Landslides In China Research Articles

Permeability characteristics, structural failure characteristics, and triggering process of loess landslides in two typical strata structures

Landslides occurring at the interface of strata are among the most common forms of loess landslides in China. Statistics indicate that significant loess-red silty clay interface landslides induced by irrigation in the Heifangtai Platform than loess-paleosol interface landslides in the South Jingyang Platform. To uncover the permeability characteristics, structural failure patterns, and triggering processes of two typical strata structures. This study employs Nuclear Magnetic Resonance (NMR) and Scanning Electron Microscopy (SEM) techniques to investigate the permeability and structural failure of two soil combination types: loess-red silty clay and loess-paleosol. The results revealed a positive correlation between the stagnant water effect and flow rate, but a negative correlation with the initial water content. Notably, these two typical strata exhibited distinct differences in the stagnant water effect. In loess-red silty clay, continuous filling of mesopores and macropores by fine clay particles, while at the same time the agglomerates disintegration at the interface, thereby enhancing the stagnant water effect. In contrast, loess-paleosol exhibited good connectivity between the mosaic pores at the interface. This facilitated the formation of several elongated microcracks, which acted as dominant channels for infiltration and weakened the stagnant water effect. However, the macroscopic triggering mechanism for loess landslides in both loess-red silty clay and loess-paleosol combination strata remains similar. Irrigation water stagnates within the relatively impermeable layers, saturating and structurally damaging the bottom of loess layer, ultimately inducing landslides. These findings provide a scientific basis for the future study of loess landslide hazards in different strata structures, which is of great significance.

Spatiotemporal patterns of non-seismic fatal landslides in China from 2010 to 2022

Spatiotemporal patterns of non-seismic fatal landslides in China from 2010 to 2022

Integrating Feature Selection with Machine Learning for Accurate Reservoir Landslide Displacement Prediction

Accurate prediction of reservoir landslide displacements is crucial for early warning and hazard prevention. Current machine learning (ML) paradigms for predicting landslide displacement demonstrate superior performance, while often relying on various feature engineering techniques, such as decomposing into different temporal lags and feature selection. This study investigates the impact of various feature selection techniques on the performance of ML algorithms for landslide displacement prediction. The Shuping and Baishuihe landslides in China’s Three Gorges Reservoir Area are used to comprehensively benchmark four prevalent ML algorithms. Both static ML models, including backpropagation neural network (BPNN), support vector machine (SVM), and dynamic models, such as long short-term memory (LSTM), and gated recurrent unit (GRU), are included. Each ML model is evaluated under three feature engineering techniques: raw multivariate time series, and feature selection under maximal information coefficient-partial autocorrelation function (MIC-PACF), or grey relational analysis-PACF (GRA-PACF). The results demonstrate that appropriate feature selection methods could significantly improve the performance of static ML models. In contrast, dynamic models effectively leverage inherent capabilities in capturing temporal dynamics within raw multivariate time series, seeing marginal gains with extensive feature engineering compared to no feature selection strategy. The optimal feature selection approach varies based on the ML model and specific landslide, highlighting the importance of case-specific assessments. The findings in this study offer guidance on integrating feature selection techniques with different machine learning models to maximize the robustness and generalizability of data-driven landslide displacement prediction frameworks.

Precipitation-induced landslide risk escalation in China’s urbanization with high-resolution soil moisture and multi-source precipitation product

