Geometrical characteristics of earthquake-induced landslides and correlations with control factors: a case study of the 2013 Minxian,Gansu, China, <Emphasis Type="Italic">Mw</Emphasis> 5.9 event

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 m²) 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 = 0.6164L + 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.     

Evaluating the susceptibility of landslide landforms in Japan using slope stability analysis: a case study of the 2016 Kumamoto earthquake

This study analyzed 267 landslide landforms (LLs) in the Kumamoto area of Japan from the database of about 0.4 million LLs for the whole of Japan identified from aerial photos by the National Research Institute for Earth Science and Disaster Resilience of Japan (NIED). Each LL in the inventory is composed of a scarp and a moving mass. Since landslides are prone to reactivation, it is important to evaluate the sliding-recurrence susceptibility of LLs. One possible approach to evaluate the susceptibility of LLs is slope stability analysis. A previous study found a good correlation (R ² = 0.99) between the safety factor (F _s ) and slope angle (α) of F _s  = 17.3α ^?0.843. We applied the equation to the analysis of F _s for 267 LLs in the area affected by the 2016 Kumamoto earthquake (M _j  = 7.3). The F _s was calculated for the following three cases of failure: scarps only, moving mass only, and scarps and moving mass together. Verification with the 2016 Kumamoto earthquake event shows that the most appropriate method for the evaluation of LLs is to consider the failure of scarps and moving mass together. In addition, by analyzing the relationship between the factors of slope of entire landslide and slope of scarp for LLs and comparing the results with the Aso-ohashi landslide, the largest landslide caused by the 2016 Kumamoto earthquake, we also found that morphometric analysis of LLs is useful for forecasting the travel distance of future landslides.     

A novel fuzzy <Emphasis Type="Italic">K</Emphasis>-nearest neighbor inference model with differential evolution for spatial prediction of rainfall-induced shallow landslides in a tropical hilly area using GIS

This research represents a novel soft computing approach that combines the fuzzy k-nearest neighbor algorithm (fuzzy k-NN) and the differential evolution (DE) optimization for spatial prediction of rainfall-induced shallow landslides at a tropical hilly area of Quy Hop, Vietnam. According to current literature, the fuzzy k-NN and the DE optimization are current state-of-the-art techniques in data mining that have not been used for prediction of landslide. First, a spatial database was constructed, including 129 landslide locations and 12 influencing factors, i.e., slope, slope length, aspect, curvature, valley depth, stream power index (SPI), sediment transport index (STI), topographic ruggedness index (TRI), topographic wetness index (TWI), Normalized Difference Vegetation Index (NDVI), lithology, and soil type. Second, 70 % landslide locations were randomly generated for building the landslide model whereas the remaining 30 % landslide locations was for validating the model. Third, to construct the landslide model, the DE optimization was used to search the optimal values for fuzzy strength (fs) and number of nearest neighbors (k) that are the two required parameters for the fuzzy k-NN. Then, the training process was performed to obtain the fuzzy k-NN model. Value of membership degree of the landslide class for each pixel was extracted to be used as landslide susceptibility index. Finally, the performance and prediction capability of the landslide model were assessed using classification accuracy, the area under the ROC curve (AUC), kappa statistics, and other evaluation metrics. The result shows that the fuzzy k-NN model has high performance in the training dataset (AUC?=?0.944) and validation dataset (AUC?=?0.841). The result was compared with those obtained from benchmark methods, support vector machines and J48 decision trees. Overall, the fuzzy k-NN model performs better than the support vector machines and the J48 decision trees models. Therefore, we conclude that the fuzzy k-NN model is a promising prediction tool that should be used for susceptibility mapping in landslide-prone areas.     

