高速远程滑坡的地貌学与沉积学研究进展

本文总结了当前有关高速远程滑坡远程机制的研究现状,探讨了滑坡地貌学和沉积学对于揭示高速远程滑坡运动学机理的重要意义。通过梳理前人关于高速远程滑坡堆积形态的分类,分析了高速远程滑坡主要的形态学特征、蕴含的运动学信息以及高速远程滑坡的脱落块体、丘体、脊结构、断层等的地貌结构特征,并进一步指出这些地貌结构的形成主要是滑坡体物质不均匀运动的结果,其受地形和内聚力的影响。高速远程滑坡的沉积学特征明显受到岩性、地形和基底等因素的影响,本文对高速远程滑坡的沉积相模式及其运动学意义进行了分析,并对地层不变性与反序结构两个重要堆积特征进行了讨论。最后,指出高速远程滑坡地貌学与沉积学研究当前主要存在的一些问题,建议将大型物理模拟与数值模拟方法相结合,加强对高速远程滑坡地貌微形态和堆积结构的形成机制研究。     

国际树轮水文学研究进展

树木年代学丰富的内容和知识体系, 成熟的研究方法, 为区域尺度上研究水文变化提供了精确的方法. 基于树轮重建水文, 认识过去长期自然水文变化格局, 合理规划与管理水资源的价值, 使树轮研究在水文气候变化研究中愈来愈受到人们的广泛重视.在短短的几十年里, 树轮水文学取得了日新月异的进展, 成为继树轮气候学之后又一快速发展的应用学科.在理解树轮水文学科学概念与内涵的基础上, 介绍了基于树轮重建水文的主要手段与方法, 从重建径流、重建极端水文事件、重建降水及重建湖泊沼泽水位等4个方面综述了国际树轮水文学的研究内容与进展, 并简要的概述了其未来发展趋势.     

树轮生态学研究进展

树轮生态学体系庞大, 涉及众多研究方向, 讨论主要限定在水平梯度和垂直梯度上树轮气候响应模式研究、森林生态系统对环境变化响应和狭义树轮生态学所定义的虫灾、火灾以及涉及人为活动影响如环境污染方面的研究. 基于讨论所限定的树轮生态学的部分研究领域, 通过与国际研究水平的对比, 找出我国树轮生态学研究的不足, 并进一步展望了未来国内树轮生态学的发展. 通过吸取国外先进成果从而弥补国内树轮生态学研究的不足, 结合全球变化研究的背景, 在不同区域开展树轮生态学不同研究领域的研究, 定能更进一步推动我国树轮生态学研究的全面发展.     

树轮气候学中树木年轮密度分析方法的研究进展

树木年轮密度（树轮密度）分析作为树轮气候学的一种研究方法,能够提取更准确的环境信息,应用范围更广,在过去环境变化研究中受到越来越广泛的重视。讨论了树轮密度测量技术与树轮密度分析方法在过去气候变化研究中的发展历程及最新研究成果,认为X射线分析法仍然是目前被广泛采用的树轮密度分析手段,基于树轮密度数据的过去温度变化重建仍是树轮密度研究的主要方向。相关研究表明：森林上限和高纬度地区树木年轮的最大晚材密度对夏季温度变化非常敏感,可以用来重建过去几百年、乃至千年的不同空间尺度的温度变化;树轮早材密度或最小早材密度对降水量的响应比较显著,但目前主要集中于重建区域小尺度的降水量变化方面。另外,树轮密度分析结果还可以用来研究某些气候要素分量的变化（季节变化、气候事件爆发的早晚和持续时间等）、区域气候变化的影响因子以及海气动力机制、气压异常等。     

树轮气候学研究的若干进展

树轮气候学研究的若干进展刘广深，魏建云（中国科学院地球化学研究所，贵阳５５０００２）关键词年轮密度，气候信息，环境事件全球变化和环境变迁研究的需要，使树轮研究工作越来越受重视。近年来，在技术方法、取样范围、研究对象、研究内容、研究的组织网络等方面都取...     

广东现代樟树树轮纤维素的碳同位素与厄尔尼诺事件的关系

本文对广东阳春河的樟树树轮纤维素△δ13C序列研究发现,厄尔尼诺对树轮C同位素的高频变化的影响有一定的滞后性,一般是厄尔尼诺强度高峰期所对应年的下一年树轮△δ13C值较低.通过谱分析发现,树轮记录的△δ13C序列变化中2～3 a周期与热带气候的"准两年振荡"(QBO)十分一致,这从另一个侧面说明了广东樟树树轮记录了大范围的ENSO信息.其中准4.57 a周期可能与影响全球气候异常的厄尔尼诺事件的周期有关,很可能就是厄尔尼诺事件引起的气候变化对树木生长影响的反映.研究表明,广东厄尔尼诺所引起的气候变化,在树轮同位素中有较好的记录.说明在热湿地区,冬季树木仍有同位素分馏,树轮δ13C依然能记录冬季气温和东亚冬季风强弱的变化.     