Landslides pose a formidable natural hazard. Accurate risk assessment of landslides triggered by precipitation heavily relies on hydrometeorological factors, specifically precipitation and soil moisture. However, the insufficient ground-based observations and the coarse spatio-temporal resolution hinder the performance of landslide prediction. It is not clear what hydrometeorological thresholds and triggering mechanisms are more likely to trigger landslides in China, particularly in the context of rapid urbanization. To address these questions, this study investigated 1504 shallow landslide events in Chinese urban and non-urban areas from 2007 to 2019. It utilized daily 1 km soil moisture at various depths (20–100 cm) and multi-source precipitation datasets, including gauge-based gridded dataset, in conjunction with three multi-source fusion precipitation products (Multi-Source Weighted-Ensemble Precipitation − MSWEP, the Climate Hazards Group InfraRed Precipitation with Station dataset − CHIRPS, and the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement − GPM-IMERG), along with dynamic urban impervious area datasets. It aims to determine the optimal multi-source precipitation predictor, the critical soil moisture depth that triggers landslides, and to establish the hydrometeorological thresholds for landslides. Additionally, the impact of urbanization on landslide occurrences was estimated by comparing antecedent precipitation accumulation, soil moisture, and impervious surface ratio dynamics between urban and non-urban areas. The results indicated that a combination of 2-day cumulative CHIRPS precipitation and 100 cm soil moisture provided the most accurate predictions for landslides in urban regions of China (accuracy = 88.5 %), outperforming interpolated ground-based observations and other fusion products. Specifically, landslides become more prone when antecedent cumulative rainfall surpasses 97.42 mm in 2 days and soil moisture exceeds 39.6 % m3/m3 saturation in China. Urban areas experienced high antecedent precipitation levels, lower precipitation (64.40 mm) threshold and soil moisture threshold (38.9 %), and shorter durations at landslide sites compared to non-urban areas (71.90 mm, 41.4 %, and 7 days, respectively). The process of urbanization is observed to decrease soil moisture levels while concurrently elevating rainfall amounts. This phenomenon, combined with anthropogenic activities, including distance from roads and urban impervious surface expansion, contributes to 44.6 % of the causes of landslides by reducing slope stability and increasing the presence of loose material. These findings have implications for landslide warnings in urban areas with limited measurements and contribute to understanding urbanization’s impact on landslide risks in developing nations.

Delineating Non-Susceptible Landslide Areas in China Based on Topographic Index and Quantile Non-Linear Model

Efficient analysis of non-susceptibility to landslides targets regions with minimal or zero landslide probability, thereby obviating the need to estimate the likelihood for low-susceptibility zones. This study assesses the effectiveness of the quantile non-linear (QNL) model in delineating the non-susceptibility of landslides in China through a topographic index. The topographic index encompassed slope angle and topographic relief, which are calculated using a 3 × 3 and 15 × 15 square cell moving window, respectively. Additionally, a global landslide susceptibility model established using a comprehensive global landslide database and fuzzy algorithm was employed for comparative analysis, providing a holistic evaluation of the QNL model’s accuracy. The results show that while the overall distribution of the two QNL models for non-susceptible landslide areas was roughly consistent, notable discrepancies were observed in localized regions, especially in the Southwest and Qinghai-Tibet geological environment areas where landslides are prone to occur. The applicability of the QNL model is significantly limited in these areas. In addition, the predicted results of the QNL_CHN model are closer to those based on the global landslide susceptibility model of the fuzzy algorithm. This study provides valuable insights to enhance the QNL model’s applicability, thereby strengthening forest ecosystem management and mitigating ecological disaster risks.

A framework for automated landslide dating utilizing SAR-Derived Parameters Time-Series, An Enhanced Transformer Model, and Dynamic Thresholding

