On 18th December 2023,an Ms 6.2 earthquake centered in Jishishan County,Gansu Province triggered a prototypical earthquake-induced liquefaction flowslide in Jintian Village and Caotan Village,Zhongchuan Township,Minhe County,Haidong City,Qinghai Province.This hazard caused numerous buildings to be engulfed and interred by several meters of deposits.Owing to the abruptness of its occurrence and extremely high fluidity,it was mistakenly identified as a"sand surge".Through on-site investigation and remote sensing imagery analysis,we confirm this flowslide to be an earthquake-triggered liquefied flowslide event,and explore its causal mechanisms.The results show that:(1)This flowslide was induced by seismic vibratory loading causing liquefaction of the saturated silt layer(loess layer)underlying the plateau,forming a landslide that transformed into a mudflow moving along the valley,rather than an in-situ"sand surge"in the conventional sense.(2)This flowslide includes two failure modes:diffuse failure and lateral spreading.(3)Earthquake-induced soil liquefaction frequently occurs in saturated granular materials(silts,fine sands,etc.)exhibiting prominent strain softening characteristics.The occurrence of such geological hazard is abrupt.Post-instability,the landslide mass flows significant distances like a fluid,readily resulting in catastrophic consequences.This warrants heightened attention.
Objectives:With the development of Shenzhen city,China,land renovation is more frequent.At the same time,affected by the subtropical monsoon climate,the area under the jurisdiction has abundant rainfall and dense vegetation coverage,making it difficult to identify the hidden dangers of geological hazards widely distributed on artificial slopes and natural slopes.Therefore,it is necessary to develop a set of hazard evaluation system of geological disaster that can solve the unique terrain and climate conditions in Shenzhen,so as to achieve the purpose of preventing disasters in advance and reducing casualties.Methods:(1)On the basis of high-precision digital elevation model of Shenzhen city obtained by airborne light detection and ranging(LiDAR),about 3 500 slope disaster prone points in Shenzhen are obtained through data collection,remote sensing interpretation and field verification.The sample library expanded 330％after proofreading.(2)Taking 3 major factors(8 factors)of terrain,geological structure and human engineering activities into comprehensive consideration,and based on the rainfall-induced disaster mechanism,a rainfall collection factor is proposed,and the weight of evidence method is used to complete the geological disaster hazard evaluation model under rainfall-induced conditions.(3)The threshold determination method of"key point control"under the actual background of single disaster is proposed,and the classification of the risk assessment model is completed.Results:The area under curve value of receiver operating characteristic curve model reaches 0.903,indicating that the model has a good effect on disaster forecasting.LiDAR technology can improve the identification accuracy of geological hazards in cities under dense vegetation coverage.Conclusions:Based on airborne LiDAR data,through a series of means such as expansion of disaster database,analysis of disaster distribution law,establishment of disaster evaluation factors,and classification of risk levels,it can form a refined evaluation system for the hazard evaluation of the slope in densely vegetated areas under the influence of the subtropical monsoon climate.
Objectives:In mid-to late-April 2024,an extreme heavy rainfall event occurred in Shaoguan City,Guangdong Province,inducing a large number of landslides in Jiangwan Town,Shaoguan.People lost connection with the outside world for nearly 36 hours,which aroused widespread social concern.Rapid-ly and accurately identifying the basic characteristics of landslides,development and distribution patterns and formation conditions is crucial for disaster emergency decision-making and risk elimination and dispos-al.Methods:Using the post-disaster optical remote sensing images and combining with deep learning mod-el,the rainfall-induced landslides in Jiangwan Town,Shaoguan,were quickly and automatically identified.Results:After manually calibration,a total of 1 192 landslides were deciphered,with a total area of about 3.14 km2.The scale of the landslides was dominated by small and medium-sized landslides,which were mainly distributed as an aggregated belt along the river in the northeast-southwest direction,with a signifi-cant characteristic of concentrated occurrence.Spatial statistical analysis showed that the landslides were mainly distributed on concave slopes with slopes of 10°-30° in the range of 200-300 m elevation.Further quantitative analysis of the geomorphic controlling factors of landslides using the random forest model and SHAP theory reveals that different topographic and geomorphic factors have different degrees of nonlinear effects on landslide formation,and that multiple factors such as elevation,slope,and catchment conditions are coupled to jointly control the formation of landslides.Conclusions:This paper highlights the great ad-vantage of deep learning-based intelligent identification and analysis technology in the emergency investiga-tion and formation conditions analysis of landslide disasters,which can provide important technical support for the rapid assessment of disaster losses and risk identification.
在高频次观测、任务突发等动态业务场景下,任务数据往往具有动态增量性,需要影像理解模型能够对新的任务及影像数据快速作出响应.然而,由于现实世界的动态开放性,新增任务数据往往偏离历史观测数据,需要人工干预以实现模型快速调整和更新.
随全球气候变化,高位崩塌灾害频发,造成了巨大人员伤亡与财产损失.然而,当前崩塌模拟软件尚存明显的不足,如地形精度较低、未考虑岩体结构特征及无法实现块体运动过程中碰撞碎裂等现象,且利用中央处理器(central pro-cessing unit,CPU)的运算效率较低也限制了模拟崩塌的规模.为此,急需一种能更真实反映崩塌运动过程的快速模拟方法.利用无人机摄影技术,结合现场调查,提取危岩岩体结构特征,建立精细化的崩塌地质-力学模型;利用集成PhysX物理引擎与中央处理器-图形处理器(central processing unit-graphics processing unit,CPU-GPU)并行计算能力的Unity3D平台,研发了大规模崩塌运动过程模拟软件,再现崩塌坠落-撞击-碎裂-堆积的全过程;系统可输出崩塌三维运动轨迹、速度、能量及弹跳高度,为崩塌防治设计提供依据.以贵州纳雍谢家岩崩塌为原型案例,开展崩塌运动过程三维模拟与验证,模拟结果显示落石堆积范围与现场调查坡底落石堆积范围较吻合,单体块石运动特征符合现实规律.
