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Small-scale landslides that occur at unexpected locations and seasons can cause severe losses of life and property. Unusually, the Shidang landslide occurred prior to the rainy season in Jinkouhe County, Sichuan Province, China, resulting in 19 deaths and missing persons. In this paper, landslide characteristics and causes are revealed through a field investigation, laboratory experiments, and numerical calculations. The results show that the Shidang landslide was a small-scale landslide with a total volume of approximately 19500 m3, originating from hollow colluvial deposits. The geomorphological hollow above the landslide provided topographic conditions for the collection of subsurface runoff. Groundwater flow was transported downward along dolomite and accumulated in the area above shale, with a low conductivity. Groundwater table rise combined with the decrease in the soil strength led to landslide occurrence. Improper siting of residential areas and insufficient vigilance against post-rainfall landslides caused this tragedy. Considering the post-disaster lessons, it is strongly recommended that in the future, more attention should be given to the investigation of landslides triggered by groundwater, especially in determining landslide hydrological system boundaries. In mountainous areas where the developable space is limited, areas with hollow landforms are potentially at risk of geohazards. This paper could provide a reference for the prevention and management of similar landslides and new insights for updating landslide investigation systems and residential housing plans. [ABSTRACT FROM AUTHOR]

The Wenchuan earthquake in 2008 induced thousands of geological hazards. Among them, earthquake-induced landslides are extremely disastrous, causing considerable social and economic losses and damage to the ecology and environment. Therefore, it is of great significance to accurately predict the spatial distribution of earthquake-induced landslides. The research in this paper focuses on ten extremely earthquake-stricken areas of the Wenchuan earthquake; landslide hazard data are collected from the study areas, and the terrain information entropy, distance to rivers, distance to faults, distance to roads, lithology, normalized difference vegetation index (NDVI), peak ground acceleration (PGA) and other landslide conditioning factors are extracted. Combined with a deep neural network (DNN), a landslide hazard spatial prediction model is constructed. Through Dataset 1 and Dataset 2, which are generated randomly, the training dataset (70%) and the validation dataset (30%) are divided to verify the robustness and accuracy of the model, and the landslide susceptibility map of the study area is obtained. Moreover, considering the area under curve (AUC), the impacts of different conditioning factors on the landslide hazard prediction model are analyzed. The research results show that the DNN-based landslide hazard spatial prediction model (AUCMean = 93.66%, RecallMean = 85.70%) has a better prediction performance in the training step. From the validation step, the influences of various factors on the spatial prediction model of landslide hazards established in this paper (from high to low) are as follows: distance to faults, lithology, distance to rivers, PGA, terrain information entropy, NDVI, and distance to roads. The landslide hazard point data and landslide susceptibility map are highly consistent, indicating that the application of a deep learning algorithm in hazard susceptibility assessment is effective and can provide a scientific basis for landslide hazard early warning and disaster prevention and mitigation in mountainous areas prone to earthquake disasters. [ABSTRACT FROM AUTHOR]

A World Natural Heritage Site, Jiuzhaigou, is the first nature reserve in China whose primary purpose is to protect natural scenery. On August 8, 2017, a Ms 7.0 earthquake caused many unstable slopes in Jiuzhaigou, Sichuan Province, China. In the extreme storm conditions that follow, the unstable slopes tend to develop into potential landslides, which can cause many casualties and property losses in scenic areas. Sentinel-1A ascending orbit data were obtained in this paper to establish a SAR database. The large-scale deformation rate map of the study area was obtained using a small baseline set InSAR technology. The potential landslides in the deformation area are preliminarily confirmed with remote sensing interpretation. The field verification is further carried out by studying the deformation information of the characteristic points on the potential landslides. The results show that 13 deformation zones were preliminarily identified, and three typical deformation zones were selected for coupling verification and identified as potential landslides. At the same time, further analysis shows that the four potential landslides have been in continuous linear deformation for a long time since the earthquake, posing a severe threat to the safety of local people's lives and property. The research results provide a reference for the early identification and warning of potential landslides in earthquake-prone regions. [ABSTRACT FROM AUTHOR]

Background: On September 5, 2022, an Ms 6.8 earthquake occurred in Luding County, Ganzi Tibetan Autonomous Prefecture, Sichuan, China. The casualties and economic losses caused by the earthquake are huge. Most of the landslides triggered by this earthquake are small and medium-sized rock collapses and destructive debris flows, with a small number of large-scale landslides. Results: There are 5007 coseismic landslides in the Luding earthquake VII-degree area and above, with a total area of 17.36 km2. The landslides are generally distributed along the NW-SE direction. The highest landslide area density is 13.8%, and the highest point density of the landslide is 35.73 km2. High-density area is mainly concentrated in the IX-degree area and the northeast side of the seismogenic fault. Coseismic landslides are more likely to develop in the area with the slope more than 40°, the slope direction of 67.5-112.5 degree, and vegetation coverage of 40–50%. More landslides are developed in granite areas and forest areas. The closer distance to active faults, the more the coseismic landslides develop. The correlation between coseismic landslide development and the distance from the active faults is better than that between coseismic landslide development and the distance from the seismogenic faults. Conclusion: The landslide database used in this paper is the most perfect at present, and the research results have also been verified by fieldwork. This is also the first applied study of this landslide inventory, which indirectly confirmed the accuracy of the inventory. The results obtained in the manuscript will provide an important guideline for future disaster prevention and mitigation in southwest China. [ABSTRACT FROM AUTHOR]

Slow-moving landslide-prone areas (SLAs) are unstable objects on the terrestrial surface that can collapse rapidly when provoked by earthquakes, leading to infrastructure damage. It is critical to identify SLAs prior to earthquake events and assess their hazard-causing probabilities when triggered. An assessment approach of earthquake-triggered geohazards is proposed in this paper by combining interferometric synthetic aperture radar (InSAR) derived SLAs and geological and geomorphological factors. Taking the Ms6.8 Luding earthquake, which occurred in the Sichuan Province of southwestern China on September 5, 2022, as an example, 1320 scenes of Sentinel-1 SAR data in western Sichuan were processed using the small baseline subset (SBAS) InSAR technique before the earthquake. After the earthquake, hazard probability assessment was performed in real-time by filtering the SLAs using a spatial analysis technique with geological and geomorphological factors, e.g., real-time peak ground acceleration (PGA), slope, distance to fault (DTF), and distance to the river (DTR) data. The results show that 11 SLAs were classified into high-risk zones. As verified by the Luding co-seismic landslide dataset from visual interpretation of optical remote sensing images, 142 coseismic landslides were triggered by the earthquake in these high-risk regions. In these areas, an ancient landslide, with high pre-earthquake displacement rates (−50 mm/year) on the scarp was reactivated under the Luding earthquake forces. This method can provide a scientific tool for disaster mitigation and rapid response emergency management. [ABSTRACT FROM AUTHOR]

Based on the mechanism of landslide dam failure caused by overtopping, a simplified mathematical model for simulating dam breach overtopping flow processes was proposed in our previous work. The model is composed of five main modules including hydrodynamic, sediment transport and erosion, headward erosion, breach lateral evolution, and breach side slope stability. However, the breach lateral evolution module is still based on the assumption existing in most available models that breach lateral spreading is linearly related to the undercutting, i.e., the lateral erosion rate is twice as much as the vertical undercutting rate in the process of dam breaking. This assumption is obviously lack of theoretical basis. Through a theoretical analysis of sediment initial motion on the river channel slope, a lateral erosion formula was deduced in this paper. Then the lateral enlargement model was developed to update the breach evolution module in the previously established simulation model. To verify the effectiveness of the modification, A case study was conducted in the same overtopping failure event of Baige landslide dam, which was located at the border of Tibet Autonomous Region and Sichuan province in China, took place on November 3, 2018. A good model performance was achieved with predicted flood volume, peak discharge and occurrence time within 10% margin of error compared with field monitoring data. Furthermore, a comparing with the previous model was carried out and the results obtained by the modified model applying the new lateral enlargement formula proposed in this study indicate that the predicting accuracy of the model can be improved significantly for the simulation of landslide dam overtopping failure. [ABSTRACT FROM AUTHOR]

