China Geology最新文献

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.

It is of crucial importance to investigate the spatial structures of ancient landslides in the eastern Tibetan Plateau's alpine canyons as they could provide valuable insights into the evolutionary history of the landslides and indicate the potential for future reactivation. This study examines the Deda ancient landslide, situated in the Chalong-ranbu fault zone, where creep deformation suggests a complex underground structure. By integrating remote sensing, field surveys, Audio-frequency Magnetotellurics (AMT), and Microtremor Survey Method (MSM) techniques, along with engineering geological drilling for validation, to uncover the landslide's spatial features. The research indicates that a fault is developed in the upper part of the Deda ancient landslide, and the gully divides it into Deda landslide accumulation zone I and Deda landslide accumulation zone II in space. The distinctive geological characteristics detectable by MSM in the shallow subsurface and by AMT in deeper layers. The findings include the identification of two sliding zones in the Deda I landslide, the shallow sliding zone (DD-I-S1) depth is approximately 20 m, and the deep sliding zone (DD-I-S2) depth is 36.2–49.9 m. The sliding zone (DD-II-S1) depth of the Deda II landslide is 37.6–43.1 m. A novel MSM-based method for sliding zone identification is proposed, achieving less than 5% discrepancy in depth determination when compared with drilling data. These results provide a valuable reference for the spatial structural analysis of large-deep-seated landslides in geologically complex regions like the eastern Tibetan Plateau.

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.

Xinqiao Gully is located in the area of the 2008 Wenchuan M_s 8.0 earthquake in Sichuan province, China. Based on the investigation of the 2023 “6-26” Xinqiao Gully debris flow event, this study assessed the effectiveness of the debris flow control project and evaluated the debris flow hazards. Through field investigation and numerical simulation methods, the indicators of flow intensity reduction rate and storage capacity fullness were proposed to quantify the effectiveness of the engineering measures in the debris flow event. The simulation results show that the debris flow control project reduced the flow intensity by 41.05% to 64.61%. The storage capacity of the dam decreases gradually from upstream to the mouth of the gully, thus effectively intercepting and controlling the debris flow. By evaluating the debris flow of different recurrence intervals, further measures are recommended for managing debris flow events.

The Xianshuihe-Anninghe fault extends SE–S and constitutes the southeastern margin of the Tibetan Plateau. However, the Dadu River which is associated with the fault does not flow following the path, but makes a 90° turn within a distance of 1 km at Shimian, heading east, and joins the Yangtze River, finally flowing into the East China Sea. Adjacent to the abrupt turn, a low and wide pass near the Daqiao reservoir at Mianning separates the N–S course of the Dadu River from the headwater of the Anning River which then flows south into the Yunnan Province along the Anninghe fault. Therefore, many previous studies assumed southward flow of the paleo-Dadu River from the Shimian to the Anning River. However, evidences for the capture of the integrated N–S paleo-Dadu-Anning River, its timing, and causes are still insufficient. This study explored the paleo-drainage pattern of the Dadu and Anning Rivers based on bulk mineral and geochemical analyses of the large quantities of fluvial/lacustrine sediments along the trunk of the Dadu and Anning Rivers. Similar with sands in the modern Dadu River, the Xigeda sediments also exhibit a granitoid affinity with the bulk major mineral compositions of quartz (>50%), anorthite (about 10%), orthoclase (about 5%), muscovite (about 5%), and clinochlore (about 4%). Correspondingly, bulk major elements show high SiO₂, with all samples >60%, and some of them >70%, low TiO₂ (⩽0.75%), P₂O₅ (⩽0.55%), FeO* (⩽5%), and relatively high CaO (1.02%–8.51%), Na₂O (1.60%–2.52%), and K₂O (2.17%–2.71%), with a uniform REE patterns. Therefore, synthesizing all these results indicate that these lacustrine sediments have similar material sources, which are mainly derived from its course in the Songpan-Ganzi flysch block, implying that the paleo-Dadu originally flowed southward into the Anning River and provided materials to the Xigeda ancient lake. The rearrangement of the paleo-Dadu River appears to be closely related to the locally focused uplift driven by strong activities of the Xianshuihe-Xiaojiang fault system.

©2024 China Geology Editorial Office.

Machine learning is currently one of the research hotspots in the field of landslide prediction. To clarify and evaluate the differences in characteristics and prediction effects of different machine learning models, Conghua District, which is the most prone to landslide disasters in Guangzhou, was selected for landslide susceptibility evaluation. The evaluation factors were selected by using correlation analysis and variance expansion factor method. Applying four machine learning methods namely Logistic Regression (LR), Random Forest (RF), Support Vector Machines (SVM), and Extreme Gradient Boosting (XGB), landslide models were constructed. Comparative analysis and evaluation of the model were conducted through statistical indices and receiver operating characteristic (ROC) curves. The results showed that LR, RF, SVM, and XGB models have good predictive performance for landslide susceptibility, with the area under curve (AUC) values of 0.752, 0.965, 0.996, and 0.998, respectively. XGB model had the highest predictive ability, followed by RF model, SVM model, and LR model. The frequency ratio (FR) accuracy of LR, RF, SVM, and XGB models was 0.775, 0.842, 0.759, and 0.822, respectively. RF and XGB models were superior to LR and SVM models, indicating that the integrated algorithm has better predictive ability than a single classification algorithm in regional landslide classification problems.

©2024 China Geology Editorial Office.