Landslide Deformation Monitoring Research Articles

Landslide Detection Based on Multi-Direction Phase Gradient Stacking, with Application to Zhouqu, China

Landslides are a common geological disaster, which cause many economic losses and casualties in the world each year. Drawing up a landslide list and monitoring their deformations is crucial to prevent landslide disasters. Interferometric synthetic aperture radar (InSAR) can obtain millimeter-level surface deformations and provide data support for landslide deformation monitoring. However, some landslides are difficult to detect due to the low-coherence caused by vegetation cover in mountainous areas and the difficulty of phase unwrapping caused by large landslide deformations. In this paper, a method based on multi-direction phase gradient stacking is proposed. It employs the differential interferograms of small baseline sets to directly obtain the abnormal region, thereby avoiding the problem where part of landslide cannot be detected due to a phase unwrapping error. In this study, the Sentinel-1 satellite ascending and descending data from 2018 to 2020 are used to detect landslides around Zhouqu County, China. A total of 26 active landslides were detected in ascending data and 32 active landslides in the descending data using the method in this paper, while the SBAS-InSAR detected 19 active landslides in the ascending data and 25 active landslides in the descending data. The method in this paper can successfully detect landslides in areas that are difficult for the SBAS-InSAR to detect. In addition, the proposed method does not require phase unwrapping, so a significant amount of data processing time can be saved.

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.

Deformation monitoring and analysis of liujiaxia reservoir landslide based on time-series InSAR and wavelet transform

The periodic storage and release of water in reservoirs have the potential to accelerate deformation or trigger ancient landslides. Interferometric synthetic aperture radar (InSAR) technology, renowned for its wide coverage and high precision, has emerged as an effective technique for the identification and monitoring of landslides. This study utilized spaceborne InSAR technology to identify and interpret landslides in the Liujiangxia reservoir area in China, leading to the detection of a total of 20 landslides. Moreover, among these 20 landslides, the landslide with substantial deformation (LJX02) was chosen, and the wavelet quantitative analysis technique was employed to investigate the correlation and time lag between various elevation positions of this landslide, the reservoir water level, and rainfall. The study successfully demonstrated that the deformation of the landslide lagged behind the reservoir water level by 152.10-182.50 days (from low to high elevation) and was delayed by 45.60-68.44 days compared to rainfall (from high to low elevation).

Deformation Monitoring and Dynamic Analysis of Long-Runout Bedding Landslide Based on InSAR and Particle Flow Code

Long-runout landslides occur frequently in the sandstone and mudstone mountainous areas in southwestern China under heavy rainfall conditions. This has been a key issue in the field of disaster prevention and reduction. Considering the Niuerwan landslide in Wulong, Chongqing, on 13 July 2020, as an example, we employed technical methodologies, including unmanned aerial vehicle (UAV) images, field investigation, geological condition analysis (including geomorphology and topography, stratigraphic structure and formation lithology, etc.), interferometric synthetic aperture radar (InSAR) monitoring and Particle Flow Code 3D (PFC3D) simulations to study failure mechanism and a long-runout motion model of flow-like landslides induced by the heavy rainfall. The results showed that (1) the large differences between the upper and lower strata are the root cause of the instability and long-runout fluidization movement; (2) heavy rainfall is the key driving factor of slope instability and deep-seated landslides, leading to long-distance movement of the upper saturated residual soil; (3) the long-runout fluidization model of bedding landslides is mainly divided into the overall sliding in the lower layer, the mixing of coarse and fine particles in the middle layer, and saturation fluidization in the upper layer; and (4) the long-runout fluidization process of bedding landslides is composed of three stages: overall instability, mixed acceleration, and fluidization accumulation. In view of these findings, in the risk evaluation and prediction of long-runout fluidization landslides in sandstone and mudstone mountainous areas, this particular disaster model can be used to provide quantitative references for disaster prevention and mitigation.

A graph deep learning method for landslide displacement prediction based on global navigation satellite system positioning

The accurate prediction of displacement is crucial for landslide deformation monitoring and early warning. This study focuses on a landslide in Wenzhou Belt Highway and proposes a novel multivariate landslide displacement prediction method that relies on graph deep learning and Global Navigation Satellite System (GNSS) positioning. First model the graph structure of the monitoring system based on the engineering positions of the GNSS monitoring points and build the adjacent matrix of graph nodes. Then construct the historical and predicted time series feature matrixes using the processed temporal data including GNSS displacement, rainfall, groundwater table and soil moisture content and the graph structure. Last introduce the state-of-the-art graph deep learning GTS (Graph for Time Series) model to improve the accuracy and reliability of landslide displacement prediction which utilizes the temporal-spatial dependency of the monitoring system. This approach outperforms previous studies that only learned temporal features from a single monitoring point and maximally weighs the prediction performance and the priori graph of the monitoring system. The proposed method performs better than SVM, XGBoost, LSTM and DCRNN models in terms of RMSE (1.35 mm), MAE (1.14 mm) and MAPE (0.25) evaluation metrics, which is provided to be effective in future landslide failure early warning.

