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Following the earthquake, prompt evaluation of the distribution of coseismic landslides and estimation of potential disaster losses are crucial for emergency response and resettlement planning. The Luding earthquake of 2022 offers a valuable opportunity to conduct a rapid assessment of coseismic landslides using various models. In this study, we utilize the Logistic Regression (LR)-based Xu2019 model, a new-generation model developed in China, alongside the Newmark model to perform the rapid hazard assessment of coseismic landslides. Assessing the accuracy and applicability of these two models based on the coseismic landslides from the Luding earthquake, we find that within intensity area of IX, the high probability area identified by the Newmark model aligns closely with the actual distribution of landslides. However, the Newmark model's prediction is overestimated in the intensity area of VIII. For the Xu2019 model, the prediction results are in good agreement with the distribution of actual landslides. Most landslides are located in high probability areas, such as Detuo town, Wandong, and Xingfu villages, indicating that the model has a higher prediction accuracy. Overall, two models have good practical utility in emergency hazard assessment of coseismic landslides. However, the Newmark model requires multi-input parameters and the assignment of these parameters will increase the uncertainty and subjectivity in the practical application of the modeling assessment. [ABSTRACT FROM AUTHOR]

This study uses automatic linear regression (LINEAR) and artificial neural network (ANN) models to statistically analyze the area of landslides triggered by the 2021 SW Chelgard earthquake (ML = 6) based on controlling parameters. We recorded and mapped the number of 632 landslides into four groups (based on the Hungr et al. 2014): rock avalanche-rock fall, debris avalanche-debris flow, rock slump, and slide earth flow-soil slump using remote sensing method, satellite images (before and after the earthquake), and field observation. The spatial distribution of landslides showed that the highest values of the landslide area percentage (LAP %) and of the landslide number density (LND, N/km2) occurred in the northern part of the fault on the hanging wall. The ANN models with R2 = 0.51–0.80 provided more accurate predictions of landslide area (LA, m2) than the LINEAR models, with R2 = 0.40–0.61 using multiple parameters. The LINEAR models revealed that the most influential controlling parameters for landslides were the topographic factors and ANN models showed that seismic parameters are effective on the coseismic landslides (e.g., the distance from the epicenter on the rock slumps; the PGA on debris avalanches- debris flow; the distance from the rupture surface of the fault and Ia on the rock avalanches-rockfall and slide earth flow-soil slump). Therefore, the classification of coseismic landslides can be helpful for predicting the LA more accurately and better understanding the failure mechanism. [ABSTRACT FROM AUTHOR]

The rapid and accurate extraction of wide-area coseismic landslide locations is critical in earthquake emergencies. At present, the extraction of coseismic landslides is mainly based on post-earthquake site investigation or the interpretation of human–computer interactions based on remote sensing images. However, the identification efficiency is low, which seriously delays the earthquake emergency response. On the basis of the available multisource and multiscale remote sensing data, numerous studies have been carried out on the methods of coseismic landslide extraction, such as pixel analysis, object-oriented analysis, change detection, and machine learning. However, the effectiveness of coseismic landslide extraction was low in wide areas with complex topographic and geomorphic backgrounds. Therefore, this paper offers a comprehensive study of the factors influencing coseismic landslides and researches rapid and accurate wide-area coseismic landslide extraction methods with multisource remote sensing and geoscience technology. These techniques include digital elevation modeling (DEM) and its derived slopes and aspects. An embedded multichannel spectral–topographic feature fusion model for coseismic landslide extraction based on DeepLab V3+ is proposed, and a knowledge-enhanced deep learning information extraction method integrating geological knowledge is formed. Using the Jiuzhaigou Ms7.0 earthquake (seismic intensity VIII) in Sichuan Province, China, a comparison of landslide extraction models and strategies is carried out. The results show that the model proposed in this paper achieves the best balance in the accuracy and efficiency of wide-area extractions. Using multiple feature data of coseismic landslides, the problem of mixed pixels is solved. The rate of the misidentification of landslides as clouds, snow, buildings, and roads is significantly lower than in other methods. The identified landslide boundaries are smoother and more accurate, and the connectivity is better. Compared with other methods, ours can more accurately eliminate landslides not triggered by the Jiuzhaigou earthquake. While using the image block strategy to ensure extraction efficiency, it also improves the extraction accuracy of wide-area coseismic landslides in complex backgrounds. [ABSTRACT FROM AUTHOR]

