Deformation,structure and potential hazard of a landslide based on InSAR in Banbar county,Xizang(Tibet)

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 6.57×10^(4)m^(2),and the landslide volume is about 1.45×10^(6)m^(3).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.

Automated machine learning for rainfall-induced landslide hazard mapping in Luhe County of Guangdong Province,China

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 of92.36%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.

Analysis of debris flow control effect and hazard assessment in Xinqiao Gully,Wenchuan M_(s)8.0 earthquake area based on numerical simulation

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 by41.05%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.

Application of oblique photogrammetry technique in geological hazard identification and decision management

With the continuous development of the oblique photography technique, it has been used more and more widely in the field of geological disasters. It can quickly obtain the three-dimensional(3D) real scene model of dangerous mountainous areas under the premise of ensuring the safety of personnel while restoring the real geographic information as much as possible. However, geological disaster areas are often accompanied by many adverse factors such as cliffs and dense vegetation. Based on this, the paper introduced the flight line design of oblique photogrammetry, analyzed the multi-platform data fusion processing, studied the multi-period data dynamic evaluation technology and proposed the application methods of data acquisition, early warning, disaster assessment and decision management suitable for geological disaster identification through the analysis of actual cases, which will help geologists to plan and control geological work more scientifically and rationally, improve work efficiency and reduce the potential personnel safety hazards in the process of geological survey, to offer technical support to the application of oblique photogrammetry in geological disaster identification and decision making and provide the scientific basis for personal and property safety protection and later-stage geological disaster management in disaster areas.

A Study on the Implementation Path of the Blended Teaching Model in Geological Hazards Investigation and Evaluation

Geological Hazards Investigation and Evaluation is the core course of Environmental Geological Engineering,aiming to cultivate skilled talents with solid theoretical knowledge and excellent practical skills.At present,the course faces several issues,including a teaching environment disconnected from real-world work scenarios,course content that deviates from job-related tasks,a lack of digital teaching resources,and reliance on a single teaching method,leading to students’poor feedback from employers.Based on the concept of outcome-based education,the course team of Geological Hazards Investigation and Evaluation establishes a“five-step double-rotation”blended teaching model with the help of a Small Private Online Course platform.The program is designed to improve the teaching environment and expand the digitalized teaching resources in order to improve students’learning motivation,enhance learning effectiveness,and cultivate skillful talents who meet employers’satisfaction.

Present state,basic theories,methods and progresses of investigation and assessment on marine hazardous geology in China

The achievement progresses of investigation and studies on marine hazardous geology are summarized and presentsd in the late 20 century in China. The importance, research value and present-day studies of marine hazardous geology, a newly developing branch of geoscience, are well expatiated. Several often confused concepts and theories are explained and redefined here. The comment on the means of investigations, assessment of marine hazardous geology, as well as its evolution, innovation, existing questions and future tasks are also introduced and presented. The concepts of 'hazard geology', geohazard', 'map of marine hazard geology', 'integrated evaluaton on seafloor stablity' are respectively discussed, including their definition, research objects, methods and contents. The types and classification of marine hazardous geology, principles and methods of marine hazardous geology map compilation, the assessment methods and models of marine hazardous geology environment and seafloor stability and so on are also discussed.

Recent Advances of Deep Learning in Geological Hazard Forecasting

Geological hazard is an adverse geological condition that can cause loss of life and property.Accurate prediction and analysis of geological hazards is an important and challenging task.In the past decade,there has been a great expansion of geohazard detection data and advancement in data-driven simulation techniques.In particular,great efforts have been made in applying deep learning to predict geohazards.To understand the recent progress in this field,this paper provides an overview of the commonly used data sources and deep neural networks in the prediction of a variety of geological hazards.

Thinking on Effective Methods of Successful Prediction of Geological Hazards in Shaanxi, China

In the process of human survival and development, it is inevitable to transform the original state of the world, thus forming a contradiction between the earth and the earth. The violent form of this contradiction is geological disasters. Geological disasters pose a threat to human life and property, and cause damage caused by natural or human factors, often causing casualties. The destruction process of geological disasters is usually a gradual process, showing many pre-disaster symptoms, such as local landslides, surface cracks, building deformation, tree skew, and ground sound. Evacuation can be avoided in advance according to the disaster precursors, so as to avoid casualties and achieve successful prediction. By reviewing the general situation of geological disasters in Shaanxi Province and the casualties in 2020, the difficulties in the prevention and control of geological disasters are summarized. In view of these difficulties, an on-site investigation, visit and analysis of geological disaster points and successful forecast points in Shaanxi Province in 2020 were conducted. In addition, combined with actual cases and years of work experience, the successful prediction experience of geological disasters was discussed from 8 aspects. Finally, the “Regulations on the Reward for Successful Geological Disaster Forecasting in Shaanxi Province” was revised in order to improve the successful prediction ability of geological disasters in Shaanxi Province and even the whole country, provide reference for future prevention and control of geological disasters, and effectively protect the safety of people’s lives and property.

