China Geology最新文献

Long runout landslides involve a massive amount of energy and can be extremely hazardous owing to their long movement distance, high mobility and strong destructive power. Numerical methods have been widely used to predict the landslide runout but a fundamental problem remained is how to determine the reliable numerical parameters. This study proposes a framework to predict the runout of potential landslides through multi-source data collaboration and numerical analysis of historical landslide events. Specifically, for the historical landslide cases, the landslide-induced seismic signal, geophysical surveys, and possible in-situ drone/phone videos (multi-source data collaboration) can validate the numerical results in terms of landslide dynamics and deposit features and help calibrate the numerical (rheological) parameters. Subsequently, the calibrated numerical parameters can be used to numerically predict the runout of potential landslides in the region with a similar geological setting to the recorded events. Application of the runout prediction approach to the 2020 Jiashanying landslide in Guizhou, China gives reasonable results in comparison to the field observations. The numerical parameters are determined from the multi-source data collaboration analysis of a historical case in the region (2019 Shuicheng landslide). The proposed framework for landslide runout prediction can be of great utility for landslide risk assessment and disaster reduction in mountainous regions worldwide.

Loess has distinctive characteristics, leading to frequent landslide disasters and posing serious threats to the lives and properties of local residents. The involvement of water represents a critical factor in inducing loess landslides. This study focuses on three neighboring cities sequentially situated on the Loess Plateau along the direction of aeolian deposition of loess, namely Lanzhou, Dingxi, and Tianshui, which are densely populated and prone to landslide disasters. The variations in hydraulic properties, including water retention capacity and permeability, are investigated through Soil Water Characteristic Curve (SWCC) test and hydraulic conductivity test. The experimental findings revealed that Tianshui loess exhibited the highest water retention capacity, followed by Dingxi loess, while Lanzhou loess demonstrated the lowest water retention capacity. Contrastingly, the results for the saturated permeability coefficient were found to be the opposite: Tianshui loess showed the lowest permeability, whereas Lanzhou loess displayed the highest permeability. These results are supported and analyzed by scanning electron microscopy (SEM) observation. In addition, the water retention capacity is mathematically expressed using the van Genuchten model and extended to predict unsaturated hydraulic properties of loess. The experimental results exhibit a strong accordance with one another and align with the regional distribution patterns of disasters.

The periphery of the Qinghai-Tibet Plateau is renowned for its susceptibility to landslides. However, the northwestern margin of this region, characterised by limited human activities and challenging transportation, remains insufficiently explored concerning landslide occurrence and dispersion. With the planning and construction of the Xinjiang-Tibet Railway, a comprehensive investigation into disastrous landslides in this area is essential for effective disaster preparedness and mitigation strategies. By using the human-computer interaction interpretation approach, the authors established a landslide database encompassing 13003 landslides, collectively spanning an area of 3351.24 km² (36°N–40°N, 73°E–78°E). The database incorporates diverse topographical and environmental parameters, including regional elevation, slope angle, slope aspect, distance to faults, distance to roads, distance to rivers, annual precipitation, and stratum. The statistical characteristics of number and area of landslides, landslide number density (LND), and landslide area percentage (LAP) are analyzed. The authors found that a predominant concentration of landslide origins within high slope angle regions, with the highest incidence observed in intervals characterised by average slopes of 20° to 30°, maximum slope angle above 80°, along with orientations towards the north (N), northeast (NE), and southwest (SW). Additionally, elevations above 4.5 km, distance to rivers below 1 km, rainfall between 20-30 mm and 30–40 mm emerge as particularly susceptible to landslide development. The study area's geological composition primarily comprises Mesozoic and Upper Paleozoic outcrops. Both fault and human engineering activities have different degrees of influence on landslide development. Furthermore, the significance of the landslide database, the relationship between landslide distribution and environmental factors, and the geometric and morphological characteristics of landslides are discussed. The landslide H/L ratios in the study area are mainly concentrated between 0.4 and 0.64. It means the landslides mobility in the region is relatively low, and the authors speculate that landslides in this region more possibly triggered by earthquakes or located in meizoseismal area.

