Risk Assessment of Secondary Geological Disasters Induced by the Yushu Earthquake

The Yushu Ms 7.1 earthquake occurred on April 14,2010 in Qinghai Province,China.It induced a mass of secondary geological disasters,such as collapses,landslides,and debris flows.Risk assessment maps are important for geological disaster prevention and mitigation,and also can serve as a guide for post-earthquake reconstruction.Firstly,a hazard assessment index system of secondary geological disasters in the earthquake region was built in this paper,which was based on detailed analysis of environmental and triggering factors closely related to geological disasters in the study area.GIS technology was utilized to extract and analyze the assessment index.Hazard assessment maps of secondary geological disasters were obtained by spatial modeling and overlaying analysis.Secondly,an analysis of the vulnerability of hazard bearing bodies in the area was conducted,important information,such as, population density,percentage of arable land, industrial and agricultural outputs per unit area were regarded as assessment indices to evaluate socioeconomic vulnerability.Thirdly,the risk level of secondary geological disasters of the area was obtained by the formula:Risk=Hazard×Vulnerability. Risk assessment maps were categorized into four levels,including"low","moderate","high"and"very high".These results show that some urban areas are at very high risk,including Jiegu,Chengwen,Xiaxiula and Sahuteng towns.This research can provide some references and suggestions to improve decisionmaking support for emergency relief and post- earthquake reconstruction in the study area.

Planning and construction of Xiong’an New Area(city of over 5 million people):Contributions of China’s geologists and urban geology

China established Xiong’an New Area in Hebei Province in 2017,which is planned to accommodate about 5 million people,aiming to relieve Beijing City of the functions non-essential to its role as China’s capital and to expedite the coordinated development of the Beijing-Tianjin-Hebei region.From 2017 to 2021,the China Geological Survey(CGS)took the lead in multi-factor urban geological surveys involving space,resources,environments,and disasters according to the general requirements of“global vision,international standards,distinctive Chinese features,and future-oriented goals”in Xiong’an New Area,identifying the engineering geologic conditions and geologic environmental challenges of this area.The achievements also include a 3D engineering geological structure model for the whole area,along with“one city proper and five clusters”,insights into the ecology and the background endowment of natural resources like land,geothermal resources,groundwater,and wetland of the area before engineering construction,a comprehensive monitoring network of resources and environments in the area,and the“Transparent Xiong’an”geological information platform that is open,shared,dynamically updated,and three-dimensionally visualized.China’s geologists and urban geology have played a significant role in the urban planning and construction of Xiong’an New Area,providing whole-process geological solutions for urban planning,construction,operation and management.The future urban construction of Xiong’an New Area will necessitate the theoretical and technical support of earth system science(ESS)from various aspects,and the purpose is to enhance the resilience of the new type of city and to provide support for the green,low-carbon,and sustainable development of this area.

Object-based classification of cloudy coastal areas using medium-resolution optical and SAR images for vulnerability assessment of marine disaster

Efficient and accurate access to coastal land cover information is of great significance for marine disaster prevention and mitigation.Although the popular and common sensors of land resource satellites provide free and valuable images to map the land cover,coastal areas often encounter significant cloud cover,especially in tropical areas,which makes the classification in those areas non-ideal.To solve this problem,we proposed a framework of combining medium-resolution optical images and synthetic aperture radar(SAR)data with the recently popular object-based image analysis(OBIA)method and used the Landsat Operational Land Imager(OLI)and Phased Array type L-band Synthetic Aperture Radar(PALSAR)images acquired in Singapore in 2017 as a case study.We designed experiments to confirm two critical factors of this framework:one is the segmentation scale that determines the average object size,and the other is the classification feature.Accuracy assessments of the land cover indicated that the optimal segmentation scale was between 40 and 80,and the features of the combination of OLI and SAR resulted in higher accuracy than any individual features,especially in areas with cloud cover.Based on the land cover generated by this framework,we assessed the vulnerability of the marine disasters of Singapore in 2008 and 2017 and found that the high-vulnerability areas mainly located in the southeast and increased by 118.97 km2 over the past decade.To clarify the disaster response plan for different geographical environments,we classified risk based on altitude and distance from shore.The newly increased high-vulnerability regions within 4 km offshore and below 30 m above sea level are at high risk;these regions may need to focus on strengthening disaster prevention construction.This study serves as a typical example of using remote sensing techniques for the vulnerability assessment of marine disasters,especially those in cloudy coastal areas.

