Landslide Deformation Research Articles

Elucidating the complex interplay between natural and anthropogenic factors in the deformation of the Muyubao landslide through time-series InSAR analysis

In the Three Gorges Reservoir area, landslide disasters occur frequently, making scientific monitoring and risk prediction crucial for disaster prevention and mitigation. However, most previous studies have been constrained by analysis of singular influencing factors. In this study, we employed multi-temporal InSAR techniques coupled with multivariate geospatial statistical analysis to monitor and analyze the dynamic evolution of the Muyuba landslide in Zigui County, Hubei Province, China from 2016 to 2023. The findings indicate that the Muyuba landslide was predominantly characterized by continuous, gradual subsidence. Key factors inducing deformation included well-developed drainage networks, gentle slopes of 15–30°, and the orientation of rock strata. Deformation rates in residential areas and along roadways exceeded background levels, implicating anthropogenic activities in the heightened landslide risk. A significant correlation was observed between landslide deformation and reservoir water level fluctuations, as opposed to rainfall patterns, highlighting reservoir regulation disturbances as a critical landslide triggering factor.

Creep deformation monitoring of landslides in a reservoir area

Effective utilization of reservoirs facilitates the distribution of water resources in both time and space, providing strong support for the sustainable growth of the economy and society. However, the periodic water level fluctuations of a reservoir during its operation may lead to geological hazards such as landslides. Here, we conducted a comparative analysis of the deformation processes of reservoir and non-reservoir landslides in the Jilintai area between 2017 and 2022 using Interferometric Synthetic Aperture Radar (InSAR) technology and wavelet analysis. Results show that more unstable locations were found on shaded slopes with a soil moisture of 6–10% than on sunny ones with a soil moisture of 15–19%. Both reservoir and non-reservoir landslides showed continuous and prolonged creep displacement over time. The deformation curve of the reservoir landslides displayed a step-like trend, whereas it was a linear trend without the discernible acceleration period for the non-reservoir landslides. Furthermore, the wavelet analysis revealed that the deformation of reservoir landslides follows a one-year period, almost synchrony with reservoir level changes. The results from this work deepen our understanding of kinematic processes of reservoir and non-reservoir landslides, which is crucial for reasonable and effective landslide monitoring and prevention.

Slope stability analysis with a hypoplastic constitutive model: Investigating a stochastic anisotropy model and a hydro‐mechanical coupled simulation

AbstractA reliable constitutive model is essential for accurately predicting slope deformation in numerical analysis, which includes assessing slope stability as an integral component. However, calculating slope stability in numerical models can be challenging due to complex boundary conditions and advanced constitutive models, especially when probabilistic and hydro‐mechanical coupled simulations are required. In this paper, we use a vector‐sum method‐based framework for slope stability analysis that incorporates a critical state hypoplastic constitutive model. The rationality and accuracy of the stability results are initially verified through critical slip surface analysis and a factor of safety evaluation. Subsequently, we investigate the probability‐based slope stability and failure characteristics by incorporating the spatial variability of strength parameters into the hypoplastic model. Finally, the Baishuihe landslide in the Three Gorges Reservoir Region is examined as a case study, where hydro‐mechanical coupled simulations are conducted under actual water level and rainfall conditions. The time‐varying stability and deformation characteristics of the landslide are analyzed and compared with the results obtained from the Mohr‐Coulomb model. The analysis results demonstrate the practicality and efficiency of using a hypoplastic model for probabilistic and time‐varying slope stability calculations. Furthermore, it highlights the superior ability of the hypoplastic model to accurately describe reservoir landslide deformation processes.

