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.

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.

Co-seismic Faults and Geological Hazards and Incidence of Active Fault of Wenchuan Ms 8.0 Earthquake,Sichuan,China

There are two co-seismic faults which developed when the Wenchuan earthquake happened. One occurred along the active fault zone in the central Longmen Mts. and the other in the front of Longmen Mts. The length of which is more than 270 kin and about 80 km respectively. The co-seismic fault shows a reverse flexure belt with strike of N45°-60°E in the ground, which caused uplift at its northwest side and subsidence at the southeast. The fault face dips to the northwest with a dip angle ranging from 50° to 60°. The vertical offset of the co-seismic fault ranges 2.5-3.0 m along the Yingxiu- Beichuan co-seismic fault, and 1.5-1.1 m along the Doujiangyan-Hanwang fault. Movement of the coseismic fault presents obvious segmented features along the active fault zone in central Longmen Mts. For instance, in the section from Yingxiu to Leigu town, thrust without evident slip occurred; while from Beichuan to Qingchuan, thrust and dextral strike-slip take place. Main movement along the front Longmen Mts. shows thrust without slip and segmented features. The area of earthquake intensity more than IX degree and the distribution of secondary geological hazards occurred along the hanging wall of co-seismic faults, and were consistent with the area of aftershock, and its width is less than 40km from co-seismic faults in the hanging wall. The secondary geological hazards, collapses, landslides, debris flows et al., concentrated in the hanging wall of co-seismic fault within 0-20 km from co-seismic fault.

Distribution Characteristics and Influencing Factors of Geological Hazards in Tibet

Tibet is one of the areas with most serious geological hazards in China, and the distribution of disasters has obvious local charac teristics. Tibet can be classified as three parts through zoning the danger degree, the mountain canyon high danger zone of east and southeast Tibet, the plateau mountain lake basin and valley middle danger zone of south Tibet, and the Plateau Mountain lake basin low danger zone of south Tibet. This paper takes the debris flow, collapse, landslide as the key points to analyze the distribution characteristics of geological hazards, and analyze the factors which influence the distribution of geological hazards, such as terrain landform, formation lithology, geologic structure pattern, precipitation, earthquake, human activity and so on. finally, as a conclusion., in whole Tibet, the geological hazards are more in southeast than in northwest, more in mountainous area which in the edge of plateau and river valley than in the interior of plateau and lake basin. And most hazards distribute in the regions where human activity is stronger than in other regions, for example towns or strips along the highway.

Types and Distribution of Geological Hazards in the South China Sea

Various types of geological hazards exist in the South China Sea. In dynamics sense, they can be categorized into 5 principal genetic types related to effects of hydraulic dynamics, gaseous activity, soil mechanics, gravity and tectonism, respectively. Integrated analyses indicate that the geological hazards associated with volcanoes, earthquakes and fractures are mainly distributed in tectonically active regions, whereas those resulting from mudflows, landslides and diapirs are usually concentrated in the region of slope, that shallow gas, high pressure gas pockets and soft intercalations are major potential geological hazards in the inner shelf, and that strong hydraulic dynamics, especially storm tide, is one of the major causes of geological hazards in the littoral areas. The geological hazards that occurred in the South China Sea are also characterized by periodicity, succession and, to a certain extent, unpredictability in addition to regionalization.

Regional Integrated Meteorological Forecasting and Warning Model for Geological Hazards Based on Logistic Regression

Information model is adopted to integrate factors of various geosciences to estimate the susceptibility of geological hazards. Further combining the dynamic rainfall observations, Logistic regression is used for modeling the probabilities of geological hazard occurrences, upon which hierarchical warnings for rainfall-induced geological hazards are produced. The forecasting and warning model takes numerical precipitation forecasts on grid points as its dynamic input, forecasts the probabilities of geological hazard occurrences on the same grid, and translates the results into likelihoods in the form of a 5-level hierarchy. Validation of the model with observational data for the year 2004 shows that 80% of the geological hazards of the year have been identified as ＂likely enough to release warning messages＂. The model can satisfy the requirements of an operational warning system, thus is an effective way to improve the meteorological warnings for geological hazards.

