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The sliding process and characteristics of earthquake-induced landslides are complex, and many factors affect landslide movement. The damage caused by landslides is often closely related to their movement characteristics. Therefore, establishing an efficient and quick analysis model for earthquake-induced landslide movement is very important in considering multiple control factors in the sliding process. Based on the typical characteristics of earthquake-induced landslide movement, considering the influences of earthquake action, friction characteristics of sliding surfaces, topographic relief, and morphological changes in landslide masses, a widely applicable quick analysis model for earthquake-induced landslide movement (ELQA) is constructed based on energy conservation. Compared with the existing landslide movement models, this model can consider multiple control factors and significantly improve the applicability of the model. Through comparisons with theoretical analysis, model tests, and actual landslide (Kolka glacial detrital flow), the movement distance of the landslide front edge obtained by the ELQA model matches well with the comparison results, indicating that the ELQA model can reproduce earthquake-induced landslide movement more accurately and quickly. Based on the ELQA model, the influences of control parameters (terrain sampling point density, time step, calculated point number) and calculation parameters (internal friction angle, slip mass volume, terrain concavity, and convexity characteristics) on the calculation results are studied. The results show that control parameters such as terrain sampling point density, time step, and calculated point number have limited influence on the movement characteristics; as long as appropriate values are selected, the error rate can be controlled within 5%. The calculation parameters have a significant influence on the movement characteristics. For example, the influence of the internal friction angle on the coverage area reaches up to 83.75%. The slip mass volume has an insignificant influence on the maximum movement speed, but it on the coverage area reaches approximately 20%. The influence of the terrain concavity and convexity characteristics on the results is less than 10%. The ELQA model is suitable for in-situ estimation and can provide effective suggestions for disaster prevention and reduction in earthquake-prone areas. [ABSTRACT FROM AUTHOR]

In the period of October–December 2019, the Cotabato–Davao del Sur region (Philippines) was hit by a seismic sequence comprising four earthquakes with magnitude MW > 6.0 (EQ1-4; max magnitude MW 6.8). The earthquakes triggered widespread environmental effects, including landslides and liquefaction features. We documented such effects by means of field surveys, which we supplemented with landslide mapping from satellite images. Field surveys allowed us to gather information on 43 points after EQ1, 202 points after EQs2–3 and 87 points after EQ4. Additionally, we built a multi-temporal inventory of landslides from remote sensing, comprising 190 slope movements triggered by EQ1, 4737 after EQs2–3, and 5666 at the end of the sequence. We assigned an intensity value to each environmental effect using the environmental seismic intensity (ESI-07) scale. Our preferred estimates of ESI-07 epicentral intensity are VIII for the first earthquake and IX at the end of the sequence, which is in broad agreement with other events of similar magnitude globally. This study, which is the first case of the application of the ESI-07 scale to a seismic sequence in the Philippines, shows that repeated documentation of environmental damage and the evaluation of the progression through time may be useful for providing input data for derivative products, such as susceptibility assessment, evaluation of residual risk or investigation of the role played by ground shaking and by other mechanisms able to trigger environmental effects. [ABSTRACT FROM AUTHOR]

Earthquake-induced landslides can cause severe surface damage and casualties, posing a serious threat to the overall ecological environment and social stability. Traditional landslide susceptibility prediction (LSP) techniques often suffer from low effectiveness and precision, necessitating the exploration of remote sensing technology. However, this research in this area is limited, and the development of high-performance prediction models remains a pressing scientific issue. This study focuses on the Ms7.0 earthquake in Jiuzhaigou on 8 August 2017. To investigate the optimal integration of remote sensing technology with traditional LSP techniques, the study applies collaborative factor analysis and contingency matrix methods to create four new coupling models (SVM-I, SVM-II, RF-I, RF-II), followed by a comprehensive performance evaluation of these models. The results indicate that the integration of SAR-derived surface deformation data significantly enhances the accuracy of Landslide Susceptibility Mapping (LSM). Comparing the model performance with the receiver operating characteristic curve and landslide density, the reliability and prediction performance of the RF-I model are outstanding, reflecting that the improved method based on the InSAR collaborative machine learning model with shape variables along the slope direction can optimize the accuracy of the LSM, and has better performance and robustness in earthquake landslide susceptibility evaluation.

In this study, we defined screening maps for Italy that classify sites based on their potential for triggering landslides. To this end, we analyzed seismic hazard maps and hazard disaggregation results on a national scale considering four spectral periods (0.01 s, 0.2 s, 0.5 s, and 1.0 s) and three return periods (475, 975, and 2475 years). First, joint distributions of magnitude ( M ) and distance ( R ) from hazard disaggregation were analyzed by means of an innovative approach based on image processing techniques to find all modal scenarios contributing to the hazard. In order to obtain the M - R scenarios controlling the triggering of earthquake-induced landslides at any computation node, mean and modal M - R pairs were compared to empirical curves defining the M - R bounds associated with landslide triggering. Three types of landslides were considered (i.e., disrupted slides and falls, coherent slides, and lateral spreads and flows). As a result, screening maps for all of Italy showing the potential for triggering landslides based on the level of seismic hazard were obtained. The maps and the related data are freely accessible. [ABSTRACT FROM AUTHOR]

Earthquake-induced landslides cause extensive harm, necessitating accurate predictions for effective risk management. To tackle the dual challenges posed by inadequate model accuracy and the absence of frequency-derived landslide intensity as critical information, this paper presents a robust modelling method that generates high-resolution landslide hazard map for risk assessment. Firstly, the Frequency Ratio (FR) model is employed to quantitatively explore the correlation between geo-environmental factors and landslide occurrences, enabling the exclusion of less significant factors. Subsequently, these FR values are integrated into the Light Gradient Boosting Machine (LightGBM) model, resulting in the composite model, FR-LightGBM. To test the proposed model, data from the areas affected by the Luding earthquake based on the seismic landslide intensity model following the 2022 Ms 6.8 earthquake in Sichuan, China, is examined. Model validation, using the area under the curve (AUC) of the receiver operating characteristic curve, highlighted the superior predictive accuracy of the FR-LightGBM model, marking a 3.5 % improvement in AUC value compared to both the Convolutional Neural Network (CNN) and Logistic Regression (LR) models. The results have identified high-risk areas along the Dadu riverside and the Xianshuihe fault zone correspond well with the actual landslide distribution. Overall, the practical applicability of the FR-LightGBM model is significantly enhanced by its capacity to prioritize lithology, faults, and aspect as crucial factors. Additionally, based on an extended landslide inventory established using high spatial resolution (2 m) satellite imagery from the Chinese Gaofen satellite series, the proposed approach delivered a high-resolution (12.5 m) hazard map across the Luding earthquake area, contributing to relevant studies and risk management.