Time-Lag Response of Landslide to Reservoir Water Level Fluctuations during the Storage Period: A Case Study of Baihetan Reservoir

Fluctuations in reservoir water levels exert a strong triggering effect on landslides along reservoir banks, constituting a long-term concern in the safe operation of hydroelectric projects and in the prevention and management of geological disasters. While existing research has investigated the impact of periodic water level changes on the deformation of reservoir bank landslides, observation and detection of such deformation are challenging, with noticeable gaps in understanding how these deformations respond to water level changes during the water impoundment period. To address this, our study targets the Baihetan Reservoir, leveraging 567 ascending and descending LiCSAR data and LiCSBAS (the small-baseline subset within LiCSAR) technology to construct a time series of ground deformations in the study area from 2019 to 2023. The TLCC (Time Lag Cross Correlation) model was employed to examine the time-lag response pattern of reservoir bank landslide deformations to reservoir water level changes during the impoundment period. Our findings indicate a clear time-lag response in reservoir bank landslide deformations to water level changes during the impoundment process. The rise in water levels emerged as a primary factor influencing the instability of reservoir bank landslides. During the half-year impoundment period of the Baihetan Reservoir, a time lag of 5–7 days was observed between landslide deformations and increases in water levels, with landslides on the eastern and western banks exhibiting differing time-lag response patterns. Our study illuminates the time-lag effect between water level changes during reservoir impoundment and reservoir bank landslide deformation monitoring. By proposing a quantitative analysis methodology utilizing LiCSBAS technology and the TLCC model, our findings can inform decision-making in the field of disaster prevention and reduction in reservoir engineering.

Deformation Monitoring and Trend Analysis of Reservoir Bank Landslides by Combining Time-Series InSAR and Hurst Index

Landslides along the Three Gorges Reservoir in China pose a threat to coastal residents and waterway safety. To reduce false positive misjudgments caused by a sudden local change in the landslide deformation curve, in this paper, we propose an effective method for predicting the deformation trend of reservoir bank landslides. We take reservoir bank landslides in the Wanzhou District of the Three Gorges Reservoir area as the research object. The Time-Series Interferometric Synthetic Aperture Radar (InSAR) method and 62 Sentinel-1A images from 2018 to 2022 were selected for landslide deformation monitoring, and the Hurst index was calculated to characterize the deformation trend. Furthermore, we propose a method for predicting the deformation trend based on the statistical distribution of deformation rates and the physical significance of the Hurst index. After the field survey and Global Positioning System (GPS) verification, the Time-Series InSAR results are shown to be reliable. We take the Sifangbei landslide as a representative case to analyze the validation results. It is found that the determined Sifangbei landslide deformation trend is consistent with the conclusions for the region. In addition, the deformation trend of a reservoir bank slope has obvious spatial and temporal differences. Changes in the reservoir water level and concentrated rainfall play roles similar to those of catalysts. The proposed method, involving the combination of Time-Series InSAR and the Hurst index, can effectively monitor deformation and predict the stability trend of reservoir bank landslides. The presented research results provide new ideas and solutions for landslide prevention and risk mitigation in reservoir areas.

The Creep-Sliding Deformation Mechanism of the Jiaju Ancient Landslide in the Upstream of Dadu River, Tibetan Plateau, China

