Comparing models of debris-flow susceptibility in the alpine environment

Debris-flows are widespread in Val di Fassa (Trento Province, Eastern Italian Alps) where they constitute one of the most dangerous gravity-induced surface processes. From a large set of environmental characteristics and a detailed inventory of debris flows, we developed five models to predict location of debris-flow source areas. The models differ in approach (statistical vs. physically-based) and type of terrain unit of reference (slope unit vs. grid cell). In the statistical models, a mix of several environmental factors classified areas with different debris-flow susceptibility; however, the factors that exert a strong discriminant power reduce to conditions of high slope-gradient, pasture or no vegetation cover, availability of detrital material, and active erosional processes. Since slope and land use are also used in the physically-based approach, all model results are largely controlled by the same leading variables.Overlaying susceptibility maps produced by the different methods (statistical vs. physically-based) for the same terrain unit of reference (grid cell) reveals a large difference, nearly 25% spatial mismatch. The spatial discrepancy exceeds 30% for susceptibility maps generated by the same method (discriminant analysis) but different terrain units (slope unit vs. grid cell). The size of the terrain unit also led to different susceptibility maps (almost 20% spatial mismatch). Maps based on different statistical tools (discriminant analysis vs. logistic regression) differed least (less than 10%). Hence, method and terrain unit proved to be equally important in mapping susceptibility.Model performance was evaluated from the percentages of terrain units that each model correctly classifies, the number of debris-flow falling within the area classified as unstable by each model, and through the metric of ROC curves. Although all techniques implemented yielded results essentially comparable; the discriminant model based on the partition of the study area into small slope units may constitute the most suitable approach to regional debris-flow assessment in the Alpine environment.     

Managing expert-information uncertainties for assessing collapse susceptibility of abandoned underground structures

Assessing the collapse susceptibility of abandoned cavities at a regional scale is associated with large uncertainties that are mainly related to the very nature of the phenomena, but also to the difficulty in collecting exhaustive information at such a scale on often “forgotten” structures. In this context, the expert's role is essential, because he is able to synthesize the information resulting from the inventory and from the commonly imprecise, if not vague, criteria on the basis of his experience and his knowledge of the geological, historical, economic regional context.In this article, we propose mathematical tools for representing and processing this information in order to give flexibility to this step and manage the uncertainty inherent in the expert's information. The first tool, based on the weight of evidence theory, is for managing the uncertainty due to the heterogeneous spatial distribution of the data, whereas the second tool, based on the fuzzy set theory, is for managing the imprecision and incompleteness of available data, which hinder the definition of the class boundaries of the quantitative decision criteria. Based on an appropriate representation of the uncertainty sources (related to the input data and to the expert diagnostic), we then propose a methodology that integrates the uncertainty in the final output of the collapse susceptibility assessment and provides a confidence indicator useful within the decision-making process. The proposed methodology is applied to the Arras territory in the North of France, where abandoned chalk pits (dating back to the Roman ages) and war saps located in the vicinity of the First World War front lines (i.e. covered trenches), raise both difficulties for urban planning.     

GIS based landslide susceptibility mapping of Tevankarai Ar sub-watershed, Kodaikkanal, India using binary logistic regression analysis

Landslide susceptibility mapping is the first step in regional hazard management as it helps to understand the spatial distribution of the probability of slope failure in an area.An attempt is made to map the landslide susceptibility in Tevankarai Ar subwatershed,Kodaikkanal,India using binary logistic regression analysis.Geographic Information System is used to prepare the database of the predictor variables and landslide inventory map,which is used to build the spatial model of landslide susceptibility.The model describes the relationship between the dependent variable(presence and absence of landslide) and the independent variables selected for study(predictor variables) by the best fitting function.A forward stepwise logistic regression model using maximum likelihood estimation is used in the regression analysis.An inventory of 84 landslides and cells within a buffer distance of 10m around the landslide is used as the dependent variable.Relief,slope,aspect,plan curvature,profile curvature,land use,soil,topographic wetness index,proximity to roads and proximity to lineaments are taken as independent variables.The constant and the coefficient of the predictor variable retained by the regression model are used to calculate the probability of slope failure and analyze the effect of each predictor variable on landslide occurrence in thestudy area.The model shows that the most significant parameter contributing to landslides is slope.The other significant parameters are profile curvature,soil,road,wetness index and relief.The predictive logistic regression model is validated using temporal validation data-set of known landslide locations and shows an accuracy of 85.29 %.     

变形岩石磁性各向异性及其应变相关性的模拟

本文介绍处于弱磁场中的磁性砂岩人工样品,在三种应变作用下磁化率各向异性特征的研究结果.结果表明,磁化率椭球与应变椭球之间,显示了密切的相关性.文中并对上述情况下,磁化率各向异性特征及剩磁特征,从机理上进行了分析.     