The formation of the Wulipo landslide and the resulting debris flow in Dujiangyan City, China

The Wulipo landslide, triggered by heavy rainfall on July 10, 2013, transformed into debris flow,resulted in the destruction of 12 houses, 44 deaths, and 117 missing. Our systematic investigation has led to the following results and to a new understanding about the formation and evolution process of this hazard. The fundamental factors of the formation of the landslide are a high-steep free surface at the front of the slide mass and the sandstone-mudstone mixed stratum structure of the slope. The inducing factor of the landslide is hydrostatic and hydrodynamic pressure change caused by heavy continuous rainfall. The geological mechanical model of the landslide can be summarized as "instability-translational slide-tension fracture-collapse" and the formation mechanism as "translational landslide induced by heavy rainfall". The total volume of the landslide is 124.6×104 m3, and 16.3% of the sliding mass was dropped down from the cliff and transformed into debris flow during the sliding process, which enlarged 46.7% of the original sliding deposit area. The final accumulation area is found to be 9.2×104 m2. The hazard is a typical example of a disaster chain involving landslide and its induced debris flow. The concealment and disaster chain effect is the main reason for the heavy damage. In future risk assessment, it is suggested to enhance the research onpotential landslide identification for weakly intercalated slopes. By considering the influence of the behaviors of landslide-induced debris flow, the disaster area could be determined more reasonably.

Landslide susceptibility assessment in Western Henan Province based on a comparison of conventional and ensemble machine learning

Landslide is a serious natural disaster next only to earthquake and flood,which will cause a great threat to people’s lives and property safety.The traditional research of landslide disaster based on experience-driven or statistical model and its assessment results are subjective,difficult to quantify,and no pertinence.As a new research method for landslide susceptibility assessment,machine learning can greatly improve the landslide susceptibility model’s accuracy by constructing statistical models.Taking Western Henan for example,the study selected 16 landslide influencing factors such as topography,geological environment,hydrological conditions,and human activities,and 11 landslide factors with the most significant influence on the landslide were selected by the recursive feature elimination(RFE)method.Five machine learning methods[Support Vector Machines(SVM),Logistic Regression(LR),Random Forest(RF),Extreme Gradient Boosting(XGBoost),and Linear Discriminant Analysis(LDA)]were used to construct the spatial distribution model of landslide susceptibility.The models were evaluated by the receiver operating characteristic curve and statistical index.After analysis and comparison,the XGBoost model(AUC 0.8759)performed the best and was suitable for dealing with regression problems.The model had a high adaptability to landslide data.According to the landslide susceptibility map of the five models,the overall distribution can be observed.The extremely high and high susceptibility areas are distributed in the Funiu Mountain range in the southwest,the Xiaoshan Mountain range in the west,and the Yellow River Basin in the north.These areas have large terrain fluctuations,complicated geological structural environments and frequent human engineering activities.The extremely high and highly prone areas were 12043.3 km^(2)and 3087.45 km^(2),accounting for 47.61%and 12.20%of the total area of the study area,respectively.Our study reflects the distribution of landslide susceptibility in western Henan Province,which provides a scientific basis for regional disaster warning,prediction,and resource protection.The study has important practical significance for subsequent landslide disaster management.

Landslide Vulnerable Zones Based on Engineering Geological Assessment, Case Study: Wonotopo Area, Gebang District, Purworejo Regency, Central Java, Indonesia

Landslide is one of the geological disasters that frequently occur on the natural slopes, often threatening community of the adjacent area. Therefore, it is necessary to hold engineering geological research and assessment in disaster-prone regions such as Gebang district of Purworejo Regency, Central Java Province, Indonesia. This research was conducted to determine and analyze the type of mass movements, factor of safety, potential landslide areas, vulnerable zonation, factors influencing the stability of the slope, and to propose the disaster mitigation recommendation. The methods applied in the research are surface geological mapping, physical soil-rock properties testing, engineering geological assessment and analysis, and Regulation of Minister of Public Works of Republic of Indonesia number 22/PRT/M/2007, on Guidelines to Spatial Planning for Landslide Disaster Areas. To create information on the threats of land movements, a map on landslide potential zonation is developed by considering seven important aspects including: slope inclination, soil conditions, slope constituents, rainfall, slope water condition, seismicity, and vegetation or land use. The results show that, the landslide prone zone of the study area can be divided into 2 types, namely type B and type C. Landslide potential zone of type B involves high level of vulnerability and moderate level of vulnerability. Meanwhile, the landslide potential zone of type C consists of high level of vulnerability and low level of vulnerability.

