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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]

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]

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]

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]

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]

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]

This work aims to characterize the earthquake-triggered landslides (EQTLs) during the 2017 Jiuzhaigou earthquake and to describe the geological and geomorphological control of landslide hazards. The Jiuzhaigou-EQTLs were studied via field-based investigation and remote sensing techniques during few weeks immediately after the mainshock occurred. The Jiuzhaigou earthquake produced an 11.4-km2 landslide area with 5633 landslides, dominated by shallow disrupted landslides, rock falls, and rockslides/rock avalanche. Statistical analyses show that the Jiuzhaigou-EQTLs had low mobility with a modal apparent friction angle of 40–44°. The Jiuzhaigou-EQTLs mostly occurred at elevations of 2700–3500 m and below 900 m relative relief to the main riverbed, where small-scale landslides were situated at the lower to middle parts of hillslope and large-scale landslides were situated close to the ridge crests. Jiuzhaigou-EQTLs clustered on river valley-side at the upstream of Baihe River (Jiuzhai Paradise and Ganhaizi village), Rize gully, and Danzhu gully on the tectonically active region at the southwest side of seismogenic fault F2. Landslides are prone to failure on the carbonate rocks with anaclinal hillslopes, following the cataclinal and cata-plagioclinal hillslopes. Most Jiuzhaigou-EQTLs preferentially occur on the below low-level break line (L2) in V-shaped valley-side and between first-level (L2) and high-level break line (L1) in U-shaped valley-side formed from long-term paleo-glacier and river incision. We found that landslide frequency is determined by a combination of the earthquake source with ground deformation, slope distribution, and rock strength due to weathering and dissolution along the fractures. Ground surface deformation, rock type (strength), geological structures, and valley-side slope breaks dictated the Jiuzhaigou-EQTL distribution pattern and hazards. This study elucidates the roles that local conditions play in influencing the occurrence, distribution, and frequency of slope failures and provides a better understanding of EQTL phenomenon during the Jiuzhaigou earthquake. [ABSTRACT FROM AUTHOR]