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Efficient landslide mapping is crucial for disaster mitigation and relief. Recently, deep learning methods have shown promising results in landslide mapping using satellite imagery. However, the sample sparsity and geographic diversity of landslides have challenged the transferability of deep learning models. In this paper, we proposed a universal adapter module that can be seamlessly embedded into existing segmentation models for transferable landslide mapping. The adapter can achieve high-accuracy cross-regional landslide segmentation with a small sample set, requiring minimal parameter adjustments. In detail, the pre-trained baseline model freezes its parameters to keep learned knowledge of the source domain, while the lightweight adapter fine-tunes only a few parameters to learn new landslide features of the target domain. Structurally, we introduced an attention mechanism to enhance the feature extraction of the adapter. To validate the proposed adapter module, 4321 landslide samples were prepared, and the Segment Anything Model (SAM) and other baseline models, along with four transfer strategies were selected for controlled experiments. In addition, Sentinel-2 satellite imagery in the Himalayas and Hengduan Mountains, located on the southern and southeastern edges of the Tibetan Plateau was collected for evaluation. The controlled experiments reported that SAM, when combined with our adapter module, achieved a peak mean Intersection over Union (mIoU) of 82.3 %. For other baseline models, integrating the adapter improved mIoU by 2.6 % to 12.9 % compared with traditional strategies on cross-regional landslide mapping. In particular, baseline models with Transformers are more suitable for fine-tuning parameters. Furthermore, the visualized feature maps revealed that fine-tuning shallow encoders can achieve better effects in model transfer. Besides, the proposed adapter can effectively extract landslide features and focus on specific spatial and channel domains with significant features. We also quantified the spectral, scale, and shape features of landslides and analyzed their impacts on segmentation results. Our analysis indicated that weak spectral differences, as well as extreme scale and edge shapes are detrimental to the accuracy of landslide segmentation. Overall, this adapter module provides a new perspective for large-scale transferable landslide mapping. [ABSTRACT FROM AUTHOR]

ABSTRACT High‐precision, multi‐source landslide monitoring data are crucial for disaster prevention and management. These datasets provide explicit landslide descriptions. Integrating this data with landslide mechanisms, using knowledge graphs can enhance early monitoring and forecasting. However, traditional knowledge graphs often oversimplify landslide knowledge, failing to capture the complexity of geological environments and landslide evolution. Spatio‐temporal knowledge graphs face challenges in representing intricate relationships. A hypergraph (HG), where an edge connects multiple nodes, offers a better representation of these complexities. This paper proposes an HG‐based method for landslide knowledge representation, organizing multi‐source information and knowledge through binary or multiple relationships under specific temporal and spatial conditions. A case study of the Miaodian landslide, which experienced multiple sliding events, shows that the proposed landslide knowledge HG outperforms other knowledge graphs like YAGO, Geographic Knowledge Graph (GeoKG), and Geographic Evolutionary Knowledge Graph (GEKG) in completeness, accuracy, and redundancy, demonstrating its effectiveness. [ABSTRACT FROM AUTHOR]

Efficient landslide mapping is crucial for disaster mitigation and relief. Recently, deep learning methods have shown promising results in landslide mapping using satellite imagery. However, the sample sparsity and geographic diversity of landslides have challenged the transferability of deep learning models. In this paper, we proposed a universal adapter module that can be seamlessly embedded into existing segmentation models for transferable landslide mapping. The adapter can achieve high-accuracy cross-regional landslide segmentation with a small sample set, requiring minimal parameter adjustments. In detail, the pre-trained baseline model freezes its parameters to keep learned knowledge of the source domain, while the lightweight adapter fine-tunes only a few parameters to learn new landslide features of the target domain. Structurally, we introduced an attention mechanism to enhance the feature extraction of the adapter. To validate the proposed adapter module, 4321 landslide samples were prepared, and the Segment Anything Model (SAM) and other baseline models, along with four transfer strategies were selected for controlled experiments. In addition, Sentinel-2 satellite imagery in the Himalayas and Hengduan Mountains, located on the southern and southeastern edges of the Tibetan Plateau was collected for evaluation. The controlled experiments reported that SAM, when combined with our adapter module, achieved a peak mean Intersection over Union (mIoU) of 82.3 %. For other baseline models, integrating the adapter improved mIoU by 2.6 % to 12.9 % compared with traditional strategies on cross-regional landslide mapping. In particular, baseline models with Transformers are more suitable for fine-tuning parameters. Furthermore, the visualized feature maps revealed that fine-tuning shallow encoders can achieve better effects in model transfer. Besides, the proposed adapter can effectively extract landslide features and focus on specific spatial and channel domains with significant features. We also quantified the spectral, scale, and shape features of landslides and analyzed their impacts on segmentation results. Our analysis indicated that weak spectral differences, as well as extreme scale and edge shapes are detrimental to the accuracy of landslide segmentation. Overall, this adapter module provides a new perspective for large-scale transferable landslide mapping. [ABSTRACT FROM AUTHOR]

In the era of big SAR data, it is urgent to develop dynamic time series DInSAR processing procedures for near-real-time monitoring of landslides. However, the dense vegetation coverage in mountainous areas causes severe decorrelations, which demands high precision and efficiency of phase optimization processing. The common phase optimization using single-polarization SAR data cannot produce satisfactory results due to the limited statistical samples in some natural scenarios. The novel polarimetric phase optimization algorithms, however, have low computational efficiency, limiting their applications in large-scale scenarios and long data sequences. In addition, temporal changes in the scattering properties of ground features and the continuous increase of SAR data require dynamic phase optimization processing. To achieve efficient phase optimization for dynamic DInSAR time series analysis, we combine the Sequential Estimator (SE) with the Total Power (TP) polarization stacking method and solve it using eigen decomposition-based Maximum Likelihood Estimator (EMI), named SETP-EMI. The simulation and real data experiments demonstrate the significant improvements of the SETP-EMI method in precision and efficiency compared to the EMI and TP-EMI methods. The SETP-EMI exhibits an increase of more than 50% and 20% in highly coherent points for the real data compared to the EMI and TP-EMI, respectively. It, meanwhile, achieves approximately six and two times more efficient than the EMI and TP-EMI methods in the real data case. These results highlight the effectiveness of the SETP-EMI method in promptly capturing and analyzing evolving landslide deformations, providing valuable insights for real-time monitoring and decision-making. [ABSTRACT FROM AUTHOR]

Subaqueous mass transport deposits (MTDs) attributed to the Mw 6.1 1935 CE Témiscaming earthquake were mapped at 17 sub-bottom acoustic profile survey areas on 11 lakes near Témiscaming, Quebec. Distributed over about 1270 km2, MTDs are the product of shallow failures, up to several metres thick, that occurred along planar surfaces and involved primarily lacustrine sediments. Core samples of unfailed deposits indicate that the failure planes occurred within soft sediments at the top of a glaciolacustrine unit or at the base of overlying lacustrine deposits. Radioisotope dating of sediment samples from six coring sites on Tee and Kipawa lakes confirm that the MTDs are the product of failures triggered by the 1935 CE earthquake. To assess the application of such a mapping study to a paleoseismic investigation, the minimum magnitude of an earthquake that can generate an MTD distribution of 1270 km2 was extrapolated from a published empirical plot. The resulting magnitude of Mw/Ms 5.7–5.8 is less than the instrumental Mw 6.1 magnitude and deemed a reasonable estimation of minimum earthquake magnitude. The distribution of MTDs triggered by the 1935 CE earthquake forms the only such signature within the Témiscaming study area since roughly 8 ka cal B.P. The lack of an analogous, older signature(s) is consistent with the absence of equivalent shaking to the 1935 CE earthquake over this period, but the actual timespan may be shorter and begin when gyttja deposits on slopes became thick enough to be prone to failure from such an event. [ABSTRACT FROM AUTHOR]