锦州市规划区地质环境安全评价研究

地质环境问题是影响城市国土空间安全开发利用的重要因素,在城市规划开发阶段开展地质环境综合评价可有效控制地质灾害风险。锦州市规划区尚处于开发前期,本文基于该规划区的地质条件和开发需求,构建了地质环境安全评价指标和评价方法体系,基于主导因素综合法开展了地质环境安全综合评价。根据评价结果,锦州市规划区地质环境安全程度可划分为3级,空间分布上以相对安全区为主,其次为次不安全区和安全区。按照空间位置和区域构造稳定性、地质灾害易发性等不同成因划分为23个亚区,并指出了每个亚区安全程度主要影响因素。该评价结果反映了锦州市规划区的地质环境安全程度,确定了影响地质环境安全问题的关键因素,有助于规避该区国土空间规划开发可能面临的地质灾害风险。

铁路地质灾害勘察识别与监测预警

地质灾害对铁路建设、安全运营构成了极大威胁,“天空地”一体化综合勘察技术有效解决了地质灾害勘察识别难题。“天”基多源立体卫星遥感技术实现艰险复杂山区地质灾害的大范围精准判识,长时序InSAR与高精度GPS实现高陡岸坡的稳定性监测分析;“空”基真实感大场景、机载倾斜摄影和机载LiDAR扫描技术实现高植被覆盖区隐蔽性地质灾害判识;“地”基三维立体勘探技术获取岩土体结构、属性、参数;共同构建沟谷山地灾害链风险评价方案,为建设工程合理选址和工程设置提供依据。介绍地质灾害监测预警的方法、内容、技术和预警模型,对监测预警技术发展方向进行展望。

基于新型智慧城市视角的地质灾害监测——以广州市为例

新型智慧城市建设为广州地质灾害监测提供了新机遇、新思路。笔者结合近年来地灾监测的科学研究和工程实践,从感知、调度、共治的角度浅谈对于广州地灾监测的一些思考。感知方面:广州市地貌结构复杂,北部丘陵台地作为地灾易发区,建议构建重点区域天-空-地三查体系,及时识别潜在风险源。预警调度:广州市地灾的发生与降雨有很强的时间相关性和空间耦合性,建议在广州市雨窝、降雨集中区构建精细化气象预警网格,提高监测预警精准度;针对地灾监测预警存在的重监测、轻预警现象,建议构建基于数据-知识双驱动的预警模型,实现高精度可解释的地灾预测建模;针对疑难边坡,建立专家研判系统,实现精准“把脉”,精准“治疗”。共建共治:加强跨部门跨层级合作,加强新建工程项目地灾评估,消减地灾存量,控制地灾增量。

气候灾害、农产品价格策略和农户生计脆弱性——来自生态搬迁安置区的考察

在生态搬迁大背景下,农户面临的外部环境、内在心理和应对策略均发生了明显转变,有必要从外部扰动与内部适应的视角探究农户生计脆弱性。基于生计脆弱性理论,探讨实际气候灾害、感知气候灾害和农产品价格策略对农户生计脆弱性的影响,基于广东韶关南水湖生态搬迁安置区425位农户的调研数据进行验证。通过单因素方差分析、回归分析和Bootstrap中介效应检验,得出结论:首先,生态搬迁安置区农户感知气候灾害有其特殊性,与村子位置、社会关系和生计转换能力有关;其次,实际气候灾害和感知气候灾害均会加剧农户生计脆弱性,但感知气候灾害的影响力大于实际气候灾害,这与以往研究观点不同,也反映出农户对于气候灾害的担忧心理较为突出;最后,气候灾害及其后续的农产品价格策略是影响农户生计脆弱性的重要因素。

小尺度山区地质灾害隐患的无人机精细化识别方法与实践

本文针对小空间尺度范围内以滑坡、崩塌为主的山区地质灾害,提出了一套基于小型无人机摄影测量的精细化隐患识别方法。首先,针对工作区开展至少两期无人机摄影测量作业,经处理后得到实景三维模型、数字正射影像(DOM)、数字表面模型(DSM)等精细化成果;其次,以两期DOM与DSM变化检测为主实现灾害体识别;然后,基于灾害体共性特征建立典型识别标志,并依此采用三维实景目视解译为主方法实现孕灾体识别;最后,通过地面核查确认或排除隐患。将该套方法应用到三峡库首秭归泄滩河左岸顺向斜坡区域,共识别出10处不同类型隐患,证明了方法的可行性。