The use of the anisotropy of magnetic remanence in the resolution of the anisotropy of magnetic susceptibility into its ferromagnetic and paramagnetic components

The anisotropy of magnetic susceptibility (AMS) is often controlled by both ferromagnetic (sensu lato) and paramagnetic minerals. The anisotropy of magnetic remanence (AMR) is solely controlled by ferromagnetic minerals. Jelínek (Trav. Geophys. 37 (1993)) introduced a tensor derived from the isothermal AMR whose normalized form equals the normalized susceptibility tensor provided that the ferromagnetic fraction is represented by multi-domain magnetite. The present paper shows the close correlation between these tensors for a collection of strongly magnetic specimens containing multi-domain magnetite. In addition, acceptable correlation between the tensors was also found for a collection of specimens containing single-domain magnetite. A new method is developed for the AMS resolution into ferromagnetic and paramagnetic components using the AMR. Some examples are presented of this resolution in mafic microgranular enclaves in granodiorite and in gneisses of the KTB borehole.     

GIS-based weights-of-evidence modelling of rainfall-induced landslides in small catchments for landslide susceptibility mapping

Landslide susceptibility mapping is a vital tool for disaster management and planning development activities in mountainous terrains of tropical and subtropical environments. In this paper, the weights-of-evidence modelling was applied, within a geographical information system (GIS), to derive landslide susceptibility map of two small catchments of Shikoku, Japan. The objective of this paper is to evaluate the importance of weights-of-evidence modelling in the generation of landslide susceptibility maps in relatively small catchments having an area less than 4 sq km. For the study area in Moriyuki and Monnyu catchments, northeast Shikoku Island in west Japan, a data set was generated at scale 1:5,000. Relevant thematic maps representing various factors (e.g. slope, aspect, relief, flow accumulation, soil depth, soil type, land use and distance to road) that are related to landslide activity were generated using field data and GIS techniques. Both catchments have homogeneous geology and only consist of Cretaceous granitic rock. Thus, bedrock geology was not considered in data layering during GIS analysis. Success rates were also estimated to evaluate the accuracy of landslide susceptibility maps and the weights-of-evidence modelling was found useful in landslide susceptibility mapping of small catchments.     

中国大陆科学钻探（CCSD）主孔100-2000m区间磁化率的变异特征及其地质意义

对中国大陆科学钻探主孔100—2000m区间岩心样品磁化率与钻孔原位测井磁化率进行系统时比研究。结果表明,控制岩石磁化率的主要因素是岩性和退变质作用,其中蛇纹石化石榴石橄榄岩的磁化率最大,样品平均磁化率为86．277×10-3SI,正片麻岩,退变质榴辉岩,榴辉岩,副片麻岩次之,角闪岩的磁化率最小,样品平均磁化率为1．698×10-3SI。不同退变质程度榴辉岩的磁化率特征具有明显的差异:中-强程度退变质榴辉岩由于退变质过程所产生的磁铁矿和钛铁矿-钛磁铁矿,其磁化率最大;随着退变质作用的进一步加剧,这些磁铁矿和钛铁矿-钛磁铁矿被完全分解,导致完全退变质榴辉岩的磁化率最小;新鲜榴辉岩的磁化率介于两者之间。在该深度区间,约70％岩心样品磁化率与测井磁化率的比值小于4．0,其中以<2．0为主,相同岩性岩石(如榴辉岩,退变质榴辉岩和副片麻岩)磁化率的比值随深度变化较小。岩心样品磁化率和测井磁化率之间的欧氏距离位于3．0-6．4范围,且随深度变化不明显,如在100～600m和1500～2000m两个深度范围的欧氏距离分别为3．0-5．2和3．2～5．3,表现出相似的变化特征,显示了在此区间深度范围内,地层压力对磁化率的基本没有影响。     

山地环境地质灾害易发性县级区划研究以重庆城口县为例

本文以重庆市城口县作为研究区域, 基于地理空间数据库, 结合逻辑回归模型, 开展山地环境地质灾害易发性县级区划研究。在研究过程中选取坡度、坡向、剖面曲率、微地貌、坡位、距水系距离、岩性、距断层距离、距道路距离、高程、归一化植被指数(NDVI)、顺逆向坡、年平均降雨量等13个因子作为诱发地质灾害的影响因子, 应用GIS技术将各个因子专题图层栅格化, 建立地理空间数据库。采用逻辑回归模型, 在R软件中进行逻辑回归模型训练, 得到各个影响因子的回归系数。建立地灾等级评价图, 并将危险等级划分为极低、低、中、高、极高5种类型。对于研究结果运用受试者工作特征曲线(ROC曲线)进行合理性检验, 位于ROC曲线下的面积AUC值为0.901, 结果表明逻辑回归模型适用于重庆市城口县山地环境地质灾害易发性区划研究。     

青海湖南岸黑马河黄土剖面碳酸盐含量与记录的古气候变化

在青海湖南岸黑马河2.2 m厚的黄土剖面中,以5 cm的间隔采样,进行了碳酸盐、碳酸盐中Sr含量以及磁化率的测定,通过对比分析研究了12.7 ka以来青海湖湖区古气候的变化,结果表明碳酸盐、碳酸盐中Sr含量、磁化率等基本较真实的记录了该区自12.7 kaBP以来经历的多次暖湿—干冷古气候变化过程。