The failure of fractured rock masses in slopes due to freeze–thaw processes is a common occurrence in cold regions, leading to significant issues such as soil erosion and landslide disasters, particularly in mining areas. This study utilizes a self-developed true triaxial grouting apparatus to perform full-stress state grouting on freeze–thawed fractured rock. Furthermore, a computational simulation of CT permeability is conducted on the rock mass post-grouting. The primary focus of this research is the quantitative assessment of the waterproofing properties of grout masses utilizing microsilica powder-enhanced cement, along with an analysis of freeze–thaw durability at the grout–rock interface. Experimental findings indicate that microsilica facilitates particle aggregation through nucleation effects and expedites cement hydration via pozzolanic reaction, thereby significantly improving the impermeability and frost resistance of the interface transition zone. Compared to ordinary Portland cement (PC) grouting for waterproofing, the anti-seepage rate is increased by 17% and 20% when microsilica cement is used to grout rock masses that have undergone freeze–thaw cycles of 25 and 45, respectively. The outcomes of this investigation hold substantial importance in enhancing the grouting reinforcement mechanisms of fractured rock masses in cold regions and in implementing targeted preventive and control strategies. Highlights: Grouting in a freeze–thaw rock mass under a true triaxial state. CT reconstruction of the grouting vein and seepage simulation. Microsilica enhancement of the compactness and freeze‒thaw damage resistance of the grout–rock interfacial transition zone. [ABSTRACT FROM AUTHOR]

To explore the influence of different sampling strategies on landslide susceptibility assessment, this study takes landslides in Taojiang County, Hunan Province, China, as the research object. Based on three positive sample sampling methods (discrete point sampling, buffer integral sampling, and buffer discrete sampling) and three sampling resolutions (30 m, 60 m, and 90 m cell size), nine sampling strategies were combined. Extreme gradient boosting, random forest, and convolutional neural network machine learning algorithms were used to establish landslide susceptibility models under different sampling strategies, and the performance of each model was evaluated using the Receiver Operating Characteristic (ROC) curve. The results show that the model accuracy is highest when using the buffer discrete sampling strategy, while the accuracy is lowest under the buffer integral sampling strategy. The performance of various sampling strategies fluctuates greatly at the 30 m cell size, while the model exhibits higher stability at the 90 m cell size. The results indicate that different sampling strategies have a significant impact on the performance of landslide susceptibility assessment models, and some sampling strategies exhibit overfitting and false segmentation phenomena. The area under the ROC curve (AUC) cannot fully reflect the quality of the model. Although the AUC of the buffer integral sampling strategy is not high, it avoids overfitting and false segmentation problems, making its results more reliable. [ABSTRACT FROM AUTHOR]

The Taihang Mountains in China span numerous cities, where landslide disasters occur frequently in the mountainous areas, jeopardizing the lives and properties of residents. Consequently, it is of great significance to focus on prevention and control of landslide disasters in the region. Currently, a single model is commonly employed to analyze landslide susceptibility mapping (LSM), but the accuracy of the results fails to meet the demands of early warning, prevention, and control. This paper focuses on the Taihang Mountain area as the research area, organizes the collection of landslide disaster potential points and related influence factor data, and employs the information quantity method to derive a composite machine learning model by coupling with Random Forest (RF) and Extreme Gradient Boosting (XGB), subsequently utilizing the Genetic Optimization Algorithm (GA) to optimize the model. The performance of the composite model is enhanced using the Genetic Algorithm (GA), employing accuracy, regression rate, precision, F1 score, AUC value, and Taylor diagram to evaluate the comprehensive accuracy of the model results, with a susceptibility map generated for comparative analysis. The results demonstrate that the IV-GA-RF model performs optimally (accuracy = 0.956, precision = 0.96, recall = 0.953, F1 score = 0.957, AUC = 0.946 for the testing set, AUC = 0.929 for the training set), with all-around improvement in performance metrics compared to the unoptimized composite model, with metric values improving by 0.044, 0.051, 0.046, 0.044, 0.021 and 0.020, respectively. The IV-GA-RF model exhibits a significant advantage over the IV-GA-XGB algorithm, also optimized using the GA algorithm. The accuracy of the susceptibility map produced by the IV-GA-RF model is superior, as assessed by the Seed Cell Area Index (SCAI) method. The four factors of slope, rainfall, seismicity, and stratigraphic lithology are crucial in determining the occurrence of landslides in the study area. In summary, the IV-GA-RF model can be utilized as an effective model for analyzing landslide disasters, providing a reference for research in this field and contributing scientific insights to disaster prevention and control efforts in the study area; simultaneously, the concept of the composite optimization model introduces new perspectives into this field. [ABSTRACT FROM AUTHOR]

Rapid identification and detection of landslides is of significance for disaster damage assessment and post-disaster relief. However, U-net for rapid landslide identification and detection suffers from semantic gap and loss of spatial information. For this purpose, this paper proposed the U-net with a progressive Convolutional Block Attention Module (CBAM-U-net) for landslide boundary identification and extraction from high-precision aerial imagery. Firstly, 109 high-precision aerial landslide images were collected, and the original database was extended by data enhancement to strengthen generalization ability of models. Subsequently, the CBAM-U-net was constructed by introducing spatial attention module and channel attention module for each down-sampling process in U-net. Meanwhile, U-net, FCN and DeepLabv3 + are used as comparison models. Finally, 6 evaluation metrics were used to comprehensively assess the ability of models for landslide identification and segmentation. The results show that CBAM-U-net exhibited better recognition and segmentation accuracies compared to other models, with optimal values of average row correct, dice coefficient, global correct, IoU and mean IoU of 98.3, 0.877, 95, 88.5 and 90.2, respectively. U-net, DeepLab V3 + , and FCN tend to confuse bare ground and roads with landslides. In contrast, CBAM-U-net has stronger ability of feature learning, feature representation, feature refinement and adaptation.The proposed method can improve the problems of semantic gap and spatial information loss in U-net, and has better accuracy and robustness in recognizing and segmenting high-precision landslide images, which can provide certain reference value for the research of rapid landslide recognition and detection. [ABSTRACT FROM AUTHOR]

Many landslides occurred every year, causing extensive property losses and casualties in China. Landslide susceptibility mapping is crucial for disaster prevention by the government or related organizations to protect people's lives and property. This study compared the performance of random forest (RF), classification and regression trees (CART), Bayesian network (BN), and logistic model trees (LMT) methods in generating landslide susceptibility maps in Yanchuan County using optimization strategy. A field survey was conducted to map 311 landslides. The dataset was divided into a training dataset and a validation dataset with a ratio of 7:3. Sixteen factors influencing landslides were identified based on a geological survey of the study area, including elevation, plan curvature, profile curvature, slope aspect, slope angle, slope length, topographic position index (TPI), terrain ruggedness index (TRI), convergence index, normalized difference vegetation index (NDVI), distance to roads, distance to rivers, rainfall, soil type, lithology, and land use. The training dataset was used to train the models in Weka software, and landslide susceptibility maps were generated in GIS software. The performance of the four models was evaluated by receiver operating characteristic (ROC) curves, confusion matrix, chi-square test, and other statistical analysis methods. The comparison results show that all four machine learning models are suitable for evaluating landslide susceptibility in the study area. The performances of the RF and LMT methods are more stable than those of the other two models; thus, they are suitable for landslide susceptibility mapping. [ABSTRACT FROM AUTHOR]

Landslides are among the most dangerous hazards in Asia, posing a significant threat to human lives, infrastructure, and sustainable development. Landslide susceptibility maps provide useful insights into hazard potential but lack quantitative exposure assessments to develop targeted mitigation strategies and resource allocation. This study aims to propose an integrated approach for landslide hazards and exposure evaluation using machine learning models on the Google Earth Engine environment and geospatial analysis techniques. A geospatial database was established to predict hazards and evaluate exposure, including data on topography, geology, hydrology, climate features, land use, and building data. The landslide susceptibility map was created with advanced machine-learning algorithms, including Classification And Regression Tree (CART), Random Forest (RF), Gradient Boosting (GB), and Support Vector Machine (SVM). The ROC curve and the Wilcoxon signed-rank test were employed to evaluate the performance differences among the CART, GB, RF, and SVM models. The results indicated that the RF model demonstrated the highest performance, leading to its selection for creating a landslide susceptibility map. Landslide exposure was evaluated by overlaying the landslide susceptibility map with building data to quantify the number of affected houses by landslides across districts and communes. The analysis results identified the Cho Don district as the most exposed, with 46,237 households located in high and very high landslide susceptibility zones, followed by Ba Be district (39,631 households), Bac Kan city (37,266 households), Bach Thong district (28,495 households), Cho Moi district (28,436 households), Na Ri district (17,723 households), Ngan Son district (14,142 households), and Pac Nam district (13,034 households). These findings enable the identification of the potential consequences of landslides on infrastructure, human settlements, and livelihoods, contributing to the promotion of disaster reduction and prevention strategies. [ABSTRACT FROM AUTHOR]