Shear wave velocity imaging of landslide debris deposited on an erodible bed and possible movement mechanism for a loess landslide in Jingyang,Xi’an,China

The South Jingyang Plateau, with a total area of 70 km², is located in Shaanxi Province, China. Since 1976, more than 50 landslides of different types have occurred repeatedly on the edge slopes of the plateau due to the start of diversion irrigation on the plateau, resulting in great loss of lives and property. To better understand the initiation and movement mechanisms of these loess landslides, we surveyed them and carried out a detailed investigation of a large landslide in the Xihetan area. Our field survey results revealed that although most of these landslides had a long runout with high mobility, most of the landslide materials originating from the edge slopes may have been in an unsaturated state when the landslide occurred. This suggests that the materials at the toe of the edge slope as well as on the travel path along the river terrace might have played a key role in landslide movement. To examine how the materials on the travel path were involved in the landsliding, we used a multichannel surface wave technique and surveyed shear wave velocity (V _s ) profiles of the landslide deposits. We also examined the internal geometry of the deposits that outcropped on the right-side slope of the landslide foot. The longitudinal profile of V _s along the direction of movement showed that terrace deposits near the toe of the edge slope may have been sheared upward, indicating that at the toe, the surface of rupture might be located inside the terrace deposits. The V _s contours showed an A-shaped fold within the landslide deposits in the middle part of the travel path and became greater in the most distal toe part. The V _s profile across the deposits showed a U-shaped belt, in which the soil layers have smaller V _s . This belt may be the boundary between the sliding landslide debris and terrace deposits. The observed internal geometry of the landslide deposits indicates that a sliding surface developed within the sandy layer underlying the gravel layer. Therefore, we inferred that after failure, the displaced landslide materials overrode and sheared the terrace deposits along its main sliding direction, resulting in the formation of thrust folds within the terrace deposits, and greater V _s on the distal toe part of the landslide.     

Developing an algorithm for automated geometric analysis and classification of landslides incorporating LiDAR-derived DEM

Amending landslides inventories is immensely important to policy and decision makers alike. Sliding creates geometric shapes on the Earth’s surface. This study presents the utilization of LiDAR high-resolution digital elevation model (DEM) in the Alborz Mountains, Iran to refurbish the existing landslide inventory dataset by implementing the proposed algorithm. The method consists of the automated derivation of landslide geometry (length, width, and area) followed by classification of landslide types considering length, width and flow direction. This study has used the trapezoidal rule for numerical integration to develop the proposed algorithm. The landslides were then classified into four types (very long, long, very wide, and wide) based on slope, length, and width. This geometric classification of landslides is based on the geographical coordinates, slope angle (θ), length (L), and width (W), and further failure flow direction. A total of 95 landslides were updated from the existing inventory database. The proposed method was verified and evaluated by field observations; and 14 samples were tested to determine the relative error. The results demonstrated that the mean percentage relative error is 0.496% in length and width and 0.008% in area, related to the GIS analysis. The accuracy performance of determining the landslide’s type is 92%. The purposefulness of this algorithm is to increase the accuracy performance of landslides geometry analysis and automated measurements associated with the usual GIS platforms such as ArcGIS.     

A novel genetic algorithm for optimization of conditioning factors in shallow translational landslides and susceptibility mapping