Determining the timing of landslide occurrence is crucial for establishing an accurate, comprehensive and systematic landslide inventory while assessing the potential for reducing landslide risk. Unfortunately, many existing landslide inventories lack temporal information such as the precise time of landslide events. Optical and Synthetic Aperture Radar (SAR) sensors are the most commonly used remote sensing technologies for landslide detection. Unlike optical sensors, SAR sensors are not affected by cloudy conditions and provide valuable imagery regardless of sunlight availability. Therefore, SAR-derived parameters, i.e., SAR amplitude, interferometric coherence, and polarimetric features (alpha and entropy), offer a higher temporal resolution for detecting landslide occurrence times compared to optical data. Despite the advantages, there is currently no universally accepted automatic method for determining the time of landslide events using SAR data. This is due to the lack of anomaly labels and the high time-series volatility in detecting landslide occurrence times. Despite advances in deep-learning methods for anomaly detection in time-series, only a few of them can address these challenges in our case. In this paper, we propose an unsupervised multivariate transformed-based deep-learning model to automatically and efficiently estimate landslide occurrence times using multivariate SAR-derived parameters time-series analysis. The designed gated relative position can increase robustness and temporal context information, by learning global temporal trends in the time-series. Subsequently, the time-series of the anomaly score derived from the proposed Transformer model is analyzed using an adaptive thresholding strategy to dynamically and automatically mark anomalies related to the landslide occurrence. Our research focuses on collapsed landslides characterized by dramatic changes in ground surface topography, with a particular attention for the need of a prior knowledge about landslide boundaries. We assess the performance of the proposed methodology for several collapsed landslides including the July 21, 2020 Shaziba and 23 July, 2019 Shuicheng landslides in China, March 19, 2019 Takht landslide in Iran, June 15, 2018 Jalgyz-Jangak and May 25, 2018 Kugart landslides in Kyrgyzstan, July 7, 2018 Hitardalur landslide in Iceland, and January 25, 2019 Brumadinho landslide in Brazil. In comparison to commonly used neural networks like the LSTM algorithm, our proposed framework leads to a more accurate estimate for the time of landslide failure using time-series of SAR-derived parameters. Furthermore, our results suggest the great potential of SAR data to narrow the time period detected from optical data when used in conjunction with them.

Revisiting spatiotemporal evolution process and mechanism of a giant reservoir landslide during weather extremes

An updated comprehension of landslide kinematics is a prerequisite for developing effective risk assessment and emergency management in the context of climate variability and anthropogenic disturbances. This study employs a multidisciplinary approach, taking the Outang landslide in China's Three Gorges Reservoir area as a case study. We examined the spatiotemporal surface processes and time- and subzone-dependent hydrometeorological factors from 2007 to 2021, particularly identifying rainfall regimes and reservoir water levels that trigger landslide movements. Given the uneven distribution of GNSS surface markers, we introduced weighted monthly mean displacement (D) and weighted marker frequency (f), enhancing the accuracy of our insights into distinct active subzones of the landslide. Preliminary results revealed July, September and June as months of higher susceptibility, as well as months with low water levels. The early-stage deformation control by reservoir water levels is being increasingly overshadowed by rainfall, due to extreme weather events and localized anti-slip measures. Long-term surface processes have also highlighted the transformation of landslide failure modes towards a compound pattern, from a predominantly retrogressive to a thrust-dominated pattern, and even overall translation coupled with localized rotation. Furthermore, the comparative analysis of normalized displacement/strain demonstrates the power and potential of integrating multi-source monitoring data. By revisiting the evolution processes and potential deformation mechanisms of the Outang landslide, we are getting closer to understanding more about the dynamics of giant landslides in reservoir areas, and illuminate the pathway towards enhanced risk management and emergency response strategies.

LiteTransNet: An interpretable approach for landslide displacement prediction using transformer model with attention mechanism

Accurate landslide displacement prediction is crucial for effective early warning systems to mitigate hazards. The importance of historical information varies with time during prediction due to the underlying landslide deformation mechanism. Despite advances in dynamic machine learning models like Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), they struggle to fully capture and interpret the varying importance of historical information during prediction, resulting in decreased accuracy and limited understanding of landslide physical mechanisms. Additionally, these approaches rely on manual feature selection, which is independent of the learning process and therefore is prone to estimation errors. To address these challenges, we propose LiteTransNet, a Lightweighted Transformer Network tailored for landslide displacement prediction. Built on the revolutionary Transformer model for sequential data, LiteTransNet leverages localized self-attention to selectively focus on relevant timestamps and provides interpretable results through its attention heatmap. Furthermore, LiteTransNet performs end-to-end time series modeling without extensive feature engineering. We validate LiteTransNet on two landslides in China's Three Gorges Reservoir Area and demonstrate its improved accuracy over recurrent baselines utilizing feature selection methods. Notably, the attention heatmap generated by LiteTransNet provides interpretable insights into the model's temporal dependency learning. It uncovers the varying importance of historical information at different timestamps, showcasing how LiteTransNet's attention aligns with specific periods of the external environment that cause significant disruptions in the landslide system. Our findings reveal that these disruptions continuously impact displacement prediction until their effects fade or are replaced by subsequent intense disturbances, which traditional recurrent methods are unable to capture. Further experiment shows that LiteTransNet enhances efficiency through its streamlined architecture and its parallelization capability. Overall, LiteTransNet innovates landslide prediction by providing accuracy, interpretability, and efficiency, enhancing understanding of deformation mechanisms to facilitate effective early warning systems.