Susceptibility of Landslide Debris Flow in Yanghe Township Based on Multi-Source Remote Sensing Information Extraction Technology (Sichuan, China)

The extraction of real geological environment information is a key factor in accurately evaluating the vulnerability to geological hazards. Yanghe Township is located in the mountainous area of western Sichuan and lacks geological survey data. Therefore, it is important predict the spatial and temporal development law of landslide debris flow in this area and improve the effectiveness and accuracy of monitoring changes in landslide debris flow, this article proposes a method for extracting information on the changes in landslide debris flows combined with NDVI variation, which is based on short baseline interferometry (SBAS-InSAR) and optical remote sensing interpretation. In this article, we present relevant maps based on six main factors: vegetation index, slope, slope orientation, elevation, topographic relief, and formation lithology. At the same time, different remote sensing images were compared to improve the accuracy of landslide debris flow sensitivity assessments. The research showed that the highest altitude of the region extracted by multi-source remote sensing technology is 2877 m, and the lowest is 630 m, which can truly reflect the topographic relief characteristics of the region. The pixel binary model’s lack of regional restrictions enables a more accurate estimation of the Normalized Difference Vegetation Index (NDVI), bringing it closer to the actual vegetation situation. The study uncovered a bidirectional relationship between vegetation coverage changes and landslide deformation in the study area, revealing spatial–temporal evolution patterns. By employing multi-source remote sensing technology, the research effectively utilized changes in multi-period imagery and feature extraction methods to accurately depict the development process and distribution characteristics of landslide debris flow. This approach not only offers technical support but also provides guidance for evaluating the vulnerability of landslide debris flow in the region.

The influence of chemical and technological parameters of materials in the design of objects in industrial design on the example of state facilities

AbstractThe purpose of this study is to generalise the thermodynamic model describing the fragmentation of metals or alloys at integrated project delivery, and to study the features and conditions for the formation of limit (stationary) structures of various types. Thus, a Project Delivery is constructed in the two‐defect model approximation with dislocation density and grain boundaries taken into account, which establishes the conditions for the formation of limit (stationary) structures of different types. In addition, the article studies in detail the evolution of the main structural defects and their interaction during the implementation of the stationary regime, and also establishes the dependence of the type of limit structure formed on the values of landslide deformation and the initial state of the material. It is found that the change of states of the system is of solid‐state phase transformation nature. It follows from the methods of description that the limit (stationary) structure is not immutable, but represents a dynamic equilibrium of the processes of generation and annihilation of structural defects.

Reservoir landslide monitoring and mechanism analysis based on UAV photogrammetry and sub-pixel offset tracking: a case study of Wulipo landslide

Introduction: Reservoir landslides undergo large deformations during the early stages of impoundment and maintain long-term persistent deformations during the operational period of the reservoir. The management of reservoir landslides mostly focuses on the early identification, risk assessment during the large deformations, and long-sequence monitoring during long-term persistent deformations, which requires sufficient continuity and integrity of the landslide monitoring data.Methods: Taking the Wulipo (WLP) landslide in Baihetan Reservoir as example, this paper proposes a reservoir landslide monitoring method that integrates field survey, unmanned aerial vehicle (UAV) photogrammetry and global navigation satellite system (GNSS) monitoring, which can effectively eliminate the practical monitoring gaps between multiple monitoring methods and improve the continuity and completeness of monitoring data.Results and discussion: First, this study determined the initiation time of the landslide through the field investigation and collected five period of UAV data to analyze the overall displacement vector of the WLP landslide using sub-pixel offset tracking (SPOT). On the basis of the above data, we compensated for the missing data in GNSS system due to the practical monitoring vacancies by combining the field survey and the landslide-water level relationship. Based on these monitoring data, this paper points out that the WLP landslide is a buoyancy-driven landslide, and whether or not accelerated deformation will occur is related to the maximum reservoir water level. Finally, this study analyzed and discussed the applicability of UAV photogrammetry for reservoir landslide monitoring in the absence of ground control points (GCPs), and concluded that this method can be quickly and flexibly applied to the stage of large deformation of reservoir landslides.

