Xu₂₀₁₉ outperforms the Newmark model, with a prediction accuracy of 0.77, while the prediction accuracy of the Newmark model is 0.74. Overall, both two models have good practicability in the rapid evaluation of cosesimic landslide. However, the Newmark model needs multi parameter input, and these parameters themselves and the way of human acquisition are uncertain, which results in that the model evaluation is greatly affected by subjectivity.

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.

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.

Large earthquakes in mountainous areas often induce landslides, which always lead to serious disasters. A detailed and accurate inventory of earthquake-triggered landslides is an important basis for scientific research on co-seismic landslides in the future. High quality landslide inventory should cover the whole earthquake area, spanning all scales of co-seismic landslides that can be detected, accurate landslide locations and boundaries, polygon-based description of the true landslide shape, and separate individual landslides from contiguous landslides group. Building the inventory of landslides triggered by earthquake based on the traditional ground survey will take a long time and a large amount of manpower and material resources, while the remote sensing image, by virtue of its comprehensive coverage and economy, can make up for the shortcomings of the former. In this study, a new inventory of landslides triggered by the 2014 Ludian M_W6.2 earthquake is presented on the basis of visual interpretation of pre- and post-earthquake satellite images in very high resolution (~0.5m), and verified by selecting filed investigation. Results show the earthquake triggered at least 10^￣￣559 landslides around the epicenter. Statistics reveals a total horizontal projection area of 14.975km², an oval distribution area about 360km² and an estimated total volume of 1.24×10^8￣￣m³. The landslide number density, landslide area percentage, and landslide volume density are 29.6km^-2, 4.2%, 0.35m, respectively. Comparing with previous studies, the inventory of landslides triggered by earthquake in this study is much more detailed and accurate in spatial distribution and the boundaries of landslides are more exquisite, which is attributed to the image quality, resolution and coverage of remote sensing we used in this study and strict compliance with cataloging standards of co-seismic landslides inventory during interpretation. This study provides a detailed and complete inventory related to the 2014 Ludian earthquake, which is an important and essential data for subsequent landslide spatial distribution analyses and assessments. In addition, this study also reminds us that when we establish the inventory of landslides triggered by earthquake with satellite images, it is necessary to select better quality and high-resolution remote sensing images and strictly comply with the standard of co-seismic landslides inventory during interpretation, so as to establish a complete and detailed inventory of landslides triggered by earthquake that can be used to seismic geological disaster analysis and quantitative research.

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.

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. In this paper, we use the trinity method of space, sky and earth to create a panorama of the landslides triggered by this event. There are 4 roads in the distribution area of the coseismic landslides. The Jinglinghai-Xiamo and Jiudaoguai-Jiuzhaitiantang road sections register the most serious coseismic landslides. The landslides are mainly of moderate-and small-scales, and also with a few large landslides and avalanches. A detailed visual interpretation of the coseismic landslides is performed in two areas of Wuhuahai(11.84km²) and Zharusi-Shangsizhai village(47.07km²), respectively. The results show the overall intensity of landsliding(1088 landslides, a total area 1.514km²) in the Wuhuahai area is much higher than those in the Zharusi-Shangsizhai village area(528 landslides, a total area 0.415km²). On the basis of a scene of post-earthquake Geoeye -1 satellite images, we delineate more than 4 800 coseismic landslides with a total occupation area 9.6km². The spatial pattern of these landslides is well related with the locations of the inferred seismogenic fault and aftershocks. Widely distributed earthquake-affected weakened slopes, residual loose materials staying at high-position slopes and in valleys have greater possibilities to fail again and generate new landslides or debris flows under the conditions of strong aftershocks or heavy rainfalls in the future. Geological hazard from these events will become one of the most serious problems in the recovery and reconstruction of the earthquake-affected area which should receive much attention.