On 5th September 2022，an Mw 6.6 earthquake struck Luding and it is the largest earthquake on the Xianshuihe fault, Eastern Tibet in the past 40 years. It is of great significance for investigating the tectonic activity and assessing future seismic hazards in the region. Methods: In this study, we used Sentinel-1 and ALOS-2 synthetic aperture radar (SAR) images to retrieve coseismic surface displacements and then to determine the fault geometry parameters and slip distribution with a dislocation model in an elastic halfspace. Results: The results show that the 2022 Luding Mw 6.6 earthquake is a left-lateral sliding event with a maximum surface displacement of 15 cm and 21 cm for the Sentinel-1 and ALOS-2 ra‐ dar images, respectively. The fault ruptured along an NNW-SSE strike, and westward at a dip of 72°. The slip was concentrated at depths of 4-12 km with a maximum fault slip of 2.23 m occurring at a depth of 5.8 km. Conclusions: By analyzing the distribution of the coseismic landslides interpreted by optical images, we found that the coseismic landslides were mainly located on the west side of the fault, which is consistent with the aftershock distribution and can be considered due to hanging wall effects. [ABSTRACT FROM AUTHOR]

Near real-time spatial prediction of earthquake-induced landslides (EQILs) can rapidly forecast the occurrence position of widespread landslides just after a violent earthquake; thus, EQIL prediction is very crucial to the 72-hour 'golden window' for survivors. This work focuses on a series of earthquake events from 2008 to 2022 occurring in the Tibetan Plateau, a famous seismically-active zone, and proposes a novel interpretable self-supervised learning (ISeL) method for the near real-time spatial prediction of EQILs. This new method innovatively introduces swap noise at the unsupervised mechanism, which can improve the generalization performance and transferability of the model, and can effectively reduce false alarm and improve accuracy through supervised fine-tuning. An interpretable module is built based on a self-attention mechanism to reveal the importance and contribution of various influencing factors to EQIL spatial distribution. Experimental results demonstrate that the ISeL model is superior to the excellent state-of-the-art machine learning and deep learning methods. Furthermore, according to the interpretable module in the ISeL method, the critical controlling and triggering factors are revealed. The ISeL method can also be applied in other earthquake-frequent regions worldwide because of its good generalization and transferability. [ABSTRACT FROM AUTHOR]

Near real-time prediction of earthquake-triggered landslides can rapidly forecast the spatial distribution of coseismic landslides just after a great earthquake, and provide effective support for emergency response. However, the prediction of earthquake-triggered landslides has always been a great challenge because of low accuracy and high false alarms. This work proposes a novel fuzzy deep learning (FuDL) model for near real-time earthquake-triggered landslide spatial prediction. Fuzzy learning theory is for the first time employed in earthquake-triggered landslide prediction. The FuDL has high generalization and robustness, effectively improving the accuracy of earthquake-triggered landslide prediction. Eighteen earthquake-triggered landslide inventories worldwide from 2008 to 2022 are employed to conduct ETL prediction. According to the chronological order, 15 earthquake-triggered landslides from 2008 to 2018 are adopted to train the FuDL model, and 3 earthquake-triggered landslides from 2019 to 2022 are utilized for near real-time earthquake-triggered landslide prediction. Furthermore, this work reveals that ground movement, relatively steep and high topography, and strong seismic intensity are critical factors affecting the spatial distribution of earthquake-triggered landslides. In addition, this work conducted a detailed analysis of the distribution patterns of earthquake-triggered landslides on a global scale. • A novel FuDL model is suggested and achieves high accuracy and good generalization. • FuDL model outperforms the state-of-the-art machine learning or deep learning models. • Topography, ground shake, and seismic intensity dominate the distribution of ETLs. [ABSTRACT FROM AUTHOR]