The Jiaju ancient landslide is a giant landslide located upstream of the Dadu River, eastern Tibetan Plateau, with a volume of approx. 7.04 × 108 m3. The Jiaju ancient landslide is complex and comprises five secondary sliding bodies, e.g., the Jiaju landslide (H01), Niexiaping landslide (H02), Xiaobawang landslide (H03), Niela landslide (H04), and Mt.-peak landslide (H05). Affected by regional neotectonic movement, heavy rainfall, river erosion, and lithology, the secondary sliding bodies of the Jiaju ancient landslide are undergoing significantly different creep-sliding deformation, which will cause great damage to villages, roads, and rivers around the sliding bodies. Combined with the SBAS-InSAR method, Sentinel-1A data from June 2018 to August 2021, remote sensing and field surveys, this study obtained the Jiaju ancient landslide deformation characteristics and deformation rate in the line-of-sight direction (VLOS), slope (VSlope), and vertical (VVertical). It is concluded that the maximum deformation rate of the Jiaju ancient landslide is significant. The maximum of VLOS, VSlope, and VVertical are −179 mm/a, −211 mm/a, and −67 mm/a, respectively. The Niela landslide (H04), Jiaju landslide (H01), and Mt.-peak landslide (H05) are very large and suffer strong deformation. Among these, the Niela landslide (H04) is in the accelerative deformation stage and at the Warn warning level, and the Jiaju landslide (H01) is in the creep deformation and attention warning level, especially heavy rainfall, which will accelerate landslide deformation and trigger reactivation. Because the geological structure is very complex for the Jiaju ancient landslide and strong neotectonic movement, under heavy rainfall, the secondary landslide creep-sliding rate of the Jiaju ancient landslide is easily accelerated and finally slides in part or as a whole, resulting in river blocking. It is suggested to strengthen the landslide deformation monitoring of the Niela landslide and Jiaju landslide and provide disaster mitigation and prevention support to the government and residents along the Dadu River watershed.

N-BEATS deep learning method for landslide deformation monitoring and prediction based on InSAR: a case study of Xinpu landslide

N-BEATS deep learning method for landslide deformation monitoring and prediction based on InSAR: a case study of Xinpu landslide

A Data-Driven Based Method for Pipeline Additional Stress Prediction Subject to Landslide Geohazards

Pipelines that cross complex geological terrains are inevitably threatened by natural hazards, among which landslide attracts extensive attention when pipelines cross mountainous areas. The landslides are typically associated with ground movements that would induce additional stress on the pipeline. Such stress state of pipelines under landslide interference seriously damage structural integrity of the pipeline. Up to the date, limited research has been done on the combined landslide hazard and pipeline stress state analysis. In this paper, a multi-parameter integrated monitoring system was developed for the pipeline stress-strain state and landslide deformation monitoring. Also, data-driven models for the pipeline additional stress prediction was established. The developed predictive models include individual and ensemble-based machine learning approaches. The implementation procedure of the predictive models integrates the field data measured by the monitoring system, with k-fold cross validation used for the generalization performance evaluation. The obtained results indicate that the XGBoost model has the highest performance in the prediction of the additional stress. Besides, the significance of the input variables is determined through sensitivity analyses by using feature importance criteria. Thus, the integrated monitoring system together with the XGBoost prediction method is beneficial to modeling the additional stress in oil and gas pipelines, which will further contribute to pipeline geohazards monitoring management.

GNSS/Accelerometer Adaptive Coupled Landslide Deformation Monitoring Technology

Global Navigation Satellite System (GNSS) positioning technology has become the most effective method for real-time three-dimensional landslide monitoring. However, the GNSS observation signal is easily affected by the presence of a complex landslide environment with high occlusion and strong interference, in which case its accuracy and reliability cannot meet the requirements of landslide deformation monitoring. Although the accelerometers have strong autonomous working capacities and can complement the GNSS landslide monitoring technology, regular GNSS/accelerometer coupled deformation monitoring technology relies on high-quality GNSS measurement information in order to obtain high-precision accelerometer-reckoned results, derived by accurately estimating the baseline shift error (BSE). When the GNSS signal suffers severe interference, the GNSS monitoring error will be partially absorbed by the BSE component after Kalman filtering, resulting in the divergence of the deformation solution. In this study, an abnormal observation variance inflation model was used to process the simulated landslide monitoring data (GNSS and accelerometer raw observation) for three typical scenes—GNSS signal normally locked, signal partially lost, and short-term interruption. The results were as follows: (1) When the GNSS signal was normally locked, the accuracy was comparable to that of the coupled solution employing an accelerometer (the Root Mean Square (RMS) values in the East (E), North (N) and Upward (U) directions were 0.11 cm, 0.33 cm, and 0.30 cm, respectively). (2) When the GNSS signal was partially lost, the accelerometer could effectively suppress the low-precision float solution of the GNSS, but here, the accuracy of the coupled solution would also decrease with the duration of the floats (the RMS values were E—1.21 cm, N—0.31 cm, and U: 0.58—cm, respectively, when the floats lasted for 10 s, and increased to E—3.09 cm, N—0.39 cm, and U—1.14 cm when they lasted for 20 s, wherein E was the main simulated sliding direction). (3) When the GNSS signal was interrupted for a short time, the accuracy of the coupled solution gradually decreased during continuous interruption, and decreased more quickly during the sliding period of the landslide (when the interruption persisted for 10 s, the RMS values in the simulated landslide stability period were E—0.61 cm, N—0.24 cm, and U—0.25 cm, respectively, while in the simulated landslide sliding period they reached E—4.10 cm, N—6.84 cm, and U—2.30 cm). However, raw observations of the accelerometer could still effectively be used to assist in identifying the real state of the landslide, thereby providing auxiliary information pertinent to early landslide disaster warning.

