China Geology最新文献

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.

This study makes a significant progress in addressing the challenges of short-term slope displacement prediction in the Universal Landslide Monitoring Program, an unprecedented disaster mitigation program in China, where lots of newly established monitoring slopes lack sufficient historical deformation data, making it difficult to extract deformation patterns and provide effective predictions which plays a crucial role in the early warning and forecasting of landslide hazards. A slope displacement prediction method based on transfer learning is therefore proposed. Initially, the method transfers the deformation patterns learned from slopes with relatively rich deformation data by a pre-trained model based on a multi-slope integrated dataset to newly established monitoring slopes with limited or even no useful data, thus enabling rapid and efficient predictions for these slopes. Subsequently, as time goes on and monitoring data accumulates, fine-tuning of the pre-trained model for individual slopes can further improve prediction accuracy, enabling continuous optimization of prediction results. A case study indicates that, after being trained on a multi-slope integrated dataset, the TCN-Transformer model can efficiently serve as a pre-trained model for displacement prediction at newly established monitoring slopes. The three-day average RMSE is significantly reduced by 34.6% compared to models trained only on individual slope data, and it also successfully predicts the majority of deformation peaks. The fine-tuned model based on accumulated data on the target newly established monitoring slope further reduced the three-day RMSE by 37.2%, demonstrating a considerable predictive accuracy. In conclusion, taking advantage of transfer learning, the proposed slope displacement prediction method effectively utilizes the available data, which enables the rapid deployment and continual refinement of displacement predictions on newly established monitoring slopes.

The frequent occurrence of extreme weather events has rendered numerous landslides to a global natural disaster issue. It is crucial to rapidly and accurately determine the boundaries of landslides for geohazards evaluation and emergency response. Therefore, the Skip Connection DeepLab neural network (SCDnn), a deep learning model based on 770 optical remote sensing images of landslide, is proposed to improve the accuracy of landslide boundary detection. The SCDnn model is optimized for the over-segmentation issue which occurs in conventional deep learning models when there is a significant degree of similarity between topographical geomorphic features. SCDnn exhibits notable improvements in landslide feature extraction and semantic segmentation by combining an enhanced Atrous Spatial Pyramid Convolutional Block (ASPC) with a coding structure that reduces model complexity. The experimental results demonstrate that SCDnn can identify landslide boundaries in 119 images with MIoU values between 0.8 and 0.9; while 52 images with MIoU values exceeding 0.9, which exceeds the identification accuracy of existing techniques. This work can offer a novel technique for the automatic extensive identification of landslide boundaries in remote sensing images in addition to establishing the groundwork for future investigations and applications in related domains.

This study proposed a novel friction law dependent on velocity, displacement and normal stress for kinematic analysis of runout process of rapid landslides. The well-known Yigong landslide occurring in the Tibetan Plateau of China was employed as the case, and the derived dynamic friction formula was included into the numerical simulation based on Particle Flow Code. Results showed that the friction decreased quickly from 0.64 (the peak) to 0.1 (the stead value) during the 5s-period after the sliding initiation, which explained the behavior of rapid movement of the landslide. The monitored balls set at different sections of the mass showed similar variation characteristics regarding the velocity, namely evident increase at the initial phase of the movement, followed by a fluctuation phase and then a stopping one. The peak velocity was more than 100 m/s and most particles had low velocities at 300s after the landslide initiation. The spreading distance of the landslide was calculated at the two-dimension (profile) and three-dimension scale, respectively. Compared with the simulation result without considering friction weakening effect, our results indicated a max distance of about 10 km from the initial unstable position, which fit better with the actual situation.