Spatiotemporal deformation characteristics of Outang landslide and identification of triggering factors using data mining

Since the impoundment of Three Gorges Reservoir (TGR) in 2003, numerous slopes have experienced noticeable movement or destabilization owing to reservoir level changes and seasonal rainfall. One case is the Outang landslide, a large-scale and active landslide, on the south bank of the Yangtze River. The latest monitoring data and site investigations available are analyzed to establish spatial and temporal landslide deformation characteristics. Data mining technology, including the two-step clustering and Apriori algorithm, is then used to identify the dominant triggers of landslide movement. In the data mining process, the two-step clustering method clusters the candidate triggers and displacement rate into several groups, and the Apriori algorithm generates correlation criteria for the cause-and-effect. The analysis considers multiple locations of the landslide and incorporates two types of time scales: long-term deformation on a monthly basis and short-term deformation on a daily basis. This analysis shows that the deformations of the Outang landslide are driven by both rainfall and reservoir water while its deformation varies spatiotemporally mainly due to the difference in local responses to hydrological factors. The data mining results reveal different dominant triggering factors depending on the monitoring frequency: the monthly and bi-monthly cumulative rainfall control the monthly deformation, and the 10-d cumulative rainfall and the 5-d cumulative drop of water level in the reservoir dominate the daily deformation of the landslide. It is concluded that the spatiotemporal deformation pattern and data mining rules associated with precipitation and reservoir water level have the potential to be broadly implemented for improving landslide prevention and control in the dam reservoirs and other landslide-prone areas.

Physics-informed data assimilation model for displacement prediction of hydrodynamic pressure-driven landslide

Due to the complex and non-linear characteristics of landslide evolution, the deformation law of landslides is always hard to be predicted. Considering the dynamical evolution process of landslide physical mechanism, a Physics-Informed Data Assimilation (PIDA) method is first proposed for landslide displacement forecasting. It can enhance the prediction ability of the physical forecasting model, and solves the problem of ignoring the physical significance of landslides in mathematical prediction model. Additionally, the superiority of Particle Filter (PF) in nonlinear and non-Gaussian system determines its application in complex problem. Aiming at the hydrodynamic pressure-driven landslide, the Sliding Zone Deterioration (SZD) model is constructed as the state equation of the PF algorithm. And the observation equation is formed by the relationship between landslide deformation and monitoring datasets. The physics-informed landslide displacement prediction model serves as a bridge, consisting of the shear strength parameter dynamic updated model and physical mechanism analysis model, for key parameters feedback, modification, and update. Comparing the results to traditional Long Short-Term Memory (LSTM) and Back Propagation Neural Network (BPNN) methods, the PIDA method combining triggering and instability mechanisms performs better in accuracy. Moreover, it enables dynamic and reliable landslide deformation prediction.

Impact of precipitation on Beishan landslide deformation from 1986 to 2023

Investigating the response of landslide activity to climate change is crucial for understanding the disastrous effects of climate change on high mountains. However, the lack of long-term, spatial–temporal consistent measurement of landslide activity prohibits the study of this relationship. In this work, we used two methods to derive the time series of a landslide’s deformation and study its relationship with precipitation in the northeastern Tibetan Plateau. The small baseline subset-interferometric synthetic aperture radar (SBAS-InSAR) method with Sentinel-1A images is first applied to derive time series of the landslide’s deformation from 2020 to 2021. A recently developed method to derive cumulative deformations of optical images was used with Landsat 5 and Sentinel-2 images to derive the long-term deformation time series from 1986 to 2023. Centimeter-scale deformations detected by using the InSAR method are mainly located in the upper and eastern parts of the landslide, whereas meter-scale deformations detected by using the optical method are in the middle of the landslide. Time-series results from both methods show that intra-annual initiations of the landslide’s deformation occurred in rainy months (from July to October). Although there seems to be no direct relations between inter-annual deformations and precipitation, significant displacements since 2020 occurred after exceptionally wet years from 2018 (with a record-breaking precipitation year in 2020). With optical images, we found that the maximum cumulative deformation of the landslide has been >35 m since 1986 with major deformations (>20 m) found after 2020, which may indicate an imminent risk to the Lijie town near the toe of the landslide. With climate change, increased precipitation is expected in future, which may trigger more similar landslides in the vicinity of this region. This work demonstrates an executable framework to assess landslide hazard risk under climate change.