The preferred plane often exists in a layered slope, which has a main influence on the slope stability. Such a plane can evolve into the failure surface, due to the internal and external triggers of landslides. This paper proposes a case study of the failure of a highway slope in Sichuan, China. The site investigations were conducted using a number of methods, such as geological survey, unmanned aerial vehicle survey, slope monitoring, in-situ and laboratory tests. Through the case study, the failure mechanism of the layered slope was discussed, where the slope stability analysis was conducted using the limit equilibrium method (slip-wedge method) and strength reduction method (SRM). According to the circumstance of the landslide area and the requirement for the highway, this paper also proposes the stabilization measures, which was subsequently validated by SRM. Finally, the sliding mechanism of the landslide was discussed. [ABSTRACT FROM AUTHOR]

Landslides which occur at unexpected locations and times, and which have unrecognizable risk areas, often cause mass casualties. Despite there being few preceding landslides in the same season, the catastrophic Qijiaba landslide occurred in the new government-selected settlement of Wufu village, Sichuan Province, China, on September 5, 2021, killing three people and destroying 13 houses. In this paper, the landslide features and causes are discussed based on field investigations, remote sensing interpretations, and laboratory-based soil experiments. The results showed that it was a typical soil landslide within a geomorphic hollow of the slope with a volume of 20,230 m3, recurrence interval of at least 69 years, and runout distance of 137.5 m. This type of landslide is difficult to predict. The soil strength in the sliding surface decreased suddenly as the water content increased, and the soil's rich clay mineral and high clay particle contents determined its high mobility (Heim's ratio of 0.44). The occurrence time of the Qijiaba landslide lagged 1.3 days behind the peak rainfall, and the 2-day cumulative rainfall was a crucial factor in the landslide occurrence. Finally, the large water confluence area behind the main scarp was determined to be 3.9 times the area of the landslide itself, thereby providing abundant water conditions to induce failure. The factors underlying the casualties included inappropriate site selection, insufficient awareness of potential landslides, and failure to evacuate in time. The results of this study provide a basis for future prevention, mitigation, and early warning of similar landslides. [ABSTRACT FROM AUTHOR]

Machine learning models have been widely used in landslide susceptibility prediction. However, landslide multidimensional feature extraction, model generalization ability, and prediction quantification of deep learning are still challenging. This paper proposes a new approach, the landslide density-based bidirectional long short-term memory (LD-BiLSTM) model with multichannel input and an optimized sampling strategy to predict and map landslide susceptibility in active seismic mountainous areas of Sichuan Province, China. First, to ensure the generalization ability of the LD-BiLSTM model, other regions in Sichuan were selected as the model training area independent of the prediction area (Luding County). Multichannel landslide datasets were constructed to extract high-dimensional geospatial features of landslides. Subsequently, the landslide density of each grid cell was utilized as the label for the corresponding input sample. The LD-BiLSTM model was improved by using transfer learning and sampling optimization strategies, which makes our method attenuate the impact of historical landslide inventory deviation on the spatial susceptibility mode compared with the existing DL model, which usually uses landslide objects (LO) as input sample labels. Model performance evaluation results show that the LD-BiLSTM model (precision = 0.903, recall = 0.899, F1-score = 0.901, Area under receiver operating characteristic curve (AUC) = 0.940) outperformed the LO-BiLSTM model (precision = 0.812, recall = 0.815. F1-score = 0.813, AUC = 0.910) in the case areas. Meanwhile, the performance of the LD-BiLSTM model (AUC = 0.9407) significantly outperformed both the information value (IV) (AUC = 0.7207) model and the random forest (RF) (AUC = 0.8116) models in the landslide prediction area (Luding), which confirms that the proposed LD-based method is superior to traditional LO-based methods. Significantly, our approach can effectively extract the spatial distribution of landslides and predict potential landslides in complex high-mountain environments. [ABSTRACT FROM AUTHOR]

The Himalayan volcanic earthquake zone has significantly impacted China's Sichuan-Tibet region. Many barriers have formed as a result of the earthquake and secondary disasters, such as landslides, which have blocked the river. The breach of the barrier lake seriously threatens the lives and property safety of downstream personnel. There has been little research on the surrounding ecology for the later treatment of the barrier lake. This paper aims to scientifically predict the risk of dam break in a barrier lake as well as to explore its impact on the ecological environment and put forward controllable measures. Based on four major barrier lake events in the Sichuan-Tibet area, Diexihaizi, Tangjiashan barrier lake, and so on, we extract water bodies from remote sensing images and use the HEC-RAS (Hydrologic Engineering Center of River Analysis System) model to investigate whether there is a dam break risk and the route of the dam break is predictable. Simultaneously, from 1990 to 2020, the smallest administrative region is located. The InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) model is utilized to evaluate and analyze its habitat and create an evaluation based on flood inundation data. The results suggest that a stable barrier lake (such as Diexihaizi) has a sound effect on the habitat quality index following engineering treatment. The development of the barrier lake has altered the types of neighboring lands used and the natural patterns of the region's landscape. The habitat quality index will marginally deteriorate within a 1-km radius of the barrier lake. However, the quality of habitats in the area ranging within 3 km and 5 km has improved. It is necessary to discharge and strengthen the barrier lake artificially. Human-controlled regions, according to studies, will recover higher habitat quality index values than other locations. Whether the barrier lake has a positive impact on the surrounding area, on the other hand, is primarily dependent on the original ecology. The development of barrier lakes is damaging and unprofitable in Tibet, where the actual ecology is better in the short term. Still, in Sichuan Province, where the habitat quality is relatively low, the appearance of dammed lakes has played a role in correcting the ecology. [ABSTRACT FROM AUTHOR]

On 5 April 2021, a high-position landslide occurred in Tiejiangwan, Liujiang Town of Hongya County, Sichuan Province, China. The detached mass transformed into a debris avalanche traveled about 1300 m with an elevation difference of 665 m and caused three fatalities. Then landslide deposits were initiated by continuous rainfall and evolved to a debris flow five days later, forming a secondary disaster chain. The rapid characterization of a landslide-debris flow chain (LDC) is a critical step in remediation works and future early warning of failure. This paper proposes a rapid disaster information integration and assessment method for providing information on LDC occurrence, failure mechanisms, and potential hazard-affected areas via examining and characterizing the Tiejiangwan LDC based on multi-method including geomorphological mapping, structural characterization, kinematic analysis and numerical simulation. The results suggested that the first landslide initiated from a mountain ridge that experienced historical deformation extended over the fractured argillaceous siltstones. Constrained by three discontinuity sets, the failure mode was determined as planar failure along a sub-vertical defect. The cause of first landslide can be attributed to a jointed effect of structural plane, tectonic activity, terrain and slope structure, while rainfall is the dominating triggering factor. Deposits with loose structure subjected to persistent rainfall and gully topography contribute to the occurrence of secondary debris flow. Using UAV and LiDAR identification, the unstable elements in the surrounding cliffs of the source area are derived and a three-dimensional simulation was implemented to detect the energy, velocity, height and reach of detached blocks. The proposed method leads to rapid characterization of LDC and assists the evaluation of cliff instability of these environments. Highlights: We proposed a rapid and integrated assessment system for landslide-debris flow chain occurrence, failure mechanisms, and potential hazard-affected areas identification. Quantitative study was conducted on the evolution from landslide deposits to debris flow. 3D simulation was performed to reveal the runout behaviors of surrounding potential dangerous rockfall areas. [ABSTRACT FROM AUTHOR]