ABSTRACTOn the 25 February 2018, an earthquake of magnitude Mw7.5 struck the region of Porgera in Papua New Guinea (PNG), triggering numerous landslides. Planetscope images are used to derive a partial inventory of 2941 landslides in a cloud-free area of 2686 km2. The average area of landslides in the study area is 18,500 m2. We use the Environmental Seismic Intensity (ESI) scale to assess the damage due to the triggered landslides. Local intensity values are assigned to individual landslides by calculating their volume using various area–volume relations. We observe that different empirical relations yield similar volume values for individual landslides (local ESI intensity ≥ X). The spatial variation of landslide density and areal coverage within the study area in cells of 1 km2 is investigated and compared to the probability predicted by the USGS model. We observe that high probability corresponds to a significant number of landslides. An ESI epicentral intensity of XI is estimated based on primary and secondary effects. This study represents the first application of the ESI scale to an earthquake in PNG. The Porgera earthquake fits well with past case studies worldwide in terms of ESI scale epicentral intensity and triggered landslide number as a function of earthquake magnitude.

Abstract 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.

On September 5, 2022, a magnitude 6.8 earthquake occurred along the Xianshuihe Fault Zone in Luding County, Tibetan Plateau, China, leading to a significant outbreak of landslides. The urgent need for a swift and accurate evaluation of earthquake-induced landslides distribution in the affected area prompted this study. This research delves into regional geological data, scrutinizes post-earthquake Peak Ground Acceleration (PGA) and Arias Intensity (/a) associated with the Luding earthquake, and conducts earthquake-induced landslides risk assessments within the Luding earthquake zone using the Newmark model. Validation of the earthquake-induced landslides risk assessment outcomes rooted in PGA and /a relies on an earthquake-induced landslides database, revealing Area Under the Curve (AUC) values of 0.73 and 0.84 in respective ROC (Receiver Operating Characteristic) curves. These results unequivocally affirm the exceptional accuracy of earthquake-induced landslides evaluation using /a calculations, emphasizing its suitability for the swift prediction and evaluation of earthquake-induced landslides. The earthquake-induced landslides risk assessment based on /a computation reveals the area with extremely high-risk and high-risk of earthquake-induced landslides encompass 0.71% of the entire study area. Notably, these areas are predominantly clustered within seismic intensity VII zones and primarily trace the Moxi fault zone, extending from the southern portion of the middle east along the Dadu River and the Moxi fault, with reach up to Dewei Township in the north and Caoke Township in the south. Hazard-prone regions predominantly align with slopes featuring gradients of 30°-45° and bear a strong correlation with fault activity. Furthermore, the results of this evaluation are harmonious with the findings from remote sensing interpretation and on-site field investigations pertaining to the earthquake-induced landslides. This body of knowledge can serve as a crucial reference for expedited assessment, emergency response and subsequent supplementation of earthquake-induced landslide databases when confronting similar earthquake-induced landslide scenarios. [ABSTRACT FROM AUTHOR]

The Valmarecchia area, located between the Emilia-Romagna and Marche administrative regions, is characterized by numerous landslides and has been affected by earthquakes occurring both outside and inside its territory. As earthquakes are one of the main causes of gravitational phenomena, we investigated the occurrence of earthquake-induced landslides, starting from the events that were reported as felt in any of the Valmarecchia municipalities. We analyzed their magnitude and distance from the area and identified the seismotectonic areas that generated them. We found that more than half of the earthquakes that hit Valmarecchia historically could have generated shaking-induced landslides; and yet, based on the ample documentation stored in online portals, we also found that the number of earthquakes for which there exists historical evidence for such phenomena is very limited. This is likely the result to lack of information on the correlation among earthquakes and landslides, possibly justified by the local historical and geographical context and by the seismotectonic marginality of Valmarecchia, located off the main seismogenic trends of the Italian peninsula. We aim to improve knowledge on Valmarecchia seismicity and to illustrate a methodology for identifying both earthquake-induced effects and the areas that are prone to these phenomena. [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 September 5, 2022, a magnitude 6.8 earthquake occurred along the Xianshuihe Fault Zone in Luding County, Tibetan Plateau, China, leading to a significant outbreak of landslides. The urgent need for a swift and accurate evaluation of earthquake-induced landslides distribution in the affected area prompted this study. This research delves into regional geological data, scrutinizes post-earthquake Peak Ground Acceleration (PGA) and Arias Intensity (Ia) associated with the Luding earthquake, and conducts earthquake-induced landslides risk assessments within the Luding earthquake zone using the Newmark model. Validation of the earthquake-induced landslides risk assessment outcomes rooted in PGA and Ia relies on an earthquake-induced landslides database, revealing Area Under the Curve (AUC) values of 0.73 and 0.84 in respective ROC (Receiver Operating Characteristic) curves. These results unequivocally affirm the exceptional accuracy of earthquake-induced landslides evaluation using Ia calculations, emphasizing its suitability for the swift prediction and evaluation of earthquake-induced landslides. The earthquake-induced landslides risk assessment based on Ia computation reveals the area with extremely high-risk and high-risk of earthquake-induced landslides encompass 0.71% of the entire study area. Notably, these areas are predominantly clustered within seismic intensity VII zones and primarily trace the Moxi fault zone, extending from the southern portion of the middle east along the Dadu River and the Moxi fault, with reach up to Dewei Township in the north and Caoke Township in the south. Hazard-prone regions predominantly align with slopes featuring gradients of 30°–45° and bear a strong correlation with fault activity. Furthermore, the results of this evaluation are harmonious with the findings from remote sensing interpretation and on-site field investigations pertaining to the earthquake-induced landslides. This body of knowledge can serve as a crucial reference for expedited assessment, emergency response and subsequent supplementation of earthquake-induced landslide databases when confronting similar earthquake-induced landslide scenarios.