On 8 August 8 2017, an M_S7.0 earthquake occurred in Jiuzhaigou County, Sichuan Province. Field geological investigations did not find any co-seismic surface rupture in the epicenter area, implying that the seismogenic structure is likely a hidden active fault. Based on the results of the relocated aftershocks, the seismogenic fault was simulated and characterized using the SKUA-GOCAD software. The three-dimensional model of the seismogenic fault was preliminarily constructed, which shows that the main shock of the Jiuzhaigou M_S7.0 earthquake occurred at the sharp bending area of the fault surface, similar to the geometry of the active fault that generated several major earthquakes in the Songpan area during 1973-1976. Our study suggests that high seismicity of this area may be closely related to the inhomogeneous geometry of the fault surface. In this work, we collected the historical earthquakes of M ≥ 6.5, and analyzed the geometric and kinematic features of the active faults in the study area. A three-dimensional fault model for the 10 main active faults was constructed, and its limitation in fault modeling was discussed. It could provide evidence for analyzing the seismotectonics of historical earthquakes, exploring the relationships between earthquakes and active faults, and predicting major earthquakes in the future.

Geomorphic information entropy is a quantitative indicator used to determine the evolutionary stage and express the erosion degree of watershed geomorphic surface, which is a reflection of topography factors. In order to do a better job for the upcoming rainy season's debris-flow hazard zone planning and provide a reference for disaster prevention, with GIS technology as a platform and the Lushan seismic landslides' volume data as source material of debris flow, and by combining geomorphic information entropy method, the paper carries out debris-flow hazard evaluation for 55 debris flows based on landslide material sources. The results show that: The range of the value of geomorphic information entropy is between 0.003 2~0. 938 1 in debris-flow valley basin of the study area, and valley geomorphic evolution is distributed from childhood to old age; the area of debris-flow hazard zone shows a decreasing trend basically from high to low hazard zone, with 80. 77% of the study region locating in the debris flow prone area, and the presence of debris-flow valley in its juvenile to mature stage increases the risk of debris flow. The response rate of seismic slope mass movement(RRSSMM)of the debris flow basin varies from 0~133. 24mm, and the area of material source sensitive area of low and very low degree accounts for 72. 93% of the valley basin area of the study area, which indicates that nearly 3/4 of debris-flow valley basins are insensitive to landslides material source. The result of debris-flow hazard evaluation based on landslides material source indicates that more than 2/5 of the debris flow valley basin is located in moderate or above hazard zone, where debris flow activity seems more active.

On August 3, 2014, an M_S6.5 earthquake happened in the Ludian County, Zhaotong City of Yunnan Province. This earthquake caused a large number of landslides. In order to study the spatial distribution of the coseismic and pre-earthquake landslides, a 44.13km² area at the junction of Ludian County, Qiaojia County and Huize County along the Niulanjiang River is selected in this study. By visual interpretation of the Google Earth pre-earthquake high resolution images and the coseismic aerial data of 0.2m resolution of this area, the landslide databases of pre-earthquake and coseismic are established. The result shows that there are 284 pre-earthquake landslides, and 1053 earthquake-induced landslides. Then with the help of 10m×10m resolution DEM data and the GIS, the extracted factors of elevation, slope angle, slope aspect, curvature, lithology, earthquake intensity and drainages are used to analyze the spatial distribution of the coseismic and pre-earthquake landslides by adopting LAP(Landslide Areas Percentage)and LND(Landslide Number Density). The results show that areas with elevation <1 200m and 1 200～1 300m are prone to landslides whatever pre-earthquake or coseismic. With the slope gradient increasing, it is much more prone to landslide, and the area of <10°, close to the rivers, is also much susceptible. The advantage slope aspect is almost near S direction. Concave slope(when the curvature is negative)is much susceptible to landslides, and with the curvature decreasing, the landslide susceptibility gets higher. The region of limestone with dolomitic limestone is sensitive to landslide; in the areas consisting of basalt and volcanic breccia, the slope stability is greatly reduced under the effect of seismic force. The larger the intensity is, the more landslides happened. For either pre-earthquake or coseismic landslides, there is a positive correlation between landslide spatial distribution and the distance to rivers. The large pre-earthquake landslides have effective influence on LAP.