At 3:08 on 6 September 2018 (UTC +9), massive landslides were triggered by an earthquake of Mw 6.6 that occurred in Hokkaido, Japan. In this paper, a coseismic landslide inventory that covers 388 km2 of the earthquake-impacted area and includes 5828 coseismic landslides with a total landslide area of 23.66 km2 was compiled by using visual interpretations of various high-resolution satellite images. To analyze the spatial distribution and characteristics of coseismic landslides, five factors were considered: the peak ground acceleration (PGA), elevation, slope gradient, slope aspect, and lithology. Results show more than 87% of the landslides occurred at 100 to 200 m elevations. Slopes in the range of 10~20°are the most susceptible to failure. The landslide density of the places with peak ground acceleration (PGA) greater than 0.16 g is obviously larger than those with PGA less than 0.02 g. Compared with the number and scale of coseismic landslides caused by other strong earthquakes and the mobility of the coseismic landslides caused by the Haiyan and Wenchuan earthquakes, it was found that the distribution of coseismic landslides was extremely dense and that the mobility of the Hokkaido earthquake was greater than that of the Wenchuan earthquake and weaker than that of the Haiyuan earthquake, and is described by the following relationship: L = 18.454 ∗ H0.612. Comparative analysis of coseismic landslides with similar magnitude has important guiding significance for disaster prevention and reduction and reconstruction planning of landslides in affected areas. [ABSTRACT FROM AUTHOR]

The coseismic landslide is one of the important hazard phenomena in the hilly and seismically active mountainous region. It is, therefore, essential to map the areas susceptible to coseismic landslides, especially for the seismically active region. In the present work, the probabilistic assessment of coseismic landslides has been carried out for Goriganga valley located in the Kumaun Himalaya, India, which lies in the highest seismically active zone of the seismic zoning map of India. Several studies suggest that this region is prone to a great future earthquake of Mw ≥8.0. In this context, mapping of the coseismic landslide has been made for the future scenario earthquakes of 7.0, 8.0, and 8.6 Mw using modified Newmark's analysis. The modified Newmark's analysis provides the permanent displacement of the potential landslide, by integrating (1) joint strength of rock mass, (2) critical acceleration of the slope, and (3) peak ground acceleration of the region. Newmark permanent displacement has been estimated, which provides the distribution of predicted slope failure in the area. It has been observed that 41% of the area exhibits ˃40 cm Newmark's permanent displacement corresponding to Mw 8.6 earthquake and thus susceptible to failure, followed by 8.0 and 7.0 Mw earthquake with 36 and 14% of the area susceptible to the coseismic landslide, respectively. Further, the maximum permanent displacements for the simulated earthquakes of Mw 7.0, 8.0, and 8.6 are 76, 279, and 502 cm, respectively. [ABSTRACT FROM AUTHOR]

Yogyakarta is one of the large cities in Central Java, located on Java Island, Indonesia. The city, and the Pleret sub-district, where the study has taken place, is prone to earthquake hazards, because it is close to several seismically active zones, such as the Sunda Megathrust and the active fault known as the Opak Fault. Since a devastating earthquake of 2006, the population of the Pleret sub-district has increased significantly. Thus, the housing demand has increased, and so is the pace of low-cost housing that does not meet earthquake-safety requirements, and furthermore are often located on unstable slopes. The local alluvial material covering a jigsaw of unstable blocks and complex slope is conditions that can amplify the negative impacts of earthquakes. Within this context, this study is aiming to assess the multi-hazards and risks of earthquakes and related secondary hazards such as ground liquefaction, and coseismic landslides. To achieve this, we used geographic information systems and remote sensing methods supplemented with outcrop study and existing seismic data to derive shear-strain parameters. The results have revealed the presence of numerous uncharted active faults with movements visible from imagery and outcrops. show that the middle part of the study area has a complex geological structure, indicated by many unchartered faults in the outcrops. Using this newly mapped blocks combined with shear strain data, we reassessed the collapse probability of buildings that reach level >0.75 near the Opak River, in central Pleret sub-district. Classifying the buildings and from population distribution, we could determine that the highest risk was during nighttime as the buildings susceptible to fall are predominantly housing buildings. The secondary hazards follow a slightly different distribution with a concentration of risks in the West. [ABSTRACT FROM AUTHOR]