Earthquake-induced landslides are ubiquitous on slopes in terrestrial environments, which can pose a serious threat to local communities and infrastructures. Data-driven landslide assessments play a crucial role in preventing future landslide occurrences and recurrences. We present a novel granular computing approach that assesses landslide risk by combining fuzzy information granulation and a stacked autoencoder algorithm. The stacked autoencoder is trained using an end-to-end learning strategy to obtain a central latent vector with reduced dimensionality. The multivariate landslide dataset was used as both the input and output to train the stacked autoencoder algorithm. Subsequently, in the central latent vector of the stacked autoencoder, the Fuzzy C-means clustering algorithm was applied to cluster the landslides into various groups with different risk levels, and the intervals for each group were computed using the granular computing approach. An empirical case study in Wenchuan County, Sichuan, China, was conducted. A comparative analysis with other state-of-the-art approaches including Density-based spatial clustering of applications with noise (DBSCAN), K-means clustering, and Principal Component Analysis (PCA), is provided and discussed. The experimental results demonstrate that the proposed approach using a stacked autoencoder integrated with fuzzy information granulation provides superior performance compared to those by other state-of-the-art approaches, and is capable of studying deep patterns in earthquake-induced landslide datasets and provides sufficient interpretation for field engineers.

Strong earthquakes induce a large number of secondary disasters, such as landslides, which bring serious challenges to post-disaster emergency rescue, and the rapid and accurate assessment of earthquake-induced landslide disasters is crucial for post-earthquake emergency rescue. This research aims to propose an emergency assessment model that is suitable for post-earthquake landslides, specifically targeting the first 72 h after an earthquake for emergency rescue guidance. The model combines remote sensing technology and the Newmark physical mechanics assessment model to form the InSAR Data–Newmark Physical Fusion Driver Model (IDNPM), which comprehensively considers the dynamic deformation of the ground surface and geological features. To validate the predictive performance of the IDNPM, the model is applied to the 5 September 2022 Luding earthquake event and the 8 August 2017 Jiuzhaigou earthquake event. The landslide qualitative evaluation, confusion matrix and Receiver Operating Characteristic (ROC) curve are utilized for quantitative assessment. The results show that the IDNPM can effectively reduce the false negative and false positive errors in landslide prediction by utilizing the SAR deformation information, and to a certain extent, it accounts for the dependence of the Newmark model on the accuracy of empirical formulas and geotechnical parameters. For the Luding earthquake event, the IDNPM shows an accuracy improvement of 10.296% compared to the traditional Newmark model. For the Jiuzhaigou earthquake event, there is also an improvement of 3.152%, with a promising generalization performance. The simplicity and ease of operation in constructing the model are accompanied by high reliability and accuracy. The research findings provide essential references for the development of post-earthquake landslide emergency prediction models and offer robust data support for emergency rescue and recovery efforts in earthquake-stricken areas in the future. [ABSTRACT FROM AUTHOR]

In order to prevent and mitigate disasters, it is crucial to immediately and properly assess the spatial distribution of landslide hazards in the earthquake-affected area. Currently, there are primarily two categories of assessment techniques: the physical mechanism-based method (PMBM), which considers the landslide dynamics and has the advantages of effectiveness and proactivity; the environmental factor-based method (EFBM), which integrates the environmental conditions and has high accuracy. In order to obtain the spatial distribution of landslide hazards in the affected area with near real-time and high accuracy, this study proposed to combine the PMBM based on Newmark method with EFBM to form Newmark-Information value model (N-IV), Newmark-Logic regression model (N-LR) and Newmark- Support Vector Machine model (N- SVM) for seismic landslide hazard assessment on the Ludian Mw 6.2 earthquake in Yunnan. The predicted spatial hazard distribution was compared with the actual cataloged landslide inventory, and frequency ratio (FR), and area under the curve (AUC) metrics were used to verify the model's plausibility, performance, and accuracy. According to the findings, the model's accuracy is ranked as follows: N-SVM>N-LR>N-IV>Newmark. With an AUC value of 0.937, the linked N-SVM was discovered to have the best performance. The research results indicate that the physics-environmental coupled model (PECM) exhibits accuracy gains of 46.406% (N-SVM), 30.625% (N-LR), and 22.816% (N-IV) when compared to the conventional Newmark technique. It shows varied degrees of improvement from 2.577% to 12.446% when compared to the single EFBM. The study also uses the Ms 6.8 Luding earthquake to evaluate the model, showcasing its trustworthy in forecasting power and steady generalization. Since the suggested PECM in this study can adapt to complicated earthquake-induced landslides situations, it aims to serve as a reference for future research in a similar field, as well as to help with emergency planning and response in earthquake-prone regions with landslides. [ABSTRACT FROM AUTHOR]

In this study, we defined screening maps for Italy that classify sites based on their potential for triggering landslides. To this end, we analyzed seismic hazard maps and hazard disaggregation results on a national scale considering four spectral periods (0.01 s, 0.2 s, 0.5 s, and 1.0 s) and three return periods (475, 975, and 2475 years). First, joint distributions of magnitude (M) and distance (R) from hazard disaggregation were analyzed by means of an innovative approach based on image processing techniques to find all modal scenarios contributing to the hazard. In order to obtain the M-R scenarios controlling the triggering of earthquake-induced landslides at any computation node, mean and modal M-R pairs were compared to empirical curves defining the M-R bounds associated with landslide triggering. Three types of landslides were considered (i.e., disrupted slides and falls, coherent slides, and lateral spreads and flows). As a result, screening maps for all of Italy showing the potential for triggering landslides based on the level of seismic hazard were obtained. The maps and the related data are freely accessible.

