It is crucial to ensure the safety and stability of pipelines buried in slopes during installation and operation. In this paper, the interaction between a pipe and soil was investigated via laboratory model tests. The effects of the slope angle and pipe position on the slope horizontal deformation and pipe mechanical properties were investigated. Furthermore, the restraint effect of tire strip reinforcement (TSR) on slope deformation and its impact on pipe stress and strain were analyzed. The results revealed that the potential sliding surface is located at the middle of the slope. The pipe location has a significant effect on the horizontal surface deformation of the slope, whereas the slope angle has a small effect on the stress and strain of the pipe. In addition, the use of the TSR not only reduces the horizontal surface deformation of the slope but also partially alleviates the vertical stress on the crown of the pipe. As the pipe moves away from the loading plate, the circumferential stress distribution changes from a symmetric state to an asymmetric state, with the most critical location moving from the spring line to the top. The test results provide reliable experimental data to support the design of pipes buried within a slope.

This study employs the Global Navigation Satellite System-Interferometric Reflectometry (GNSS-IR) technique, along with in situ hydrothermal data, to explore the details and mechanisms of permafrost ground surface deformation in the hinterland Tibetan Plateau. Through analyzing GNSS data collected from November 2021 to April 2024, seasonal deformation of up to approximately 5 cm, caused by active layer freeze-thaw cycles, was identified. Additionally, more than 2 years of continuous monitoring revealed a clear ground subsidence rate of 2.7 cm per year due to permafrost thawing. We compared the GNSS-IR monitored deformation with simulated deformation using in situ soil moisture and temperature profiles at 5-220 cm depth and found that the correlation reached 0.9 during the active-layer thawing and freezing period; we also observed that following an exceptionally thawing season, the subsequent thawing season experiences notably greater thaw subsidence. Furthermore, we analyzed the differences in GNSS-IR monitoring results with and without the inclusion of Beidou Navigation Satellite System (BDS) signals, and found that the inclusion of BDS signals reduced the standard deviation of GNSS-IR results by an average of 0.24 mm on snow-free periods, but increased by an average of 0.12 mm during the snow cover periods. This may be due to the longer wavelength of the BDS signal, which exhibits greater diffraction through snow and reduces signal reflectivity compared to other satellite systems. The research results demonstrate the potential and ability of continuous GNSS-IR ground surface deformation monitoring in revealing and exploring the hydrothermal processes within permafrost under climate change.

Introduction Surface deformation in the Three Gorges Reservoir area poses significant threats to infrastructure and safety due to complex geological and hydrological factors. Despite existing studies, systematic exploration of long-term deformation characteristics and their driving mechanisms remains limited. This study combines SBAS-InSAR technology and machine learning to analyze and predict surface deformation in Fengjie County, Chongqing, China, between 2020 and 2022, focusing on riverside urban ground, riverside road slopes, and ancient landslides in the reservoir area.Methods SBAS-InSAR technology was applied to 36 Sentinel-1A images to monitor surface deformation, complemented by hydrological and meteorological data. Machine learning models-Random Forest (RF), Extremely Randomized Trees (ERT), Gradient Boosting Decision Tree (GBDT), Support Vector Regression (SVR), and Long Short-Term Memory (LSTM)-were evaluated using six metrics, including RMSE, R2, and SMAPE, to assess their predictive performance across diverse geological settings.Results Deformation rates for riverside urban ground, road slopes, and ancient landslides were -3.48 +/- 2.91 mm/yr, -5.19 +/- 3.62 mm/yr, and -6.02 +/- 4.55 mm/yr, respectively, with ancient landslides exhibiting the most pronounced deformation. A negative correlation was observed between reservoir water level decline and subsidence, highlighting the influence of seasonal hydrological adjustments. Urbanization and infrastructure development further exacerbated deformation processes. Among the models, LSTM demonstrated superior predictive accuracy but showed overestimation trends in ancient landslide areas.Discussion Reservoir water level adjustments emerged as a critical driver of subsidence, with rapid water level declines leading to increased pore pressure and soil compression. Seasonal effects were particularly evident, with higher subsidence rates during and after the rainy season. Human activities, including urbanization and road construction, significantly intensified deformation, disrupting natural geological conditions. Progressive slope failure linked to road expansion underscored the long-term impacts of engineering activities. For ancient landslides, accelerated deformation patterns were linked to prolonged drought and reservoir-induced hydrological changes. While LSTM models showed high accuracy, their limitations in complex geological settings highlight the need for hybrid approaches combining machine learning with physical models. Future research should emphasize developing integrated frameworks for long-term risk assessment and mitigation strategies in reservoir environments.Conclusions This study provides new insights into the complex surface dynamics in the Three Gorges Reservoir area, emphasizing the interplay of hydrological, geological, and anthropogenic factors. The findings highlight the need for adaptive management strategies and improved predictive models to mitigate subsidence risks.