Expansive soils cause frequent surface deformation due to their expansion and contraction, which is a serious engineering hazard, and long-term subsidence monitoring is a prerequisite for preventing and controlling expansive soil disasters. Currently, the conventional monitoring methods for the above issue include Interferometric Synthetic Aperture Radar (InSAR) technology, but InSAR is not suitable for uninterrupted monitoring of surface deformation and has low sensitivity. Meanwhile, it can't obtain multiple surface environmental parameters around the station. The Global Navigation Satellite System (GNSS), a system that can directly acquire surface deformation, has been widely used in landslide disaster monitoring, and in recent years, this technology has also been applied to the field of expansive soil disaster monitoring. At the same time, GNSS can also provide a constant stream of L-band microwave signals to obtain ground environmental information such as precipitable rainfall and soil moisture around the station. In previous studies of expansive soil hazards, GNSS technology has been mainly used to provide surface deformation information without exploring its potential to invert ground environmental information around stations. This paper proposes a ground-based GNSS remote sensing integrated monitoring system that integrates expanding land surface parameters such as "precipitable rainfall, soil moisture, and three-dimensional deformation" and analyses the ability of ground-based GNSS to be used for integrated monitoring of expanding soil hazards by combining ten years of consecutive observational data from GNSS stations along the coastal area of Houston. The experimental results show that the GNSS is capable of providing highly accurate time-series characterization of deformation, and inelastic subsidence in recent years has resulted in a cumulative permanent elevation loss of 2 cm along the Houston coast. The correlation coefficient between soil moisture extracted by the fifth-generation European reanalysis data (ERA5) and soil moisture inverted by ground-based GNSS is 0.514. At the same time, the GNSS was also able to monitor the zenithal precipitable water vapor (PWV) and soil moisture changes around the GNSS station and further analyze the response relationship among the three parameters, which could comprehensively evaluate the stability of expansive soils, avoiding the unreliability of relying on a single piece of monitoring information to assess the stability of expansive soils. We hope to construct a more comprehensive ground-based GNSS remote sensing monitoring system to better monitor expansive soil hazards. [ABSTRACT FROM AUTHOR]

The Beiluo River basin, which flows through the central part of the Loess Plateau, has experienced intense soil erosion and significant geomorphic change, which has provided favorable conditions for the occurrence of a large number of landslides. Landform indexes, which can express geomorphologic development state and internal rules, can transfer the development process information of surface morphology into the evaluation of landslide susceptibility, and help get more accurate landslide susceptibility prediction results. Taking the Beiluo River Basin as an example, a landslide susceptibility prediction model based on landform index is proposed by comparing the importance of landform index. In order to improve the accuracy of LSP, 10 kinds of general predictors indexes, 5 kinds of landform predisposing indexes and 1821 landslide points were compiled, and the geographic information system of Beiluo River Basin was constructed. Through the correlation test and CF model, the environmental indexes were evaluated to obtain the sensitive index results, and the combination of different environmental predictors indexes were classified according to the sensitive index results. Based on the combined classification results, the Max Entropy (MaxEnt) model was used to evaluate the Landslide susceptibility prediction (LSP), while the calculated results were evaluated and compared using the receiver operating characteristic (ROC) curve and landslide density. The results show that the vertical erosion factor, elevation, rainfall, horizontal erosion factor, slope angle and NDVI play a key role in controlling the spatial distribution of landslides in the study area. At the same time, the accuracy of landslide susceptibility is compared by AUC value. According to the calculation results, the Group5 (AUC = 0.803) with reasonable terrain index performs better in the training and test stages, and the relative accuracy is improved by 6.22 % compared with the non-introduction of terrain index and the omission rate difference is the best (omission rate difference = 0.0005), indicating that the introduction of landform index can effectively improve the landslide susceptibility prediction. The distribution of different sensitive areas was observed. The high sensitive areas and very high sensitive areas are mainly distributed in the southern Luochuan loess tableland and the northern Wuqi loess hilly area. The research results provide a scientific basis for landslide susceptibility prediction with rational introduction of landform indexes and regional infrastructure construction. [ABSTRACT FROM AUTHOR]

Mining landslides in southwestern China pose a serious threat to people's property and safety. In order to study the destabilisation and damage mechanism of karst mountains under the combined action of mining and rainfall, based on the landslides in the mountainous area of the Maidi Coal Mine in Guizhou Province, and combining with field investigations, we have analysed the characteristics of the landslides, investigated the stability of the bearing structure of the bedrock of the mountain, the composition of the mineral components, and the microscopic characteristics of the rocks, and simulated the excavation of the coal seams as well as the infiltration of the rainfall. The destabilisation mechanism of the karst mountain under the coupling of mining stress and rainfall infiltration was investigated. The obtained destabilisation and destruction mechanism of karst mountain destabilisation under the coupled action of mining and rainfall lays the foundation for the control of karst landslides. [ABSTRACT FROM AUTHOR]

The article discusses a long-runout loess landslide triggered by a 6.2 magnitude earthquake in Qinghai, China, resulting in significant casualties and property damage. The study highlights the geological and topographic factors controlling groundwater in the loess layer, emphasizing that long-term irrigation activities are not the primary cause of groundwater accumulation. The research aims to elucidate the failure process and motion characteristics of the landslide, providing insights into the mechanisms and prevention measures of deep-seated flows triggered by earthquakes in the region. [Extracted from the article]

Flash flood after the breaching of South Lhonak Lake (glacier lake outburst flood (GLOF)) along with prolonged rainfall event in the first week of October 2023 significantly triggered numerous landslides along the side of the river, roads, and vulnerable slopes within the upper catchment of Teesta basin, Sikkim. In this paper, we present documentation on the occurrences of series of landslides just after the flood and heavy rainfall event in upper Teesta basin and try to find out the relevant causes. Primarily, flash flood due to glacial lake outburst and extensive rainfall accelerated series of landslides; however, the landslides are controlled by some other secondary factors including soil and slope condition, lithological diversity, geological discontinuities, and anthropogenic influences. The InSAR coherence analysis significantly shows the lowering of coherence value (decorrelation) in the moraine dam area within pre and post GLOF event which indicates breaching of the dam. The result of the slope stability assessment showed that majority slopes along the river Teesta are highly vulnerable with less safety factor and low cohesiveness of soil materials. Furthermore, the northern part of the MCT (Main Central Thrust) is composed by high-grade metamorphic rocks with exposed structures (high lineament density) that provoke landslides. Consequently, the result also highlighted that most of the slides along the side of Teesta occurred due to high water discharge and elevated gauge height after the GLOF event. Here, NH-10 (National Highway 10) runs on the high grade vulnerable litho-units and in between Dikchu and Chungthang, NH-10 is passing at the close proximity of river Teesta. So, different pockets of North Sikkim along the NH-10 were engulfed by high river discharge and gauge height due to large scale destruction of Chungthang Hydroelectric Power Plant constructed across the river Teesta. [ABSTRACT FROM AUTHOR]

An ancient landslide located in Xichang, China, that was reactivated by mining excavation and rainfall was investigated in this study. The volume of the reactivated landslide was approximately 1200 × 104 m3, thus posing a major threat to the mining area's safety. Field surveys, drilling, on-site monitoring, laboratory studies, and numerical analyses were performed to investigate the landslide deformation characteristics and reactivation mechanism. The reactivated landslide was divided into four zones: the leading-edge collapse, the sliding, the uplifting, and the traction sliding zones. X-ray diffraction and ring shear tests indicate that the sliding zone soil exhibits significant strength-weakening characteristics when exposed to water, and the residual cohesion and internal friction angle decrease by 26.9% and 28.9%, respectively, as the moisture content increases from 15 to 24%. Additionally, a three-dimensional numerical simulation was conducted to quantitatively analyze the stability evolution of the landslide. The results showed that the topographic, stratigraphic lithology, and sliding zone soil properties provided the basic conditions for landslide formation, while mining excavation and concentrated rainfall triggered landslide reactivation. Furthermore, a conceptual model characterizing the reactivation process was constructed, and the reactivation process was divided into five stages: leading-edge collapse, sliding, extrusion and bulging, deformation expansion, and accelerated creep deformation. This study provides a basis for understanding the reactivation mechanism of ancient open-pit mine landslides. [ABSTRACT FROM AUTHOR]