Chinese public participation monitoring and warning system for geological hazards

In China,many geological hazards occurred in remote mountainous regions,and it was time-consuming to disseminate disaster information for the responsible parties to make timely judgements.Besides,only relying on professionals to monitor and manage disasters was demanding and costly.The Chinese government created a system to engage residents in the process of disaster risk management,namely’Public Participation Monitoring and Warning’(PPMW),to disseminate timely disaster information and bring down management costs.The objective of this system was to reduce casualties with minimum cost by organizing residents to evacuate from disasters in advance.This paper introduced the PPMW system,including its structure,operation mechanism by reviewing government documents and research articles,and its implementation by a case study of a landslide at Boli village(E 101°01’,N 27°29’),Yanyuan County,Sichuan Province,China on July 19th 2018.Further,this paper analyzed the strengths and limitations of the system and discussed its future development.It had the potential to become an affordable disaster risk management tool for other countries facing similar situations to China.

Geological Hazards Occurred on the Road Connecting Vashlijvari-Lisi (M. Machavariani Street) in Tbilisi

The capital of Georgia—Tbilisi has a very convenient location and is a node of the transit corridor. Along with natural-geological conditions, its complexity is due to the rapid demographic growth of the city in a highly “sensitive” area of the geological environment and the pressure of high engineering and agricultural activities. In Tbilisi, it is observed almost all types of geological hazards, including landslide-gravitational, suffosion, debris/mudflows, river bank erosion and inundation were caused by groundwater. These hazards cause high damages to the residential houses and other infrastructure facilities. Most importantly and most tragically is that these kind of negative geological events are often accompanied by human casualties. The study discusses the geological processes developed in March 2021 in the corridor of the Vashlijvari-Lisi road (Machavariani Street). The information obtained from the study, reflects the triggering factors of the geological hazards, also damages caused by them, and provides recommendations for short-term and long-term protective measures that should ensure the sustainable operation of the road and other infrastructure facilities.

Machine Learning-Based Evaluation of Susceptibility to Geological Hazards in the Hengduan Mountains Region, China

The Hengduan Mountains Region(HMR) is one of the areas that experience the most frequent geological hazards in China. However, few reports are available that address the geological hazard susceptibility of the region.This study developed six machine learning models to assess the geological hazard susceptibility. The results show that areas with medium and high susceptibility to geological hazards as a whole account for almost 21% of the total area, while both are 18% when it comes to the single hazard of landslide and rockfall respectively. Medium and high geological hazard susceptibility is found in three parts of the HMR with different characteristics:(1)the central and southern parts, where the population of the region concentrates;(2) the northern part, where higher geological hazard susceptibility is found along the mountain ranges;and(3) the junction of Tibet, Yunnan, and Sichuan in the eastern part, which is prone to larger-scale geological hazards. Of all the potential influencing factors,topographic features and climatic variables act as the major driving factors behind geological hazards and elevation,slope, and precipitation are crucial indicators for geological hazard susceptibility assessment. This study developed the geological hazard susceptibility maps of the HMR and provided information for the multi-hazard risk assessment and management of the region.

Application of remote sensing for investigating mining geological hazards

To investigate geological mining hazards using digital techniques such as highresolution remote sensing,a semi-automatically geological mining hazards extraction method is proposed based on the case of the Shijiaying coal mine,located in Fangshan District,Beijing,China.In the method,the vegetation is first removed using the normalized difference vegetation index(NDVI)on the GeoEye-1 data.Then,geological mining hazards interpretation features are determined after color enhancement using principal component analysis(PCA)transformation.Bitmaps mainly covered by geological mining hazards are isolated by masking operation in the environment for visualizing images software.Next,each bitmap is classified into a two-valued imagery using support vector machine algorithm.In the two-valued imagery,1 denotes the geological mining hazards,while 0 denotes none.Afterwards,the two-valued imagery is converted into a vector graph by corresponding functions in the ArcGIS software and no geological mining hazards regions in the vector graph are deleted manually.Finally,the correlation between factors(such as mining activity,lithology,geological structure,and slope)and geological mining hazards is analyzed using a logistic regression and a hazardous-area forecasting model is built.The results of field verification show that the accuracy of the geological mining hazards extraction method is 98.1%and the results of the hazardous-area forecasting indicate that the logistic regression is an effective model in assessing geological hazard risks and that mining activity is the main contributing factor to the hazards,while geological structure,slope,lithology,roughness of the surface,and aspect are the secondary.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.