Genetic algorithm (GA) is an effective approach in selecting the best factors without considering all possible combinations in landslide susceptibility mapping (LSM). The approach experienced a local optimal solution for hazard mapping. In this study, we propose a novel genetic algorithm (NGA) for solving the problems of optimal precision in selecting conditioning factors based on the crossover and mutation. In the southwestern part of China, including Wenchuan, Ludshan, and Ludian areas, the findings of this study confirm the applicability of NGA, which has a strong robustness compared to GA obviously. Results indicated that the highest area under curve (AUC) of GA is 93.47, 83.45, and 82.21% in Wenchuan, Lushan, and Ludian, respectively. Cumulative error of the precision (?R) is 3.19, 10.48, and 6.05%, and error of the highest precision (?P) is 0.01, 0.03, and 0.12% for Wenchuan, Lushan, and Ludian, respectively. Compared to the GA, the highest accuracy of NGA is 93.48% (Wenchuan), 83.48% (Lushan), and 82.28% (Ludian). It also revealed that ?R is 0.77, 1.26, and 1.82%, and ?P is 0.00, 0.04, and 0.05% for Wenchuan, Lushan, and Ludian, respectively. By comparing with GA, the novel approach of NGA has stronger robustness and higher accuracy on selecting the optimal conditioning factors of landslide. Additionally, the relationship of landslide occurrence with controlling factors was assessed in every study area. According to the results, lithology, distance to roads, elevation, and slope were regarded as the most effective factors for shallow translational landslides. These factors implied that internal structure and composition of rock, anthropogenic activity, and topography factors posed the main impacts on landslide occurrence. Finally, we implemented landslide susceptibility assessment in three study areas. Results showed that high landslide susceptibility was in the east and northeastern parts of Wenchuan; central region northward of Lushan; and southwest, central region, and west of Ludian.     

Setting up evaluate indicators for slope control engineering based on spatial clustering analysis

Rainfall-induced landslides (RILs) have been a source of social and economic disruption in the mountainous Baguio area in northern Philippines. Prolonged heavy rainfall usually happens during tropical cyclone and southwest monsoon activity. A pragmatic approach to RIL mitigation is to develop rainfall-based early warning. We implemented a modified regression method to derive the empirical minimum intensity (I)–duration (D) threshold I = 6.46 D ^?0.28 and a normalized ID threshold NI = 0.002 D ^?0.28 for rainfall duration ranging between 24 and 264 h. Using a separate data set to evaluate the applicability of the threshold, 93% of the landslide-triggering rainfall events fell above the derived threshold. RILs also occurred when 24-h rainfall was 0.02–28% of the mean annual precipitation or after accumulating at least 500 mm of rainfall from the onset of the rainy season. The thresholds may be further refined as more landslide data become available in the future.     

Coseismic landslides triggered by the 8th August 2017 <Emphasis Type="Italic">M</Emphasis><Subscript>s</Subscript> 7.0 Jiuzhaigou earthquake (Sichuan,China): factors controlling their spatial distribution and implications for the seismogenic blind fault identification

On 8th August 2017, a magnitude M_s 7.0 earthquake struck the County of Jiuzhaigou, in Sichuan Province, China. It was the third M_s ≥?7.0 earthquake in the Longmenshan area in the last decade, after the 2008 M_s 8.0 Wenchuan earthquake and the 2013 M_s 7.0 Lushan earthquake. The event did not produce any evident surface rupture but triggered significant mass wasting. Based on a large set of pre- and post-earthquake high-resolution satellite images (SPOT-5, Gaofen-1 and Gaofen-2) as well as on 0.2-m-resolution UAV photographs, a polygon-based interpretation of the coseismic landslides was carried out. In total, 1883 landslides were identified, covering an area of 8.11 km², with an estimated total volume in the order of 25–30?×?10⁶ m³. The total landslide area was lower than that produced by other earthquakes of similar magnitude with strike-slip motion, possibly because of the limited surface rupture. The spatial distribution of the landslides was correlated statistically to a number of seismic, terrain and geological factors, to evaluate the landslide susceptibility at regional scale and to identify the most typical characteristics of the coseismic failures. The landslides, mainly small-scale rockfalls and rock/debris slides, occurred mostly along two NE-SW-oriented valleys near the epicentre. Comparatively, high landslide density was found at locations where the landform evolves from upper, broad valleys to lower, deep-cut gorges. The spatial distribution of the coseismic landslides did not seem correlated to the location of any known active faults. On the contrary, it revealed that a previously-unknown blind fault segment—which is possibly the north-western extension of the Huya fault—is the plausible seismogenic fault. This finding is consistent with what hypothesised on the basis of field observations and ground displacements.     