Landslide hazard mapping is essential for regional landslide hazard management. The main objective of this study is to construct a rainfall-induced landslide hazard map of Luhe County, China based on an automated machine learning framework (AutoGluon). A total of 2241 landslides were identified from satellite images before and after the rainfall event, and 10 impact factors including elevation, slope, aspect, normalized difference vegetation index (NDVI), topographic wetness index (TWI), lithology, land cover, distance to roads, distance to rivers, and rainfall were selected as indicators. The WeightedEnsemble model, which is an ensemble of 13 basic machine learning models weighted together, was used to output the landslide hazard assessment results. The results indicate that landslides mainly occurred in the central part of the study area, especially in Hetian and Shanghu. Totally 102.44 s were spent to train all the models, and the ensemble model WeightedEnsemble has an Area Under the Curve (AUC) value of 92.36% in the test set. In addition, 14.95% of the study area was determined to be at very high hazard, with a landslide density of 12.02 per square kilometer. This study serves as a significant reference for the prevention and mitigation of geological hazards and land use planning in Luhe County.

Airblasts, as one common phenomenon accompanied by rapid movements of landslides or rock/snow avalanches, commonly result in catastrophic damages and are attracting more and more scientific attention. To quantitatively analyze the intensity of airblast initiated by landslides, the Wangjiayan landslide, occurred in the Wenchuan earthquake, is selected here with the landslide propagation and airblast evolution being studied using FLUENT by introducing the Voellmy rheological law. The results reveal that: (1) For the Wangjiayan landslide, its whole travelling duration is only 12 s with its maximum velocity reaching 36 m/s at t=10 s; (2) corresponding to the landslide propagation, the maximum velocity, 28 m/s, of the airblast initiated by the landslide also appears at t=10 s with its maximum pressure reaching 594.8 Pa, which is equivalent to violent storm; (3) under the attack of airblast, the load suffered by buildings in the airblast zone increases to 1300 Pa at t=9.4 s and sharply decreased to –7000 Pa as the rapid decrease of the velocity of the sliding mass at t=10 s, which is seriously unfavorable for buildings and might be the key reason for the destructive collapse of buildings in the airblast zone of the Wangjiayan landslide.

Catastrophic geological disasters frequently occur on slopes with obliquely inclined bedding structures (also referred to as obliquely inclined bedding slopes), where the apparent dip sliding is not readily visible. This phenomenon has become a focal point in landslide research. Yet, there is a lack of studies on the failure modes and mechanisms of hidden, steep obliquely inclined bedding slopes. This study investigated the Shanyang landslide in Shaanxi Province, China. Using field investigations, laboratory tests of geotechnical parameters, and the 3DEC software, this study developed a numerical model of the landslide to analyze the failure process of such slopes. The findings indicate that the Shanyang landslide primarily crept along a weak interlayer under the action of gravity. The landslide, initially following a dip angle with the support of a stable inclined rock mass, shifted direction under the influence of argillization in the weak interlayer, moving towards the apparent dip angle. The slide resistance effect of the karstic dissolution zone was increasingly significant during this process, with lateral friction being the primary resistance force. A reduction in the lateral friction due to karstic dissolution made the apparent dip sliding characteristics of the Shanyang landslide more pronounced. Notably, deformations such as bending and uplift at the slope's foot suggest that the main slide resistance shifts from lateral friction within the karstic dissolution zone to the slope foot's resistance force, leading to the eventual buckling failure of the landslide. This study unveils a novel failure mode of apparent dip creep-buckling in the Shanyang landslide, highlighting the critical role of lateral friction from the karstic dissolution zone in its failure mechanism. These insights offer a valuable reference for mitigating risks and preventing disasters related to obliquely inclined bedding landslides.

A detailed and accurate inventory map of landslides is crucial for quantitative hazard assessment and land planning. Traditional methods relying on change detection and object-oriented approaches have been criticized for their dependence on expert knowledge and subjective factors. Recent advancements in high-resolution satellite imagery, coupled with the rapid development of artificial intelligence, particularly data-driven deep learning algorithms (DL) such as convolutional neural networks (CNN), have provided rich feature indicators for landslide mapping, overcoming previous limitations. In this review paper, 77 representative DL-based landslide detection methods applied in various environments over the past seven years were examined. This study analyzed the structures of different DL networks, discussed five main application scenarios, and assessed both the advancements and limitations of DL in geological hazard analysis. The results indicated that the increasing number of articles per year reflects growing interest in landslide mapping by artificial intelligence, with U-Net-based structures gaining prominence due to their flexibility in feature extraction and generalization. Finally, we explored the hindrances of DL in landslide hazard research based on the above research content. Challenges such as black-box operations and sample dependence persist, warranting further theoretical research and future application of DL in landslide detection.