The Qilian Mountains, located on the northeastern edge of the Qinghai-Tibet Plateau, are characterized by unique high-altitude and cold-climate terrain, where permafrost and seasonally frozen ground are extensively distributed. In recent years, with global warming and increasing precipitation on the Qinghai-Tibet Plateau, permafrost degradation has become severe, further exacerbating the fragility of the ecological environment. Therefore, timely research on surface deformation and the freeze-thaw patterns of alpine permafrost in the Qilian Mountains is imperative. This study employs Sentinel-1A SAR data and the SBAS-InSAR technique to monitor surface deformation in the alpine permafrost regions of the Qilian Mountains from 2017 to 2023. A method for spatiotemporal interpolation of ascending and descending orbit results is proposed to calculate two-dimensional surface deformation fields further. Moreover, by constructing a dynamic periodic deformation model, the study more accurately summarizes the regular changes in permafrost freeze-thaw and the trends in seasonal deformation amplitudes. The results indicate that the surface deformation time series in both vertical and east-west directions obtained using this method show significant improvements in accuracy over the initial data, allowing for a more precise reflection of the dynamic processes of surface deformation in the study area. Subsidence is predominant in permafrost areas, while uplift mainly occurs in seasonally frozen ground areas near lakes and streams. The average vertical deformation rate is 1.56 mm/a, with seasonal amplitudes reaching 35 mm. Topographical (elevation; slope gradient; aspect) and climatic factors (temperature; soil moisture; precipitation) play key roles in deformation patterns. The deformation of permafrost follows five distinct phases: summer thawing; warm-season stability; frost heave; winter cooling; and spring thawing. This study enhances our understanding of permafrost deformation characteristics in high-latitude and high-altitude regions, providing a reference for preventing geological disasters in the Qinghai-Tibet Plateau area and offering theoretical guidance for regional ecological environmental protection and infrastructure safety.

The 2022 Paktika earthquake (moment magnitude: 6.2) occurred on June 22, 2022, near the border between the Khost and Paktika Provinces of Afghanistan, causing heavy damage and casualties in Paktika Province. This study evaluated the crustal deformation and associated strong motions induced by the Paktika earthquake. Crustal deformations were determined using the Differential Interferometric Synthetic Aperture Radar (DInSAR) technique and three-dimensional finite element method (3DFEM) and the results were compared. The permanent ground displacements obtained from the DInSAR and 3D-FEM analyses were similar in terms of amplitude and areal distribution. Strong motions were estimated using the 3D-FEM with and without considering regional topography. The estimations of maximum ground acceleration, velocity, and permanent ground deformations were compared among each other as well as with those inferred from failures of some simple structures in the Spera and Gayan districts. The inferred maximum ground acceleration and velocity from the failed adobe structures were more than 300 Gal and 50 cm/s, respectively, nearly consistent with the estimates obtained using empirical methods. The empirical method yielded a maximum ground acceleration of 347 Gal, whereas the maximum ground velocity was approximately 50 cm/s. In light of these findings, some surface expressions of crustal deformations and strong ground motions, such as failures of soil and rock slopes and rockfalls, have been presented. The rock slope failures in the epicentral area were consistent with those observed during various earthquakes in Afghanistan and worldwide.