Rainfall thresholds for the possible landslide occurrence in Sicily (Southern Italy) based on the automatic reconstruction of rainfall events

Review of the literature on the reconstruction of the rainfall responsible for slope failures reveals that criteria for the identification of rainfall events are lacking or somewhat subjective. To overcome this problem, we developed an algorithm for the objective and reproducible reconstruction of rainfall events and of rainfall conditions responsible for landslides. The algorithm consists of three distinct modules for (i) the reconstruction of distinct rainfall events, in terms of duration (D, in h) and cumulated event rainfall (E, in mm), (ii) the identification of multiple ED rainfall conditions responsible for the documented landslides, and (iii) the definition of critical rainfall thresholds for possible landslide occurrences. The algorithm uses pre-defined parameters to account for different seasonal and climatic settings. We applied the algorithm in Sicily, southern Italy, using rainfall measurements obtained from a network of 169 rain gauges, and information on 229 rainfall-induced landslides occurred between July 2002 and December 2012. The algorithm identified 29,270 rainfall events and reconstructed 472 ED rainfall conditions as possible triggers of the observed landslides. The algorithm exploited the multiple rainfall conditions to define objective and reproducible empirical rainfall thresholds for the possible initiation of landslide in Sicily. The calculated thresholds may be implemented in an operational early warning system for shallow landslide forecasting.     

Accuracy assessment of near-shore bathymetry information retrieved from Landsat-8 imagery

The improvement in the capabilities of Landsat-8 imagery to retrieve bathymetric information in shallow coastal waters was examined. Landsat-8 images have an additional band named coastal/aerosol, Band 1: 435–451 nm in comparison with former generation of Landsat imagery. The selected Landsat-8 operational land image (OLI) was of Chabahar Bay, located in the southern part of Iran (acquired on February 22, 2014 in calm weather and relatively low turbidity). Accurate and high resolution bathymetric data from the study area, produced by field surveys using a single beam echo-sounder, were selected for calibrating the models and validating the results. Three methods, including traditional linear and ratio transform techniques, as well as a novel proposed integrated method, were used to determine depth values. All possible combinations of the three bands [coastal/aerosol (CB), blue (B), and green (G)] have been considered (11 options) using the traditional linear and ratio transform techniques, together with five model options for the integrated method. The accuracy of each model was assessed by comparing the determined bathymetric information with field measured values. The standard error of the estimates, correlation coefficients (R ² ) for both calibration and validation points, and root mean square errors (RMSE) were calculated for all cases. When compared with the ratio transform method, the method employing linear transformation with a combination of CB, B, and G bands yielded more accurate results (standard error = 1.712 m, R ² _calibration = 0.594, R ² _validation = 0.551, and RMSE =1.80 m). Adding the CB band to the ratio transform methodology also dramatically increased the accuracy of the estimated depths, whereas this increment was not statistically significant when using the linear transform methodology. The integrated transform method in form of Depth = b ₀  + b ₁ X _CB  + b ₂ X _B  + b ₅ ln(R _CB )/ln(R _G ) + b ₆ ln(R _B )/ln(R _G ) yielded the highest accuracy (standard error = 1.634 m, R ² _calibration = 0.634, R ² _validation = 0.595, and RMSE = 1.71 m), where R _i (i = CB, B, or G) refers to atmospherically corrected reflectance values in the i ^th band [X _i  = ln(R _i -R _{deep water})].     

<Emphasis Type="Italic">Landslides</Emphasis>: a review of achievements in the first 5 years (2004–2009)

The international journal Landslides was initiated in April 2004. It is the core project (IPL-C100) of the International Programme on Landslides, a joint initiative of the International Consortium on Landslides and the United Nations and other global organizations. The aims of Landslides are to promote landslide sciences, technology, and capacity building and strengthen global cooperation for landslide risk reduction within the United Nations International Strategy for Disaster Reduction. This paper presents an analysis of the first 5 years of Landslides, the study methods employed, the types and major causes of landslides, the number of different contributors per country, and the “times cited” per issue and most frequently cited papers and briefly discusses some of these. Strategies for future development of the journal involve obtaining input and suggestion from researchers and readers worldwide.     