On 24 June 2017, a rockslide-debris avalanche occurred in Xinmo Village, Maoxian County, Sichuan Province, China. The displaced materials traveled approximately 2.6 km and caused 83 casualties. Combined with the field investigation, seismic signal, and numerical simulation, the dynamic process and deposit characteristics of the Xinmo landslide were analyzed. Based on the Arias intensity and Hibert time–frequency analysis of the seismic signal, the Xinmo landslide is determined to have a total duration of 120 s and with three-stage dynamic processes, namely, the initial accelerating stage (50 s), erosion impact stage (50 s), and accumulation stage (20 s). Then, taking the field investigation results, the total duration, and kinetic parameters inverted from the seismic signal as criteria, the DAN3D (Dynamic Analysis of Landslides) was employed to reconstruct the dynamic process of the Xinmo landslide. The results show that the erosion and deposit depth distributions obtained from the simulation are in agreement with the pre- and post-landslide DEM (digital elevation model) data and field results. Further, the three-stage dynamic processes (40 s, 60 s, 20 s) obtained from the numerical simulation are also close to the seismic analysis results. In this study, the results of the field investigation and seismic analysis provide a more detailed set of inversion criteria for the numerical simulation work, which contributes to reconstructing the dynamic processes of the Xinmo landslide more accurately. Based on the approach of multi-source data collaboration in this paper, those landslides that lack direct observation data can be better understood and the numerical parameters used for landslide dynamics inversion are better calibrated, which can be applied to predict the runout behavior and disaster scope of potential landslides in the immediate area. [ABSTRACT FROM AUTHOR]

Landslides are a serious natural hazard in the world. A map of landslide susceptibility can help to effectively reduce losses. In this paper, a hybrid ensemble technique based on random subspace (RS) and a J48 decision tree named RS–J48T was proposed for landslide susceptibility mapping. This model could enhance the effect of a single classifier significantly and solve the problem of overfitting. Qingchuan County, Sichuan (China) was taken as a study area. A geospatial database which consisted of 640 landslide locations and 12 factors was constructed for this study. The J48 decision tree, artificial neural network (ANN), and other ensemble techniques like AdaBoost and Bagging, were selected for comparison. Receiver operating curves and some statistical indices were used for model validation. The results showed that the RS–J48T model had the better fitting capability (AUC = 0.875), and the best prediction capability (AUC = 0.769) compared to other models. Overall, the novel hybrid model could be a promising way for generating landslide susceptibility maps for other prone areas. [ABSTRACT FROM AUTHOR]

In China, many geological hazards occurred in remote mountainous regions, and it was time-consuming to disseminate disaster information for the responsible parties to make timely judgements. Besides, only relying on professionals to monitor and manage disasters was demanding and costly. The Chinese government created a system to engage residents in the process of disaster risk management, namely 'Public Participation Monitoring and Warning' (PPMW), to disseminate timely disaster information and bring down management costs. The objective of this system was to reduce casualties with minimum cost by organizing residents to evacuate from disasters in advance. This paper introduced the PPMW system, including its structure, operation mechanism by reviewing government documents and research articles, and its implementation by a case study of a landslide at Boli village (E 101°01′, N 27°29′), Yanyuan County, Sichuan Province, China on July 19th 2018. Further, this paper analyzed the strengths and limitations of the system and discussed its future development. It had the potential to become an affordable disaster risk management tool for other countries facing similar situations to China. [ABSTRACT FROM AUTHOR]

Maoxian Country in Sichuan Province, China is a quake-prone mountainous area. Within 300 km of it, there have been more than ten strong earthquakes higher than Ms6.0 on record. The nearest one is the Diexi Earthquake of 1933, 8.7 km from Maoxian County. A catastrophic scale landslide, the Xinmo landslide, took place on the steep cataclinal slope of Xinmo Village on June 24, 2017. Shear failure happened to the phyllite layer of rich fissures during the landslide. In this paper, shaking table tests have been conducted on the Xinmo landslide as a prototype, and a non-contact deformation measurement based on the digital image correlation (DIC) technique has been made to analyze the dynamic response and deformation of the slope under accumulated seismic actions. As the results, stress amplification is found at the end of joints on the potential slide face. Failure starting from the middle of the potential slide face in the beginning extends upwards and downwards along the face, and leads to a steep tension crack on the top of the slope and a buckling failure of radial-arc cracks at the foot. A semi-steep radial crack in buckling part has a shear failure. The tensile stress amplification on the discontinuous face contributes to the failure accumulation of it and asymptotic failure of the slope. It proposed that the closure and downward displacement of tensile crack on the top of slope, buckling phenomenon at the foot of slope and shear failure of semi-steep crack are all the bad omens of landsliding. [ABSTRACT FROM AUTHOR]

The assessment of earthquake-induced landslide hazards is an important prerequisite for disaster prevention and reduction in tectonic active areas. However, few studies have considered the amplification effect of site and topography on ground motion parameters and made the peak ground acceleration (PGA) correction. Based on Newmark's method, taking Litang County, Sichuan Province, China, as the study area, considering the site amplification effect and topographic amplification effect, this paper carried out the assessment of landslide hazards under the action of occasional earthquakes with an exceedance probability of 10% and rare earthquakes with an exceedance probability of 2%. The results show that the hazard of earthquake-induced landslides is higher in the high slopes of loose rock in the middle of Litang County and the steep rock slopes with large topographic relief in the northeast, and low in the southern plateau and central basins. The site and topographic conditions have a significant effect on the nonlinear amplification of PGA, and the corrected PGA is even magnified by 2–3 times in steep mountains. Compared with the occasional earthquakes, the influence of rare earthquakes on the initiation and movement distance of landslides is remarkably improved. This study can provide a valuable reference for potential earthquake-induced landslide hazard assessment and seismic landslide emergency response in Litang County. [ABSTRACT FROM AUTHOR]

Landslide hazards have attracted increasing public attention over the past decades due to a series of catastrophic consequences of landslide occurrence. Thus, the mitigation and prevention of landslide hazards have been the topical issues. Thereinto, numerous research achievements on landslide susceptibility assessment have been springing up in recent years. In this paper, four benchmark models including best-first decision tree (BFTree), functional tree, support vector machine and classification regression tree (CART) and were integrated with bagging strategy. Then, these bagging-based models were applied to map regional landslide susceptibility in Jiange County, Sichuan Province, China. Fifteen conditioning factors were employed in establishing landslide susceptibility models, respectively, slope aspect, slope angle, elevation, plan curvature, profile curvature, TWI, SPI, STI, lithology, soil, land use, NDVI, distance to rivers, distance to roads and distance to lineaments. Then utilize correlation attribute evaluation method to weigh the contribution of each factor. Finally, the comprehensive performance of various bagging-based models and corresponding benchmark models was evaluated and systematically compared applying receiver operating characteristic curve and area under curve (AUC) values. Results demonstrated that bagging-based ensemble models significantly outperformed their corresponding benchmark models with validation dataset. Among them the Bag-CART model has the highest AUC value of 0.874; however, the AUC value of CART model is only 0.766, which reflected satisfying predictive capacity of integrated models in some degree. The achievements obtained in this study have some reference values for landslides prevention and land resource planning in Jiange County. [ABSTRACT FROM AUTHOR]

Tunnel excavation in mountainous areas can sometimes reactivate or trigger landslides. The problem of tunneling-induced landslides may be influenced by not only the tunnel location and slope geological conditions but also construction deficiency without timely treatment. This paper presents a case study of Jimei landslide in Sichuan Province, China. This dormant landslide was largely induced by the excavation of a twin tunnel in the landslide body. Field investigation, slope movement monitoring, and numerical simulations show that the formation mechanism of the Jimei landslide triggered by tunneling can be summarized as follows: (1) The adverse geological conditions of stratified sliding mass consists of multilayer sliding surfaces and cracked sliding blocks. (2) The rock surrounding the tunnel underwent large and continuous deformation because of construction deficiency without timely treatment. (3) There was a reduction in the shear strength and increase in the bulk density of the sliding mass because of rainfall infiltration during the rainy season. [ABSTRACT FROM AUTHOR]

A rainfall-induced catastrophic landslide occurred at UTC + 8 03:50 local time on August 21, 2020, in Zhonghaicun village (N 29°20′30″, E 102°41′40″), Hanyuan County, Sichuan Province, China, resulting in damage to 6 buildings and 9 casualties (7 fatalities and 2 missing). In this paper, multitemporal remote sensing images, comprehensive field investigations, drilling holes, deformation monitoring, and numerical simulations are used to identify the main characteristics and the formation mechanism of the event. The results showed that long-term intensive rainfall in 2020 played an essential role in the failure of the Zhonghaicun landslide and that strong earthquakes in Wenchuan County (Ms 8.0, on 12 May 2008) and Lushan County (Ms 7.0, on 20 April 2013) also exerted a negative influence on the evolution process of this catastrophic landslide. Several traction zones appeared after the occurrence of the landslide. Fortunately, no evident deformation was observed and these slopes were in a stable state. In addition, a large-scale potential sliding slope, four small-scale landslides, and a debris flow were found in the study area. [ABSTRACT FROM AUTHOR]