An Improved Filtering Method and Application of Landslide Deformation Monitoring

Real-time early warning based on deformation monitoring is an important measure to reduce the threat related to landslides. However, due to the complexity of landslide deformation, equipment accuracy, and data transmission interference, the displacement obtained by existing monitoring equipment has random errors, resulting in inaccurate landslide warning. In this paper, a tangent angle model based on the improved filtering method is proposed for the early warning of landslides. An improved filtering method is proposed based on the least-square method to smooth the measurement errors of monitoring equipment, considering both “data smoothing effect” and “accelerated deformation real-time determination”. By comparing the least-squares method and the improved filtering method, the results demonstrate that the improved filtering method can more effectively smooth the fluctuation of the deformation rate. The amplitude of the error-induced tangent angle warning value fluctuation can be reduced, which provides correct early warning. The improved method provides a new approach for the real-time filtering of subsequent landslide deformation data.

Опасность оползневой деятельности в Моздокском районе Республики Северная Осетия – Алания

The object of the study is landslide activity in the Mozdok administrative region of the Republic of North Ossetia-Alania. The work examines the results of systematization and analysis of various sources containing information about the activity and danger of landslide processes in this area in the 21st century. The conditions, causes, activity and danger of landslide activations in the region are characterized. It is noted that the activity of landslide processes in the study area is mainly due to natural factors, however, recently the emergence of new landslide forms and the activation of old ones most often occur under technogenic influence. Landslide activity in the region is observed on the ledge of the right bank alluvial-accumulative terrace of the river. Terek, where most of the settlements of the region are located, as well as the northern slope of the Tersky ridge and in the area of the watershed ridge of the Sunzhensky ridge. The most significant activation of landslide processes (causing deformation of residential buildings and roadways of streets) was observed in the villages of Maly Malgobek, Predgornoye and Sukhotskoye. Since 2021, after the reconstruction of the Zilga – Mozdok highway, significant landslide deformations and destruction of the roadway have been observed on the section of the road between the villages of Khurikau (Mozdok district) and Old Batako (Right Bank district). The article provides information about the activity of landslide activity and the scale of deformations and destruction of residential and transport infrastructure that occurred in the area. Analysis of the socio-economic consequences of the landslide activations shows a significant degree of their danger to the life of the population in the Mozdok region.

MUSEnet: High Temporal-Frequency Estimation of Landslide Deformation Field Through Joint InSAR and Hydrological Observations Using Deep Learning

MUSEnet: High Temporal-Frequency Estimation of Landslide Deformation Field Through Joint InSAR and Hydrological Observations Using Deep Learning

Machine-learning-based nowcasting of the Vögelsberg deep-seated landslide: why predicting slow deformation is not so easy

Abstract. Landslides are one of the major weather-related geohazards. To assess their potential impact and design mitigation solutions, a detailed understanding of the slope processes is required. Landslide modelling is typically based on data-rich geomechanical models. Recently, machine learning has shown promising results in modelling a variety of processes. Furthermore, slope conditions are now also monitored from space, in wide-area repeat surveys from satellites. In the present study we tested if use of machine learning, combined with readily available remote sensing data, allows us to build a deformation nowcasting model. A successful landslide deformation nowcast, based on remote sensing data and machine learning, would demonstrate effective understanding of the slope processes, even in the absence of physical modelling. We tested our methodology on the Vögelsberg, a deep-seated landslide near Innsbruck, Austria. Our results show that the formulation of such a machine learning system is not as straightforward as often hoped for. The primary issue is the freedom of the model compared to the number of acceleration events in the time series available for training, as well as inherent limitations of the standard quality metrics such as the mean squared error. Satellite remote sensing has the potential to provide longer time series, over wide areas. However, although longer time series of deformation and slope conditions are clearly beneficial for machine-learning-based analyses, the present study shows the importance of the training data quality but also that this technique is mostly applicable to the well-monitored, more dynamic deforming landslides.