Centrifuge modeling of unreinforced and multi-row stabilizing piles reinforced landslides subjected to reservoir water level fluctuation

With the construction of the Three Gorges Reservoir dam, frequent reservoir landslide events have been recorded. In recent years, multi-row stabilizing piles (MRSPs) have been used to stabilize massive reservoir landslides in China. In this study, two centrifuge model tests were carried out to study the unreinforced and MRSP-reinforced slopes subjected to reservoir water level (RWL) operation, using the Taping landslide as a prototype. The results indicate that the RWL rising can provide lateral support within the submerged zone and then produce the inward seepage force, eventually strengthening the slope stability. However, a rapid RWL drawdown may induce outward seepage forces and a sudden debuttressing effect, consequently reducing the effective soil normal stress and triggering partial pre-failure within the RWL fluctuation zone. Furthermore, partial deformation and subsequent soil structure damage generate excess pore water pressures, ultimately leading to the overall failure of the reservoir landslide. This study also reveals that a rapid increase in the downslope driving force due to RWL drawdown significantly intensifies the lateral earth pressures exerted on the MRSPs. Conversely, the MRSPs possess a considerable reinforcement effect on the reservoir landslide, transforming the overall failure into a partial deformation and failure situated above and in front of the MRSPs. The mechanical transfer behavior observed in the MRSPs demonstrates a progressive alteration in relation to RWL fluctuations. As the RWL rises, the mechanical states among MRSPs exhibit a growing imbalance. The shear force transfer factor (i.e. the ratio of shear forces on pile of the nth row to that of the first row) increases significantly with the RWL drawdown. This indicates that the mechanical states among MRSPs tend toward an enhanced equilibrium. The insights gained from this study contribute to a more comprehensive understanding of the failure mechanisms of reservoir landslides and the mechanical behavior of MRSPs in reservoir banks.

Failure process and three-dimensional motions of mining-induced Jianshanying landslide in China observed by optical, LiDAR and SAR datasets

ABSTRACT The occurrence of collapses and landslides due to underground mining has its unique failure mechanism, especially in the Karst mountainous regions of China. Spaceborne and airborne remote sensing observations provide rapid and effective tools for assessing surface changes and monitoring surface deformation of such landslides. In this study, we take the Jianshanying landslide, a typical mining-induced and fast-deformed landslide, as an example, and reveal the failure mechanism of such landslide by investigating the historical surface displacement. First, the complete evolution of the landslide surface was investigated from its original state to the overall sliding. The data include the satellite and Unmanned Aerial Vehicle (UAV) optical images, UAV three-dimensional (3-D) real scene models, high-resolution Light Detection and Ranging (LiDAR) DEM, and field survey. The results show that the head region entered the high deformation stage after 2019, the maximum deformation rate was 12.3 m/yr. The landslide morphology was formed after the overall slide occurred in September 2020. Then, the pre-event 3-D surface deformation after the landslide entered the high deformation stage was recovered using Interferometric Synthetic Aperture Radar (InSAR), differential DEM, and SAR/optical offset-tracking techniques. The vertical deformation was recovered around −30 m from 2019 to 2020. In particular, we solved the problem of unequal accuracy of SAR and optical offset-tracking observations in 3-D deformation inversion by employing the Helmert variance component estimation method. The maximum deformation was 6 m and 3 m within 4 months in the NS and EW directions, respectively. Finally, we revealed the failure mechanism of the Jianshanying landslide based on the disparity of horizontal and vertical deformation. That is, underground mining causes a significant subsidence of the rear part of the landslide body, resulting in different stress changes in the rear and front parts of the landslide body, which eventually led to sliding of the front part of the slope along the free surface. This work investigates and monitors the typical underground mining-induced Jianshanying landslide by using multi-sensor remote sensing approaches to trace the pre-event surface motions and to reveal its failure mechanism.