Tibetan Plateau is known as the roof of the world. Due to the continuous uplift of the Tibetan Plateau, many active fault zones are present. These active fault zones such as the Anninghe fault zone have a significant influence on the formation of special geomorphology and the distribution of geological hazards at the eastern edge of the Tibetan Plateau. The Anninghe fault zone is a key part of the Y-shaped fault pattern in the Sichuan-Yunnan block of China. In this paper, high-resolution topographic data, multitemporal remote sensing images, numerical calculations, seismic records, and comprehensive field investigations were employed to study the landslide distribution along the active part of the Anninghe. The influence of active faults on the lithology, rock mass structures and slope stress fields were also studied. The results show that the faults within the Anninghe fault zone have damaged the structure and integrity of the slope rock mass, reduced the mechanical strength of the rock mass and controlled the slope failure modes. The faults have also controlled the stress field, the distribution of the plastic strain zone and the maximum shear strain zone of the slope, thus have promoted the formation and evolution of landslides. We find that the studied landslides are linearly distributed along the Anninghe fault zone, and more than 80% of these landslides are within 2–3 km of the fault rupture zone. Moreover, the Anninghe fault zone provides abundant substance for landslides or debris flows. This paper presents four types of sliding mode control of the Anninghe fault zone, e.g., constituting the whole landslide body, controlling the lateral boundary of the landslide, controlling the crown of the landslide, and constituting the toe of the landslide. The results presented merit close attention as a valuable reference source for local infrastructure planning and engineering projects. [ABSTRACT FROM AUTHOR]

Critical acceleration is an inherent property of a slope and determines the slope stability under seismic action. The critical acceleration model is a core element of regional seismic landslide hazard assessment. Therefore, the purpose of this paper is to reveal the influence of different critical acceleration models on assessments of potential earthquake–induced landslide hazards. Traditionally, the Newmark critical acceleration model has commonly been used to evaluate the potential earthquake–induced landslide hazard. This method needs to assume the failure depth of the slope, which leads to an underestimation of the predicted displacement of the seismic landslide. Recently, the prediction equations of critical acceleration based on a parametric study of the limit equilibrium method overcomes the limitation of Newmark critical acceleration model and has been applied to assessments of co-seismic landslide hazards. In this study, we use Newmark critical acceleration model and prediction equations of critical acceleration to obtain the distribution maps of potential earthquake–induced landslide hazard in Shimian County, with peak ground acceleration of 10% and 2% exceeding the probability in 50 years. In addition, the nonlinear effect of site and topographic effects on peak ground acceleration were considered. The results show that Newmark critical acceleration model underestimates the area and value of the predicted displacement, while prediction equations of critical acceleration produces seismic landslides in a wider range of mountainous areas. This indicates that the critical acceleration model has a significant influence on assessments of potential earthquake–induced landslide hazards. In addition, the study not only provides valuable reference for assessment of potential earthquake–induced landslide hazard, emergency response of seismic landslides, and land planning in the study area, it also provides a useful demonstration for the selection of a critical acceleration model in seismic landslide hazard assessments for future researches. [ABSTRACT FROM AUTHOR]

Landslides with large displacements are more dangerous than landslides with small displacements, because the first category causes more serious damages. In this paper, a new prediction method is introduced for rock planar slides with large displacements. The prediction model is not limited to a certain region but limited to a certain failure mechanism of a landslide. This case study was carried out in the red bed area, Nanjiang County, Sichuan Province, China, and validated in the red bed area of Yunyang and Fengjie Counties, Chongqing, China. The topographic factor T was proposed as a topographical indicator. The influence of the normalized rainfall R which is a combination of the duration of rainfall (D) and the average intensity (I) of rainfall was analyzed. A threshold value for rock planar sliding with large displacement was obtained by establishing a relationship between the T factor and the R factor. The primary probability factor P gives a final indication of the probability of rock planar sliding with large displacement. The prediction model is applied to the existing rock planar slides or the slopes with the presence of a number of preconditions for sliding. The prediction model obtained from Nanjiang County was validated successfully in the red bed area of the Yunyang and Fengjie Counties. It is assumed that the prediction model is suitable for other regions with a red bed structure as well. [ABSTRACT FROM AUTHOR]

A dataset of landslides from Sichuan Province in China, containing 1551 historical individual landslides, is a result of two teams' effort in the past few years to map the susceptibility to landslides. Considering complex internal relations among the triggering factors, logistic regression (LR) and shallow neural networks, such as back-propagation neural network (BPNN), are often limited. In this paper, we make a straightforward development that the deep belief network (DBN) based on deep learning technology is introduced to map the regional susceptibility to landslides. Seven factors with respect to geomorphology, geology and hydrology are considered and verified through the collinearity test. A DBN model containing three pre-trained layers of restricted Boltzmann machines by stochastic gradient descent method is configured to obtain the susceptibility to landslides. Susceptibility results evaluated by DBN model are compared with those by LR and BPNN in the receive operator characteristic (ROC) analysis, showing that DBN has a better prediction precision, with a lower rate of false alarms and fake alarms. The case study also indicates different sensitivities of the triggering factors to the landslide susceptibility, that the factors of altitude, distance to drainage network and average annual rainfall have significant impact in mapping the susceptibility to landslides in the region. This research will contribute to a better-performance model for regional-scale mapping for the susceptibility to landslides, in particular, at the area where triggering factors show complex relations and relative independence. [ABSTRACT FROM AUTHOR]

This paper reports on a typical slow-moving landslide that occurred in Shuitianba village in Wanyuan County, Sichuan Province, China, on September 16, 2017. The landslide in Wanyuan County is regionally outstanding for its features and well-developed landslide morphology. Hence, this event can provide a good case study for understanding the possible triggering conditions of the occurrences of all the landslides examined in Wanyuan County. The 225 residents of Shuitianba village did not flee the village because the deformation of the landslide was extremely slow. However, the primary emergency tasks are still necessary to reduce the landslide risk that may affect the residents. Detailed field investigations, boreholes, and exploratory trenches were performed to investigate the characteristics and triggering factors of this landslide. Rainfall and anthropogenic activities (such as informal, unplanned settlements and deforestation) were revealed to be the main triggering factors. The main engineering control measures, including anti-sliding piles, an anti-sliding retaining wall, and intercepting ditch, were applied to stabilize the landslide. However, landslide mitigation measures have not been finished, and hence, some interim measures are suggested to effectively reduce landslide risk. [ABSTRACT FROM AUTHOR]

This paper reports on a recent landslide event, which occurred in Yanyuan County at Xichang City, Sichuan Province, China, on 19 July 2018. Due to the continuous and heavy rainfall between 8 July 2018 and 18 July 2018, approximately 18 million m3 of soil detached from an ancient landslide and buried 65 buildings, the entire sliding process lasted for several minutes. Here, based on field investigations and unmanned aerial vehicle (UAV) photography, we describe the characteristics and failure mechanism of this landslide. The results showed that the landslide was triggered by the continuous and heavy rainfall. Additionally, during the first post-failure stage, the motion of this landslide involved typical successive movement, and over time, the style of movement progressively turned flow-like, which can be classified as debris slide by using the 1978 Varnes classification scheme. The landslide event described here is an outstanding case for landslide studies. In particular, this landslide represents a good illustration of the transform mechanism of landslide movement from sliding to flow-like. [ABSTRACT FROM AUTHOR]