Earthquake-triggered landslides significantly contribute to worsening the impact of seismic events; thus, comprehensive landslide inventories are essential for improving seismic hazard assessment. During complex seismic sequences, landslides are triggered by more than one event and the final inventory reflects the spatial and temporal evolution of the sequence. Here, I analyze the landslides triggered by the 2018 Lombok (Indonesia) seismic sequence. I use high-resolution satellite imagery to map 4823 landslides triggered after the 05/08/2018 event (Mw 6.9) and 9319 landslides after the 19/08/2018 event (Mw 6.9). I analyze the distribution and evolution over time of landslide density and landslide area percentage. Despite the significant increase in number and cumulative area of the landslides, the 05/08 and 19/08 events share the maximum dimension of individual landslides; this suggests that the maximum intensity is equal for the two events, i.e., X on the Environmental Scale Intensity scale. I compare the distribution of landslides with macroseismic information provided by eyewitnesses through online questionnaires. Finally, I investigate the role of earthquake environmental effects within seismic sequences, showing that effects on the natural environment provide complementary information with respect to traditional intensity and felt reports. [ABSTRACT FROM AUTHOR]

An accurate and detailed seismic landslide inventory is essential to better understand the landslide mechanism and susceptibility. The 8th August 2017 Mw 6.5 Jiuzhaigou Earthquake of China initiated a large number of coseismic landslides. The results of the post-seismic survey show the actual landslide number might be underestimated in previous publications. Coupled with field investigation and visual interpretation on high-resolution remote sensing images before and after the main shock, we established a detailed inventory of landslides triggered by the earthquake. Results show that this event caused at least 4 834 individual landslides with a total area of 9.64 km2. They are concentrated in an elliptical area of 434 km2, dominated by medium- and small-scale rock falls and debris slides. Statistics indicate that, except for slope aspect that seems not significantly correlated with the landsliding, these landslides are most common in the places with following features: elevation of 2 800-3 400 m, slope angle greater than 30°, slope positions of upper, middle and flat slopes, and Carboniferous limestone and dolomite. Besides, the landslide area percentage (LAP) and landslide number density (LND) values decrease with the increasing distance to river channels and roads, implying a positive correlation. Instead of centering around the epicenter, most of these coseismic landslides are distributed along the inferred seismogenic fault, which means that the seismogenic structure played a more important role than the location of the epicenter. Remarkable differences in landslide densities along the fault indicate the varied landslide susceptibility which may be attributed to other varied controls along the fault such as the rock mass strength. In sum, this study presents a more detailed inventory of the landslides triggered by the 2017 Mw 6.5 Jiuzhaigou Earthquake, describes their distribution pattern and analyzes its control factors, which would be helpful to understand the genesis of the coseismic landslides and further study their long-term impact on the environment of the affected area. [ABSTRACT FROM AUTHOR]

This study compares the performance of empirically- and physically-based methods for predicting coseismic landslides around Aso caldera during the 2016 M w 7.0 Kumamoto earthquake. The physically-based method couples a regional-scale wave propagation simulation with a site-scale Newmark-type sliding analysis, while the empirically-based method predicts sliding displacements based on seismic recordings and/or ground motion prediction equations. By using the well-documented landslide inventory, the predictive capacity of each method is quantitatively assessed. The case study demonstrates that the physically-based method has the advantage of simulating complicated near-fault and topographic effects. It captures around 44% of observed landslides at the caldera rim, which is significantly better than the 20% captured from the empirically-based prediction. Both methods have similar prediction performance, capturing around 56% actual landslides at the central cone region. The empirically-based method is simple to use and provides fast landslide prediction. It can be further improved by incorporating a simple prediction model for the topographic amplification effect, which increases the percentage of captured landslides by around 8% across the caldera rim. • Empirically- and physically-based methods are used to evaluate regional coseismic landslides in the 2016 Kumamoto earthquake. • Predicted landslides are validated against the well-documented landslide inventory. • The topographic amplification of ground motions is incorporated to improve the empirically-based method for fast prediction. [ABSTRACT FROM AUTHOR]