Interaction between risk perception and sense of place in disaster-prone mountain areas: a case study in China’s Three Gorges Reservoir Area

Residents of rural mountain settlements are constantly threatened by disasters such as landslides. Their risk perception directly or indirectly affects their behavioral choices. Introducing the concept of sense of place, this paper details its interactions with the risk perception of mountain hazards. Rural residents (n = 348) in landslide-threatened areas of the Three Gorges Reservoir Area were questioned about their sense of place and risk perception. Partial least squares structural equation modeling was used to explore the interactions between various dimensions, and hypothetical tests were conducted. We found that dimensions of risk perception, such as Possibility and Unknown, had direct, negative impacts on the dimensions of sense of place, such as Society bond and Place dependence. Possibility had an indirect, yet more significant effect on Place attachment and Place identity, acting through Society bond. Rural residents often overestimate disaster risks due to fatalism, and a fear of the unknown stemming from low scientific literacy, which reduces their Place dependence. A complex interaction between sense of place and risk perception was observed. The findings provide a scientific basis for the government to formulate integrated policies regarding settlement relocation, disaster prevention and mitigation, and sustainable development.     

Innovative landslide susceptibility mapping supported by geomorphon and geographical detector methods

Landslides are among the most common and dangerous natural hazards in mountainous regions that can cause damage to properties and loss of lives. Landslide susceptibility mapping (LSM) is a critical tool for preventing or mitigating the negative impacts of landslides. Although many previous studies have employed various statistical methods to produce quantitative maps of the landslide susceptibility index (LSI) based on inventories of past landslides and contributing factors, they are mostly ad hoc to a specific area and their success has been hindered by the lack of a methodology that could produce the right mapping units at proper scale and by the lack of a general framework for objectively accounting for the differing contribution of various preparatory factors. This paper addresses these issues by integrating the geomorphon and geographical detector methods into LSM to improve its performance. The geomorphon method, an innovative pattern recognition approach for identifying landform elements based on the line of sight concept, is adapted to delineate ridge lines and valley lines to form slope units at self-adjusted spatial scale suitable for LSM. The geographical detector method, a spatial variance analysis method, is integrated to objectively assign the weights of contributing factors for LSM. Applying the new integrated approach to I-Lan, Taiwan produced very significant improvement in LSI mapping performance than a previous model, especially in highly susceptible areas. The new method offers a general framework for better mapping landslide susceptibility and mitigating its negative impacts.     

Characteristics of moderate- to large-scale landslides triggered by the <Emphasis Type="Italic">M</Emphasis><Subscript><Emphasis Type="Italic">w</Emphasis></Subscript> 7.8 2015 Gorkha earthquake and its aftershocks

The M _w 7.8 2015 Gorkha earthquake and its aftershocks significantly impacted the lives and economy of Nepal. The consequences of landslides included fatalities, property losses, blockades of river flow, and damage to infrastructural systems. Co-seismic landslides triggered by this earthquake were significantly widespread and pose a major geodisaster. There were tens of thousands of landslides triggered by the earthquake, majority of which were distributed in between the epicenter of the main shock and the M _w 7.3 aftershock. Although 14,670 landslides triggered by this earthquake were identified, only approximately 23% of them were of moderate to large scale with areas greater than 100 m². Of the moderate- to large-scale landslides identified, just over 90% were triggered by the main shock and smaller aftershocks prior to the major (M _w 7.3) aftershock, while nearly 10% were triggered by the ground shaking induced by the major aftershock. Moreover, the number of landslides triggered by the 2015 Gorkha earthquake, specifically by the main shock, was slightly more than the expected number of landslides for the recorded maximum peak ground acceleration (PGA) in comparison to the co-seismic landslides triggered by 26 earthquakes. Over 90% of those moderate- to large-scale landslides were concentrated within the estimated fault rupture surface. Majority of these moderate- to large-scale landslides were disrupted failures with over 96% of which were classified as earth falls. However, the majority of small-scale landslides were rock or boulder falls. The most number of moderate- to large-scale landslides were triggered in the slate, shale, siltstone, phyllite, and schist of the Lesser Himalayan formation followed by an equally significant number in both schist, gneiss, etc. of the Higher Himalayan formation and the phyllite, metasandstone, schist, etc. of the Lesser Himalayan formation. The sizes (i.e., areas) of the landslides were lognormally distributed, with a mode area of 322.0 m². Slope inclinations of the moderate- to large-scale landslides followed a normal distribution with a mean slope inclination of 32.6° and standard deviation of 13.5°. There exists a strong correlation between the number of landslides and the peak ground acceleration within the study area, specific for different geological formations.     