A giant, high-position rockslide occurred in Xinmo village of Maoxian County, Sichuan, China, on June 24, 2017. It was the largest rockslide recorded since the 2008 Wenchuan earthquake, and caused great loss. We use field survey data and relevant information to describe the geometric and zoning of the Maoxian landslide, and we discuss its sliding mechanism and the role of active faults in its formation. The sliding mode of the Maoxian landslide involves a plane failure mechanism (sliding rupture), while the slipping process can be divided into two stages: a rock cracking and deterioration stage, and a high-speed sliding stage. The role of active faults (earthquake and fault movement) is probably the most important factor in the Maoxian landslide formation, while lithology played a catalytic role and rainfall acted as an inducing factor. The fault vertical combination model ("back thrust" dynamic model) proposed in this paper provides a reasonable explanation for the different distribution of the coseismic landslides caused by the 1933 M 7.5 Diexi earthquake. We consider that a steep slope near the active fault, especially where the active fault intersects, just like the "locked segment" of a fault, is the uppermost area to develop a large landslide. [ABSTRACT FROM AUTHOR]

Previous studies on optical remote sensing mapping of landslides mainly focused on new landslides that have occurred, but little attention was paid to the early landslide due to its high concealment. InSAR technology, a prevalent method to detect early landslides, only can be used to identify the potential hazards of slow deformation. Therefore, it is necessary to explore new method of early landslides mapping by integrating all types of direct and indirect early features, such as cracks on slopes, small collapses inside and topographic features. In this study, an object-oriented image analysis method based on slope unit division and multi-scale segmentation was proposed to obtain accurate location and boundary extraction of early landslides. In the middle- and small-scale segmentation, the object, texture, spectrum, geometric features, topographic features, and other features were obtained to determine the local feature location of early landslides. The slope unit boundary was combined with the feature of a large-scale segmentation object to determine the scope of landslides. This method was tested in the Xianshui River basin in the Daofu County, Sichuan Province, China. The results demonstrate that: (1) Such features as landslide cracks and the small collapse at the bottom of slope can effectively determine the landslide position. (2) The slope unit division and the correct setting of shape factors in multiple segmentation can effectively determine the landslide boundary. (3) The accuracy of landslide location extraction was 83.33%, and the accuracy of boundary extraction for early landslides that were completely identified was evaluated as 82.67%. It is indicated that this method can improve the accuracy of boundary extraction and meet the requirements of the early landslides mapping. [ABSTRACT FROM AUTHOR]

Two successive landslides within a month started in October 11, 2018, and dammed twice the Jinsha River at the border between Sichuan Province and Tibet in China. Both events had potential to cause catastrophic flooding that would have disrupted lives of millions and induced significant economic losses. Fortunately, prompt action by local authorities supported by the deployment of a real-time landslide early warning system allowed for quick and safe construction of a spillway to drain the dammed lake. It averted the worst scenario without loss of life and property at least one order of magnitude less to what would have been observed without quick intervention. Particularly, the early warning system was able to predict the second large-scale slope failure 24 h in advance, along with minor rock falls during the spillway construction, avoiding false alerts. This paper presents the main characteristics of both slope collapses and damming processes, and introduces the successful landslide early warning system. Furthermore, we found that the slope endured cumulative creeping displacements of > 40 m in the past decade before the first event. Twenty-five meter displacement occurred in the year immediately before. The deformation was measured by the visual interpretation of multitemporal satellite images, which agrees with the interferometry synthetic aperture radar (InSAR) measurement. If these had been done before the emergency, economic losses could have been reduced further. Therefore, our findings strengthen the case for the deployment of systematic monitoring of potential landslide sites by integrating earth observation methods (i.e., multitemporal satellite or UAV images) and in situ monitoring system as a way to reduce risk. It is expected that this success story can be replicated worldwide, contributing to make our society more resilient to landslide events. [ABSTRACT FROM AUTHOR]

Calculating the failure probability of a landslide is important in engineering related to geological process and geomorphological evolution. Strength parameters of a soil (i.e., cohesion and internal friction angle) are regarded as uncertain-but-bounded parameters. In this study, a new method is proposed for computing the landslide failure probability based on a convex set model and artificial neural network (ANN). In the new method, ANN is used to determine the limit state function of landslide stability, and the failure probability is determined using a simple iterative algorithm. The new method was applied to calculate the failure probability of the Gufenping landslide in Nanjiang, Sichuan, China. The results calculated using a Monte Carlo simulation (MCS) method confirmed that the new method accurately and quickly obtains the failure probability of a landslide. Additionally, compared with the two-dimensional calculation method, the one-dimensional analysis method overestimates the failure probability of the landslide. The results of single factor and global sensitivity analysis indicate that the average of internal friction angle is the main factor affecting the stability of the landslide. It is easy to calculate failure probability of landslides using the novel method than using the conventional methods. [ABSTRACT FROM AUTHOR]

Geological hazards, especially landslides and mudslides, are frequent in Caoke County, Sichuan Province, China. In September 2022, the mechanical parameters of the soil were obtained through a basic investigation of the landslide characteristics of Ni changgou. Upon that, the finite element-discrete element method was used to reconstruct the three-dimensional numerical model of the landslide on the right bank of Ni changgou, and the initiation mechanism of rainfall on landslide and the formation of debris flow impact dam process were simulated. Furthermore, the pore pressure, stability coefficient as well as displacement of the landslide body were analyzed. It turned out that with the increase of rainfall intensity, the pore water pressure value also increases, where pore water pressure rises rapidly. the slope is close to the unstable edge, Eventually, it tends to one under rainfall conditions, and due to gravity, the slide of the landslide is induced. The duration of landslide movement is about 200 s, the maximum average velocity of the landslide reaches 4.85 m/s, and the average movement distance is close to 500 m. In addition, this method is applied to the Chutougou debris flow, and the corresponding hazard analysis is added which could better show the treatment and application of debris flow in actual engineering. [ABSTRACT FROM AUTHOR]

The purpose of this work is to carry out seismically induced landslide probabilistic hazard mapping for future seismic scenarios of Aba Prefecture and Chengdu Plain region, Sichuan Province, China. Nine earthquake events that occurred in the regions and neighboring areas are selected, which include a total of 251,260 landslide records. This work used 12 influencing factors including elevation, slope, aspect, relief, topographic wetness index (TWI), topographic position index (TPI), peak ground motion, distance to active faults, vegetation coverage, distance to roads, lithology, and annual rainfall to establish the LR model. Based on the probabilistic seismic hazard analysis (PSHA) method, the distribution of predicted seismic motion under four earthquake scenarios is calculated including frequent, occasional, rare, and very rare earthquake occurrence. Using the PGA distribution of the four scenarios as input peak ground motion parameters, we calculated the occurrence probability of coseismic landslides in the entire Aba Prefecture and Chengdu Plain region under the action of different ground motions. The result shows that the high-hazard areas are mainly concentrated in the Longmenshan fault zone, and the southern area of Kangding is also a potential high-hazard area for landsliding. Meanwhile, as the probability of exceedance decreases, the probability of corresponding earthquake-induced landslides hazard probability and the area of high-hazard regions also significantly increase. Especially, the Pengguan complex rock mass in the southwest of the Longmenshan fault zone is the potential high-hazard area for coseismic landslides. Highlights: Establishing a coseismic landslides evaluation model suitable for Aba Prefecture and Chengdu Plain region. Calculating the occurrence probability of coseismic landslides under different peak ground motions. [ABSTRACT FROM AUTHOR]

On April 20, 2013, a Mw 6.6 earthquake struck Lushan County of Sichuan Province, China, about 80 km southwest of the epicenter of the 12 May 2008 Wenchuan Mw 7.9 event. It triggered a large number of landslides in a broad area, causing causality and injuries and damages to roads, drains, dwellings, and infrastructures. Field investigations of coseismic landslides provide a basis for better understanding and illustrating the spatial distributions and hazards related to the Lushan earthquake-triggered landslides. Therefore, this paper presents results of field investigations of the Lushan earthquake-triggered landslides, which can be classified into two main categories of disrupted landslides and coherent landslides, and constitutes seven types. The disrupted landslides include five types: rock falls, rock slides, rock avalanches, soil falls, and soil slides; and the coherent landslides have two types: soil slumps and slow earth flows. All of the seven types of landslides can be observed in many field photos, aerial photographs, and/or satellite images. Among them, seven typical large-scale landslides, distributed in a northeast trending line consistent with the Shuangshi-Dachuan fault, which is presumably related with the earthquake, are described in detail. We prepared an original, emergency-based coseismic landslide inventory map based on visual interpretation of available post-earthquake aerial photographs and pre-earthquake satellite images, combined with results from emergency-based field investigations. The original inventory map registered 3884 landslides. Later, 11,761 more landslides were identified by much more careful and time-consuming visual interpretation based on the same aerial photographs and satellite images. Subsequently, all the 15,645 coseismic landslides were correlated with six common landslide impact factors, including elevation, slope angle, slope aspect, topographic position, stratum/lithology, and peak ground accumulation (PGA). These observational and compiled coseismic landslide data allow us to make subsequent compiling of a detailed and completed landslide inventory by visual interpretation of remote sensing images as well as further studies of landslides induced by the Lushan earthquake. [ABSTRACT FROM AUTHOR]