Numerical study of soil-rock mixtures with 3D numerical manifold method: Generation of random aggregate structure and discretized model

As the main material composition of many large landslides in China and other countries, the soil-rock mixtures (SRMs) have attracted many researchers to investigate their mechanical and hydraulic characteristics in the past several decades. Although in-door and in-situ tests still play the dominant role in obtaining the material parameters of SRMs, numerical methods are seen as a better choice regarding economy and practicality. As a competitive numerical method, the numerical manifold method (NMM), which can solve continuous and discontinuous problems in a unified framework, is used in the present work to investigate the hydraulic characters of SRMs. Before investigating the hydraulic characters of SRMs with NMM, the aggregate structural model and discretized model of SRMs should be built. To effectively build the aggregate structure model of SRMs with high rock content, a simplified random sequential addition method, which is based on E(A, B) concept and background mesh, is firstly developed. Then, the recently proposed parallel preprocessing strategy of NMM is used to generate the discretized model of SRMs. Finally, an illustrative example regarding the determination of effective permeability coefficient of SRMs is solved with 3DNMM. The proposed numerical model in investigating SRMs deserves further investigation.

Rainfall early warning threshold and its spatial distribution of rainfall-induced landslides in China

In order to investigate the spatial distribution of early warning threshold for landslide induced by rainfall in China, the literatures about rainfall thresholds of landslides in China in recent 20 years are selected. Statistical analysis and visualization methods were employed to systematically analyze the research progress of rainfall early warning thresholds at various scales. Taking the typical rainfall intensity-duration (I-D) threshold model as the research object, combined with the geographical characteristics of China and the average annual rainfall of 20 years, the spatial distribution of early warning thresholds for rainfall-induced landslide in China is depicted. The results show that the inspired rain intensity coefficient α of the rainfall threshold (I-D curve) in China roughly increases gradually with the decrease of topography. Moreover, under consistent annual rainfall conditions, the scalar index β exhibits regular changes corresponding to variations in terrain. Topography and rainfall are the two main factors strongly associated with the rainfall threshold. This research establishes a clear framework for studying the early warning thresholds for rainfall-induced landslides in China and holds significant scientific implications for developing more effective rainfall threshold models.

The 21 July 2020 Shaziba landslide in China: Results from multi-source satellite remote sensing

A catastrophic landslide occurred on 21 July 2020, 30 km from Enshi city, in Mazhe County of Hubei province, China. In this paper, we aimed to investigate the kinematic evolution and volumetric change related to this landslide using multi-source remote sensing measurements from synthetic aperture radar (SAR) and optical data. C-band Sentinel-1 and X-band TerraSAR-X SAR data are analyzed using several multi-temporal interferometry (MTI) time-series techniques including Persistent Scatterer Interferometry (PSI), Small Baseline subset (SBAS), and Combined eigenvalue maximum likelihood Phase Linking (CPL). The spatial pattern of surface deformation resulting from the interferometric analysis is then statistically analyzed to retrieve the pre-failure and post-failure displacements. Co-failure motions are analyzed using an image correlation technique applied to both the Planetscope and Sentinel-2 images. Moreover, 4 pairs of bistatic TerraSAR-X/TanDEM-X (TDX) data are utilized to generate high-precision digital elevation models (DEMs) and estimate the volumetric change related to the main slope failure.The pre-failure ground deformation analysis results suggest that the landslide was already active before the July 2020 failure, with the seasonality and hydraulic diffusivity being characteristics of a slow-moving landslide. Among the three different MTI methods applied, the CPL method results in a greater measurement points (MPs) density than the PSI and SBAS method when estimating the pre-failure movement of the Shaziba landslide. The July 2020 Shaziba disaster is divided into three main parts: (1) slightly horizontal deformation of 0.5–1.5 m within the northern part with ground and house cracks, (2) less collapse in the eastern part with horizontal motions reaching 30 m and (3) a highly eroded western part where vegetation was wholly lost in the main event, resulting in an collapse volume of approximately 4.98 million m3, out of which approximately around 3.4 million m3 was deposited and approximately 1.58 million m3 was washed away into the Qing River. After the failure, the marginal scrap of the main failure body, above crown of landslide and eastern part showed instability with rates of 20–30 mm/yr, suggesting that the failure zone may continue to expand.