复杂艰险山区高速铁路慢行病害特征与治理

针对我国西南复杂艰险山区高速铁路建设与运营中路基、桥梁和隧道出现导致列车运行速度减慢的病害(慢行病害),依托南昆、沪昆、西成高铁3条复杂艰险山区高速铁路运营期间监测与补充勘察资料,分析不同慢行病害的危害方式与变形破坏特征,研究高速铁路慢行病害产生的原因,提出治理方案。研究得出结论:地层岩性、地形地貌、构造应力环境、气候降雨、岩体结构条件等多重因素造成复杂艰险山区高速铁路出现路基基底上拱、斜坡桥梁位移、隧道基底上拱、隧道混凝土腐蚀等慢行病害,针对不同慢行病害提出多种治理方案。研究成果可为复杂艰险山区高速铁路工程建设、运营维护提供借鉴。

高山峡谷地区地质灾害易发性评价——以怒江州为例

高山峡谷地区地质灾害频发,目的为探究地质灾害易发性空间分布状况,方法以怒江州为研究区,综合地质条件、气象水文、植被覆盖等因素,筛选高程、坡度、坡向、曲率、起伏度等12个共线性低的评价因子,构建区域易发性评价指标体系,并基于栅格单元采用信息量(information value,IV)模型、信息量-BP神经网络(information value-back propagation neural networks,IV-BPNN)耦合模型和信息量-支持向量机(information value-support vector machine,IV-SVM)耦合模型进行地质灾害易发性评价。结果结果表明:(1)用实际地质灾害点验证易发性结果,灾害点与3种易发性结果在空间分布上具有较好的一致性;(2)将易发性指数划分为低、中、高和极高易发4个等级,其中IV模型、IV-BPNN耦合模型与IV-SVM耦合模型的高+极高易发区面积占比分别为37.12%,32.36%,23.08%,高与极高易发区呈线状分布,主要集中在怒江、澜沧江、独龙江等水系沿岸地区、道路附近和地质构造活跃的区域;(3)IV模型、IV-BPNN耦合模型与IV-SVM耦合模型的受试者曲线(receiver operating characteristic curve,ROC)的曲线下面积(area under the curve,AUC)分别为0.884,0.889,0.901。结论3种地质灾害易发性评价模型均有较高的预测精度,其中IV-SVM耦合模型准确率最高,分区结果较可靠,可为当地政府制定地质灾害防治措施提供参考。

粤港澳大湾区复合灾害承灾体脆弱性评估

由多种自然灾害耦合而成的复合灾害对个别区域的人类健康与城市社会发展有着显著的影响,对其主要承灾体的脆弱性评估便成为防灾减灾工作开展的重要前提.基于对自然灾害形势和脆弱性影响因素的深度学习,以粤港澳大湾区境内地级市以下52个区/县、香港地区、澳门地区为研究对象,从物理脆弱性、人口、经济建设、应对能力4个方面选取地均GDP、耕地面积百分比等13项指标建立评价指标体系,并使用主成分分析法分别评估4个方面的脆弱性,再通过综合评价法得出粤港澳大湾区复合灾害承灾体综合脆弱性评价结果.结果表明:高脆弱性集中在天河区、海珠区等靠近珠江口的位置,其共同特点是物理脆弱性与经济脆弱性较高,城市用地多、道路网发达占主导位置.因此,需要尽快通过气候适应战略来缓解城市化高地区局部变暖导致的复合灾害爆发,研究成果为我国华南沿海地区复杂灾害系统的防治提供重要理论支撑.

基于D-InSAR技术的矿区地表沉陷监测研究

矿山开采可能诱发各种地质灾害,威胁矿区的社会稳定和居民的生命财产安全,快速准确监测矿区沉陷可有效预防地质灾害。本文以山西省大同煤矿为研究对象,获取2022年10月至2023年3月共13景Sentinel-1影像,基于合成孔径雷达差分干涉测量技术(Differential Interferometric Synthetic Aperture Radar,D-In SAR)进行地表沉陷监测研究,获取大同煤矿地表累计沉陷量,使用已有研究成果对比验证了本实验结果的可靠性。结果表明,大同煤矿沉陷分布较广,主要分布在大同市南郊区西部以及南郊区、怀仁市、左云县和山阴县四县市交界处,监测期间最大、平均沉陷量分别为381.43 mm、13.88 mm,总沉陷面积约为207.91 km2,根据7个重点沉陷区的时空变化特征及演化规律发现沉陷呈现持续扩张趋势,推断地表沉陷仍会持续较长时间。研究证明了In SAR技术在矿山开采沉陷监测的可靠性,为矿产资源管理工作提供新的技术方法,研究结果可为矿山开采沉陷治理和地质灾害防控提供参考依据。