In mountainous areas, roads are often damaged by earthquake-induced landslides (EL). The degree of road damage and the existing functional state will have a very important impact on the whole earthquake relief work. However, this question has long been plaguing scientists engaged in the risk assessment. Now, an MDT-based rapid assessment method for the spatial distribution of trafficable sections of roads (TSR) hit by EL is proposed. The method mainly consists of three procedures: 1) Demarcate the road sections in the evaluated region; 2) Create the data for earthquake-induced landslide sensitivity level for the evaluated region; 3) Use the sample data to devise the MDT model for the TSR hit by EL and calculate the trafficability of road sections. This method was applied in the study of the 2008 Wenchuan Ms 8.0, the 2014 Ludian Ms 6.5, the 2012 Yiliang Ms 5.6 and Ms 5.7 earthquake-stricken regions. The case of Wenchuan earthquake was adopted to establish the method. The cares of Ludian and Yiliang were used to evaluate whether the method could be transplanted into similar regions. The p-value and the kappa coefficient were used to test and evaluate the significance and the consistence of the actual situation. Our results suggest: ⅰ) The Wenchuan evaluated region had the p-value 2.52 × 10−203 and the kappa coefficient 0.91. Less than 1% of the road sections that were inferred to be trafficable with the established method were actually interrupted by EL. The interruptions in the road sections that were inferred to be un-trafficable were 12 times of those in the other road sections. ⅱ) The Ludian and Yiliang earthquake-stricken regions had the p-value 9.7 × 10−107 and the kappa coefficient 0.81. Only 1.31% of the road sections that were trafficable according to the calculation results of the model had been actually interrupted by EL. The interruptions caused by EL in the un-trafficable road sections according to the calculation results were 5.2 times of those in the trafficable road sections. This method could be applied in other similar regions when a certain error was permitted.

Ischia Island is a volcano-tectonic horst in the Phlegrean Volcanic District, Italy. We investigated rockfalls in Ischia using STONE, a three-dimensional model for simulating trajectories for given detachment locations of blocks. We propose methodological advances regarding the use of high-resolution LiDAR elevation data, the localization of possible detachments sources, and the inclusion of scenario-based seismic shaking as a trigger for rockfalls. We demonstrated that raw LiDAR data are useful to distinguish areas covered by tall vegetation, allowing realistic simulation of trajectories. We found that the areas most susceptibile to rockfalls are located along the N, N-W and S-W steep flanks of Mt. Epomeo, the S and S-W coast, and the sides of some steep exposed hydrographic channels located in the southern sector of the island. A novel procedure for dynamic activation of sources depending on ground shaking, in the event of an earthquake, helped inferring a seismically-triggered source map and the corresponding rockfall trajectories, for a scenario with 475 y return time. Thus, we obtained preliminary rockfall suceptibility in Ischia both in a “static” (trigger-independent) scenario, and in a seismic shaking triggering scenario. They must not be considered a risk map, but a starting point for a detailed field analysis.

The accuracy of rapid earthquake assessment and the emergency assessment system for earthquake-induced damages could be substantially enhanced if the casualties triggered by earthquake-induced geological disasters, such as landslides, are subjected to comprehensive scientific evaluation. However, no credible solution for this purpose has been formulated yet. This study suggests a three-step rapid assessment method designed for earthquake-induced landslide casualties based on the GIS and an associated logistic regression model, as follows: (1) Partition of the region to be evaluated as a 1 km × 1 km grid in the GIS, with assignment of a certain amount of population to each of the grid cells as its population attribute. (2) Calculation of the death rate for each grid cell based upon its earthquake-induced landslide susceptibility attribute using the logistic regression model. (3) The earthquake-induced landslide casualties are first determined for each of the kilometer grid cells, and then for the entire region under evaluation. The proposed method was implemented to test the assessment of earthquake-induced landslide casualties in three earthquake-stricken regions. The study reveals the feasibility of the extensibility and applicability of the proposed rapid assessment method for earthquake-induced landslide casualties, and its suitability for similar assessments and calculations of other regions.

In January and February 2001, El Salvador was hit by two strong earthquakes that triggered thousands of landslides, causing 1259 fatalities and extensive damage. The analysis of aerial and SPOT-4 satellite images allowed us to map 6491 coseismic landslides, mainly debris slides and flows that occurred in volcanic epiclastites and pyroclastites. Four different multivariate adaptive regression splines (MARS) models were produced using different predictors and landslide inventories which contain slope failures triggered by an extreme rainfall event in 2009 and those induced by the earthquakes of 2001. In a predictive analysis, three validation scenarios were employed: the first and the second included 25% and 95% of the landslides, respectively, while the third was based on a k-fold spatial cross-validation. The results of our analysis revealed that: (i) the MARS algorithm provides reliable predictions of coseismic landslides; (ii) a better ability to predict coseismic slope failures was observed when including susceptibility to rainfall-triggered landslides as an independent variable; (iii) the best accuracy is achieved by models trained with both preparatory and trigger variables; (iv) an incomplete inventory of coseismic slope failures built just after the earthquake event can be used to identify potential locations of yet unreported landslides. [ABSTRACT FROM AUTHOR]