Prefabricated acoustic emission array system for landslide monitoring

The quality of landslide monitoring data is crucial to the reliability of early warning systems. One effective approach to improving data quality is through innovation in monitoring techniques. In the case of existing acoustic emission (AE) monitoring methods, the achievable deformation quantification accuracy is limited by the variability of internal and external factors that affect the generated AE signal. To overcome these limitations and further improve monitoring effectiveness, this study developed a novel monitoring equipment consisting of an AE array, which facilitates the standardisation of equipment manufacturing and improves the consistency of data interpretation, thus providing a system that is simple and practical for field application. The developed AE monitoring system includes modules for on-site monitoring, data transmission, information management, and early warning analysis. The AE monitoring system was installed on different soil landslides in China and is continuously operating. Because the AE array provides a standardised and simple tool for landslide subsurface deformation monitoring, it is well-suited for low-income mountainous areas with a high risk of landslides.

Fatal landslides in China from 1940 to 2020: occurrences and vulnerabilities

Fatal landslides in China from 1940 to 2020: occurrences and vulnerabilities

Landslide Displacement Prediction Based on Multivariate LSTM Model.

There are many frequent landslide areas in China, which badly affect local people. Since the 1980s, there have been more than 200 landslides in China with a death toll of 30 or more people at a time, economic losses of more than CNY 10 million or significant social impact. Therefore, the study of landslide displacement prediction is very important. The traditional ARIMA and LSTM models are commonly used for forecasting time series data. In our study, a multivariable LSTM landslide displacement prediction model is proposed based on the traditional LSTM model, which integrates rainfall and reservoir water level data. Taking the Baijiabao landslide in the Three Gorges Reservoir area as an example, the data of displacement, rainfall and reservoir water level of monitoring point ZG323 from November 2006 to December 2012 were selected for this study. Our results show that the displacement prediction results of the multivariable LSTM model are more accurate than those of the ARIMA and the univariate LSTM models, and the mean square, root mean square and mean absolute errors are the smallest, which are 0.64223, 0.8014 and 0.50453 mm, respectively. Therefore, the multivariable LSTM model method has higher accuracy and better application prospects in the displacement prediction of the Baijiabao landslide, which can provide a certain reference for the displacement prediction of the same type of landslide.

Application of Wireless Sensors and Their Networks in Monitoring Landslides

Since the 21st century, wireless sensor network technology has been rapidly developed, and this technology has been widely concerned not only in military, medical, education and other aspects, but also in disaster prevention and control has been well applied. This paper firstly analyzes the necessity of landslide monitoring, discusses and compares the more commonly used technical means of landslide monitoring, then analyzes and compares the systems based on wireless sensor network technology in monitoring landslides in China and abroad, and finally puts forward the application defects of wireless sensor network technology.