Geometric parameters are useful for characterizing earthquake-triggered landslides. This paper presents a detailed statistical analysis on this issue using the landslide inventory of the 2013, Minxian, China Mw 5.9 earthquake. Based on GIS software and a 5-m resolution DEM, geometric parameters of 635 coseismic landslides (with areas larger than 500 m) were obtained, including height, length, width, reach angle (arc tangent of the height-length ratio), and aspect ratio (length-width ratio). The fitting relationship of height and length from these data is H = 0.6164 L + 0.4589, with an average reach angle of 31.65°. The landslide aspect ratios concentrate in the range of 1.4∼2.6, with an average of 2.11. According to the plane geometric shapes and aspect ratios, the landslides are classified into four categories: transverse landslide (LA1, L/ W ≤ 0.8), isometric landslide (LA2, 0.8 < L/ W ≤ 1.2), longitudinal landslide (LA3, 1.2 < L/ W ≤ 3), and elongated landslide (LA4, L/ W > 3). Statistics of these four types of landslides versus ten classified control factors (elevation, slope angle, slope aspect, curvature, slope position, distance to drainages, lithology, seismic intensity, peak ground acceleration, and distance to seismogenic fault) are used to examine their possible correlations and the landslide-prone areas, which would be helpful to the landslide disaster mitigation in the affected area. [ABSTRACT FROM AUTHOR]

The 25 April 2015 M w 7.8 Gorkha earthquake produced strong ground motions across an approximately 250 km by 100 km swath in central Nepal. To assist disaster response activities, we modified an existing earthquake-triggered landslide model based on a Newmark sliding block analysis to estimate the extent and intensity of landsliding and landslide dam hazard. Landslide hazard maps were produced using Shuttle Radar Topography Mission (SRTM) digital topography, peak ground acceleration (PGA) information from the U.S. Geological Survey (USGS) ShakeMap program, and assumptions about the regional rock strength based on end-member values from previous studies. The instrumental record of seismicity in Nepal is poor, so PGA estimates were based on empirical Ground Motion Prediction Equations (GMPEs) constrained by teleseismic data and felt reports. We demonstrate a non-linear dependence of modeled landsliding on aggregate rock strength, where the number of landslides decreases exponentially with increasing rock strength. Model estimates are less sensitive to PGA at steep slopes (> 60°) compared to moderate slopes (30–60°). We compare forward model results to an inventory of landslides triggered by the Gorkha earthquake. We show that moderate rock strength inputs over estimate landsliding in regions beyond the main slip patch, which may in part be related to poorly constrained PGA estimates for this event at far distances from the source area. Directly above the main slip patch, however, the moderate strength model accurately estimates the total number of landslides within the resolution of the model (landslides ≥ 0.0162 km 2 ; observed n = 2214, modeled n = 2987), but the pattern of landsliding differs from observations. This discrepancy is likely due to the unaccounted for effects of variable material strength and local topographic amplification of strong ground motion, as well as other simplifying assumptions about source characteristics and their relationship to landsliding. [ABSTRACT FROM AUTHOR]