This work aims to identify the spatial patterns of some landslides triggered by the 19 September 2017 Puebla-Morelos earthquake (Mw 7.1) in Mexico. Several landslides were activated through a vast area in the central-southern region of the country, mainly concentrated in the states of Puebla and Morelos. Landslide data was derived from multiple sources. We have recognized and manually mapped the earthquake-induced landslides (EQL) through visual interpretation of high-resolution satellite imagery (0.5–3 m). Furthermore, we conducted an extensive media investigation to document cases and reports in populated regions located in the epicentral zone affected by EQL. Shallow disrupted landslides as falls and slides were recognized through the mountainous zones of the study area. Although most of the documented landslides occurred in remote non-urbanized areas, some events disturbed populated regions, collapsed railways, and interrupted life-basic services. At least, two persons died because of mass movements triggered by this earthquake but several others were affected. Finally, we analyzed the spatial distribution of these landslides in terms of influence factors as lithology, topography (elevation, slope angle, and slope aspect), seismological features (distance to epicenter, area of distribution, PGA and MMI), and accumulated precipitation. A cluster analysis following amalgamation (linkage) rule was carried out to infer relationships between EQL and the main factors controlling the phenomena. We conclude the distribution pattern of the landslides induced by the Puebla-Morelos earthquake was controlled by the combined effect of lithology, peak ground acceleration, and rainfall accumulation. [ABSTRACT FROM AUTHOR]

On the 25 February 2018, an earthquake of magnitude Mw7.5 struck the region of Porgera in Papua New Guinea (PNG), triggering numerous landslides. Planetscope images are used to derive a partial inventory of 2941 landslides in a cloud-free area of 2686 km². The average area of landslides in the study area is 18,500 m². We use the Environmental Seismic Intensity (ESI) scale to assess the damage due to the triggered landslides. Local intensity values are assigned to individual landslides by calculating their volume using various area-volume relations. We observe that different empirical relations yield similar volume values for individual landslides (local ESI intensity ≥ X). The spatial variation of landslide density and areal coverage within the study area in cells of 1 km² is investigated and compared to the probability predicted by the USGS model. We observe that high probability corresponds to a significant number of landslides. An ESI epicentral intensity of XI is estimated based on primary and secondary effects. This study represents the first application of the ESI scale to an earthquake in PNG. The Porgera earthquake fits well with past case studies worldwide in terms of ESI scale epicentral intensity and triggered landslide number as a function of earthquake magnitude. [ABSTRACT FROM AUTHOR]

Predicting the susceptibility of a specific part of a landslide (SSPL) involves predicting the likelihood that the part of the landslide (e.g., the entire landslide, the source area, or the scarp) will form in a given area. When predicting SSPL, the landslide samples are far less than the non-landslide samples. This class imbalance makes it difficult to predict the SSPL. This paper proposes an advanced artificial intelligence (AI) model based on the dice-cross entropy (DCE) loss function and XGBoost (XGBDCE) or Light Gradient Boosting Machine (LGBDCE) to ameliorate the class imbalance in the SSPL prediction. We select the earthquake-induced landslides from the 2018 Hokkaido earthquake as a case study to evaluate our proposed method. First, six different datasets with 24 landslide influencing factors and 10,422 samples of a specific part of the landslides are established using remote sensing and geographic information system technologies. Then, based on each of the six datasets, four landslide susceptibility algorithms (XGB, LGB, random-forest (RF) and linear discriminant analysis (LDA)) and four class balancing methods (non-balance (NB), equal-quantity sampling (EQS), inverse landslide-frequency weighting (ILW), and DCE loss) are applied to predict the SSPL. The results show that the non-balanced method underestimates landslide susceptibility, and the ILW or EQS methods overestimate the landslide susceptibility, while the DCE loss method produces more balanced results. The prediction performance of the XGBDCE (average area under the receiver operating characteristic curve (0.970) surpasses that of RF (0.956), LGB (0.962), and LDA (0.921). Our proposed methods produce more unbiased and precise results than the existing models, and have a great potential to produce accurate general (e.g., predicting the entire landslide) and detailed (e.g., combining the prediction of the landslide source area with the landslide run-out modeling) landslide susceptibility assessments, which can be further applied to landslide hazard and risk assessments. [ABSTRACT FROM AUTHOR]

During an earthquake sequence, there are often multiple recurring landslides. Understanding the spatial distribution of the landslides triggered by the first earthquake can help us predict the landslide susceptibility for subsequent shakes over a short term. This study used two landslide inventories from the Lombok earthquake sequence in Indonesia in 2018 to construct a short-term secondary disaster prediction model and an overall spatial prediction model using four machine learning algorithms. The average accuracy of the positive samples predicted by the prediction model was 7.1% lower than that of the short-term model. The highest accuracy of the overall prediction model was 14.9% higher, on average, and the area under the ROC curve (AUC) score was 8.1% higher, on average, but the corresponding probability thresholds were lower. The reason for this difference is that, in the short-term prediction model, since most of the landslides in the first landslide inventory were prone to fail two or more times due to the effect of multiple earthquakes, the prediction results have a high positive rate. This feature of the short-term prediction model makes it suitable for landslide rescue guidance in a sequence of earthquakes. In contrast, the overall prediction model can better represent the spatial distribution of the earthquake-triggered landslides in the area. [ABSTRACT FROM AUTHOR]

Earthquake-induced landslides are one of the most dangerous secondary disasters in mountainous areas throughout the world. The nowcasting of coseismic landslides is crucial for planning land management, development, and urbanization in mountainous areas. Taking Wenchuan County in Western Sichuan Plateau (WPS) as the study area, a landslide inventory was built using historical records. Herein, eight causative factors were selected for a library of factors, and then a landslide susceptibility assessment (LSA) was performed based on the machine learning techniques of Random Forest (RF) and Artificial Neural Network (ANN) models, respectively. The prediction abilities of the above two LSM models were assessed using the area under curve (AUC) value of the receiver operating characteristics (ROC) curve, precision, recall ratio, accuracy, and specificity. The performances of both machine learning techniques were found to be excellent, but RF outperformed in accuracy. There were still some differences between the models' performances shown by the results: RF (AUC = 0.966) outperformed ANN (AUC = 0.914). The RF model demonstrated a higher degree of correlation between the areas classified as very low and high susceptibility in comparison to the ANN model. The results provided a theoretical framework upon which machine learning applications could be applied (e.g., RF and ANN), a reliable and low-cost tool to assess landslide susceptibility. This comparative study will provide a useful description of earthquake-induced landslides in the study area, which can be used to anticipate the features of landslides in the future, and have played a very important role in proper anthropogenic activities, resource management, and infrastructural development of the mountainous areas. [ABSTRACT FROM AUTHOR]