Combining seismic signal dynamic inversion and numerical modeling improves landslide process reconstruction

Abstract. Landslides present a significant hazard for humans, but continuous landslide monitoring is not yet possible due to their unpredictability. In recent years, numerical simulation and seismic inversion methods have been used to provide valuable data for understanding the entire process of landslide movement. However, each method has shortcomings. Dynamic inversion based on long-period seismic signals gives the force–time history of a landslide using an empirical Green's function but lacks detailed flowing characteristics for the hazards. Numerical simulation can simulate the entire movement process, but results are strongly influenced by the choice of modeling parameters. Therefore, developing a method for combining those two techniques has become a focus for research in recent years. In this study, we develop such a protocol based on analysis of the 2018 Baige landslide in China. Seismic signal inversion results are used to constrain and optimize the numerical simulation. We apply the procedure to the Baige event and, combined with a field geological survey, show it provides a comprehensive and accurate method for dynamic process reconstruction. We found that the Baige landslide was triggered by detachment of the weathered layer, with severe top fault segmentation. The landslide process comprised four stages: initiation, main slip, blocking, and deposition. Multi-method mutual verification effectively reduces the inherent drawbacks of each method, and multi-method joint analysis improves the rationality and reliability of the results. The approach outlined in this study could help us to better understand the landslide dynamic process.

InSAR stacking with atmospheric correction for rapid geohazard detection: Applications to ground subsidence and landslides in China

• Enhanced InSAR stacking with atmospheric correction for geohazard detection. • Atmospheric correction reduces stacking bias due to nonstationary signals. • Efficient for geohazard inventory generation and update. Earth observation technologies have great potential in the investigation, monitoring and assessment of various geohazards. Stacking is an efficient InSAR method for estimating deformation rates and helps in the generation and update of the geohazard inventories. However, it relies on the assumption that the atmospheric statistics are stationary, which does not always hold in large-scale interferograms processing. The nonstationary signal, caused by turbulence and stratification of atmosphere, will bias the deformation estimate and lead to misinterpretations of the geophysical processes. In this paper, we propose an enhanced InSAR stacking method integrated with atmospheric correction. Atmospheric errors in the interferograms are first corrected, and then the mean deformation rate is estimated based on least squares. Applications are conducted in ground subsidence monitoring in the Yellow River Delta as well as landslide detection along the Jinsha River, China, with the deformation results evaluated by spatial structure function, semi-variogram and correlation. Spatial dependence in the subsidence results of the Yellow River Delta decreases from 757 km to 220 km, suggesting that the influence of atmospheric turbulence on deformation is mitigated. Correlation between deformation rate and elevation along the Jinsha River reduces from 0.40 to 0.15, indicating that stratification is suppressed. The proposed method adopts the strategies of simplicity and effectiveness, and the outcomes, which can meet the requirements of geohazards general survey, will be beneficial to rapid geohazard detection.

Slope Stability Analysis Method of Unsaturated Soil Slopes Considering Pore Gas Pressure Caused by Rainfall Infiltration

The variation of pore gas pressure caused by rainfall infiltration is an important factor that leads to slope failures. The purpose of this study is to propose a new slope stability analysis method that considers pore gas pressure and examines the effect of airflow on slope stability by using a numerical method. A water-air two-phase flow analysis was conducted to investigate the distribution of pore air pressure, pore water pressure, and water saturation triggered by rainfall infiltration. Then the variation of the load resulting from pore gas pressure was incorporated into the slope stability analysis method based on the unsaturated soil shear strength theory and the residual thrust method to simulate the influence of airflow on the Tanjiahe landslide in China. In order to study the infiltration behavior with respect to initial saturation, water and gas flow analyses were performed considering various initial states of saturation under similar settings. Results showed that the pore gas pressure between the slope surface and the slip band clearly varied and that it decreased during the process from the slide bed to the deep direction. Then, the pore water pressure formed in the saturated zone was transferred by the airflow to the slope toe. As a result, because the pore gas pressure gradient increased the thrust of the slide mass, the safety factor decreased over time. Moreover, in the first step, the magnitude of infiltration decreased with an increase in initial saturation, while when the magnitude dropped to the minimum value, it then went up with an increase in initial saturation. The maximum value was usually reached at a saturation degree of 0% or 100%. When evaluating slope stability, the safety factor obtained by the slope stability analysis method that considered the water-gas coupling effect was much lower than when it was not considered during the process of a similar seepage. The impact on the slope failure was significant and may provide a practical reference for hazard assessments to control rainfall-induced landslides.