埋藏古木的树轮碳同位素作为古气候变化代用指标的可能性

本文对广东不同时代、不同树种的埋藏古木树轮纤维素碳同位素序列研究发现:樟树和山桐子分别在相对炎热潮湿的气候环境中,其树轮δ13C值相对越偏正。结论如下:(1)古山桐子反映冰后期的冷凉气候;(2)古樟代表中全新世的潮湿热带气候;(3)古水松的大批死亡代表了晚全新世和历史时期几次寒冷事件。上述结论说明了古树树轮同位素可以作为古气候变化代用指标的可能性。通过古今对比,丰富了树轮气候学的研究内容。     

古地震学:活动断裂强震复发规律的研究

古地震学是活动构造学、地震地质学和构造地貌学相结合的一个分支学科,它以野外工作为基础,以史前地震的发生位置、时间及震级为研究目标,其核心是古地震事件变形的地层和地貌证据。古地震学通过运用沉积地层学、地貌学和构造地质学等研究中常用的方法和手段来识别第四纪沉积中保存的史前强震的证据并对其进行准确年代测定,弥补了历史地震和仪器记录短而大地震原地复发间隔长的局限,获得活动断裂上多次强震的时空重复特征,为评估未来地震发生概率提供基础数据。本文对古地震学的历史、研究前沿和发展趋势进行综述。首先介绍了古地震学的定义、世界和中国古地震研究的发展历史和古地震研究中的基本研究方法,重点对古地震研究的最新进展和前沿领域,如古地震探槽方法的更新、古地震事件识别证据的分级和不确定性量化评估、古地震数据对地震复发间隔和同震位移的重复规律等理论认识的贡献以及基于震害效应的古地震研究等方面的重要研究成果进行了回顾,最后对古地震研究的未来发展趋势进行了展望,认为未来古地震研究中应加强地震事件识别证据的不确定性量化方法应用,在新的测年方法上开展探索,尝试和完善虚拟现实场景下的古地震研究。在中国,尤其像华北这样历史地震文献资料较为丰富的地区,历史地震震害记录与古地震探槽开挖的结合可以拓展震例研究的丰富程度,拓宽研究思路和方法,促进对难点地区活动断裂的古地震和区域地震危险性评价工作。     

三峡库区重庆段滑坡活动时间分布的物理分形研究

在许多研究滑坡活动分布的分形特征中,未能考虑无标度区的范围,并将滑坡分布作为数学点来描述而未考虑滑坡规模对分形结果的影响。这种研究结果主要反映了数量众多的规模较小滑坡活动的特点,掩盖了规模较大的滑坡分布特征。本文研究了重庆三峡库区1594个规模大于1×10^4m^3的滑坡活动时间分布资料,分析了滑坡活动时间的均匀分布、Poisson分布和分形分布结果。提出运用物理分形的方法,将滑坡活动按不同规模等级划分为4个子集,分析了各个子集的分维D,无标度区的范围和自然周期。表明运用物理分形的方法能描述不同规模等级滑坡活动时间分布的分形特征。     

滑坡次生灾害损失评估方法研究

针对目前滑坡灾害破坏损失评估研究还没有涉及滑坡次生灾害破坏损失评估方面的研究,提出并建立了滑坡次生灾害破坏损失的评估模型与方法。应用故障树分析法（FTA）和事件树分析法（ETA）相结合的因果图方法对滑坡次生灾害作可靠性分析,由此对滑坡次生灾害损失进行了预测及评估。     

滑坡成因决策树挖掘

文章介绍了构造决策树的C4.5算法。在预处理过的滑坡数据上利用决策树算法生成一组坡体稳定性规则。这些规则可作为分析及预测滑坡的重要参考。也可用来创建专家系统的知识库。其次,作者对决策树算法提出了改进,通过调整测试集的某些参数,构造了一棵局部最优的决策树。使得对于滑坡成因的分类具有更高的正确率。     

Spatial prediction of urban landslide susceptibility based on topographic factors using boosted trees