Landslide failure mechanisms are influenced by topography, lithology, structure and rock mass damage – factors that also control landslide susceptibility. Failure mechanisms, however, are rarely considered in regional-scale coseismic landslide susceptibility analyses. In this study, we use 3D pixel tracking in pre- and post-earthquake aerial imagery, geomorphic mapping, rock mass characterisation, and geophysical ground investigations to develop conceptual models for three earthquake-induced landslides triggered by the 2016 Mw 7.8 Kaikōura earthquake on New Zealand's South Island. Analysis of two incipient landslides in Cretaceous greywacke illustrates the failure stages of a rock mass comprising multiple sets of closely-spaced and low persistence discontinuities. Conversely, analysis of a landslide in Neogene massive siltstones illustrates the role of high-persistence bedding planes in generating large translational rockslides. These two distinct mechanisms typify failures in highly deformed basement and overlying massive sedimentary rocks respectively, and highlight the link between rock mass damage, failure mechanism, and initiation. In greywacke failures, the rupture plane initiated close to the ridgetop and propagated as a joint-step-path failure along pre-existing, but low-persistence joints whereas the landslide in Neogene siltstone initiated by sliding along weak, high persistence, bedding planes near the base of the slope where topographic stresses are highest. Analysis of landslide displacements furthermore points out that the evolution of landslides is closely related to rock mass characteristics. In highly jointed Cretaceous greywacke, relatively little displacement is needed for rock mass disintegration and avalanching to occur whereas in Neogene siltstone, the landslide displaces as a coherent body - even at large displacements. Our results suggest that (1) failure mechanisms and, as a result, coseismic landslide susceptibility factors, may vary fundamentally in different geological settings; (2) characteristic spatial landslide displacement patterns may be used to remotely identify relevant failure mechanisms; and (3) the amount of displacement that can be accommodated before a failure transitions from sliding to avalanching, is highly dependent on material characteristics and topographic constraints. • Conceptual models of three coseismic landslides triggered by the 2016 Mw 7.8 Kaikōura earthquake reveal failure mechanisms. • Slopes in greywacke rock mass with closely-spaced, low-persistent joints fail through a joint-step-path failure mechanism. • Landslide susceptibility factors vary between failures in highly jointed greywacke and weak, but massive siltstone. • Analysis of spatial displacement patterns may help to remotely identify failure mechanisms. • The amount of displacement required for rock mass disintegration to occur is smaller in greywacke than siltstone. [ABSTRACT FROM AUTHOR]

The Daguangbao landslide was the most catastrophic mass movement triggered by the 2008 Wenchuan earthquake with a magnitude scale of Ms. 8.0. The landslide, which was 4.6 km long and 3.7 km wide, had a volume of approximately 1.2 × 109 m3. Since its occurrence, many assumptions regarding its initiation and movement mechanisms have been made; however, the mechanisms remain unclear. Our recent field evidence suggested that the Daguangbao landslide occurred along a saturated fault parallel to the bedding in the Paleozoic carbonate strata. We therefore examined whether the shear behavior of the fault material could have favored the initiation and movement of the Daguangbao landslide. First, we performed monotonic and cyclic loading tests on samples taken from the bedding fault breccia using ring-shear apparatus. We then conducted Newmark displacement analysis to examine the initiation and motion of the landslide. The laboratory results showed that the carbonate fault breccia on the sliding layer of the landslide has a high liquefaction potential and the friction coefficient at its steady-state under undrained condition could be as small as 0.04. We also found that with increase of shear displacement, the friction coefficients can at first exponentially increase to the peak-failure value and then exponentially decrease to the steady-state value. These relationships between the friction and shear displacements were incorporated in the Newmark analysis of landslide initiation and motion. The numerical calculation results showed that the landslide occurred 36 s after the 2008 Wenchuan earthquake origin time (at its hypocenter), and the landslide mass, with a speed of 94 m/s, collided with a riverbank in the 76th second. We infer that, in addition to the strong seismic force, pore-water pressure built up within the bedding fault during the seismic shaking, enhancing the instability of the Daguangbao slope, and a further increase of pore-water pressure with progress of sliding elevated the mobility of the landslide. • Displacement-dependent sliding strengthening and weakening friction laws are obtained based on ring shear tests • Initiation mechanism of the Daguangbao landslide is proposed based on Newmark displacement analysis • Buildup of high pore-water pressure within the sliding surface enhanced the initiation and mobility of the Daguangbao landslide [ABSTRACT FROM AUTHOR]