During the five rainy seasons following the 2008 Wenchuan earthquake, at least eight catastrophic events of multiple debris flows occurred in the Yinchanggou area of Baishui River basin, in southwestern China. The deposits had some serious impacts on the transportation, communication, and reconstruction activities. It indicates the start of a period with a high-frequency debris flow activities. In this paper, a detailed review is made of the latest 12 debris flow events, which occurred on 18th August 2012. Field reconnaissance and measurements, supported by a remote sensing interpretation, were conducted to reveal the locations and morphological characteristics of debris flow catchments. A total volume of 50.26 million m co-seismic landslides, triggered by the Wenchuan earthquake, served as source materials for these debris flows and/or shallow landslides. Bedrocks, which were on the hanging wall of the Yingxiu-Beichuan Fault with a mean slope gradient larger than 30°, were the most susceptible for these seism-triggered failures. The volume of debris flow deposits on individual fans vary by many orders of magnitude from 2,900 to 223,500 m. The huge amount of reserves of source material compared to debris flow volumes currently deposited and taking into account the relatively low morphology index values form an indication of the early stage in the development of debris flows and the immense potential for debris flow occurrence in the future. There are two patterns of debris flow initiation which can be distinguished in this study: (a) the channel initiation pattern of concentrated overland flow erosion in the channel beds and (b) the slope initiation pattern which shows the development of new landslides and a transition into debris flows during a heavy rainfall. A catastrophic debris flow in the Haihuiqiao watershed of the Yinchanggou area was selected to show in detail the mechanism of debris flow initiation as a result of intensive erosion in loose debris materials. The critical rainfall threshold in terms of daily precipitation decreased by 77 % compared to the pre-earthquake threshold. A primary rainfall intensity-duration ( ID) threshold curve was constructed for debris flows for an early-warning system and compared with other global rainfall thresholds. The discussion is focused on the initiation mechanisms of debris flows and the assessment of the critical meteorological threshold. [ABSTRACT FROM AUTHOR]

With the increase in inclement weather conditions, many countries would experience more and more landslide hazards in the process of planning, designing and construction for engineering projects, especially in the mountainous regions. How to quickly and accurately assess potential landslide risk in a large region (> 10,000 km2) is facing challenge due to its complex geological conditions and large amount of landslides in the region. To optimize the accuracy of the existing models for a large region, in this study, the genetic algorithm (GA) and particle swarm optimization (PSO) are, respectively, coupled with the backpropagation (BP) neural network to determine the initial weights and thresholds in the BP neural network, which can be called GA-BP model and PSO-BP model. To show the reliability and accuracy of the new models in large region, the BP, GA-BP and PSO-BP models are evaluated based on root mean square error (RMSE), coefficient of determination (R2), Kappa coefficient (k), receiver operating characteristic (ROC), training time and condition factor weights by using 100 landslide samples from Sichuan Province, China. Results show that the RMSE values of the GA-BP model and the PSO model are, respectively, 22.6% and 5.1% lower than those of the BP model; the R2 values of the GA-BP model and the PSO model are, respectively, 24.9% and 6.2% higher than those of the BP model; the k values of the GA-BP model and the PSO model are, respectively, 44.3% and 15.4% higher than those of the BP model, and the areas under ROC of the GA-BP model and the PSO model are, respectively, 32.4% and 9.6% larger than those of the BP model. The GA-BP model and the PSO-BP model have better accuracy in the assessment of the overall risk value and the risk-level classification. The difference of the training time is small, and the sequences of condition factor weights given by the three models are consistent. In general, the GA-BP model is more effective for landslide risk assessment in large region. At last, this study gives proposed models under different engineering conditions, which can increase efficiency of the risk assessment for landslides. [ABSTRACT FROM AUTHOR]

On September 5, 2022, an Ms6.8 earthquake struck Luding County, Sichuan Province, China. Through creating a coseismic landslide prediction model, we obtained the spatial distribution of the triggered geological hazards immediately after the earthquake. Through collecting all available multi-source optical remote sensing images of the earthquake-affected area via UAV and satellite platforms, the exact information of coseismic landslide was achieved by pattern recognition and visual inspection. According to the current results, the Luding earthquake triggered 5336 landslides with a total area of 28.53km2. The spatial distribution of the coseismic landslides is correlated statistically to various seismic, terrain, and geological factors, to evaluate their susceptibility at regional scale and to identify the most typical characteristics of these failures. The results reveal that the coseismic landslides mainly occurred on the sides of the Xianshuihe fault (within 1.2 km) and Dadu River (within 0.5 km) in striped patterns. They are concentrated in the regions with an elevation range of 1000–1800 m, a slope range of 25–55°, and lithologies of acid plutonic rocks, mixed sedimentary rocks, and siliciclastic sedimentary rocks. Besides, the coseismic landslides of the Luding earthquake are smaller in size and shallower than those triggered by the 2008 Wenchuan earthquake and the 2017 Jiuzhaigou earthquake. Rapidly achieving the spatial locations and distribution patterns of the coseismic landslides enables to provide effective support and guidance to emergency rescue, risk mitigation, and reconstruction planning. [ABSTRACT FROM AUTHOR]

As a common phenomenon in landslides, entrainment of loose bed material along the sliding path can enhance both the volume and mobility of the sliding mass, posing a great threat to the life and property safety of people in the affected area. For the purpose of revealing the mechanism of entrainment phenomenon of sliding material in the runout process, a dynamic slice-based model was firstly proposed in the present study. In this model, the motion of landslide body is assumed to follow the rheological law proposed by Voellmy. Additionally, through Voellmy model, the increase in kinetic energy, mass and volume of landslide body and entrainment depth caused by the entrainment phenomenon were taken into consideration. Subsequently, on the basis of the dynamic slice-based model, back-analyses were performed on the Wenjia gully landslide. The effect of entrainment phenomenon on the post-failure motion was analyzed. The calculated results shows that the travel distance obtained from proposed model is closer to field data than that resulted from Scheidegger's empirical method. Moreover, the peak value of the maximum velocity calculated by this model is in good agreement with that get from the dynamic analysis model (DAN3D). When considering the entrainment phenomenon, the volume of sliding mass increases greatly in post-failure motion. This paper provides a new method for analyzing the entrainment phenomenon in landslide motion. [ABSTRACT FROM AUTHOR]

The 2008 Wenchuan earthquake triggered more than 100 rock avalanches with volumes greater than 10 million cubic metres. The rock avalanche with the longest runout amongst these destructive landslides occurred in the Wenjia valley, Mianzhu, Sichuan, China. The landslide involved the failure of about 27.5 million cubic metres of sandstone from the source area. The displaced material travelled about 4,170 m with an elevation descent of about 1,360 m, equivalent to a fahrböschung of 16.9° and covered an area of 1.5 million square metres, with the final deposited volume of approximately 49 million cubic metres. The catastrophic event destroyed the village of Yanjing, killed 48 people and buried some houses at the mouth of the Wenjia valley. On the basis of a detailed field investigation, we introduce basic characteristics of the rock avalanche and find that the rock avalanche resulted in two run-ups and a superelevation along the runout path, and downslope enlargement due to the entrainment of path materials. A numerical model (DAN3D) is used to simulate the post-failure behaviour of the rock avalanche. By means of trial and error, a combination of the frictional model and Voellmy model is found to provide the best performance in simulating this rock avalanche. The simulation results reveal that the rock avalanche had a duration of about 240 s and an average velocity of 17.4 m/s. [ABSTRACT FROM AUTHOR]