湖南红层地区滑坡地质灾害致灾因子敏感性分析

为了探究滑坡成因机制、揭示滑坡发育规律,以湖南省麻阳苗族自治县为例,选取高程、坡度、地形起伏度、地层岩性、距断层距离等10个因子为致灾因子,基于确定性因数模型开展致灾因子敏感性分析,探究湖南省红层地区滑坡发育特征和成因机制。结果表明:滑坡与土地利用类型、高程和地形起伏度等因子呈现较大的相关性,各因子间的敏感性大小为:土地利用类型>高程>地形起伏度>归一化植被指数>距道路距离>坡度>地层岩性>距河流距离>多年平均降雨>距断层距离;相应因子高敏感区间分别为:土地利用类型为建筑用地、高程[101 m,1394 m)、地形起伏度[47 m,74 m)、归一化植被指数[0.30,0.43)。

基于SRP模型的地质灾害易发区生态脆弱性评价

以甘肃省舟曲县为研究对象,以2010,2015和2020年遥感影像数据、地形数据、气象数据、地质灾害数据以及社会经济数据等为基础,结合SRP模型构建评价体系,采用层次分析法、空间自相关、地理探测器等方法,对舟曲县生态环境脆弱性进行定量评价与分析.结果表明:从县域尺度来看,2010—2020年舟曲县生态处于中等脆弱水平,在空间上呈现“北低南高,东低西高”的特征,时间上呈现“极度、重度脆弱区面积明显减少,中度脆弱区面积显著增加”趋势,整体向好趋势明显;从乡镇尺度来看,生态脆弱性状况均呈好转趋势,各乡镇生态脆弱性等级变化以极度、重度脆弱区减少,中度脆弱区增加为主;生态脆弱性空间集聚效应减弱,高高集聚类型乡镇由4个减少至3个,低聚集类型乡镇由1个增加至2个;生态脆弱性分布与变化主要受土壤、气象、地形地貌、人类活动以及地质灾害等因素综合影响,其中年降水量、地质灾害易发点密度和距离道路距离3个因子解释力最强,地形起伏度、坡度和距离河流距离3个因子解释力最弱.

基于地理信息系统(GIS)的地质灾害评价现状

近年来,地理信息系统(GIS)已成为评估和管理地质灾害风险的关键工具。地质灾害危险性评价是识别、分析和预测地质灾害潜在威胁的过程,它对于防灾减灾策略的制定和执行至关重要。本文基于GIS的地质灾害危险性评价方法的研究现状,探讨了其发展趋势。首先,从国内和国外两个方面回顾了GIS技术在地质灾害管理中的应用历史,分析了基于GIS的地质灾害调查。其次,探讨了地质易发性评价、危险性评价、易损性评价、地质灾害风险性之间的关系,详细分析了易发性和危险性评价的主要评价方法、计算逻辑、优缺点、应用场景和主要评价指标,对地质灾害易损性和风险性进行分析并指出目前主要几种分级类型,并指出了地质灾害评价过程的问题及未来的发展方向。

基于云模型的煤炭矿区隐蔽致灾地质因素评价

为明确矿井隐蔽致灾地质因素,合理制定精准防治措施。以黑龙江省东部四个煤炭矿区为研究对象,构建了以层次分析法、熵权法、云模型三种评价方法融合的煤矿隐蔽致灾地质因素危险性评价模型,计算并分析各因素的综合权重及综合评价云图,评价其危险性等级。结果表明:鸡西、鹤岗、七台河矿区评价结果为Ⅳ级,双鸭山矿区评价结果为Ⅴ级。该研究可为实现煤矿事故提前警戒预防,保障煤矿安全高效生产提供参考。

斜坡单元支持下赣西山区典型乡镇地质灾害风险评价

为组织开展地质灾害风险双控,科学实施避让搬迁与地质灾害综合治理,以及为国土空间规划、村镇规划与建设、重大工程布局等提供依据,以慈化镇为例,通过孕灾地质环境条件调查、地质灾害及隐患调查、承灾体调查和构建地质灾害风险评价体系研究了赣西典型山区地质灾害风险评价,以斜坡为单元评价其地质灾害易发性、危险性、承灾体易损性和风险性。本次研究选取10个主要指标计算斜坡单元的地质灾害易发性综合指数,再叠加不同工况计算得出危险性指数,在地质灾害危险性评价的基础上,采用定量与定性相结合的方法进行风险评价。整个评价体系基于GIS平台,再结合调查数据进行调整,得出的评价结果合理,能基本反映研究区地质灾害易发性和风险性特征。