Landslide dam formation susceptibility analysis based on geomorphic features

Global climate change has increased the frequency of abnormally high rainfall; such high rainfall events in recent years have occurred in the mountainous areas of Taiwan. This study identifies historical earthquake- and typhoon-induced landslide dam formations in Taiwan along with the geomorphic characteristics of the landslides. Two separate groups of landslides are examined which are classified as those that were dammed by river water and those that were not. Our methodology applies spatial analysis using geographic information system (GIS) and models the geomorphic features with 20?×?20 m digital terrain mapping. The Spot 6 satellite images after Typhoon Morakot were used for an interpretation of the landslide areas. The multivariate statistical analysis is also used to find which major factors contribute to the formation of a landslide dam. The objective is to identify the possible locations of landslide dams by the geomorphic features of landslide-prone slopes. The selected nine geomorphic features include landslide area, slope, aspect, length, width, elevation change, runout distance, average landslide elevation, and river width. Our four geomorphic indexes include stream power, form factor, topographic wetness, and elevation–relief ratio. The features of the 28 river-damming landslides and of the 59 non-damming landslides are used for multivariate statistical analysis by Fisher discriminant analysis and logistic regression analysis. The principal component analysis screened out eleven major geomorphic features for landslide area, slope, aspect, elevation change, length, width, runout distance, average elevation, form factor, river width, stream power, and topography wetness. Results show that the correctness by Fisher discriminant analysis was 68.0 % and was 70.8 % by logistic regression analysis. This study suggests that using logistic regression analysis as the assessment model for identifying the potential location of a landslide dam is beneficial. Landslide threshold equations applying the geomorphic features of slope angle, angle of landslide elevation change, and river width (H _L/W _R) to identify the potential formation of natural dams are proposed for analysis. Disaster prevention and mitigation measures are enhanced when the locations of potential landslide dams are identified; further, in order to benefit such measures, dam volume estimates responsible for breaches are key.     

Field monitoring of groundwater responses to heavy rainfalls and the early warning of the Kualiangzi landslide in Sichuan Basin,southwestern China