This paper investigates the setting, causes, and main dynamic characteristics of the landslide that occurred at Xinmo village, Diexi, Sichuan province, China, on June 24, 2017. By comparing the pre- and post-sliding DEMs, the landslide involved the failure of ≈ 3.0 × 106 m3 rock mass from its source area, and had a final volume of ≈ 6.3 × 106 m3. The landslide runout extended 2800 m horizontally and 1200 m vertically, and covered an area of ≈ 143.1 × 104 m2. Field investigation indicates that entrainment was one of the major causes of the increase in volume of landslide. To reproduce the behavior of the landslide runout, a depth-averaged continuum model that takes entrainment into consideration is applied. The simulated results generally agree well with the characteristics of the sliding path and distribution of the landslide deposit as observed in the field. It is demonstrated that entrainment plays a significant role in landslide mobility and volume. The final volume of the landslide is calculated about 6.21 × 106 m3, which has more than double its initial volume. [ABSTRACT FROM AUTHOR]

Arias intensity is an essential ground motion measure correlating with the potential for earthquake-induced landslides. The Sichuan–Yunnan region, which is primarily mountainous, is a high incidence region of earthquake-induced landslides in China. However, there is no available attenuation relationship for this intensity measure due to the backward construction of the stations. In this study, we developed a region-specific Arias intensity attenuation relationship using the China Strong-Motion Networks Center (CSMNC) database which was established in 2008. We recommend this relationship be applied in the Sichuan–Yunnan region for moment magnitudes ranging between 4.2 and 7.0, distances ranging between 0 and 300 km and with Vs30 (the average shear-wave velocity in the upper 30 m of a soil profile) ranging between 128 and 760 m/s. The current study finds that this relationship's intra-event, inter-event, and total standard deviations are greater than for other regions. This is likely caused by the complicated seismotectonic activities, nonlinear site effects, error from inferring Vs30, basin effects, etc. However, this relationship has the best performance in fitting and predicting the data from the Sichuan–Yunnan region. [ABSTRACT FROM AUTHOR]

Landslides are serious natural disasters frequently occurring in the mountainous areas along the Sichuan–Tibet railway. Landslides are often induced by rainfall and greatly threaten people's lives and property. Via the use of GIS technology, a probabilistic analysis model was proposed for rainfall-induced shallow landslides along the above route. The process of the proposed probabilistic analysis model could be divided into three parts: (i) an infiltration hydrogeological model, (ii) a slope stability model, and (iii) probabilistic analysis. In this model, soil cohesion and internal friction angle were regarded as random parameters. The Green–Ampt model was used to dynamically describe the soil infiltration process during rainfall. Combined with the infinite slope stability model, the failure probability in the study area was analyzed. The model was applied to simulate the Bayi catchment landslide event of August 18, 2010, to evaluate its reliability. Through comparison of the simulation results to landslide occurrence locations, it was determined that the model achieves a satisfactory prediction performance. In addition, compared to deterministic analysis methods and the r.slope model, the proposed probabilistic analysis model achieved a satisfactory evaluation performance. The presented physics-based probabilistic analysis model could provide important theoretical support for hazard prevention in regard to rainfall-induced shallow landslides in mountainous areas. [ABSTRACT FROM AUTHOR]

Landslide is regarded as one of the most prevalent and destroying geological hazards in natural terrain areas. Reliable landslide susceptibility analysis procedures are vital for policymakers to manage the regional-scale landslide risk. In the framework of physically based modelling analysis, the infinite slope model is commonly used to assess the surficial landslide susceptibility with deterministically defined geotechnical and geological parameters. This work aims to develop a user-friendly geographic information system (GIS) extension tool called the GIS-FORM landslide prediction toolbox using the Python programming language to consider the possible uncertainties in the physically based landslide susceptibility analysis in seismic areas. We implement the first-order reliability method (FORM) algorithm to calculate the probability of infinite slope failures. The proposed toolbox can produce some regional hazard distribution maps of different indexes, such as the factor of safety (FoS), reliability index (RI), and failure probability (Pf). Furthermore, the toolbox enables coseismic landslide displacement prediction using either the direct Newmark integration method and/or the empirical formula method. Outputs of the GIS-FORM landslide prediction analysis are verified using published data in the literature. Further, it is also successfully employed for landslide susceptibility analysis of the Ms 7.0 Jiuzhaigou earthquake in Sichuan Province, China. Without loss of generality, the GIS-FORM landslide prediction toolbox can serve for the rapid hazard mapping of earthquake-induced regional landslides where uncertainties in geological and geotechnical parameters should be considered. [ABSTRACT FROM AUTHOR]

A landslide always results from a progressive process of slope deformation. In recent years, an increasing number of slope instabilities have occurred with regard to human engineering activities such as hydropower or traffic construction in mountainous area, which cause even greater casualties and economic loss compared with the natural hazards. The development of such earth surface process may hold long period with mechanisms still not fully understood. Using monitoring technology is an effective and intuitive approach to assist analyzing the slope deformation process and their driving factors. This study presents an engineering slope excavated during the construction of Changheba Hydropower Station, which is located in the upper reaches of Dadu River, Sichuan Province, southwest China. The engineering slope experienced and featured a five-year continuous deformation which caused continuous high risks to the engineering activities. We conducted in-depth analysis for such a long-term deformation process based on ground and subsurface monitoring data, collected successive data with a series of monitoring equipment such as automated total station, borehole inclinometers and other auxiliary apparatus, and identified the deformation process based on the comprehensive analysis of monitoring data as well as field investigation. After analyzing the effects of engineering activities and natural factors on the continuous deformation, we found that the overburden strata provided deformable mass while the excavation-produced steep terrain initiated the slope deformation in limit equilibrium state over a long period of time; afterwards, the intense rainwater accelerated slope deformation in the rainy season. [ABSTRACT FROM AUTHOR]

The failure of the 2017 Xinmo catastrophic rockslide in Maoxian County, Sichuan, Southwest China was a combined effect of long-term and short-term triggering factors. Field investigation, historical data collection, laboratory tests, chemical and microstructure analysis and numerical simulations were adopted herein to reveal the initiation and failure mechanism of the Xinmo rockslide. The analytical results showed that this failure involved the coupling of several triggering factors. The initial cracks in the rock mass were induced by historical earthquakes due to the convex topography and the direction effect. Rock masses from the source area of the Xinmo rockslide contain water-sensitive minerals, i.e., albite and chamosite, and the easily oxidized chemical element Fe, resulting in obvious strength deterioration under the action of water. The scanning electron microscopy (SEM) experimental results indicated that the internal structure of the rock mass is conducive to weathering. The compressive and shear strengths of the rock mass were reduced due to freeze-thaw cycles and weathering. The antecedent rainfall further deteriorated the stability of the slope, and stress and deformation accumulated continually in the locked section. Finally, the locked section sheared out, and the slope failed. An entrainment effect was observed in the Xinmo rockslide due to the presence of old landslide deposits and the antecedent rainfall, resulting in an amplification of the catastrophic rockslide. A simplified three dimensional analysis model was established in this study to reveal the influence of the triggering factors on the failure mechanism of the Xinmo rockslide. [ABSTRACT FROM AUTHOR]

In 2008, the Wenchuan M 7.9 earthquake triggered a large number of landslides in the Longmenshan area. Among them, the Donghekou landslide is a gigantic one, which buried four villages and one elementary school. During the Wenchuan earthquake, large accelerations, especially vertical, were produced, which should to be taken into account in the dynamic analysis of slope stability. However, the vertical acceleration, which can largely influence the acceleration component normal to the sliding surface, is often overlooked in dynamic analysis of slope stability. In this work, the initiation of the Donghekou landslide is analyzed by using Newmark's sliding block model. Comparative analyses of conditions both with and without an acceleration component normal to the sliding surface, which is contributed mainly by the vertical acceleration, are then conducted to investigate its role in triggering the Donghekou landslide. Research results show that the initiating time of the Donghekou landslide would be earlier considering the acceleration component normal to the slide surface than without it due to larger estimated Newmark displacement. This conclusion is then verified by simulated results on the basis of DEM. It means that initiation of landslides can be influenced significantly by seismic acceleration component normal to sliding surface, especially in the area with large vertical acceleration. [ABSTRACT FROM AUTHOR]