On August 3, 2014, an earthquake with M_S6.5 occurred at Ludian County, Yunnan Province of China. The earthquake caused 617 deaths, 112 missing, and 3, 143 injured. Thousands of landslides were triggered by the earthquake and some of the landslides buried a few settlements, which resulted in significant casualties and property losses. In this study, we compiled an inventory of landslides triggered by the Ludian earthquake based on visual interpretation of high resolution satellite images, which are TH01-02 and SJ9A satellite images photoed post-earthquake and GF1 images shot pre-earthquake. Furthermore, some of the landslides were verified by field photos and/or very high resolution aerial photographs. The result shows the Ludian earthquake triggered at least 1024 landslides with an area equal to or larger than 100 m². The landslides are distributed in a 250km² area, with a total landslide area of 5.19 km² and a total volume of 2.2×10⁷m³. In the landslide-distributing area, the landslide number density is 4.03km^-2, the landslide area density is 2.04%, and the landslide erosion thickness is about 86.7mm. The statistical result of landslide number and area in different directions of the epicenter shows that the main spread direction of the landslides is northwest-southeast and most of the landslides occurred southeast of the epicenter. This suggests that the seismogenic fault of the earthquake probably trends to northwest and the rupture direction is from northwest to southeast, which is consistent with evidences from seismic, geological, geophysical, as well as other aspects. Comparing the landslide distribution area, landslide number, landslide area, and landslide volume related to the Ludian earthquake with those of other earthquakes worldwide, the result shows that the earthquake registered a smaller landslide distribution area but a larger landslide area and a much larger landslide volume. It suggests that the hypocenter of the Ludian earthquake is shallow and seismic energy attenuation of the event is quite rapid.

Since the May 12, 2008 Wenchuan M_W7.9 earthquake occurred, the authors have developed new standards for building earthquake-triggered landslide inventory, selecting remote sensing images, and building attribute database based on characteristics of earthquake-triggered landslides and optical remote sensing images. The construction of the basic earthquake-triggered landslides database related to the four earthquake events occurring at the beginning of the twenty-first century is introduced, including, the May 12, 2008 Wenchuan M_W 7.9 earthquake, China, which triggered at least 197,481 landslides, the April 14, 2010 Yushu earthquake, China, that triggered at least 2036 landslides, the January 12, 2010 Haiti M_W 7.0 earthquake, which triggered at least 30,828 landslides, and the Aysén Fjord M_W 6.2 earthquake, Chilean, which triggered at least 1000 landslides. Differences between the database constructed by us and previous results are analyzed. In conclusion, practicable value and scientific significance of these basic earthquake-triggered landslides databases are analyzed, such as, the importance on spatial distribution and hazard analysis of earthquake-triggered landslides, prevention and mitigation for landslide and debris flow disaster in earthquake struck areas, the significance in analyzing magnitudes of earthquake, movement behavior of active fault, seismic intensity information, and essential data for river and landscape evaluation in earthquake stuck areas, and the use in constructing correlation of global earthquakes and landslides triggered by the earthquakes.

The April 20,2013,M_S 7.0 Lushan earthquake occurred along the southwestern part of the Longmen Shan Fault zone. Tectonics around the epicenter area is complicated and several NE-trending faults are developed. Focal mechanisms of the main shock and inversions from finite fault model suggest that the earthquake occurred on a northeast-trending,moderately dipping reverse fault,which is consistent with the strike and slip of the Longmen Shan Fault zone. NE-trending ground fissures and soil liquefaction along the fissures,heavy landslides along the Dachuan-Shuangshi and Xinkaidian Faults were observed during the field investigations. No surface ruptures were found in the field work. GPS data indicate that the fault on which this earthquake occurred is a fault east of or near the Lushan county and the earthquake also triggered slip on the fault west of the Lushan county. Field observations,GPS data,focal fault plane,focal depth,and distribution of the aftershocks suggest, that the seismogenic structure associated with the M_S 7.0 Lushan earthquake is the décollement beneath the folds of the eastern Longmen Shan. Slip along this decollement generated the earthquake,and also triggered the slip along the Dachuan-Shuangshi and Xinkaidian Faults.