Under the interference of climate warming and human engineering activities, the degradation of permafrost causes the frequent occurrence of geological disasters such as uneven foundation settlement and landslides, which brings great challenges to the construction and operational safety of road projects. In this paper, the spatial and temporal evolution of surface deformations along the Beihei Highway was investigated by combining the SBAS-InSAR technique and the surface frost number model after considering the vegetation factor with multi-source remote sensing observation data. After comprehensively considering factors such as climate change, permafrost degradation, anthropogenic disturbance, and vegetation disturbance, the surface uneven settlement and landslide processes were analyzed in conjunction with site surveys and ground data. The results show that the average deformation rate is approximately -16 mm/a over the 22 km of the study area. The rate of surface deformation on the pavement is related to topography, and the rate of surface subsidence on the pavement is more pronounced in areas with high topographic relief and a sunny aspect. Permafrost along the roads in the study area showed an insignificant degradation trend, and at landslides with large surface deformation, permafrost showed a significant degradation trend. Meteorological monitoring data indicate that the annual minimum mean temperature in the study area is increasing rapidly at a rate of 1.266 degrees C/10a during the last 40 years. The occurrence of landslides is associated with precipitation and freeze-thaw cycles. There are interactions between permafrost degradation, landslides, and vegetation degradation, and permafrost and vegetation are important influences on uneven surface settlement. Focusing on the spatial and temporal evolution process of surface deformation in the permafrost zone can help to deeply understand the mechanism of climate change impact on road hazards in the permafrost zone.

The damage caused by surface deformation is substantial and far-reaching. Although multi-temporal interferometric synthetic aperture radar (InSAR) technology is commonly used to monitor surface deformation, it remains challenging to rapidly extract surface deformation on a national scale, especially in China, which has an area of approximately 9.6 million km2. We designed a set of robust parallel computing solutions for rapid acquisition of surface deformations throughout China. The 46,904 Sentinel-1 data covering the entire territory of China from 2018 to 2022 were processed, and a surface deformation dataset throughout China (SDDC) for this period was obtained for the first time. We used external GNSS data to evaluate the accurcy. The SDDC provided abundant deformation information that can play an important role in updating the list of geological disasters, assisting in decision-making in urban construction, and strengthening understanding of potential mechanisms. We analyzed a range of applications of this data, including the deformation of urban areas caused by the overexploitation of groundwater, facility construction, and reclamation, melting deformation of frozen soil, as well as landslide, mining, karst surface, earthquake, and reservoir dam deformation, and deformation of major transport infrastructure throughout China. Our work presents a reference for the rapid extraction of surface deformation at the national scale and provides valuable data support for scientific research and engineering applications in many fields.

The collapse of open-pit coal mine slopes is a kind of severe geological hazard that may cause resource waste, economic loss, and casualties. On 22 February 2023, a large-scale collapse occurred at the Xinjing Open-Pit Mine in Inner Mongolia, China, leading to the loss of 53 lives. Thus, monitoring of the slope stability is important for preventing similar potential damage. It is difficult to fully obtain the temporal and spatial information of the whole mining area using conventional ground monitoring technologies. Therefore, in this study, multi-source remote sensing methods, combined with local geological conditions, are employed to monitor the open-pit mine and analyze the causes of the accident. Firstly, based on GF-2 data, remote sensing interpretation methods are used to locate and analyze the collapse area. The results indicate that high-resolution remote sensing can delineate the collapse boundary, supporting the post-disaster rescue. Subsequently, multi-temporal Radarsat-2 and Sentinel-1A satellite data, covering the period from mining to collapse, are integrated with D-InSAR and DS-InSAR technologies to monitor the deformation of both the collapse areas and the potential risk to dump slopes. The D-InSAR result suggests that high-intensity open-pit mining may be the dominant factor affecting deformation. Furthermore, the boundary between the collapse trailing edge and the non-collapse area could be found in the DS-InSAR result. Moreover, various data sources, including DEM and geological data, are combined to analyze the causes and trends of the deformation. The results suggest that the dump slopes are stable. Meanwhile, the deformation trends of the collapse slope indicate that there may be faults or joint surfaces of the collapse trailing edge boundary. The slope angle exceeding the designed value during the mining is the main cause of the collapse. In addition, the thawing of soil moisture caused by the increase in temperature and the reduction in the mechanical properties of the rock and soil due to underground voids and coal fires also contributed to the accident. This study demonstrates that multi-source remote sensing technologies can quickly and accurately identify potential high-risk areas, which is of great significance for pre-disaster warning and post-disaster rescue.