The Kualiangzi landslide was triggered by heavy rainfalls in the “red beds” area of Sichuan Basin in southwestern China. Differing from other bedrock landslides, the movement of the Kualiangzi landslide was controlled by the subvertical cracks and a subhorizontal bedding plane (dip angle < 10°). The ingress of rainwater in the cracks formed a unique groundwater environment in the slope. Field measurement for rainfall, groundwater movement, and slope displacement has been made for the Kualiangzi landslide since 2013. The field monitoring system consists of two rainfall gauges, seven piezometers, five water-level gauges, and two GPS data loggers. The equipments are embedded near a longitudinal section of the landslide, where severe deformation has been observed in the past 3 years. The groundwater responses to four heavy rainfall events were analyzed between June 16 and July 24 in 2013 coincided with the flood season in Sichuan. Results showed that both of the water level and the pore-water pressure increased after each rainfall event with delay in the response time with respect to the precipitation. The maximum time lag reached 35 h occurred in a heavy rainfall event with cumulative precipitation of 127 mm; such lag effect was significantly weakened in the subsequent heavy rainfall events. In each presented rainfall event, longer infiltration period in the bedrock in the upper slope increased the response time of groundwater, compared to that of in the gravels in the lower slope. A translational landslide conceptual model was built for the Kualiangzi landslide, and the time lag was attributed to the gradual formation of the uplift pressure on the slip surface and the softening of soils at the slip surface. Another important observation is the effect on the slope movement which was caused by the water level (H _w) in the transverse tension trough developed at the rear edge of the landslide. Significant negative correlation was found for H _w and the slope stability factor (F _s), in particular for the last two heavy rainfall events, of which the drastic increase of water level caused significant deterioration in the slope stability. The rapid drop (Δ?=?22.5 kPa) of pore-water pressure in the deep bedrock within 1 h and the large increase (Δ?=?87.3 mm) of surficial displacement were both monitored in the same period. In the end, a four-level early warning system is established through utilizing H _w and the displacement rate D _r as the warning indicators. When the large deformation occurred in flood season, the habitants at the leading edge of the landslide can be evacuated in time.     

Rainfall-induced landslide in the active frontal fold–thrust belt of Northwestern Himalaya,Jammu: dynamics inferred by geological evidences and Ground Penetrating Radar

Landslides are the most established geological hazards in the frontal fold–thrust belt of Northwestern Himalaya comprising of Siwaliks and Murree strata. The continuous rainfall from 2 to 6 September, 2014 caused a massive landslide at village Sadal in Udhampur district of Jammu and Kashmir state. The landslide occurred in the early morning of September 6, 2014, destroying entire Sadal habitation comprising 45 houses, and killing 41 people and more than 500 domestic animals. Google earth images of pre and post-landslide events along with the field measurements show the kinematics of upper and lower parts of the slide. Horizontal and vertical components of displacement and mode of failure suggest the landslide as of complex nature. The shallow subsurface geophysical imaging through Ground Penetrating Radar (GPR) survey shows the failure plane composed of friable mudstone bed underlain by massive mudstone and overlain by cross-bedded sandstone. The depth of debris material above the failure plane ranges from 6 m at Site S1a-b to 10 m at Site-S2b and 20 m at Site S3a. The velocity analysis of Site-3 shows four thick layers represented from bottom to surface by L1—sandstone (V?=?0.16 m/ns, travel time?=?356.36 ns), L2—mudstone (V?=?0.17 m/ns, travel time?=?288.48 ns), L3—massive mudstone (V?=?0.19 m/ns, travel time 220.68 ns), and L4—cross-laminated sandstone (V?=?0.20 m/ns, travel time?=?77.58 ns) overlaying the failure plane. The study shows the landslide occur along the western limb of a fold identified during the present work. We mapped an old landslide on the same limb which shows 5–6 m-thick subsurface debris material with thick rock fragments involved in the landslide process. The detailed geological and geophysical investigations suggest that both the landslides were triggered by extreme rain fall events.     

Thaw-induced slope failures and stability analyses in permafrost regions of the Qinghai-Tibet Plateau,China

The distribution of permafrost-related slope failures along the Qinghai-Tibet Highway from Wuddaoliang to Fenghuoshan correlates with ice content, slope gradient, and ground temperature. Slope failures are of two types. (1) Retrogressive thaw slumps result from icy permafrost being exposed by either man-induced excavation or fluvial-thermal erosion and undercutting of basal slopes. (2) Active-layer-detachment failures are caused by thaw of icy permafrost at the active layer-permafrost interface. After initial failure, active-layer-detachment failures can lead to retrogressive thaw-slumping and localized surficial landslide. Common trigger mechanisms for failure include high summer air temperatures and heavy summer precipitation. A third possible trigger mechanism for slope failure is earthquake occurrence. A geotechnical slope stability analysis was undertaken for an active-layer-detachment failure that had progressed into a retrogressive thaw slump. A safety factor (F _s ) of 1.24 for the natural slope was determined using in situ tested strength parameters. However, the slope would lose stability when either the groundwater level over the permafrost table exceeded 1.42 m or seismic acceleration reached, or exceeded, 0.03?g.     