Sichuan Province, as one of the active seismic regions in China, has historically suffered from strong earthquakes. The Xingwen Ms5.7 earthquake in 2018 and the Changning Ms6.0 earthquake in 2019, two moderate-magnitude earthquakes that occurred in Sichuan Province in recent years, happened in quick succession and triggered numerous landslides under similar geological structural conditions. These events provide a rare case study for researching the distribution characteristics and cumulative effects of landslides triggered by multiple earthquakes. This study aims to explore the distribution characteristics and superposition effects of landslides from these two earthquakes and to reveal the complexity and regularity of landslides triggered by multiple moderate magnitude earthquakes by comparing and analyzing the spatial distribution, scale size, and influence factors of landslides from the two earthquakes. The results show that 455 landslides were triggered by the Xingwen earthquake event and 511 landslides were triggered by the Changning earthquake event. The landslides are all mainly small and medium-sized, covering a total area of about 2.33 km2, with similar area frequency distribution trends. There is a certain correlation between the number and area of landslides and each factor. In addition, the landslides are mainly distributed in the middle and lower slopes of slower slopes, and the landslide H/L values are positively correlated with the slope gradient. This study reveals the spatial distribution characteristics and morphological parameter features of landslides triggered by the two earthquakes and their aftershocks, which provides a reference basis for landslide hazard assessment and risk management, as well as a case study and inspiration for the study of landslides under the action of multiple earthquakes. [ABSTRACT FROM AUTHOR]

Landslides, as a common natural disaster, pose a significant threat to human society and the natural environment, including loss of life, economic damage, and environmental destruction. Effective landslide early warning is key to reducing these negative impacts. However, current warning methods face two major challenges: one is the reliance on static threshold judgments, which not only easily leads to false and missed alarms but also cannot adapt to complex and changing natural conditions. The second is the lack of ground data support in areas with complex terrain, which greatly limits the application range and accuracy of traditional warning methods. To overcome these challenges, this study designed an efficient processing system for Interferometric Synthetic Aperture Radar (InSAR) based on the (Artificial Intelligence) AI Earth Cloud platform, integrated with the Comprehensive Standardized Deformation Index (CSDI) approach, to provide an early warning analysis for the Zhenxiong landslide in Yunnan Province, China on January 22, 2024. Utilizing the cloud platform for rapid generation of deformation rates and selection of characteristic deformation points to reflect landslide trends, and applying the CSDI method for time-displacement curve analysis, enabled a fast and accurate landslide early warning. The research results show that the method proposed in this study can effectively warn of landslide events, significantly improving the accuracy and practicality of the warning. By combining InSAR technology with the CSDI model, this study not only addresses the challenges faced by traditional methods but also provides new insights and solutions in the field of landslide early warning, demonstrating the great potential of technological innovation in natural disaster management. [ABSTRACT FROM AUTHOR]

The occurrences of large rock slides often result in catastrophic debris flow within high mountain environments. Discontinuity intersected blocks meeting kinematic conditions stemming from deglaciation-related damage can be triggered by external factors, leading to massive rock slides with a significant downstream hazard. This study presents a comprehensive analysis underlining the mechanism and evolution of the failure during the 2017 Santa Lucía landslide, Patagonian Andes, Chile, utilizing remote sensing and numerical modelling. Due to the remote location, aerial photogrammetry was used to unravel the structural and geomorphological configuration, and four discontinuity sets were identified. Based on colour-shaded relief and slope kinematic analysis, it was found that the failure is governed by combinations of three different discontinuity sets. The failure in the crown portion is complex due to resulting planar and wedge surfaces, whereas in the toe region, the failure is governed by the wedge formation between bedding and other joint set. To further examine its mechanism and evolution, rigid block numerical models were developed in 3DEC to reproduce the failure with real topography and joint parameters. The maximum displacement was observed in the same topographical region where the actual failure occurred, thus conforming to the role of discontinuities in the evolution of the catastrophic failure. Acting on a reduced strength due to rock damage, the modelled slope boosts the instability leading to higher displacements along bonding surfaces with similar attributes as observed in the field. A detailed methodology is discussed regarding coupling remote sensing and 3D numerical modelling for detailed insights into the failure mechanism of the landslides. Overall, our results demonstrate that the Santa Lucía rock slide is a structurally controlled failure where joints provided kinematic freedom, favoured by long-term rock slope damage due to deglaciation. [ABSTRACT FROM AUTHOR]

Soil constitutive models are widely investigated and applied in soil mechanical behaviors simulation; however, the damage evolution process of soil with various shear deformation behaviors was rarely studied. This study introduces a novel shear constitutive model for slip zone soil considering its damage evolution process. Firstly, an innovative method for determining the shear stiffness is proposed to assess the damage degree of slip zone soil during shear deformation. Further, a damage evolution model based on the log-logistic function is derived to characterize the damage evolution process of slip zone soil, and a new shear constitutive model based on the damage evolution process is subsequently proposed. Both the damage evolution model and the shear constitutive model are verified by the ring shear test data of the slip zone soil from the Outang landslide in the Three Gorges Reservoir area of China. Compared to the traditional peak-solving constitutive model based on the Weibull distribution, the proposed shear constitutive model has the distinct advantage of describing not only the brittle (strain softening) mechanical behavior but also the ductile and plastic hardening mechanical behavior of soil. In summary, this method offers a rapid determination of the damage evolution process and the shear behavior constitutive relationship of slip zone soil in landslides. [ABSTRACT FROM AUTHOR]

Colluvial landslides develop in loose Quaternary deposits, with deformation generally being progressive and crack development dominant in the sliding mass surface layer. With the Tanjiawan landslide in the Three Gorges Reservoir (China) as a case study, field investigations, deformation monitoring, and groundwater level monitoring data were integrated to analyze the landslide deformation characteristics and elucidate the influence of cracks on its deformation. We used numerical simulations, including the finite element and discrete element methods, for investigating the progressive deformation mechanism of rainfall-triggered landslides in the accumulation layer and predicting the failure process. The results indicated that crack formation instigated a preferential seepage channel in the shallow layer of the sliding mass, rainfall infiltration along cracks generated water pressure, and the landslide gradually morphed from a stable into a "step-like" progressive deformation state. Preferential flow inside the cracks effectively elevated the groundwater level within the landslide, and either the number or depth of cracks significantly affected the groundwater seepage field, thereby influencing slide stability. Geological conditions controlled the deformation and failure processes of each landslide section. The uplifted bedrock on the right side blocked the sliding process of the rear sliding mass, and the middle and front sliding masses moved faster but the sliding distance was shorter. The deformation trend is deformation, crack formation, preferential flow occurrence, crack extension, and deformation. The ultimate cause of failure was a steep rise in groundwater level following short duration heavy rainfall or long duration light rainfall. [ABSTRACT FROM AUTHOR]

Climate change, earthquakes, and human activities are accelerating the degradation of permafrost, leading to loess failures and slope instability. Some engineering corridors (ECs)/infrastructures located on the northeastern margin of the Qinghai-Tibet Plateau (NE-QTP) of China are heavily influenced by landslide phenomena due to being built on permafrost, loess, and seasonally frozen ground. However, few systematic investigations have been carried out in this area. To compile a comprehensive landslide inventory, we visually interpreted 11,914 landslides in GaoFen-6 images taken from 2021 to 2022. We observe that approximately 44.85% of the infrastructures are affected by landslides. Then, based on the ground types and triggering factors, landslides are classified into three types: freeze‒thaw landslides (FTLs), loess landslides (LLs), and general landslides (GLs). More specifically, FTLs are mainly distributed in the boundary regions between permafrost and seasonally frozen ground. The LLs exhibit high-density clustered distribution characteristics. GLs have significant transitional characteristics and commonalities between FTLs and LLs. Furthermore, we apply the geographical detector to determine the controlling factors of the landslides that occurred. We find that the temperature change is the primary controller on the FTLs. The water exhibits a certain correlation with LLs. And the earthquake is the most important factor on the GLs. Our study provides a significant dataset for quantifying the analysis of landslides in NE-QTP. [ABSTRACT FROM AUTHOR]