On April 20,2013,a strong earthquake of M_S 7.0 struck the Lushan County,Sichuan Province of China. In this paper,basic information of the April 20,2013 Lushan earthquake,historical earthquakes in the Lushan earthquake struck area and associated historical earthquake-triggered landslides were introduced firstly. We delineated the probable spatial distribution boundary of landslides triggered by the Lushan earthquake based on correlations between the 2008 Wenchuan earthquake-triggered landslides and associated peak ground acceleration(PGA).According to earthquake-triggered landslides classification principles,landslides triggered by the earthquake are divided into three main categories: disrupted landslides,coherent landslides,and flow landslides. The first main category includes five types: rock falls,disrupted rock slides,rock avalanches,soil falls,and disrupted soil slides. The second main category includes two types of soil slumps and slow earth flows. The type of flow landslides is mainly rapid flow slides. Three disrupted landslides,including rock falls,disrupted rock slides,and soil falls are the most common types of landslides triggered by the earthquake. We preliminary mapped 3883 landslides based on available high-resolution aerial photographs taken soon after the earthquake. In addition,the effect of aftershocks on the landslides,comparisons of landslides triggered by the Lushan earthquake with landslides triggered by other earthquake events,and guidance for subsequent landslides detailed interpretation based on high-resolution remote sensing images were discussed respectively. In conclusion,based on quick field investigations to the Lushan earthquake,the classifications,morphology of source area,motion and accumulation area of many earthquake-triggered landslides were recorded before the landslide might be reconstructed by human factors,aftershocks,and rainfall etc. It has important significance to earthquake-triggered landslide hazard mitigation in earthquake struck area and the scientific research of subsequent landslides related to the Lushan earthquake.

On July 22,2013,an earthquake of M_S 6.6 occurred at the boundary between Minxian County and Zhangxian County,Gansu Province of China. Many landslides were triggered by the earthquake and the landslides were of various types,mainly in falls,slides,and topples occurring on loess cliffs,and also including soil deep-seated coherent landslides,large-scale soil avalanches,and slopes with cracks. Most of the landslides were distributed in an elongated area of 250km²,parallels to the Lintan-Dangchang Fault, with about 40km in length and the largest width of 8km. Landslides occurrence shows obvious difference along the central line of the elongated area,corresponding to different characteristics of different segments of the seismogenic fault. The elongated landslides main distribution area and the location of the epicenter indicate that the direction of the fault rupture propagation is from southeast-east to northwest-west. Finally,two probable reasons causing the horizontal distance of about 10km between the central line of the elongated area and the Lintan-Dangchang Fault are presented.

On July 22,2013,the Minxian-Zhanxian M_S 6.6 earthquake occurred at the central-northern part of the South-North Seismic Belt. In the area,complicated structural geometries are controlled by major strike-slip fault zones,i.e.the Eastern Kunlun Fault and the Northern Frontal Fault of West Qinling. The distribution of related seismic disasters,namely,the ellipse with its major axis trending NWW,is in good accord with the strike of the Lintan-Tanchang Fault. Severe damages in the meizoseismal area of the Minxian-Zhangxian M_S 6.6 earthquake are located within the fault zone. So it is considered that the earthquake related damages are closely related to the complicated geometry of the Lintan-Tanchang Fault,and it also indicates that the earthquake is the outcome of joint action of its secondary faults. Based on field investigations,and by integrating the results of previous studies on active tectonics,structural deformation and geophysical data,it can be inferred that the southward extension of the Northern Frontal Fault of West Qinling and the northeastward extrusion of the Eastern Kunlun Fault in the process of northeastward growth of Tibetan plateau are the main source of tectonic stress. Basic tectonic model is provided for strong earthquake generation on the Lintan-Tanchang Fault.