Rheological properties of clayey soils originating from flow-like landslides

Flow-like landslides in clayey soils represent serious threats for populations and infrastructures and have been the subject of numerous studies in the past decade. However, despite the rising need for landslide mitigation with growing urbanization, the transient mechanisms involved in the solid-fluid transition are still poorly understood. One way of characterizing the solid-fluid transition is to carry out rheometrical tests on clayey soil samples to assess the evolution of viscosity with the shear stress. In this study, we carried out geotechnical and rheometrical tests on clayey samples collected from six flow-like landslides in order to assess if these clayey soils exhibit similar characteristics when they fluidize (solid-fluid transition). The results show that (1) all tested soils except one exhibit a yield-stress fluid behavior that can be associated with a bifurcation in viscosity (described by the critical shear rate \( \dot{\gamma_c} \)) and in shear modulus G; (2) the larger the amplitude of the viscosity bifurcation, the larger the associated drop in G; and (3) the water content (w) deviation from the Atterberg liquid limit (LL) seem a key parameter controlling a common mechanical behavior of these soils at the solid-fluid transition. We propose exponential laws describing the evolution of the critical shear stress τ_c, the critical shear rate \( \dot{\gamma_c} \), and the shear modulus G as a function of the deviation w-LL.     

High-pressure displacive phase transition of a natural Mg-rich pigeonite

A high-pressure single-crystal X-ray diffraction study has been carried out on a P2₁/c natural Mg-rich pigeonite sample with composition ca. Wo₆En₇₆Fs₁₈ using a diamond anvil-cell. The unit-cell parameters were determined at 14 different pressures to 7.14 GPa. The sudden disappearance of the b-type reflections (h + k = odd) and a strong discontinuity (about 2.8%) in the unit-cell volume indicated a first-order P2₁/c–C2/c phase transition between 4.66 and 4.88 GPa. The P(V) data of the P2₁/c phase were fitted to 4.66 GPa by a third-order Birch–Murnaghan equation of state (BM3 EoS), whereas the limited number of experimental data collected within the C2/c phase between 4.88 and 7.14 GPa were fitted using the same equation of state but with K′ constrained to the value obtained for the P2₁/c fitting. The equation of state coefficients are V ₀ = 424.66(6) ų, K _T0 = 104(2) GPa and K′ = 8(1) for the P2₁/c phase, and V ₀ = 423.6(1) ų, K _T0 = 112.4(8) GPa, and K′ fixed to 8(1) for the C2/c phase. The axial moduli for a, b, and c for the P2₁/c phase were obtained using also a BM3-EoS, while for the C2/c phase only a linear calculation could be performed, and therefore the same approach was applied for comparison also to the P2₁/c phase. In general the C2/c phase exhibits axial compressibilities (β _c > β _a >> β _b) lower than those of the P2₁/c phase (β _b > β _c ≈ β _a; similar to those found in previous studies in clinopyroxenes and orthopyroxenes). The lower compressibility of the C2/c phase compared with that of the P2₁/c could be ascribed to the greater stiffness along the b direction. A previously published relationship between P _c and M2 average cation radius (i.r.) has been updated using all the literature data on P2₁/c clinopyroxene containing large cations at M2 site and our new data. The following weighted regression was obtained: P _c (GPa) = 26(4) ? 28(5) ×  i.r (Å), R ² = 0.97. This improved equation can be used to predict the critical pressure of natural P2₁/c clinopyroxene samples just knowing the composition at M2 site.