Utilization of 3D Laser Scanning for Stability Evaluation and Deformation Monitoring of Landslides.

Three-dimensional laser scanning technology can comprehensively and accurately monitor slope deformation. To conduct deformation monitoring and stability evaluation of the Changzhou Shunguoshan landslide, in this paper, the causes of the Changzhou Shunguoshan landslide were analyzed. Consequently, 3D laser scanning technology and the traditional monitoring methods such as data from the total station were compared. The point cloud data provides big data support for landslide deformation monitoring and landslide stability early warning. Meanwhile, the landslide stability was evaluated by analogy with existing studies on slope deformation monitoring data. Results show that the three-dimensional laser scanning monitoring data is similar to the total station monitoring data. The overall deformation of the Shunguoshan landslide is no more than ± 0.0015 m; the deformation of the Liyang slope is less than ±0.09 m, which is far less than the analog slope deformation monitoring data. The slope construction and monitoring process are in a stable state.

Deformation Monitoring and Analysis of Landslide in PPP with Dual-frequency Non-Ionosphere Combination

Deformation Monitoring and Analysis of Landslide in PPP with Dual-frequency Non-Ionosphere Combination

A Quantitative Analysis Method of Regional Rainfall-Induced Landslide Deformation Response Variation Based on a Time-Domain Correlation Model

Landslide deformation is the most intuitive and effective characterization of the evolution of landslides and reveals the inherent risk of landslides. Considering the inadequacy of existing deformation monitoring data for early warnings regarding landslide hazards, resulting in insufficient disaster response times, this paper proposes a time-domain correlation model. Based on the process of rainfall-induced landslide deformation, the time-domain correlation between regional rainfall and landslide deformation is proposed, which can reflect the temporal characteristics of landslide responses to rainfall, and the calculation method of the impulse response function is designed to quantitatively model and calculate the correlation. Furthermore, rainfall monitoring data are used to optimize the landslide deformation monitoring indicator system for early warnings regarding landslide instability. The feasibility of the method proposed in this paper is verified by analyzing the historical monitoring data of rainfall and landslide deformation at nine typical locations in five landslide hazard areas in Fengjie County, Chongqing city. (1) The correlation models for the XP landslide involve a delayed rainfall response time of 5 for deformation, respectively, as well as the existence of a cycle of 55–56 days, which means that the above area can advance the landslide warning by one lag time based on the cycle; (2) The correlation models for the OT landslide show consistent correlations under a 48–50-day cycle, which means that the deformation in the above areas can be predicted based on rainfall accumulation. (3) The HJWC landslide presents a turbulence correlation, which means that other monitoring data need to be supplemented and analyzed.

A Fusion Method Using Terrestrial Laser Scanning and Unmanned Aerial Vehicle Photogrammetry for Landslide Deformation Monitoring Under Complex Terrain Conditions

Landslides are one of the major factors threatening human life and social development. Monitoring and early warning of landslides are important parts of disaster prevention and control. However, due to complex terrain conditions, landslide monitoring in mountainous watersheds and high mountain valleys usually faces issues of high difficulty, high danger, poor data integrity and low data accuracy compared with plains or hilly areas. Based on the fusion of terrestrial laser scanning (TLS) and unmanned aerial vehicle (UAV) photogrammetry, this paper proposes a landslide deformation monitoring method that can be applied to complex terrain conditions where there are access restrictions and blind areas. The method is based on TLS to increase the number and range of ground control points (GCPs) of UAVs within the visual range and then uses assumed control points (ACPs) to reconstruct the UAV model and calculate landslide displacement in invisible areas. In addition, this paper proposes and verifies a formula based on an allometricl equation to estimate the error between the calculated and real displacements based on this method. Then, we applied this method to a real landslide case and successfully realized landslide monitoring under the conditions of access limitation and blind areas. The proposed method enables landslide monitoring to overcome terrain restrictions, and it makes it possible to perform large-scale and high-precision ground deformation measurements with limited working ranges, which can be an effective tool for landslide monitoring in emergency situations and long-term risk evaluation.