The Longmen Shan,located at the eastern margin of the Tibetan plateau,is a steep and high exhumation area. In recent years,the 2008 Wenchuan M_W7.9 earthquake and the 2013 Lushan M_S7.0 earthquake occurred,and researchers presented a lot of low-temperature thermochronology data of the Longmen Shan and adjacent area. In this paper,we provide 4 ZFT ages and 4 AFT ages for the southern segment of the Longmenshan Thrust Belt(LTB),where the low-temperature thermochronology data are still few. Combining with previous researches,we get the Cenozoic exhumation history of the Baoxing Massif,located at the southern segment of the LTB,and new knowledge about the Cenozoic activity of the southern segment of the LTB.The Baoxing Massif began quickly cooling in the early Cenozoic,with the cooling range exceeding 225℃,while the cooling range of the Pengguan Massif in the central segment of the Longmen Shan is between 185～225℃.The four AFT ages in the Baoxing Massif are between 2.7～5.0Ma,which are younger than that in the Pengguan Massif,and it indicates that the late-Cenozoic cooling rate of the Baoxing Massif is bigger than that of the Pengguan Massif. Under this assumption that the surface temperature is 15℃ and the paleo-geothermal gradient is 30℃/km,the average exhumation rate from 3～5Ma to present is about 0.63～1.17mm/yr. The low-temperature thermochronology data indicate that the differential exhumation is concentrated in the Beichuan-Yingxiu Fault and the Jiangyou-Guanxian Fault in the central segment of the LTB,while it is dispersed in a wider region along the two branches of the Shuangshi-Dachuan Fault and the faults and folds to the east,in the southern segment.

In this paper,a rectangle area of 20km?10km at the northeast of Taiping Town,which suffered strong shaking during the April 20,2013 M_S 7.0 Lushan earthquake,was selected as the study area for spatial analyses of landslides triggered by the earthquake. Landslide distribution map of the study area was prepared based on quick field investigations and visual interpretation of high-resolutions aerial photographs. It is showed that at least 688 landslides were triggered by the Lushan earthquake and the landslide number density(LND)of the study area is 3.44 landslides/km².Correlations of landslide number density with topographic,geologic and seismic parameters were analyzed based on the landslide inventory. The results show that the steeper the slopes,the greater the landslide number density values; the highest LND value appears at ranges from 1 600m to 1 800m in elevation. The landslides have preferred orientations,dominated by the east and SE directions. LND values of convex slopes are relatively higher. The limestone and dolomite of the Yangxin Group,Permian(Py)and granitic rocks of Proterozoic(Pt)experienced more concentrated landslides. In general,the higher the PGA value and seismic intensity zone,the greater the LND value. Correlation of landslide number density with distance from the Shuangshi-Dachuan Fault shows that there was not sudden change of LND value near the fault. The factor interaction statistics show that the slope angle and PGA affect the occurrence of earthquake-triggered landslides independently.

2036 landslides were triggered by the 2010 Yushu earthquake from aerial photographs and remote sensing images interpreting,verified by selected field checking. In this paper,twelve factors that influence landslide occurrence,including distance from main co-seismic surface ruptures,peak ground acceleration (PGA),elevation,slope angle,slope aspect,slope curvature,slope position,distance from drainages,lithology,distance from faults,distance from roads,normalized difference vegetation index (NDVI),are selected as landslide hazard evaluation factors. Two types of landslide hazard index map are derived using two "weight of evidence" methods based on Geographical Information Systems (GIS) technology.The success rate of Add-"weight of evidence" method is 80.32%,and the success rate of Subtract-"weight of evidence" method is 80.19%,both are satisfactory.The resulting hazard evaluation maps are divided into five categories, i.e.extremely high,high,moderate,low,and extremely low,respectively.The landslide hazard maps can be used to identify and delineate unstable hazard-prone areas. It can also help planners to choose favourable locations for development schemes,such as infrastructures,buildings,road construction,and environmental protection.

On April 14,2010 at 07:49 (Beijing time), a catastrophic earthquake with M_S 7.1 struck Yushu County, Qinghai Province, China. About 2036 landslides, covering an area of about 1.194km², were interpreted from aerial photographs and remote sensing imageries and verified by field check. And based on the above, the spatial distribution of the Yushu earthquake triggered landslides is presented in this paper. The distribution of the landslides was strongly dominated by main surface ruptures, and their types are varied, with the collapse-type landslide as the dominant. There are five genetic mechanisms of Yushu earthquake triggered landslides, they are: the slope-toe excavation type, the surface water infiltration induced slope slip type, the fault dislocation type, the shaking type, and post-quake snow melting and rainfall penetration type. Besides the main seismic surface ruptures, there are many slope fissures developed mainly on the SE end of the surface rupture zone on the SW wall, an area undergoing intensive compression in the earthquake.