As global warming accelerates, abnormal weather events are occurring more frequently. In the twenty-first century in particular, hydrological disruption has increased as water flows have changed globally, causing the strength and frequency of hydrological disasters to increase. The damage caused by such disasters in urban areas can be extreme, and the creation of landslide susceptibility maps to predict and analyze the extent of future damage is an urgent necessity. Therefore, in this study, probabilistic and data mining approaches were utilized to identify landslide-susceptible areas using aerial photographs and geographic information systems. Areas where landslides have occurred were located through interpretation of aerial photographs and field survey data. In addition, topographic maps generated from aerial photographs were used to determine the values of topographic factors. A frequency ratio (FR) model was utilized to examine the influences of topographic, soil and vegetation factors on the occurrence of landslides. A total of 23 variables that affect landslide frequency were selected through FR analysis, and a spatial database was constructed. Finally, a boosted tree model was applied to determine the correlations between various factors and landslide occurrence. Correlations among related input variables were calculated as predictor importance values, and sensitivity analysis was performed to quantitatively analyze the impact of each variable. The boosted tree model showed validation accuracies of 77.68 and 78.70% for the classification and regression algorithms using receiver operating characteristic curve, respectively. Reliable accuracy can provide a scientific basis to urban municipalities for policy recommendations in the management of urban landslides.     

西藏易贡大滑坡成因探讨

2000年4月9日, 西藏林芝地区波密县易贡藏布札木弄沟发生了体积超过3×108 m3的特大规模滑坡, 这是该沟100年(1900—2000年)来发生的第2次大规模滑坡.在卫星遥感影像分析基础上, 结合地质、气象、地震等资料, 对札木弄沟100年内2次发生特大规模滑坡的原因进行了分析, 表明这2次滑坡是地质构造、地层岩性、新构造运动、水文和气象因素共同作用的结果.分析结果还显示, 札木弄沟已进入下一个物质与能量积累过程, 在适当的外部条件作用下, 有可能再次发生滑坡.      

The assessment of landslide susceptibility mapping using random forest and decision tree methods in the Three Gorges Reservoir area,China

Landslide susceptibility mapping is an indispensable prerequisite for landslide prevention and reduction. At present, research into landslide susceptibility mapping has begun to combine machine learning with remote sensing and geographic information system (GIS) techniques. The random forest model is a new integrated classification method, but its application to landslide susceptibility mapping remains limited. Landslides represent a serious threat to the lives and property of people living in the Zigui–Badong area in the Three Gorges region of China, as well as to the operation of the Three Gorges Reservoir. However, the geological structure of this region is complex, involving steep mountains and deep valleys. The purpose of the current study is to produce a landslide susceptibility map of the Zigui–Badong area using a random forest model, multisource data, GIS, and remote sensing data. In total, 300 pre-existing landslide locations were obtained from a landslide inventory map. These landslides were identified using visual interpretation of high-resolution remote sensing images, topographic and geologic data, and extensive field surveys. The occurrence of landslides is closely related to a series of environmental parameters. Topographic, geologic, Landsat-8 image, raining data, and seismic data were used as the primary data sources to extract the geo-environmental factors influencing landslides. Thirty-four layers of causative factors were prepared as predictor variables, which can mainly be categorized as topographic, geological, hydrological, land cover, and environmental trigger parameters. The random forest method is an ensemble classification technique that extends diversity among the classification trees by resampling the data with replacement and randomly changing the predictive variable sets during the different tree induction processes. A random forest model was adopted to calculate the quantitative relationships between the landslide-conditioning factors and the landslide inventory map and then generate a landslide susceptibility map. The analytical results were compared with known landslide locations in terms of area under the receiver operating characteristic curve. The random forest model has an area ratio of 86.10%. In contrast to the random forest (whole factors, WF), random forest (12 major factors, 12F), decision tree (WF), decision tree (12F), the final result shows that random forest (12F) has a higher prediction accuracy. Meanwhile, the random forest models have higher prediction accuracy than the decision tree model. Subsequently, the landslide susceptibility map was classified into five classes (very low, low, moderate, high, and very high). The results demonstrate that the random forest model achieved a reasonable accuracy in landslide susceptibility mapping. The landslide hazard zone information will be useful for general development planning and landslide risk management.     

How do machine learning techniques help in increasing accuracy of landslide susceptibility maps?

Landslides are abundant in mountainous regions.They are responsible for substantial damages and losses in those areas.The A1 Highway,which is an important road in Algeria,was sometimes constructed in mountainous and/or semi-mountainous areas.Previous studies of landslide susceptibility mapping conducted near this road using statistical and expert methods have yielded ordinary results.In this research,we are interested in how do machine learning techniques help in increasing accuracy of landslide susceptibility maps in the vicinity of the A1 Highway corridor.To do this,an important section at Ain Bouziane(NE,Algeria) is chosen as a case study to evaluate the landslide susceptibility using three different machine learning methods,namely,random forest(RF),support vector machine(SVM),and boosted regression tree(BRT).First,an inventory map and nine input factors were prepared for landslide susceptibility mapping(LSM) analyses.The three models were constructed to find the most susceptible areas to this phenomenon.The results were assessed by calculating the receiver operating characteristic(ROC) curve,the standard error(Std.error),and the confidence interval(CI) at 95%.The RF model reached the highest predictive accuracy(AUC=97.2%) comparatively to the other models.The outcomes of this research proved that the obtained machine learning models had the ability to predict future landslide locations in this important road section.In addition,their application gives an improvement of the accuracy of LSMs near the road corridor.The machine learning models may become an important prediction tool that will identify landslide alleviation actions.     