Strike-slip salt diapir systems have been scarcely investigated and have probably been underrecognized. The western Fars Arc of the Zagros Mountains hosts the most remarkable example of salt extrusions associated with seismogenic strike-slip faults within an oblique collisional setting. This study, mainly based on field mapping and observations carried out in the Kareh Bas fault system and the Dandenjan Diapir, explores several issues with practical implications related to these active fault-salt systems: (1) role of precursor diapirs on the initiation of shortening structures; (2) fault segmentation controlled by precursor diapirs, emergence of diapirs, and the development of pull-apart basins; (3) the relative timing of emergence of salt extrusions associated with a propagating strike-slip fault; (4) Quaternary deformation and paleoseismic evidence associated with the Kareh Bas fault; (5) the impact of the salt detachment depth on the seismogenic potential of the segmented Kareh Bas fault system; and (6) clustering of large potentially coseismic landslides in the vicinity of the Kareh Bas fault system. • Strike-slip fault segmentation controlled by precursor diapir. • Active pull-apart basin associated with salt extrusion. • Chronological sequence of salt extrusions associated with propagating fault. • Sesimogenic potential of tear strike-slip fault above deep salt detachment. • Spatial association between active fault and large coseismic? landslides. [ABSTRACT FROM AUTHOR]

Earthquake-induced landslides are associated with significant risks to human lives and infrastructure. Spatially distributed infrastructure systems, such as pipelines, power lines, and transportation networks, are at particular risk to seismic landslides due to their large spatial extent. To conduct a comprehensive seismic landslide risk assessment for these systems, there is the need to evaluate the seismic landslide characteristics (i.e., location, size, displacement, direction) on a broad, regional scale. This paper presents a framework for seismic landslide analysis that provides this information for subsequent risk assessments. The approach computes seismic landslide displacements using a sliding block approach while accounting for the uncertainties in the input variables and displacement models using a logic tree. The computed displacements are then aggregated based on geomorphic landforms to define landslide zones. For each landslide zone, the statistical distributions of landslide features, such as landslide size, displacement level, and direction of movement, are defined. These attributes are presented in a format that can be integrated with fragility models for distributed infrastructure systems to quantify risk on a regional scale. The application of the approach is demonstrated through assessments for gas pipeline networks across the state of California in the United States. • A framework for regional seismic landslide risk assessment is described. • A sliding block approach with a logic tree accounts for uncertainties. • Geomorphic landforms (slope units) are integrated to define landslide zones. • Distributions of landslide attributes are defined for input into fragility models. • Results are shown for a region of southern California. [ABSTRACT FROM AUTHOR]

Understanding the soil–root mechanical interaction is crucial to advancing the utilisation of vegetation as a nature-based approach to designing more stable slopes and resilient urban forestry against tree windthrown. Although numerical and analytical models for detailed analysis of soil–root interaction exist, these models are seldom validated due to the lack of field data and the significant challenges in quantifying such interactions due to the complex nature of root system. The centrifuge modelling technique is an effective alternative for unravelling the complexities of the hydromechanical behaviour of vegetated soils by recreating prototype stress levels in small-scale physical models and testing them under more controlled conditions. This work presents a critical review on existing centrifuge modelling methods for vegetated soils, paying particular attention on the (i) fundamentals of centrifuge modelling, where principles, scaling laws and applications relevant to modelling vegetated soils are detailed; (ii) methods for modelling soils, including choice of soil material and sample preparation; and (iii) methods for modelling roots by means of natural plants and root analogues, where the replication of root morphology, mechanical properties and capabilities of modelling transpiration effects are discussed. In every topic, the challenges that could further advance the centrifuge modelling of vegetated soils and the possible ways to address them are highlighted. Finally, the prospect for future studies is discussed, highlighting the potential to enhance the understanding of the underlying mechanisms amongst plant roots, soil, water and external loading. • Obeying scaling laws for roots and soils is critical for centrifuge modelling of vegetated soils. • Root modelling should consider morphological, mechanical, and hydrological properties. • 3D-printed root analogues are an alternative to addressing scaling difficulties of real roots. [ABSTRACT FROM AUTHOR]

Strong earthquakes induce a large number of secondary disasters, such as landslides, which bring serious challenges to post-disaster emergency rescue, and the rapid and accurate assessment of earthquake-induced landslide disasters is crucial for post-earthquake emergency rescue. This research aims to propose an emergency assessment model that is suitable for post-earthquake landslides, specifically targeting the first 72 h after an earthquake for emergency rescue guidance. The model combines remote sensing technology and the Newmark physical mechanics assessment model to form the InSAR Data–Newmark Physical Fusion Driver Model (IDNPM), which comprehensively considers the dynamic deformation of the ground surface and geological features. To validate the predictive performance of the IDNPM, the model is applied to the 5 September 2022 Luding earthquake event and the 8 August 2017 Jiuzhaigou earthquake event. The landslide qualitative evaluation, confusion matrix and Receiver Operating Characteristic (ROC) curve are utilized for quantitative assessment. The results show that the IDNPM can effectively reduce the false negative and false positive errors in landslide prediction by utilizing the SAR deformation information, and to a certain extent, it accounts for the dependence of the Newmark model on the accuracy of empirical formulas and geotechnical parameters. For the Luding earthquake event, the IDNPM shows an accuracy improvement of 10.296% compared to the traditional Newmark model. For the Jiuzhaigou earthquake event, there is also an improvement of 3.152%, with a promising generalization performance. The simplicity and ease of operation in constructing the model are accompanied by high reliability and accuracy. The research findings provide essential references for the development of post-earthquake landslide emergency prediction models and offer robust data support for emergency rescue and recovery efforts in earthquake-stricken areas in the future.