Understanding the slope hydrology and failure processes of rainfall-induced landslides is key to landslide early warning; the heterogeneity of soil (e.g., grain-size distribution in different layers) can markedly affect rainfall infiltration and slope failure patterns. However, the hydrological and failure processes of heterogeneous slopes layered by different soil groups have received little attention. In this study, we use a typical landslide soil composition of rainfall-induced landslide in fault zones as a prototype and via flume experiments to simulate the hydrological evolution, failure processes, and patterns under rainfall conditions on material heterogeneity slopes with a combination of colluvial deposit and fault gouge. Our results showed that rainfall-induced slope settlement and rapid saturation of shallow layers of colluvial deposits led to the occurrence of layer-by-layer shallow flow-slides. The spatial variability of infiltration led to the generation of a relatively dry‒wet interface in deeper layers, causing differential changes in the mechanical properties of the fault gouge; this was conducive to the formation of a steep landslide back wall, perched water table in the shallow layer of the fault gouge, and a rapid increase in porewater pressure, which triggered deep sliding, with a change in the failure pattern to a retrogressive mode. There was a strong linear correlation between the displacement rate before slope instability and the Arias intensity (IA) of the seismic signal; an abrupt change and rapid increase in IA may indicate that the slope entered an accelerating creep stage before failure. The results of this study provide a physical basis for related numerical simulation research and a reference for landslide early warning based on seismic signals. [ABSTRACT FROM AUTHOR]

This research focuses on the essential task of defining rainfall thresholds in regions with complex geological features, specifically at a regional scale. It examines a variety of methodologies, from traditional empirical-statistical methods to cutting-edge machine learning (ML) techniques, for establishing these thresholds. The Emilia-Romagna region in Italy, known for its intricate geological structure and prevalence of weak rocks that often lead to large and deep-seated landslides, serves as the study area. The region's complex interplay between rainfall and landslide incidences poses a significant challenge in accurately determining rainfall thresholds. The effectiveness of ML methods is compared against conventional empirical-statistical approaches, evaluating factors such as prediction accuracy, model complexity, and the interpretability of results for use by regional landslide warning system operators. The findings indicate that machine learning techniques have an edge over traditional methods, yielding higher performance scores and fewer false positives. Nevertheless, these advancements are modest when considering the increased complexity of ML methods and the incorporation of additional rainfall parameters. This underlines the continued need for improvements in data quality and volume. The study stresses the importance of enhancing data collection and analysis techniques, especially in an era where advanced AI tools are increasingly available, to improve the accuracy of predicting rainfall thresholds for effective landslide warning systems. [ABSTRACT FROM AUTHOR]

Complex or compound landslides, which combine different movement types with sliding planes at various depths and with varying movement acceleration frequencies, are highly demanding for landform mapping, movement monitoring and reliable hazard assessment. In this work, several techniques including dendrogeomorphological investigation were combined to describe surface morphology, underground structures and movement dynamics of the compound and deep-seated landslide aiming to provide reliable information for its hazard assessment. Interpretation of high-quality digital elevation model and detailed field morphological mapping along with geological information provided context for the interpretation of electric resistivity tomography profiles and enabled the description of properties of two distinct landforms, which are typically identified on a compound or complex deep-seated landslides in the studied region—shallow slides and landslide blocks. Dendrogeomorphological investigation proved for the first time the movement accelerations of the landslide blocks, which reactivate approximately half as often as shallow slides. It also showed different trees' responses to the movements of these two landforms. Trees on the shallow landslide responded mainly with abrupt growth suppression (54.4%) to movements of its highly disturbed material. In contrast, trees on landslide blocks exhibited a dominant response (84.7%) with reaction wood to tilting of the landslide blocks composed of more coherent rock material. The research demonstrated that the dendrogeomorphological investigations provide reliable identification of years with accelerated movements, which corresponds well to instrumental, near-surface monitoring of the landslide. And at the same time, the method provided densely spatially distributed information about partial landslide reactivations during several decades in conditions (e.g. dense forests), where remote sensing methods are difficult to apply. Therefore, we argue that the dendrogeomorphological research is well applicable for hazard assessment of partial failures (cf., shallow slides and landslide blocks) of compound or complex landslides providing information also about the type of landslide movements (sliding vs. surface tilting) and character of the deformed material (highly disturbed vs. more coherent). [ABSTRACT FROM AUTHOR]

Landslide disasters are one of the frequently occurring geological hazards, posing a significant threat to human life and property safety. Swift and accurate identification of landslide areas is crucial for disaster prevention and mitigation. Current object detection algorithms have limitations in the localization and recognition of landslide areas. To address this issue, this paper proposes a cross-spatial network based on efficient multi-scale attention (EMA-Net) landslide recognition model. The proposed EMA-Net model incorporates the efficient multi-scale attention (EMA) for cross space learning, enhancing the model's focus on landslide areas. Additionally, by employing convolution with absolute positioning (CoordConv), the positional information of features is retained to enhance the capability of multiscale feature extraction. The utilization of the SCYLLA-IoU (SIoU)loss function enhances regression learning ability for model prediction borders, thereby improving the efficiency and accuracy of the model. To assess its performance, EMA-Net is evaluated against other models, including Yolov5 - 5.0, Yolov5 - 6.1, Yolov7, and Faster-R-CNN. The evaluation demonstrates that the proposed EMA-Net achieves a precision of 0.980, recall of 0.982, and mAP of 0.717, exhibiting clear improvement over the compared networks. Furthermore, through visualized analysis, the proposed network is capable of effectively identifying landslides within a smaller range. Comparative analysis of the aforementioned experiments validates the superiority of the proposed network. [ABSTRACT FROM AUTHOR]

Solid material sources, such as sediment, large wood, and vehicles, intensify flash flood hazards. This paper provides a detailed review of processes involving the recruitment, entrainment, transport, and blockage dynamics of various solid material sources. Results indicate that sediment supplied by processes like landslides and debris flows can obstruct river channels, leading to a sudden increase in flash flood levels. The failure of a barrier dam results in an expansion of downstream inundation areas. Large wood and floating vehicles transported by flash floods and debris flows may directly impact and destroy built structures or form blockages at built structures. Blockages lead to a backwater rise, and the sudden amplification of flow during the failure of these blockages causes more severe disasters. Based on these analyses, the paper proposes future research directions primarily focusing on the changes in sediment burial processes caused by the sheltering effects of building groups. Furthermore, the study aims to investigate the flow amplification effects of large wood and vehicle blockage. [ABSTRACT FROM AUTHOR]

Catastrophic landslides are generally evolved from potential active landslides, and early identification of active landslides over an extensive region is vital to effective prevention and control of disastrous landslides in urban areas. Interferometric Synthetic Aperture Radar (InSAR) has immense potential in mapping active landslides. However, artificial interpretation of InSAR measurements and manual recognition of active landslides are very laborious and time-consuming, with a relatively high missing and false alarms. That hinders the application of InSAR technique and the identification of active landslides in wide areas. Automatic recognition of active landslides has always been a great challenge and has been relatively rarely investigated by previous studies. This work establishes comprehensive identification indices of geoenvironmental, disaster-triggering, and surface deformation features. Moreover, it suggests a novel deep learning algorithm of SDeepFM to conduct automatic identification of active landslides across a vast and landslide-serious area of Hualong County. Some new viewpoints are suggested as follows. (1) The identification indices consist of disaster-controlling, disaster-inducing, and active deformation characteristics and are constructed in terms of the cause characteristics of active landslides. Thus, it can effectively decrease the false alarms of active landslide identification. (2) The proposed SDeepFM algorithm features a spatial-perception ability and can adequately extract and fuse the low-level and high-level semantic features. It outperforms the classification and regression tree (CART), multi-layer perceptron (MLP), convolutional neural network (CNN), and deep neural network (DNN) algorithms. The test accuracy attains 0.91, 99.73%, 90.21%, 0.92, 0.96, and 0.91 in F1-score, Accuracy, Precision, Recall, AUC, and Kappa, respectively. (3) A total of 164 active landslides are exactly recognized, and 39 active landslides are newly identified in this work. (4) In Hualong County, the characteristics of slope deformation, spatial context, lithology, tectonic movement, human activity, and topography play important roles in active landslide identification. River distribution and rainfall also contribute to active landslide recognition. [ABSTRACT FROM AUTHOR]