In tectonically active steep mountain belts, such as those in west Sichuan in China, frequent landslide disasters can occur during the rainy season. High-locality landslides often initiate with the failure of fractured rock masses or regolith near the ridge of a high and steep slope. Often, these events occur suddenly in the sense that people downslope are caught by surprise. High-locality landslides can be destructive owing to the large momentum and entrainment potential of the moving mass. Increasing attention has been given to these events since the 2008 Wenchuan earthquake. Indeed, slope ridges are particularly susceptible of topographic amplification of the seismic shaking, resulting in mechanical weakening that predisposes them to enhanced weathering and rain-triggered failures years or decades after the mainshock. Risk reduction in such cases must begin with a systematic surveying of the ridges to identify criticalities. Then, quantitative (numerical) approaches must be used to evaluate volumes and describe the movement. Risk assessments can then be produced, and mitigations suggested. Here, we describe the Tonghua landslide in Li County, which is currently in a creeping stage but threatens critical infrastructures and dwellings. We monitored the site for 3 years and identified additional potentially unstable areas before performing physically based modeling and hazard zonation. Our results could be useful in decision-making for risk reduction. [ABSTRACT FROM AUTHOR]

A large, deep-seated ancient landslide was partially reactivated on 17 June 2020 close to the Aniangzhai village of Danba County in Sichuan Province of Southwest China. It was initiated by undercutting of the toe of this landslide resulting from increased discharge of the Xiaojinchuan River caused by the failure of a landslide dam, which had been created by the debris flow originating from the Meilong valley. As a result, 12 townships in the downstream area were endangered leading to the evacuation of more than 20000 people. This study investigated the Aniangzhai landslide area by optical and radar satellite remote sensing techniques. A horizontal displacement map produced using cross-correlation of high-resolution optical images from Planet shows a maximum horizontal motion of approximately 15 meters for the slope failure between the two acquisitions. The undercutting effects on the toe of the landslide are clearly revealed by exploiting optical data and field surveys, indicating the direct influence of the overflow from the landslide dam and water release from a nearby hydropower station on the toe erosion. Pre-disaster instability analysis using a stack of SAR data from Sentinel-1 between 2014 and 2020 suggests that the Aniangzhai landslide has long been active before the failure, with the largest annual LOS deformation rate more than 50 mm/yr. The 3-year wet period that followed a relative drought year in 2016 resulted in a 14% higher average velocity in 2018–2020, in comparison to the rate in 2014–2017. A detailed analysis of slope surface kinematics in different parts of the landslide indicates that temporal changes in precipitation are mainly correlated with kinematics of motion at the head part of the failure body, where an accelerated creep is observed since spring 2020 before the large failure. Overall, this study provides an example of how full exploitation of optical and radar satellite remote sensing data can be used for a comprehensive analysis of destabilization and reactivation of an ancient landslide in response to a complex cascading event chain in the transition zone between the Qinghai-Tibetan Plateau and the Sichuan Basin. [ABSTRACT FROM AUTHOR]

Landslides following rainfall occurrence are a widespread phenomenon. The neglect of this phenomenon leads to serious loss of life when disasters occur. At 03:45 (GMT + 8) on August 21, 2020, a semi-diagenetic landslide occurred in Zhonghai Village, Hanyuan County, China, which occurred 42 h after earlier rainfall. Nine people privately returned to their homes after evacuation of the dangerous area. In this disaster, eight people were lost and one injured. This study explores the failure characteristics, inducement, and mechanisms of the landslide via field investigations, resident interviews, multi-temporal images, field drilling, and geotechnical tests. Hydrological numerical calculations were also performed to uncover the seepage and transfer processes of the groundwater in the slope. Finally, problems in the current community early warning system were analyzed and corresponding suggestions put forward. The results show that the maximum sliding depth of the landslide was 27.5 m, the total area was 80,000 m2, and the sliding volume was about 58,0000 m3, making it a medium-sized deep landslide. In addition to the vertical seepage of rainfall in the landslide area, the downward movement of rainfall in the back and upper catchment areas along the silt sand strata also affected the stability of the landslide. More needs to be done to make the population aware of this lag phenomenon to achieve scientific disaster reduction. This study not only provides a case study of a lagging semi-diagenetic landslide, but also provides insight into hydrological boundary determination and landslide early warning system construction. [ABSTRACT FROM AUTHOR]

The intrusion of debris avalanches into reservoirs frequently occurs in mountainous regions and is a complex solid-fluid interaction. A coupled discrete element method (DEM) and smooth particle hydrodynamics (SPH) method is developed to forecast the entire process of disaster chains including landslide motion, wave generation, and wave propagation on Kaiding slopes at the Houziyan Reservoir, Sichuan Province, China. The sliding masses are treated as granular assemblies with collisions instead of rigid solid. The simulation results indicate that the maximum velocity of debris avalanches when swarming into the water surface is beyond 30 m/s, and the maximum wave elevation on the watercourse and the maximum run-up wave elevation generated by landslides reach 1867.4 m and 1883.0 m, respectively, and the minimum wave trough is 1822.1 m occurring near opposite bank of landslide. Furthermore, the range of landslide-generated waves is estimated to be approximately a propagation distance of 800.0 m along the axis of the river. The coupled DEM-SPH simulation method provides important inspiration for hazard prevention on reservoir landslides. [ABSTRACT FROM AUTHOR]

On 6 July 2020, 3 h 40 min after rainfall stopped, a delayed debris-flow disaster occurred due to colluvium deposits in a hollow region (CDH) in the Chenghuangmiao Gully, Sichuan Province, China, resulting in 4 deaths and 27 injuries. This study explores the initiation process of the delayed debris flow and the cause for the delay. Field investigations, catchment geometry interpretation, laboratory tests, theoretical calculations, and fluid–solid coupling numerical simulation were performed to obtain landslide parameters and understand the mechanisms of the event. Results show that (1) the event was a giant low-frequency viscous debris flow. (2) It was initiated by the delayed landslide process under the influence of back-end confluence. (3) The debris-flow discharge in the main gully increased over 19.5 min. (4) The seepage process inside the CDH continued for 3 h 20 min after the rainfall stopped before the pore pressure and reduction in strength were sufficient to initiate the debris flow. This research provides new insights on delayed debris-flow disasters and can be a reference for improving disaster management systems, especially monitoring and early warning systems, thereby avoiding future casualties. [ABSTRACT FROM AUTHOR]

On 25 August 1933, a 7.5-magnitude earthquake struck the eastern margin of the Tibetan Plateau in Sichuan, China. The Diexi earthquake is among the largest known geohazard events worldwide and is frequently cited by those studying the effects of large earthquakes. And yet, the attention focused on this event has failed to deliver a clear picture of landslides and their geomorphic impacts—key attributes of the Diexi earthquake and its aftermath remain obscure and debated. By integrating present-day LiDAR topographic data with existing records (including studies published in Chinese), and a series of unique archival photographs (from 1910, 1920, and 1934), we present the first inventory of coseismic landslides from the epicentral region of this catastrophic 1933 event. We find that the earthquake-triggered landslides were mainly of shallow to deep rock/debris fall/avalanche type, containing mass detached from steep slopes at the top of the mountain ridge source. We reinterpreted three major landslide dams on the Minjiang River based on their geomorphology and sedimentology, and we reconstructed the processes of impoundment and the maximum area of the dammed lake that breached 45 days after the earthquake. Since 1933, we estimate that 43.15–47.68 million m3 of post-earthquake sediment has been delivered to valley floors from the erosion of the three landslide dams, with a sediment yield of up to 131.71 t km−2 year−1. Retrospective studies of the effects of historical earthquakes are challenging, but long-term observations like these are the cornerstone of emerging knowledge of earthquake-induced landsliding and related landscape response. [ABSTRACT FROM AUTHOR]