| Citation: |
Guan-hua Zhao, Heng-xing Lan, Hui-yong Yin, Lang-ping Li, Alexander Strom, Wei-feng Sun, Chao-yang Tian, 2024. Deformation, structure and potential hazard of a landslide based on InSAR in Banbar county, Xizang (Tibet), China Geology, 7, 203-221. doi: 10.31035/cg2023130
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Deformation, structure and potential hazard of a landslide based on InSAR in Banbar county, Xizang (Tibet)
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Abstract
The Tibetan Plateau is characterized by complex geological conditions and a relatively fragile ecological environment. In recent years, there has been continuous development and increased human activity in the Tibetan Plateau region, leading to a rising risk of landslides. The landslide in Banbar County, Xizang (Tibet), have been perturbed by ongoing disturbances from human engineering activities, making it susceptible to instability and displaying distinct features. In this study, small baseline subset synthetic aperture radar interferometry (SBAS-InSAR) technology is used to obtain the Line of Sight (LOS) deformation velocity field in the study area, and then the slope-orientation deformation field of the landslide is obtained according to the spatial geometric relationship between the satellite’s LOS direction and the landslide. Subsequently, the landslide thickness is inverted by applying the mass conservation criterion. The results show that the movement area of the landslide is about 6.57×104 m2, and the landslide volume is about 1.45×106 m3. The maximum estimated thickness and average thickness of the landslide are 39 m and 22 m, respectively. The thickness estimation results align with the findings from on-site investigation, indicating the applicability of this method to large-scale earth slides. The deformation rate of the landslide exhibits a notable correlation with temperature variations, with rainfall playing a supportive role in the deformation process and displaying a certain lag. Human activities exert the most substantial influence on the spatial heterogeneity of landslide deformation, leading to the direct impact of several prominent deformation areas due to human interventions. Simultaneously, utilizing the long short-term memory (LSTM) model to predict landslide displacement, and the forecast results demonstrate the effectiveness of the LSTM model in predicting landslides that are in a continuous development and movement phase. The landslide is still active, and based on the spatial heterogeneity of landslide deformation, new recommendations have been proposed for the future management of the landslide in order to mitigate potential hazards associated with landslide instability.
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Keywords:
- Landslide /
- Human activity /
- InSAR /
- Deformation /
- LSTM model /
- Engineering construction /
- Structure /
- Neural network /
- Thickness /
- Machine learning /
- Prediction and prevention /
- Tibetan Plateau
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References
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Guan-hua Zhao, Heng-xing Lan, Hui-yong Yin, Lang-ping Li, Alexander Strom, Wei-feng Sun, Chao-yang Tian, 2024. Deformation, structure and potential hazard of a landslide based on InSAR in Banbar county, Xizang (Tibet), China Geology, 7, 203-221. doi: 10.31035/cg2023130
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Figure 1.
Banbar landslide.
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Figure 2.
a-Aerial wide-angle photo of the landslide in Banbar County captured by UAV. b-Engineering geological plan of the landslide.
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Figure 3.
Photos of Banbar landslide. a-Overview of the study area captured by UAV and the specific locations of b, c, d, e, f, and g have been marked. d-The east side of the front portion of the landslide captured by UAV. g-The west side of the front portion of the landslide captured by UAV. c, f-The cracking in houses near the front portion of the landslide. b, e-The loose gravel soil behind the anti-slide piles.
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Figure 4.
Flowchart showing available data and methods used in this study.
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Figure 5.
Spatial and temporal baselines of the interferograms. a-Ascending data. b-Descending data. The red dots represent master images.
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Figure 6.
Landslide deformation decomposition. a-The coordinate system of the sliding surface of landslide, where ON represents North-South, OE represents East-West, OH represents Vertical. And on the sliding surface, OI represents normal, OT represents perpendicular and OK represents average aspect. b, c-The geometric relationship between the sliding surface and “North-East-High” coordinate system.
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Figure 7.
Deformation velocity fields along the LOS. a-Ascending orbit of the landslide area. b-Descending orbit of the landslide area. ⅰ and ⅱ are areas with higher deformation velocity.
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Figure 8.
Cumulative LOS deformation and deformation rates. The green dashed and solid lines represent the cumulative deformation value and deformation rate of the maximum deformation point in the descending orbit result, respectively. The yellow dashed and solid lines represent the cumulative deformation value and deformation rate of the maximum deformation point in the ascending orbit result.
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Figure 9.
The slope-orientation deformation velocity fields of the landslide. a-Slope normal direction. b-Slope direction. A and B are high deformation velocity areas with opposite directions. C is area with higher deformation velocity.
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Figure 10.
Thickness distribution of landslide. I, II, and III are areas with relatively high prediction thickness values.
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Figure 11.
Distribution of landslide thickness along profile aa'. b and c are monitoring points for deep-seated displacement (the actual values of landslide thickness at points b and c are known).
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Figure 12.
Constraints data of landslide. The red curve and the purple curve respectively represent the deformation rate of the maximum deformation point in the slope deformation result and the deformation rate of the maximum deformation point in the descending orbit result. The dark green dashed line represents temperature, and the blue bar graph represents precipitation.
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Figure 13.
Spatial heterogeneity of landslide deformation velocity fields. a-Slope normal direction. b-Slope direction. c-Overview of study area captured by UAV. The red dashed lines in (c) correspond to area Ⅰ, area Ⅱ, and area Ⅲ in Fig. 10. The blue shaded area corresponds to regions with higher velocity of deformation in a, b.
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Figure 14.
Displacement prediction results based on daily precipitation data and displacement interpolation data from the maximum deformation point in the descending orbit. The green dotted line represents the actual values, the red dotted line represents the predicted values, and the blue bar graph represents the precipitation.
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Figure 15.
Displacement prediction results based on daily temperature data and displacement interpolation data from the maximum deformation point in the descending orbit. The green dotted line represents the actual values, the yellow dotted line represents the predicted values, and the dark green dashed line represents the temperature.
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Figure 16.
Suggestion sites for anti-slide piles installation. Ⅰ, Ⅱ, and Ⅲ correspond to area Ⅰ, area Ⅱ, and area Ⅲ in the thickness prediction result (Fig. 10).