The article provides new data on the structure of the Laptev Sea flank of the Gakkel Ridge. The intensive supply of clastic material from the Laptev Sea shelf leads to the development of a thick alluvial fan at the continental rise, which determines the structure of the bottom topography. In the northwestern direction, the influence of the fan decreases and tectonics becomes the main relief-forming factor. The bathymetric survey traced the asymmetrical rift valley of the Gakkel Ridge, the western flank of which is complicated by terraces. The presence of fault structures, bottom subsidence, extensive sediment supply, and the widespread development of subaqueous slump processes indicate the high neotectonic activity of the Laptev Sea flank of the Gakkel Ridge. For the first time in this region, numerous carbonate rocks have been discovered, the authigenic cement of which is represented by magnesian calcite or aragonite with an admixture of terrigenous material. The palynological and micropaleontological analysis of the carbonate rocks indicates the Quaternary formation of authigenic carbonate cement. An important role in the formation of authigenic carbonates was played by diagenetic solutions coming from the sedimentary cover together with methane and oxidation products of gases and organic matter. The authigenic carbonates were precipitated mainly in an isotopic equilibrium with bottom water at a temperature of about 0°C. The negative correlation between 87Sr/86Sr and δ13C indicates the presence of at least two different sources of carbonate-forming solutions. [ABSTRACT FROM AUTHOR]

Landslide is a common hazard in Tamil Nadu's Nilgiri district of. While much work on landslide susceptibility have been done worldwide, understanding society's vulnerability to landslides, considering house structure and socio-economic conditions, remains lacking. This research presents landslide vulnerability mapping using advanced computing deep learning models in the Nilgiri district, India. Compared to traditional ML techniques, the deep learning neural network (DLNN) architecture demonstrates greater accuracy, particularly when dealing with more samples or significant amounts of big data. Although the standardized characteristics of multi-layer NNs are widely known, the key benefit of DL is its organized method for training DLNN-layer organizations how to govern themselves. Therefore, one deep learning neural network and three conventional machine learning models i.e., MLP classifier and RBF neural network were opted. A total of twenty-eight physical, climatological, hydrological and socio-economic factors were considered to produce socio-economic and relative landslide vulnerability maps. Multi-collinearity diagnosis was performed to select the appropriate factors. Several physical as well as human related factors are highly important for making the area vulnerable to land-slide. To justify the vulnerability maps, several statistical methods were applied. The best model DLNN, with an area under the curve of 89.07%, shows that 43.31%, and 37.72% of areas are highly to very-highly vulnerable to landslides. The framework presented in this work establishes an ideal link between human activities and landslide vulnerability, aiding planners in making informed decisions for landslide management. [ABSTRACT FROM AUTHOR]

Landslides are prominent geomorphological processes in active mountain belts hindering urban development and food production projects. Generally, heterogeneous geomorphological contexts are the most affected, particularly in the Mediterranean rim. On the other hand, landslides in homogenous contexts remain under-investigated. In such conditions, the monotony of certain causative factors may conceal the parameters controlling the landslide's magnitude and typological differences. In this paper, the frequency–size distribution of landslides was performed to investigate the effect of the main categories of the landslide causative factors to identify the key features connected to the magnitude of these gravitational processes. Results show that the main typological difference is related to the land use and lithological categories in a way that marls and cereal farming slopes promote small-size and hence shallow movements, while olive growing terrain for the land use and calcareous marls and colluvium for the lithological factor promote larger and deeper processes. Furthermore, the slope direction has also proved to be an influencing parameter on landslide typology, where slopes remaining in the shade during wet seasons (northern slopes) promote more shallow movements than those in the Sun. These findings show that even in monotonous contexts such as the foreland of the Rif chain (the Prerif), entities controlling the typological difference can be found and investigated, which improves our knowledge about the landslide hazard. [ABSTRACT FROM AUTHOR]

The district of Maçka in Trabzon, in the Eastern Black Sea Region of Turkey, frequently experiences landslides, resulting in the highest number of disaster victims. In this study, Landslide Susceptibility Maps (LSMs) were generated via the Statistical-based Frequency Ratio (FR) and Modified Information Value (MIV) models using 10 factors. Out of the 150 landslides in the region, 105 (70%) were utilized in creating the maps, and the remaining 45 (30%) were reserved for validation. The models demonstrated success rates of 87.5% and 84.9%, along with prediction rates of 84.8% and 83.1%, respectively, as determined by the receiver operating characteristics curve and area under the curve values. While both models achieved acceptable levels of accuracy, MIV outperformed FR. Additionally, the risk status of 5413 buildings and forested areas was examined. The results showed that 78.64% (FR) and 80.79% (MIV) of the buildings were situated in high landslide risk areas. Regarding forest areas, 39.30% (FR) and 41.35% (MIV) were observed in high-risk landslide areas. In the next step, neighborhood landslide risk statuses were examined, revealing risks ranging from 90 to 100% in some areas. The final step concentrated on risk analyses for construction plans in a chosen pilot neighborhood using two criteria. 88.75% of all parcels were observed in high-risk areas, with hazelnut groves at 79.67% in high-risk zones. Conversely, 71.89% of fruit trees were in low-risk areas. The results align with the literature, indicating that LSMs can serve as a versatile base map. [ABSTRACT FROM AUTHOR]

The accumulation of waterborne large woody debris is a critical issue facing bridges spanning active waterways. In addition to collision forces, drift buildup constricts flow, producing increased hydraulic pressures and exacerbating scour, the cause of nearly one-third of bridge failures. Accurate methods of predicting debris generation, transport, entrapment, and dimensions are consequently necessary to improve public safety, bridge designs, and informed decision-making. However, debris buildup characteristics are site-specific, varying with the regional vegetative, hydraulic, topographical, and geotechnical properties. As sufficient data on these properties is limited to create empirical predictive models, a probabilistic physics-based framework incorporating the risk assessment of local vegetation to estimate debris accumulation and scour is developed. Based on the upstream tree, soil, weather, and river conditions, fragility assessment of the riparian trees from landslides and windthrow is conducted to estimate the probability of debris generation and subsequent entrapment. The framework is demonstrated in a case study of a bridge in Vermont subject to Hurricane Irene. Sensitivity analysis is conducted to quantify the importance of various contributing factors, including slope, rainfall, wind gust, flow rate, and pier geometry. The experimental results highlight the importance of including the debris accumulation in the scour projections. [ABSTRACT FROM AUTHOR]

Landslide susceptibility map (LSM) plays an important role in providing the knowledge of slopes prone to future landslides. However, the applicability of LSM is often hindered due to high cost of data collection especially in mountainous region such as Himalayas. Therefore, this study proposes transfer learning approach (TL) to improve the performance of LSM by transferring the information from the data rich region (source) to data scare region (target). Two machine learning based model are trained in source area which is then transferred for prediction in target area and a source trained model combined with the knowledge of target is used for the prediction in the target area. The applicability of the proposed approach is tested considering Mandi district as source area and Kullu and Rudraprayag districts as target areas. Independent variables which included 11 landslide conducive factors from the source and target area, the independence was critically analysed using multi-collinearity test, while the geomorphic similarity was assessed using KL divergence method. Efficient machine learning method such as random forest (RF) and multi-layer perceptron (MLP) was used to train the models in both areas, statistical measure such as AUC-ROC, precision, recall, F-score, and accuracy were used to evaluate the performance of the LSMs. The results demonstrated the proposed approach for target area 1, the AUC value increased from 0.908 (Target trained on itself), to 0.942 (Target-Transfer Learned (TL)) and 0.959 (Target Combined) for RF and 0.896, 0.907 and 0.946 for MLP. Additionally, an increase in the precision was observed in RF while all other statistical measures increased to 0.023 for precision, 0.022 for recall, 0.023 for F-score and 0.066 for accuracy in the case of MLP. While on the other hand, in the case of target area 2, the AUC value for RF, ranged from 0.95 (Target area trained on itself), 0.80 (target TL) and 0.98 (target combined) and for MLP the AUC ranged from 0.82 for the source trained model while 0.84 for LSM obtained when both target and source data was used. Thus, the results revealed that proposed TL approach can be effective in improving the performance of LSM in data scare region in Himalayan region, thereby providing a promising approach in overcoming the issue of data limitation. [ABSTRACT FROM AUTHOR]