Ascending and Descending Time Series Interferometric Synthetic Aperture Radar Monitoring and Analysis of Landslide Deformation

Ascending and Descending Time Series Interferometric Synthetic Aperture Radar Monitoring and Analysis of Landslide Deformation

Application of the method for prediction of the failure location and time based on monitoring of a slope using synthetic aperture radar

Synthetic aperture radar (SAR) technology has been widely used in landslide deformation monitoring in the past decade. It has the advantages of high monitoring accuracy, a wide range, and flexibility allowing all-weather continuous monitoring. The self-developed S-SAR synthetic aperture radar (slope radar) is the first completely domestic-made radar used in slope deformation monitoring equipment in China, and its performance and technical parameters are equal to or better than similar products made abroad. The characteristics of deformation data collected by S-SAR slope radar deployed in the front open pit mine are analyzed to further develop a spatio-temporal landslide prediction method which is applicable to the massive monitoring data within the monitoring range of slope radar. The intersection points of short-term moving average velocity curve and long-term moving average velocity curve of slope deformation, which are onset of acceleration (OOA) and termination of acceleration (TOA). When OOA occurs, the deformation will accelerate, and when TOA occurs, the deformation will tend to stabilize. The OOA can identify areas at risk in the monitored area before failure, so that the spatial position prediction of landslide early warning can be realized. Based on the assumptions of the inverse velocity method, a T-log (t) logarithmic model is established, and the updated monitoring data are corrected to approximate the time of failure, thus improving the accuracy of landslide location and time prediction. In an open-pit copper mine in Serbia, the accurate prediction of landslide location and time has been successfully applied, guaranteeing safe mining.

Research review of large deformation monitoring of rock and soil

Landslide is a kind of extremely harmful global geological disaster, which often cause serious property losses and casualties. Therefore, scientific and effective landslide monitoring is of great significance for disaster prevention and mitigation. This article introduces the status quo and development trend of landslide deformation monitoring technology in the form of a review. Firstly, the deformation monitoring technologies about rock and soil are summarized and evaluated from three aspects: air (GNSS technology, InSAR technology), sky (UAV technology, 3D laser scanning), and ground (MEMS and Optical fiber technology). Then, the article focuses on ground (deep inclinometer) of the above three aspects, mainly talks about some advanced monitoring technologies used in inclinometer such as fiber Bragg grating and MEMS. The research results show that the inclinometer based on fiber Bragg grating has high monitoring accuracy with 1 με, and the data can be transmitted in time without electromagnetic interference. The MEMS-based inclinometer can measure the vertical tilt angle and horizontal torsion angle, which can realize the three-dimensional deformation monitoring in space. The above research and development of the novel inclinometer provide an effective mean for rock and soil deformation monitoring. Finally, the article summarizes the realization of accurate and reliable monitoring of landslide deformation.

Application of Multi-System Combination Precise Point Positioning in Landslide Monitoring

To verify the positioning performance and reliability of multi-system combination Precise Point Positioning in landslide monitoring, we carried out a multi-system combination Precise Point Positioning calculation experiment on the monitoring data of a single landslide disaster area in Fujian Province. The coordinates of the monitoring points obtained by a continuously operating reference station and the monitoring station for static relative positioning were used as reference values. The GPS system was used as the standard system and the combined PPP solution mode of G/R/C, G/R/E and G/R/E/C was used to obtain the surface displacement of the landslide area. The research showed that multi-system combination PPP converges to the centimeter level in about 30 min. The average value of internal accordant precision was more than 1 mm after convergence, and that of the external accordant precision was more than 5 cm, which meets the centimeter-level accuracy requirements in rapid landslide deformation monitoring.

Research on application of GPS-based wireless communication system in highway landslide

Machine learning is a branch of the field of artificial intelligence. Deep learning is a complex machine learning algorithm that has unique advantages in image recognition, speech recognition, natural language processing, and industrial process control. Deep learning has It is widely used in the field of wireless communication. Prediction of geological disasters (such as landslides) is currently a difficult problem. Because landslides are difficult to detect in the early stage, this paper proposes a GPS-based wireless communication continuous detection system and applies it to landslide deformation monitoring to achieve early treatment and prevention. This article introduces the GPS multi-antenna detection system based on deep learning wireless communication, and introduces the time series analysis method and its application. The test results show that the GPS multi-antenna detection system of the wireless communication network has great advantages in response time, with high accuracy and small error. The horizontal accuracy is controlled at 0–2 mm and the vertical accuracy is about 1 mm. The analysis method is simple and efficient, and can obtain good results for short-term deformation prediction.