Permafrost, known for its high sensitivity to climate change and human activities, plays a crucial role in comprehending the environmental sustainability of Arctic cities. Yakutsk, being the largest city situated in continuous permafrost, has suffered irreversible damage to surface buildings due to the widespread permafrost thaw. Therefore, it is imperative to conduct detailed monitoring and assessment of surface deformation to ensure the stable operation of building facilities and to plan urban development situated above the permafrost. This study utilized 138 scenes of Sentinel-1B images captured between 2017 and 2021 to process the surface deformation variations of Yakutsk using the Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) method. The surface deformation rate map reflects that the study area is undergoing extensive surface subsidence. The cumulative deformation time series maps effectively trace the temporal and spatial evolution of surface deformation, with the highest surface subsidence rates exceeding 40 mm/year and maximum cumulative subsidence exceeding 250 mm. In-depth analysis of the obtained results indicated that the permafrost in Yakutsk is undergoing degradation, and the primary reason for surface subsidence is the thawing of foundation soils and the degradation of ice-bearing permafrost. This study provides basic data for the investigation of geohazards caused by surface subsidence in Yakutsk. Permafrost, due to its high sensitivity to climate change and human activities, plays a crucial role in understanding the environmental sustainability of Arctic cities. However, widespread global climate warming has caused irreversible damage to the urban surface situated on permafrost. Despite this, there still remains a significant amount of uncertainty in research pertaining to this issue. Therefore, this study is designed to investigate the surface deformation of Yakutsk, the largest city situated on permafrost, using times series remote sensing techniques.image

As one of the best indicators of the periglacial environment, ice-wedge polygons (IWPs) are important for arctic landscapes, hydrology, engineering, and ecosystems. Thus, a better understanding of the spatiotemporal dynamics and evolution of IWPs is key to evaluating the hydrothermal state and carbon budgets of the arctic permafrost environment. In this paper, the dynamics of ground surface deformation (GSD) in IWP zones (2018-2019) and their influencing factors over the last 20 years in Saskylakh, northwestern Yakutia, Russia were investigated using the Interferometric Synthetic Aperture Radar (InSAR) and Google Earth Engine (GEE). The results show an annual ground surface deformation rate (AGSDR) in Saskylakh at -49.73 to 45.97 mm/a during the period from 1 June 2018 to 3 May 2019. All the selected GSD regions indicate that the relationship between GSD and land surface temperature (LST) is positive (upheaving) for regions with larger AGSDR, and negative (subsidence) for regions with lower AGSDR. The most drastic deformation was observed at the Aeroport regions with GSDs rates of -37.06 mm/a at tower and 35.45 mm/a at runway. The GSDs are negatively correlated with the LST of most low-centered polygons (LCPs) and high-centered polygons (HCPs). Specifically, the higher the vegetation cover, the higher the LST and the thicker the active layer. An evident permafrost degradation has been observed in Saskylakh as reflected in higher ground temperatures, lusher vegetation, greater active layer thickness, and fluctuant numbers and areal extents of thermokarst lakes and ponds.