Discriminating landslides from other events in seismic records is challenging due to unclear phases and overlapped frequency content. We analyze the seismic waveform power spectral density (PSD) and its skewness to discriminate landslides from earthquakes and background noise. By comparing PSDs of landslides with small‐magnitude earthquakes and noise in the Alaskan region, we find distinct power decay trends in the 0.01–5 Hz frequency range. The method was successfully tested on the seismic waveforms of seven global landslides. Further, the statistical significance of the approach was tested on 835 landslide waveforms using probability density, skewness and crosscorrelation of waveform PSD. This novel integration of seismic waveform PSDs and their skewness analysis is found to be robust and statistically significant for automatic landslide detection in continuous seismic data, with vast potential for early warning through real‐time seismic networks. Plain Language Summary: In this study, we aim to tell apart landslide signals from earthquake signals and background noise by analyzing how the power of seismic data changes with frequency. We found that the way power decreases with frequency is different for landslides compared to earthquakes and noise. Additionally, the distinct shape of the power distribution further shows the effectiveness of our method for identifying landslides in seismic data. We successfully tested the method on recordings from seven landslides around the world, and we confirmed its statistical significance using 835 seismic recordings of landslides. Our results show that the frequency‐power patterns and their distribution are useful for detecting landslides in continuous seismic data. This approach could be very effective for real‐time monitoring and early warning systems in the future. Key Points: We discriminate landslides from earthquakes and background noise using trends of the seismic waveform power spectral density and its skewnessThe spectral decay characteristics of landslides and their trends in distinct frequency bands are quite distinct from those of earthquakes and background noiseSuccessfully applied and tested the technique on 835 landslide waveforms worldwide using the reference power spectral density [ABSTRACT FROM AUTHOR]

Landslides cause severe damage to life and property with a wide-ranging impact. Infiltration of rainfall is one of the significant factors leading to landslides. This paper reports on a phase creep landslide caused by long-term rainfall infiltration. A detailed geological survey of the landslide was conducted, and the deformation development pattern and mechanism of the landslide were analyzed in conjunction with climatic characteristics. Furthermore, reinforcement measures specific to the landslide area were proposed. To monitor the stability of the reinforced slope, a Beidou intelligent monitoring and warning system suitable for remote mountainous areas was developed. The system utilizes LoRa Internet of Things (IoT) technology to connect various monitoring components, integrating surface displacement, deep deformation, structural internal forces, and rainfall monitoring devices into a local IoT network. A data processing unit was established on site to achieve preliminary processing and automatic handling of monitoring data. The monitoring results indicate that the reinforced slope has generally stabilized, and the improved intelligent monitoring system has been able to continuously and accurately reflect the real-time working conditions of the slope. Over the two-year monitoring period, 13 early warnings were issued, with more than 90% of the warnings accurately corresponding to actual conditions, significantly improving the accuracy of early warnings. The research findings provide valuable experience and reference for the monitoring and warning of high slopes in mountainous areas. [ABSTRACT FROM AUTHOR]

Landslides are geological disasters that are harmful to both humans and society. Digital elevation model (DEM) time series data are usually used to monitor dynamic changes or surface damage. To solve the problem of landslide deformation monitoring without ground control points (GCPs), a multidimensional feature-based coregistration method (MFBR) was studied to achieve accurate registration of multitemporal DEMs without GCPs and obtain landslide deformation information. The method first derives the elevation information of the DEM into image pixel information, and the feature points are extracted on the basis of the image. The initial plane position registration of the DEM is implemented. Therefore, the expected maximum algorithm is applied to calculate the stable regions that have not changed between multitemporal DEMs and to perform accurate registrations. Finally, the shape variables are calculated by constructing a DEM differential model. The method was evaluated using simulated data and data from two real landslide cases, and the experimental results revealed that the registration accuracies of the three datasets were 0.963 m, 0.368 m, and 2.459 m, which are 92%, 50%, and 24% better than the 12.189 m, 0.745 m, and 3.258 m accuracies of the iterative closest-point algorithm, respectively. Compared with the GCP-based method, the MFBR method can achieve 70% deformation acquisition capability, which indicates that the MFBR method has better applicability in the field of landslide monitoring. This study provides an idea for landslide deformation monitoring without GCPs and is helpful for further understanding the state and behavior of landslides. [ABSTRACT FROM AUTHOR]

The detection of potential rural mountain landslide displacements using time-series interferometric Synthetic Aperture Radar has been challenged by both atmospheric phase screens and decoherence noise. In this study, we propose the use of a combined distributed scatterer (DS) and the Prophet_ZTD-NEF model to rapidly map the landslide surface displacements in Diqing Tibetan Autonomous Prefecture, China. We conducted tests on 28 full-resolution SENTINEL-1A images to validate the effectiveness of our methods. The conclusions are as follows: (1) Under the same sample conditions, confidence interval estimation demonstrated higher performance in identifying SHPs compared to generalized likelihood ratio test. The density of DS points was approximately eight times and five times higher than persistent scatterer interferometry and small baseline subset methods, respectively. (2) The proposed Prophet_ZTD-NEF model considers the spatial and temporal variability properties of tropospheric delays, and the root mean square error of measured values was approximately 1.19 cm instead of 1.58 cm (PZTD-NEF). (3) The proposed Prophet_ZTD-NEF method reduced the mean standard deviation of the corrected interferograms from 1.88 to 1.62 cm and improved the accuracy of the deformation velocity solution by approximately 8.27% compared to Global Position System (GPS) measurements. Finally, we summarized the driving factors contributing to landslide instability. [ABSTRACT FROM AUTHOR]

Soil piping erosion is an underground soil erosion process that is significantly underestimated or overlooked. It can lead to intense soil erosion and trigger surface processes such as landslides, collapses, and channel erosion. Conducting susceptibility mapping is a vital way to identify the potential for soil piping erosion, which is of enormous significance for soil and water conservation as well as geological disaster prevention. This study utilized airborne radar drones to survey and map 1194 sinkholes in Sunjiacha basin, Huining County, on the Loess Plateau in Northwest China. We identified seventeen key hydrogeomorphological factors that influence sinkhole susceptibility and used six machine learning models—support vector machine (SVM), logistic regression (LR), Convolutional Neural Network (CNN), K-Nearest Neighbors (KNN), random forest (RF), and gradient boosting decision tree (GBDT)—for the susceptibility assessment and mapping of loess sinkholes. We then evaluated and validated the prediction results of various models using the area under curve (AUC) of the Receiver Operating Characteristic Curve (ROC). The results showed that all six of these machine learning algorithms had an AUC of more than 0.85. The GBDT model had the best predictive accuracy (AUC = 0.94) and model migration performance (AUC = 0.93), and it could find sinkholes with high and very high susceptibility levels in loess areas. This suggests that the GBDT model is well suited for the fine-scale susceptibility mapping of sinkholes in loess regions. [ABSTRACT FROM AUTHOR]

Landslides are characterized by intricate formation mechanisms and multiple triggering factors, posing serious threats to human life and property. Interferometric synthetic aperture radar (InSAR) has been extensively applied in landslide monitoring due to its all-weather operation, high precision, and wide spatial coverage. However, one-dimensional line-of-sight (LOS) deformation limits the ability to measure actual landslide displacement. Presently, many regions without the latest descending data, but they are in the overlapping position of two adjacent latest ascending track. Compared to using a single track, the method measuring three-dimensional deformation of landslides, combined with two ascending track InSAR datasets and pixel-level surface-parallel flow (PSPF) assumption, is presented. Based on the small baseline subset (SBAS) InSAR, the LOS deformation in two observations in the middle reaches of the Qingjiang River in China was measured. With pixel-level surface-parallel flow model, the three-dimensional deformation was obtained. It was further converted into the slope surface coordinate system, thus determining the direction of landslide movement. The findings effectively elucidate the primary displacement mode of landslides, with deformation rates ranging from −80 to 20 mm yr $^{ - 1}$ − 1 , which are significantly influenced by the slope angle and slope aspect. It is highly important to remotely identify the movement direction and analyse the failure mode of landslides. [ABSTRACT FROM AUTHOR]

Swiftly and accurately acquiring the spatial distribution, location, and magnitude of landslides while documenting them in a landslide cataloging database can furnish crucial information for precise disaster mitigation measures and secondary hazard prevention. The extraction of landslides using existing semantic segmentation algorithms may give rise to issues such as false detection and missed detection due to the diverse shape and texture features of landslides in remote sensing images, the abundance of spectral features, and the complexity of the environment. In this article, we proposed LandslideNet, a novel model specifically designed for accurate segmentation of landslides in single-temporal high spatial resolution optical remote sensing images. By constructing a landslide image dataset and employing the LandslideNet model, we successfully identify and segment landslides with high precision. Quantitative experimental results demonstrate that our LandslideNet achieves superior performance compared to widely used semantic segmentation models including U-Net, PSPNet, Deeplabv3+, HRNetv2, Segformer and GELAN-c with F1-score , mIoU , FWIoU , mPA and OA reaching 72.53 %, 78.41 %, 99.86 %, 83.33 % and 99.93 % respectively. Moreover, our model exhibits lower complexity while demonstrating improved capability in detecting landslides with complex shapes and different sizes. [ABSTRACT FROM AUTHOR]

