Pre-Event Deformation and Failure Mechanism Analysis of the Pusa Landslide, China with Multi-Sensor SAR Imagery

Chen, Liquan; Zhao, Chaoying; Kang, Ya; Chen, Hengyi; Yang, Chengsheng; Li, Bin; Liu, Yuanyuan; Xing, Aiguo

doi:10.3390/rs12050856

Cited by 21 publications

(6 citation statements)

References 37 publications

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“…In 2013, a mountain in Yudong Village, Kaili City, collapsed, killing five people, blocking roads, and forming a dammed lake [9]. In addition, the Pusa landslide in Nayong County in 2017 caused 35 deaths and affected over 500 people [10]. All of the above mining-induced landslides occurred in mountainous areas that have the characteristics of "soft-hard alternant (or hard on soft), upper steep and lower gentle" dual structure.…”

Section: Introductionmentioning

confidence: 99%

Failure Mechanism Analysis of Mining-Induced Landslide Based on Geophysical Investigation and Numerical Modelling Using Distinct Element Method

et al. 2022

Remote Sensing

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Underground mining activity in the karst mountain in southwestern China has induced several large-scale rocky landslides and has caused serious casualties. At present, there is a lack of systematic research on the formation mechanism of landslides in this area using multi-method fusion technology. First, the orthophoto images of the landslide area obtained by UAV photography were used to analyze the deformation characteristics of the landslide. Second, the failure characteristics of the strata overlying the goaf were analyzed by geophysical detection. Finally, the deformation response characteristics of the mountain under underground mining were analyzed by UDEC numerical simulation. The results revealed that during the underground mining, the failure process of the mountain occurred in four stages: fracture expansion, subsidence and collapse, shear sliding, and multi-level sliding. Gently dipping soft–hard alternant strata and a blocky rock mass structure formed the geological foundation of the landslides. Underground mining accelerated the fracturing of the overlying strata and the formation of a stepped penetrating sliding surface. Tensile movement of the structural planes of hard sandstone in the free face, and shear sliding of the weak mudstone layer, were the main causes of the landslides. The slope instability mode was tension-shear fracturing, shear sliding, back toppling, and compressive shear failure. In addition, the fracture propagation in the overlying strata and damaged geological structure revealed by the geophysical detection were consistent with the simulation results. This study provides ideas for the precise countermeasures of disaster prevention and mitigation for similar landslides in this area.

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Section: Introductionmentioning

confidence: 99%

Failure Mechanism Analysis of Mining-Induced Landslide Based on Geophysical Investigation and Numerical Modelling Using Distinct Element Method

et al. 2022

Remote Sensing

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“…Finally, a discrete element numerical model was utilized to study the damage and contributing factors. In addition, the deformation results of the model were verified by the InSAR data provided by Chen et al [38].…”

Section: Methodsmentioning

confidence: 64%

“…Their occurrences are (1) 110 °∠87 °, (2) 326 °∠86 °, (3) 140 °∠88 °, and (4) 225 °∠88 °, respectively. According to the investigation by Fan et al [36] and Chen et al [38], there existed many long and deep ground fissures at the trailing edge of the mountain before 2015.…”

Section: Engineering Geological Conditionsmentioning

confidence: 99%

Failure Mechanism of Anti-Inclined Karst Slope Induced by Underground Multiseam Mining

et al. 2022

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There occurred a lot of catastrophic landslides in Southwest China karst areas in recent years. This study numerically simulated the failure process of Pusa landslide by distinct element method (UDEC). The Voronoi diagram algorithm was used to discretize the rock blocks in the upper part of the mining area, and the erosion in sliding source area caused by heavy rainfall was simulated by increasing the block density and reducing the joint strength. Results obtained using UDEC matched the failure process recorded by UAV, and the subsidence of monitoring points on the slope surface was well coincident with the InSAR results. The DEM results are as follows: firstly, under the action of underground coal mining, the overlying strata of the mountain deform toward the goaf and the deformation of the stratum increase significantly with the caving of the roof stratum; and the deep karst fissures in the overlying stratum have an important influence on the deformation of the mountain; the upper strata form a subsidence zone along the fissures, expanding the range of tensile fissures. Secondly, due to the excavation and unloading of the rock masses at the leading edge of the sliding area, the extrusion deformation occurs to the outside of the slope, resulting in the critical instability of sliding area. Finally, with the strength decreasing of rock masses and structural planes by heavy rainfall, the stability of the mountain continues to decrease until the sliding area collapses.

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“…By using mutitemporal SAR images, MT-InSAR can minimize the influence of spatiotemporal decorrelation [13], atmospheric delay [14,15], and satellite orbit error [16] of traditional differential InSAR (D-InSAR) [17] and can obtain high-precision and long time series of surface deformation results. The time series deformation information obtained by MT-InSAR can be used to retrieve the formation mechanism and kinematic evolution process of landslides, including landslide types, trigger factors, failure modes, depth and volume estimation, and risk assessment [18][19][20][21][22][23][24]. Scholars have carried out a considerable among research on the application of MT-InSAR for landslide monitoring.…”

Section: Introductionmentioning

confidence: 99%

Landslide Identification and Gradation Method Based on Statistical Analysis and Spatial Cluster Analysis

et al. 2022

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As a type of earth observation technology, interferometric synthetic aperture radar (InSAR) is increasingly widely used in the field of geological disaster detection. However, the application of InSAR in low-coherence areas, such as alpine canyon areas and vegetation coverage areas, is subject to considerable limitations. How to accurately identify landslides from InSAR measurement data in these areas remains the subject of several challenges and shortcomings. Based on statistical analysis and spatial cluster analysis, in this paper, we propose an automatic landslide identification and gradation method suitable for low-coherence areas. The proposed method combines the small baseline subset InSAR (SBAS-InSAR) method and the interferogram stacking (stacking-InSAR) method to obtain a deformation map in the study area, using statistical analysis and spatial cluster analysis to extract deformation regions and landslide polygons to propose a landslide screening model (LSM) based on multivariate features to screen landslides and reduce the interference of noise in landslide identification, in addition to proposing a landslide gradation model (LGM) based on signum function to grade the identified landslides and provide support to distinguish landslides with different deformation degrees. The method was applied to landslide identification in the upper section of the Jinsha River basin, and 47 potential landslides were identified, including 15 high-risk landslides and 13 landslides endangering villages. The experimental results show that the proposed method can identify landslides accurately and hierarchically in low-coherence areas, providing support for geological hazard investigation agencies and local departments.

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Pre-Event Deformation and Failure Mechanism Analysis of the Pusa Landslide, China with Multi-Sensor SAR Imagery

Cited by 21 publications

References 37 publications

Failure Mechanism Analysis of Mining-Induced Landslide Based on Geophysical Investigation and Numerical Modelling Using Distinct Element Method

Failure Mechanism Analysis of Mining-Induced Landslide Based on Geophysical Investigation and Numerical Modelling Using Distinct Element Method

Failure Mechanism of Anti-Inclined Karst Slope Induced by Underground Multiseam Mining

Landslide Identification and Gradation Method Based on Statistical Analysis and Spatial Cluster Analysis

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