Estimating the period of probable landslide event using advanced D-InSAR technique for time-series deformation study of Kotrupi region

Himalayan region is prone to landslides and every year many landslides occur. Some of these events are reported while, many of these landslides are not reported due to their remote location of occurrence, or nature of impact, and its date of occurrence is also unknown. This study tries to find a method to estimate the probable dates of landslide occurrences using time-series deformation analysis applying advanced DInSAR approach by adopting a short temporal baseline graph. We used 118 Sentinel 1-A SLC images to identify the nature, pattern, and trends of landslide deformation. Based on the time series cumulative deformation values, we found a ‘V’-shaped kink that matches with the landslide events. Such kinks were observed around 12 June 2015, 25 May 2016, 05 August 2016, 23 June 2017, 15 September 2019, 27 May 2018, 08 June 2018, and 15 July 2020. The majority of these kinks are visible during monsoon season which is expected. High-resolution Planet satellite data around these time ranges were used to verify the existence of new landslides. Moreover, the performance is evaluated using AUC-ROC, precision, recall; and F-Score. This approach has good prediction capabilities with the precision of 87%, AUC-ROC of 77% and F-score of 74%.

A research on a new mapping method for landslide susceptibility based on SBAS-InSAR technology

The acquisition of landslide inventory represents a pivotal challenge in landslide susceptibility mapping. Existing landslide susceptibility maps(LSMs) predominantly rely on manually obtained landslide inventories, leading to an overdependence on expert insights and susceptibilities to topographic and geomorphic influences. In regions characterized by steep terrain, obtaining a landslide inventory can be arduous or even unattainable, subsequently constraining the utility of LSMs. Addressing the limitations of conventional LSMs, this study introduces an innovative method for landslide inventory compilation and LSM creation, utilizing Small Baselines Subset Interferometry Synthetic Aperture Radar(SBAS-InSAR) technology. The study area selected for illustration is the Dongchuan district, notorious for frequent landslide occurrences. The application of SBAS-InSAR facilitated the extraction of surface deformation data, subsequently enabling the selection of landslide deformation points as samples. These samples underwent analysis through a particle swarm optimization-backpropagation neural network(PSO-BPNN) guided by deformation thresholds and the landslide developmental environment. This produced the LSM for the Dongchuan district. Subsequent validation of the LSM employed both qualitative and quantitative measures. Results elucidate that the LSM, as derived from the presented approach, primarily highlights high to very high susceptibility zones in landslide-prone areas, mirroring the spatial distribution of historical landslides. The method also achieved a commendable accuracy(ACC) of 79.59% and an area under the curve(AUC) value of 0.88. Notably, the landslide density exhibited a direct correlation with increasing susceptibility class. Such findings align with previous studies, endorsing the feasibility and reliability of the proposed approach.

Deformation Characteristics and Stability Prediction of Mala Landslide at Miaowei Hydropower Station under Hydrodynamic Action

In recent years, with the completion of the construction of large-scale hydropower projects in China, a series of engineering geological problems that occurred during the operation of the hydropower station have become an important issue affecting the normal operation of hydropower stations. Landslides on reservoir slopes triggered especially by water storage and other factors related to the construction of hydropower stations seriously affect the normal operation of the hydropower station and lead to other geological disasters. Research indicates that many reservoir-area landslides are triggered by hydrodynamic forces resulting from water level fluctuations in hydroelectric power stations. The Mala landslide of Miaowei Hydropower Station in the Lancang River Basin of China is taken as the engineering example to study the influence of hydrodynamic forces on the deformation characteristics and stability trends of the landslide. This paper explores the formation mechanism and influencing factors of the Mala landslide by conducting a field investigation of the Mala landslide and analyzing the monitoring data. Additionally, this paper also discusses the impacts of water storage, rainfall, and engineering construction on landslide induction. It is considered that the evolution of the Mala landslide from the initial stage of water storage to the current state mainly includes four stages: small-scale bank collapse stage, creep deformation stage, accelerated sliding stage, and uniform sliding stage. Moreover, the changes in the trend of landslide stability are analyzed using the two-dimensional finite element method. The research results provide a valuable reference for understanding the formation mechanism and predicting the deformation of reservoir landslides, which has considerable engineering practical significance.