Slow and very slow landslides can cause severe economic damage to structures. Due to their velocity of propagation, it is possible to take action such as programmed maintenance or evacuation of affected zones. Modeling is an important tool that allows scientists, engineers, and geologists to better understand their causes and predict their propagation. There are many available models of different complexities which can be used for this purpose, ranging from very simple infinite landslide models which can be implemented in spreadsheets to fully coupled 3D models. This approach is expensive because of the time span in which the problems are studied (sometimes years), simpler methods such as depth‐integrated models could provide a good compromise between accuracy and cost. However, there, the time step limitation due to CFL condition (which states that the time step has to be slower than the ratio between the node spacing Δx$\Delta x$ and the physical velocity of the waves results in time increments which are of the order of one‐10th of a second on many occasions. This paper extends a technique that has been used in the past to glacier evolution problems using finite differences or elements to SPH depth‐integrated models for landslide propagation. The approach is based on assuming that (i) the flow is shallow, (ii) the rheological behavior determining the velocity of propagation is viscoplastic, and (iii) accelerations can be neglected. In this case, the model changes from hyperbolic to parabolic, with a time increment much larger than that of classic hyperbolic formulations. [ABSTRACT FROM AUTHOR]

Urbanization has resulted in the widespread development of built-up areas, often without considering the local geology and geomorphology. To improve risk assessments related to landslides, it is essential to determine the physical and chemical properties of sediments. The aim of this study is to exemplify an already mobilized and reworked layer based on granulometric properties of the sediments and characterize the chemical and physical properties which have changed during or after the mass movement. Ten red clay layers were sampled near Kulcs (Hungary) from a sliding surface and its environment. We measured grain size distribution, major element and modal composition, furthermore, conduct particle shape analysis, respectively. Grain size distribution suggest that samples from the sliding surface are weathered, with the exhibiting reworking characteristics. We calculate the weathering index from the major elements, which, in combination with the particle shape analyses of the silt fractions, indicate that the morphological parameters of the samples are in relation with weathering index. According to our results, while a fresh sliding event leaves marks on the granulometric properties, the main process which affects the grain morphology is the chemical abrasion (post the mass movement) and not the landslide event. [ABSTRACT FROM AUTHOR]

Loess landslides represent a significant natural hazard, especially in arid and semi-arid regions where slopes are exposed to prolonged snowmelt and rainfall infiltration. To analyze landslide stability and conduct early warning evaluations effectively, a robust monitoring system and appropriate equipment are crucial. This study utilizes historical data from the Kalahaisu landslide in Xinyuan County, Ili region, which was monitored using various techniques. The research assesses the time resolution, spatial resolution, and data accuracy of these monitoring methods. Additionally, the study explores the landslide disaster patterns and early warning indicators for the Kalahaisu landslide. It suggested that the deformation of the rainfall- and snowmelt- landslide is not only affected by the seasonal climates but also closely related to the internal structures of the loess. The other observation is that the respective time and spatial resolution of monitoring instrument would significantly affect the the interpretation time of the landslide deformation. Instruments with higher spatial resolution can more effectively identify unstable areas within a landslide, while instruments with higher temporal resolution can provide detailed time-series deformation data. Therefore, real-time and comprehensive monitoring of landslides using a suitable combination of monitoring equipment can provide strong data support for landslide stability evaluation and deformation trend prediction. Full-area monitoring techniques, such as Synthetic Aperture Radar (SAR) and equipment that measures changes in internal properties like deep water content, deep displacement, and pore pressure, offer a more comprehensive and effective approach to monitoring and interpreting landslide deformation. The insights gained from this research may enhance the prediction and prevention of loess landslides in the Ili River valley. [ABSTRACT FROM AUTHOR]

Urbanization has resulted in the widespread development of built-up areas, often without considering the local geology and geomorphology. To improve risk assessments related to landslides, it is essential to determine the physical and chemical properties of sediments. The aim of this study is to exemplify an already mobilized and reworked layer based on granulometric properties of the sediments and characterize the chemical and physical properties which have changed during or after the mass movement. Ten red clay layers were sampled near Kulcs (Hungary) from a sliding surface and its environment. We measured grain size distribution, major element and modal composition, furthermore, conduct particle shape analysis, respectively. Grain size distribution suggest that samples from the sliding surface are weathered, with the exhibiting reworking characteristics. We calculate the weathering index from the major elements, which, in combination with the particle shape analyses of the silt fractions, indicate that the morphological parameters of the samples are in relation with weathering index. According to our results, while a fresh sliding event leaves marks on the granulometric properties, the main process which affects the grain morphology is the chemical abrasion (post the mass movement) and not the landslide event. [ABSTRACT FROM AUTHOR]

The article "Satellite Data-based Structural Mapping Reveals Active Panjal Traps Fault (PTF) in Kashmir, NW Himalaya" published in Acta Geologica Sinica discusses the identification of an active fault, the Panjal Traps Fault (PTF), in the Kashmir region of the Himalayan orogenic belt. The study uses satellite data to map evidence of the fault's activity, indicating its potential to cause major earthquakes. The fault is associated with tectonic landforms, glacial features, and river tilting, highlighting its impact on the region's geological and hazard landscape. [Extracted from the article]

With the large-scale exploitation of mineral resources, the safety problems of open-pit slopes and dumps become increasingly prominent. In this work, the safety demonstration of the interaction between the eastside slope of an open-pit quarry and the dump was carried out. Firstly, the influence of blasting vibration on the stability of the eastside slope and the dump was analyzed, and the propagation law of blasting vibration wave and the attenuation law of velocity were obtained. Secondly, the finite element analysis of the seepage field of the slope was carried out, and the basic seepage parameters of the rock and soil were determined. Then, the limit equilibrium analysis method was used to quantitatively calculate the stability of the eastside slope of the stope under the load of the dump to assess landslide risk. The results show that no landslides would occur at the dump or affect the performance of the slope, and that the slope was able to reach the required safety reserve factor. Then, through the finite element analysis of the 3-3 profile, it was found that the deformation caused by the excavation unloading of the eastside slope was mainly concentrated in the lower part of the slope, and the displacement variation caused by the top area of the slope was about 0~10 mm. The research verified the stability of slope and dump, which provides technical support for slope project design and blast operation in open-pit quarries. [ABSTRACT FROM AUTHOR]

Landslides cause significant human and financial losses in different regions of the world. A high-accuracy landslide susceptibility map (LSM) is required to reduce the adverse effects of landslides. Machine learning (ML) is a robust tool for LSM creation. ML models require large amounts of data to predict landslides accurately. This study has developed a stacking ensemble technique based on ML and optimization to enhance the accuracy of an LSM while considering small datasets. The Boruta–XGBoost feature selection was used to determine the optimal combination of features. Then, an intelligent and accurate analysis was performed to prepare the LSM using a dynamic and hybrid approach based on the Adaptive Fuzzy Inference System (ANFIS), Extreme Learning Machine (ELM), Support Vector Regression (SVR), and new optimization algorithms (Ladybug Beetle Optimization [LBO] and Electric Eel Foraging Optimization [EEFO]). After model optimization, a stacking ensemble learning technique was used to weight the models and combine the model outputs to increase the accuracy and reliability of the LSM. The weight combinations of the models were optimized using LBO and EEFO. The Root Mean Square Error (RMSE) and Area Under the Receiver Operating Characteristic Curve (AUC-ROC) parameters were used to assess the performance of these models. A landslide dataset from Kermanshah province, Iran, and 17 influencing factors were used to evaluate the proposed approach. Landslide inventory was 116 points, and the combined Voronoi and entropy method was applied for non-landslide point sampling. The results showed higher accuracy from the stacking ensemble technique with EEFO and LBO algorithms with AUC-ROC values of 94.81% and 94.84% and RMSE values of 0.3146 and 0.3142, respectively. The proposed approach can help managers and planners prepare accurate and reliable LSMs and, as a result, reduce the human and financial losses associated with landslide events. [ABSTRACT FROM AUTHOR]