Projecting Spring Consecutive Rainfall Events in the Three Gorges Reservoir Based on Triple-Nested Dynamical Downscaling

Spring consecutive rainfall events(CREs) are key triggers of geological hazards in the Three Gorges Reservoir area(TGR), China. However, previous projections of CREs based on the direct outputs of global climate models(GCMs) are subject to considerable uncertainties, largely caused by their coarse resolution. This study applies a triple-nested WRF(Weather Research and Forecasting) model dynamical downscaling, driven by a GCM, MIROC6(Model for Interdisciplinary Research on Climate, version 6), to improve the historical simulation and reduce the uncertainties in the future projection of CREs in the TGR. Results indicate that WRF has better performances in reproducing the observed rainfall in terms of the daily probability distribution, monthly evolution and duration of rainfall events, demonstrating the ability of WRF in simulating CREs. Thus, the triple-nested WRF is applied to project the future changes of CREs under the middle-of-the-road and fossil-fueled development scenarios. It is indicated that light and moderate rainfall and the duration of continuous rainfall spells will decrease in the TGR, leading to a decrease in the frequency of CREs. Meanwhile, the duration, rainfall amount, and intensity of CREs is projected to regional increase in the central-west TGR. These results are inconsistent with the raw projection of MIROC6. Observational diagnosis implies that CREs are mainly contributed by the vertical moisture advection. Such a synoptic contribution is captured well by WRF, which is not the case in MIROC6,indicating larger uncertainties in the CREs projected by MIROC6.

Exploring deep learning for landslide mapping:A comprehensive review

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 highresolution satellite imagery,coupled with the rapid development of artificial intelligence,particularly datadriven 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,77representative 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.

Dynamic simulation insights into friction weakening effect on rapid long-runout landslides:A case study of the Yigong landslide in the Tibetan Plateau,China

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 characteritics 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.

Identification and distribution of 13003 landslides in the northwest margin of Qinghai-Tibet Plateau based on human-computer interaction remote sensing interpretation

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 km^(2)(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.

Airblast evolution initiated by Wangjiayan landslides in the M_(s)8.0 Wenchuan earthquake and its destructive capacity analysis

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 reaching594.8 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.

Short-term displacement prediction for newly established monitoring slopes based on transfer learning

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 pretrained 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.

Extensive identification of landslide boundaries using remote sensing images and deep learning method

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 0.8and 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 inve stigations and applications in related domains.

Exploring mechanism of hidden,steep obliquely inclined bedding landslides using a 3DEC model:A case study of the Shanyang landslide in Shaanxi Province,China

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.

Spatial structural characteristics of the Deda ancient landslide in the eastern Tibetan Plateau:Insights from Audio-frequency Magnetotellurics and the Microtremor Survey Method

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 feature s.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 zoneⅠand Deda landslide accumulation zoneⅡin 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 DedaⅡlandslide 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-deepseated landslides in geologically complex regions like the eastern Tibetan Plateau.

Unraveling the hydraulic properties of loess for landslide prediction:A study on variations in loess landslides in Lanzhou,Dingxi,and Tianshui,China

Loess has distinctive characteristics,leading to frequent landslide disasters and posing serious threats to the lives and properties of local re sidents.The involvement of water repre sents 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.

Runout prediction of potential landslides based on the multi-source data collaboration analysis on historical cases

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.

Predicting geological hazards during tunnel construction

The complicated geological conditions and geological hazards are challenging problems during tunnel construction,which will cause great losses of life and property.Therefore,reliable prediction of geological defective features,such as faults,karst caves and groundwater,has important practical significances and theoretical values.In this paper,we presented the criteria for detecting typical geological anomalies using the tunnel seismic prediction（TSP） method.The ground penetrating radar（GPR） signal response to water-bearing structures was used for theoretical derivations.And the 3D tomography of the transient electromagnetic method（TEM） was used to develop an equivalent conductance method.Based on the improvement of a single prediction technique,we developed a technical system for reliable prediction of geological defective features by analyzing the advantages and disadvantages of all prediction methods.The procedure of the application of this system was introduced in detail.For prediction,the selection of prediction methods is an important and challenging work.The analytic hierarchy process（AHP） was developed for prediction optimization.We applied the newly developed prediction system to several important projects in China,including Hurongxi highway,Jinping II hydropower station,and Kiaochow Bay subsea tunnel.The case studies show that the geological defective features can be successfully detected with good precision and efficiency,and the prediction system is proved to be an effective means to minimize the risks of geological hazards during tunnel construction.