Adaptability Analysis of Sentinel−1A and ALOS/PALSAR−2 in Landslide Detection in the Qinling-Daba Mountains

Due to the complex terrain and intense tectonic activity, and harsh climate in the Qinling-Daba Mountains, many landslides occur in the area. Most of these landslides are extremely active, posing a serious threat to the safety and property of local residents. As a mature deformation-monitoring technology, InSAR has been widely used in landslide detection, but the steep terrain and dense vegetation in the Qinling-Daba Mountains make detection challenging. Hence, it is important to choose suitable data sources and methods for landslide detection via InSAR in this area. This study was the first to collect ALOS/PALSAR−2 and Sentinel−1A images to detect landslides in the Qinling-Daba Mountains, applying a method combining IPTA and SBAS. In total, 88 landslides were detected and validated. The results show that the deformation-detection error rate of Sentinel−1A is 2% higher than that of ALOS/PALSAR−2 and that its landslide-recognition rate is 47.7% lower than that of ALOS/PALSAR−2. Upon comparing and analyzing the visibility, coherence, closed−loop residuals, and typical time series of landslide deformation from the two kinds of data, it was found that the extremely low quality of available Sentinel−1 A summer data is a major factor influencing that system’s performance. ALOS/PALSAR−2 is more likely to detect landslides in areas with high vegetation coverage, meeting more than 90% of the monitoring needs. It is thus highly suitable for landslide detection in the Qinling–Daba Mountains, where seasonality is significant. In this paper, for the first time, multiple data sources are compared in detail with regard to their utility in landslide detection in the Qinling–Daba Mountains. A large number of accuracy metrics are applied, and the results are analyzed. The study provides important scientific support for the selection of data sources for future landslide monitoring in the Qinling–Daba Mountain area and similar areas and for the selection of methods to evaluate the accuracy of InSAR monitoring.

PROBABILISTIC APPROACH IN MODELING SLOPE STABILITY – IMPLEMENTATION AND FEATURES

The limitations of the existing deterministic approaches to the quantitative assessment of slope stability are analyzed. The basic principles of probabilistic analysis in mathematical simulation of slope stability are considered. The possibility of obtaining probabilistic assessment of the development of landslide deformations is shown, which can later be used in the analysis of geological risk. An assessment of the objectivity of probabilistic modeling of slope stability is carried out.

An optimized DEM‐SPH model for surge waves induced by riverside landslides

AbstractRiverside landslides can induce surge waves during the process of entering water. The landslide‐induced surge wave is often regarded as a significant secondary disaster, as its impact region is likely to be even more remarkable than the landslide itself. Due to the large deformation of the soil landslide and water body during the evolution of surge waves, commonly adopted multi‐phase flow models and mesh‐based methods coupled with discrete element models have some limitations, such as weak accuracy, and simulation distortion with immature coupling algorithms. As a result, the pure meshless method of smoothed particle hydrodynamics (SPH) coupling discrete element methods (DEM) become promising. Modified from the traditional empirical formulae‐based coupling algorithm, this study proposes a semi‐resolved algorithm that effectively eliminates the particle distortion at the fluid‐solid coupling interface. The proposed model demonstrates its superiority through a comparison of the pressure and porosity near the coupling interface. Furthermore, the optimized DEM‐SPH model is applied to simulate landslide‐induced surge waves, and the simulation results have also validated the accuracy of the model. This model is capable of accurately simulating the entire propagation process from landslide entry into water to the wave reaching the opposite shore, and therefore provides an effective tool for the prediction and mitigation of wave hazards caused by landslides.

A self-powered angle measurement sensor based on triboelectric nanogenerators for slope deformation monitoring and landslides pre-warning

The inclination angle is an important factor for slope deformation monitoring. This paper introduces a self-powered pendulum inclination sensor (PI-TENG) based on triboelectric nanogenerator, which can effectively monitor slope deformation. PI-TENG adopts raster-sliding mode as the main structure. The fabricated PI-TENG, based on a raster-sliding working mode, using the linear relationship between the rotation angle of the monitoring object and the number of output pulses to establish an intuitive rotation signal mapping, which can realize self-powered monitoring of the rotation direction and angle not being affected by the environment (such as temperature and humidity). The corresponding minimum resolution of PI-TENG is 3° and can be further improved by increasing the number of Nylon grids on the stator. Importantly, the sensor has a simple structure and low price, and can realize self-powered monitoring without additional power supply. This work provides new ideas for monitoring slope deformation and early warning of landslides.