Abstract Earthquake-triggered landslides are common disasters of active mountain belts. Due to the lack of earthquake-triggered landslide inventory in Taiwan, it is not intuitive to observe spatial relationships and discover unique patterns between landslides and essential triggers. We examined strong earthquake events in Taiwan after the 1999 M w7.6 Chi-Chi earthquake and targeted the 2013 M L6.5 Nantou earthquake to create the landslide inventory. We adopted two Landsat-8 satellite images before and after the event to detect landslides, and incorporated a 20-m DEM and rock type data of Taiwan to represent key factors triggering earthquake-induced landslides such as peak ground acceleration (PGA), lithology, slope roughness, slope, and aspect. Based on the analysis of the density of landslides, there are strong correlations between the landslide occurrence and seismic and geomorphic factors. Furthermore, we noticed that the landslide aspects have a systematic tendency towards the northeast, which is not correlated with the dip directions and wave propagation directions. Instead, we found that the northeastward landslide aspect is more associated with the westward–southwestward surface movement at the landslides. We found that the included angles between the landslide aspects and the displacement directions for all the landslides are ~ 100°–180°. The relationship indicated that the coseismic deformation of the Nantou earthquake may play a role in the landslide distribution. Graphical Abstract

Risk of landslide hazards strongly depends on how far landslide sediment travels, known as landslide mobility. Previous studies mentioned enhanced mobility of earthquake-induced landslides in volcanic deposits compared to those from other geologic/soil settings. A flume apparatus constructed at a 1:300 scale was used to examine the mobility of landslides with pumice. Four pumice samples were collected from landslides induced by the 2018 Eastern Iburi earthquake, Hokkaido, Japan. Laboratory tests confirmed the unique low specific gravity of the pumice (1.29–1.33), indicating numerous voids within pumice particles. These voids allowed pumice to absorb a substantial amount of water (95–143%), about 9–15 times higher than other coarse-grained soils. Our flume experiments using various saturation levels (0–1) confirmed the influence of this inner-particle water absorption on pumice mobility. Because a low value of specific gravity indicates a low strength of soil, grain crushing may occur on the pumice layer, causing water from the internal voids to discharge and fluidize the transported landslide mass. Our findings indicate that such earthquake-induced landslides can be as mobile as those induced by rainfall, depending on the initial water content of the pumice layers. These conditions might be associated with water accumulation from previous rainfall events and the water-holding capability on pumice layers. [ABSTRACT FROM AUTHOR]

Earthquake-induced landslides represent a significant seismic hazard since they can largely increase the damage and losses due to a seismic event, an issue that must be considered in land-use and risk management purposes. However, it can be difficult to consider all the natural variables, such as geotechnical parameters, that predispose the occurrence of landslides under a specific dynamic triggering, especially for wide areas. Among these, the most important and critical ones to quantify at large scale, are represented by the hydraulic conditions in both unsaturated and saturated media. For this reason, in this work we present a newly developed GIS tool that was specifically designed for the automation of a pseudo-dynamic Newmark model to estimate the co-seismic displacements over wide areas. The tool takes into account reactivations of landslides under different rupture mechanisms and parametrically weighs the role of variable initial soil moisture or pressure head conditions, as well as the influence of ground shaking resulting from local amplification effects. The proposed tool was tested in the Molise region (central-southern Italy), where almost 23,000 existing landslides have been selected for evaluating potential reactivations. The obtained results point out the importance of local conditions on the displacement amount, even by considering a unique return period of the seismic action. Strengths and weaknesses of the proposed model have been also highlighted in view of potential future applications in the framework of co-seismic landslide risk assessment and mitigation measures. [ABSTRACT FROM AUTHOR]

Roads can have a significant impact on the frequency of mass wasting events in mountainous areas. However, characterizing the extent and pervasiveness of mass movements over time has rarely been documented due to limitations in available data sources to consistently map such events. We monitored the evolution of a road network and assessed its effect on mass movements for a 11-year window in Arhavi, Turkey. The main road construction projects run in the area are associated with a hydroelectric power plant as well as other road extension works and are clearly associated with the vast majority (90.1%) of mass movements in the area. We also notice that the overall number and size of the mass movements are much larger than in the naturally occurring comparison area. This means that the sediment load originating from the anthropogenically induced mass movements is larger than its counterpart associated with naturally occurring landslides. Notably, this extra sediment load could cause river channel aggregation, reduce accommodation space and as a consequence, it could lead to an increase in the probability and severity of flooding along the river channel. This marks a strong and negative effect of human activities on the natural course of earth surface processes. We also compare frequency-area distributions of human-induced mass movements mapped in this study and co-seismic landslide inventories from the literature. By doing so, we aim to better understand the consequences of human effects on mass movements in a comparative manner. Our findings show that the damage generated by the road construction in terms of sediment loads to river channels is compatible with the possible effect of a theoretical earthquake with a magnitude greater than Mw = 6.0. [ABSTRACT FROM AUTHOR]

In January and February 2001, El Salvador was hit by two strong earthquakes that triggered thousands of landslides, causing 1259 fatalities and extensive damage. The analysis of aerial and SPOT-4 satellite images allowed us to map 6491 coseismic landslides, mainly debris slides and flows that occurred in volcanic epiclastites and pyroclastites. Four different multivariate adaptive regression splines (MARS) models were produced using different predictors and landslide inventories which contain slope failures triggered by an extreme rainfall event in 2009 and those induced by the earthquakes of 2001. In a predictive analysis, three validation scenarios were employed: the first and the second included 25% and 95% of the landslides, respectively, while the third was based on a k-fold spatial cross-validation. The results of our analysis revealed that: (i) the MARS algorithm provides reliable predictions of coseismic landslides; (ii) a better ability to predict coseismic slope failures was observed when including susceptibility to rainfall-triggered landslides as an independent variable; (iii) the best accuracy is achieved by models trained with both preparatory and trigger variables; (iv) an incomplete inventory of coseismic slope failures built just after the earthquake event can be used to identify potential locations of yet unreported landslides.