滑坡灾害超前预判技术研究

以地质构造控制论为理论核心,以地层、构造、地形等要素的系统分析为指导,以滑坡的形成、演化及特征为对象,首次提出了滑坡地质灾害超前地质预判的概念:滑坡灾害超前地质预判主要是指在各级勘察工作前期,不借助任何勘探手段,运用各种超前地质预判的方法,根据滑坡的地质地貌特征确定滑坡的空间位置及滑坡类型,预测其未来发展状况的一种技术方法。以此建立了该技术的基本体系、判别方法及工作程序,并在陕南公路沿线地质灾害调查过程中进行了初步应用。     

黄土滑坡研究现状与设想

黄土滑坡因其危害大、分布广成为近年工程地质研究热点问题。首先对黄土滑坡类型划分进行了讨论,基于滑坡物质组成及主滑面发育位置的黄土滑坡分类方案反映了我国黄土滑坡的主控因素,各类滑坡活动特征明确,建议将其作为黄土滑坡分类的首选方案。对黄土滑坡的发生、滑动机制以及滑坡液化机制进行了评述,并就两类典型液化型黄土滑坡滑动模式、进一步研究思路进行了讨论。对地震诱发黄土滑坡进行了专门的述评。最后,对今后黄土滑坡研究提出了几点设想。     

Quantitative assessment on the influence of heavy rainfall on the crystalline schist landslide by monitoring system -case study on Zentoku landslide, Japan

On Shikoku Island, which is one of the four main islands of Japan, a large number of large-scale crystalline schist landslides have been revealed and are being monitored by an observation system. Seasonal heavy rainfall is the most active meteorological factor that can threaten the stability of this kind of site-specific landslide. In this paper, on the basis of the study of the rainfall-related behavior of a typical crystalline schist landslide, the Zentoku landslide, by analyzing the precisely and continuously observed piezometric and movement data, a method was developed to quantitatively assess the effect of heavy rainfall on a large-scale landslide. The results indicated that heavy rainfall-induced landslide displacement shows good correlation with the variation of groundwater levels. Variations of groundwater level have been simulated with the use of a tank model. The simulation using this model permits the change in water levels for future rainfall events to be predicted. By combining the predicted results with the empirical relation between displacements and water levels, rainfall-induced landslide movement during extreme rainfall events can be estimated in advance. The effect of heavy rainfall on sliding behavior can be quantified in terms of the change in displacement. Thus warning information or advisories for the local residents can be provided.     

福建南平安丰自来水厂滑坡特征及治理对策

持续强降雨导致安丰水厂后山坡坡面变形开裂,形成了一个规模较大的滑坡,严重威胁水厂安全。采用钻探、物探（地质雷达和面波法）和地表工程地质测绘等综合手段,查明了滑坡发育机制,认为坡体土层松软,风化差异大,透水性强,坡面相对较陡是滑坡发生的内因,降雨是诱发滑坡最主要的外在因素。稳定性计算表明,该滑坡目前处于蠕滑极限平衡状态。应急排险及后期治理施工措施的成功,可为类似滑坡地质灾害的防治提供经验。     

湖北省香溪河流域白家堡滑坡稳定性分析与评价

三峡大坝建成蓄水后,将导致库岸部分古滑体复活、新滑体产生,香溪河流域白家堡滑坡就是其中之一。文章在对该滑坡的工程地质条件、深部位移及伸缩计监测资料的研究基础上,分析了滑坡变形机理,得出白家堡滑坡只有一个滑动面,其总体变形趋势为推移式,目前仍具有微小的变形。滑坡变形与降雨及库水有密切联系。结合试验资料,针对滑坡变形的实际情况,采用反演分析方法进行了滑移面抗剪强度参数的反演计算。利用反演结果,在三峡水库蓄水4种不同水位工况下进行稳定性计算。结果表明,滑坡的稳定性系数经历了大→小→大的过程。正常蓄水位时稳定性处于较低状态。滑坡体饱水处于蠕滑或失稳状态,需尽快进行治理。