Rainfall and earthquakes are the most frequent landslide-triggering parameters throughout the Indian Himalayan region. This region is susceptible to both rainfall- and earthquake-induced landslides. Therefore, landslide susceptibility zonation (LSZ) based on the individual triggering parameter is insufficient for this region. The primary aim of this work is to assess the combined effect of rainfall- and earthquake-triggering parameters on LSZ using a GIS-based relative frequency ratio (RFR) approach. Consequently, the objective is to develop rainfall- and earthquake-induced LSZ maps for the study area. In this paper, the study area considered is a part of Chamoli district in the Uttarakhand state of India. For this study, the landslide inventories were derived from the pre- and post-Chamoli earthquake (1999). Landslide inventory includes 220 landslides that occurred before the Chamoli earthquake, considered as rainfall-induced landslides (RIL) and 56 earthquake-induced landslides (EIL). The variation between landslides' spatial distribution and controlling parameters for both cases, i.e. RIL and EIL, are assessed and compared. Then, rainfall- and earthquake-induced LSZ maps in the same study area are produced and compared. [ABSTRACT FROM AUTHOR]

Cephalonia, located in the middle of the central Ionian Islands, has been affected by destructive earthquakes during both the instrumental and the historical period. Despite the fact that it is widely studied from several scientific viewpoints, limited research has been conducted so far regarding the earthquake-triggered landslides (ETL) and the related susceptibility. In the context of the present study, an inventory with 67 ETL from 11 earthquakes that occurred from 1636 to 2014 is presented. Given this record, the study further examines the ETL susceptibility exploiting 10 landslide causal factors in the frame of a GIS-based Analytic Hierarchy Process (AHP). Four factors (i.e., slope, PGA, tectonic structures and lithology) were associated in a higher degree to the locations where ETL occurred on the island. Based on the comparison of the ETL inventory and the landslide susceptibility index (LSI) map, the distribution of ETL in Cephalonia is not random, as their majority (82%) were generated within high to critically high susceptible zones. This fact, along with the AUC values of 80.3%, reveals a fair-to-good accuracy of the landslide susceptibility assessment and indicate that the contribution of the studied variables to the generation of ETL was effectively determined. [ABSTRACT FROM AUTHOR]

Landslides are the main secondary effects of earthquakes in mountainous areas. The spatial distribution of these landslides is controlled by the local seismic ground motion and the local slope stability. While gravitational instabilities in arid and semi-arid environments are understudied, we document the landslides triggered by the Sarpol Zahab earthquake (November 12, 2017, Mw7.3, Iran/Iraq border), the largest event ever recorded in the semi-arid Zagros Mountains. An original earthquake-induced landslide inventory was derived, encompassing landslides of various sizes and velocities (from rapid disrupted rockfalls to slow-moving coherent landslides). This inventory confirms the low level of triggered landslides in semi-arid environments. It also displays clear differences in the spatial and volumetric distributions of earthquake-induced landslides, having 386 rockfalls of limited size triggered around the epicenter, and 9 giant (areas of ca. 106 m2) active and ancient deep-seated landslides coseismically accelerated at locations up to 180 km from the epicenter. This unusual distant triggering is discussed and interpreted as an interaction between the earthquake source properties and the local geological conditions, emphasizing the key role of seismic ground motion variability at short spatial scales in triggering landslides. Finally, the study documents the kinematics of slow-moving ancient landslides accelerated by earthquakes, and opens up new perspectives for studying landslide triggering over short (~ 1–10 years) and long-time (~ 1000–10,000 years) periods. [ABSTRACT FROM AUTHOR]

Earthquake-triggered landslides are common disasters of active mountain belts. Due to the lack of earthquake-triggered landslide inventory in Taiwan, it is not intuitive to observe spatial relationships and discover unique patterns between landslides and essential triggers. We examined strong earthquake events in Taiwan after the 1999 Mw7.6 Chi-Chi earthquake and targeted the 2013 ML6.5 Nantou earthquake to create the landslide inventory. We adopted two Landsat-8 satellite images before and after the event to detect landslides, and incorporated a 20-m DEM and rock type data of Taiwan to represent key factors triggering earthquake-induced landslides such as peak ground acceleration (PGA), lithology, slope roughness, slope, and aspect. Based on the analysis of the density of landslides, there are strong correlations between the landslide occurrence and seismic and geomorphic factors. Furthermore, we noticed that the landslide aspects have a systematic tendency towards the northeast, which is not correlated with the dip directions and wave propagation directions. Instead, we found that the northeastward landslide aspect is more associated with the westward–southwestward surface movement at the landslides. We found that the included angles between the landslide aspects and the displacement directions for all the landslides are ~ 100°–180°. The relationship indicated that the coseismic deformation of the Nantou earthquake may play a role in the landslide distribution. [ABSTRACT FROM AUTHOR]

A permanent displacement model (PDM) is often used for hazard assessment of earthquake-induced landslides (ELHA) at regional scale. Pulse-like ground motions (PLGM) are rarely considered in the existing PDMs, although they have been proved to have significant influence on the initiation of landslides in near-fault area. In this paper, taking an area affected by 1994 Mw 6.7 Northridge earthquake as an example, 3 PDMs composed of PLGM, and 11 other models were selected to compare and verify the effects of PLGM. According to the results of multiple evaluation metrics such as the ground failures captured (%GFC), receiver operating characteristics (ROC), and quality sum (Qs), it indicates that the PDMs composed of PLGM use less ground motion records, but the accuracy of evaluation results is higher. This shows that PLGM have significant effect on ELHA. The ELHA method considering the near-fault PLGM was applied to a certain area along the Sichuan-Tibet Railway. The results show that the displacement model considering PLGM predicted more landslides cells. Among them, 8.45% of the slopes are in the extremely high hazard area, and about 30% of the extremely high hazard area is within 5 km of the railway. The slope with high hazard of earthquake-induced landslide can be investigated and protected in advance to avoid casualties and property losses. [ABSTRACT FROM AUTHOR]