小清河滞洪区洪灾监测信息系统的研究与建立   总被引：2，自引：0，他引：2  

陈曦川 《国土资源遥感》1997,8(3):40-45

应用机载合成孔径雷达监测洪水势态,离不开洪灾监测信息系统的支持,两者有机的结合,能充分发挥遥感和地理信息系统在防洪减灾应用中的优越性。本文以小清河滞洪区为实验区,从系统目标、数据层及其来源、数据编码、分析模型等几个方面,介绍了该区洪灾监测信息系统的建立情况。  相似文献   

中国南方晚二叠世乐平煤的成因及成煤物质     

陈能贵 《海相油气地质》1996,1(2):29-33,T001

乐平煤是分布于苏浙皖赣地区龙潭组中的特有煤种(俗称树皮煤),也是一种具良好生烃潜力的烃源岩。乐平煤中,壳质组和可溶有机质含量高,母质类型为高等植物和低等生物双重来源,元素组成具富氢、低氧、高含硫的特征,应属海相成因的泥炭坪沉积。成煤物质主要来源于真蕨、种子蕨和科达类等植物的树皮体。树皮体又可分为茎皮体和根皮体两种亚组分。依据煤的显微组分组成和树皮体富集程度的不同,乐平煤可分为原地型、过渡型和微异地型三种成因类型。乐平煤的研究对整个南方二叠纪煤成烃资源的评价具重要的地质意义。  相似文献   

试谈超大型铀矿床分类及找矿问题     

胡绍康 《铀矿地质》1996,12(6):331-335

随着核电事业在我国的发展，对铀资源保证的要求将越来越迫切。这里所指的铀资源保证应包含数量和效益两层意思，而超大型铀矿床则能同时满足对这两方面的要求。因此，在我国寻找超大型铀矿床是一项刻不容缓的任务。为了确立找矿目标，作者尝试提出了一种将矿床产出地质特征和经济效益相结合的超大型铀矿床分类方案，共划分出３大类１３个亚类。结合我国情况，作者认为在７个有远景的类型中，有３个（即Ⅰ－２、Ⅱ－１和Ⅲ－５）在我国最有突破的希望，特别是Ⅲ－５。最后，作者参考国外超大型铀矿床的找矿经验，对于加强我国对超大型铀矿床的探索提出了四点具体意见。  相似文献   

Qualitätsanforderungen an Oberflächengewässer: Zielvorgaben,Qualitätsziele und chemische Gewässergüteklassifizierung     

U. Irmer  W. Rocker  K. Blondzik 《洁净——土壤、空气、水》1997,25(2):62-70

Quality Requirements for Fresh Waters: Water Quality Targets, Water Quality Objectives, and Chemical Water Quality Classification In the Federal Republic of Germany, water quality requirements for the protection of inland surface waters against hazardous substances are formulated on the basis of a quality targets derivation concept developed jointly by the Federal Government and the Federal States. The quality requirements were termed “water quality targets” in order to make it clear that the values derived are orientational values rather than legally binding limit values. The international comparison of quality requirements for surface waters shows that, on the whole, the national quality targets ensure a high level of protection. According to present scientific knowledge, impairments of uses, such as supply of drinking water, or risks to aquatic communities need not to be expected if the quality targets are complied with. A comparison of water quality data with the water quality targets makes it possible, on the one hand, to identify those substances whose inputs must be further reduced; on the other hand, it also shows that, for a number of substances, there is no need at present for concern over their adversely water quality. A further differentiation of the aquatic hazard potential of pollutants allows a water quality classification system to be developed on the basis of the quality targets derivation concept. The basic elements of this water quality classification system are presented, and its application is explained by way of examples.  相似文献   

Land degradation in the source region of the Yellow River, northeast Qinghai-Xizang Plateau: classification and evaluation   总被引：6，自引：0，他引：6  

Jianmin Feng  Tao Wang  Shanzhong Qi  Changwei Xie 《Environmental Geology》2005,47(4):459-466

Land degradation imposes a great threat to the world. It is not merely an environmental issue, but also a social and economic problem. Land desertification is among the main aspects of environment changes in the source region of the Yellow River. Previous studies focused on water resource utilization and soil erosion, but land degradation in the source region of the Yellow River even the whole Qinghai-Xizang Plateau received little attention. Based on the data obtained by field investigation and TM satellite images of 2000, this study provides the classification and evaluation information of the land degradation in the source region of the Yellow River. There are six types of land degradation in this region: water erosion in the northern mountains around the Gonghe Basin, sandy desertification in the Gonghe Basin and Upland Plain Area, aridization in the lower reaches, salinization in the Gonghe Basin, vegetation degradation in the intramontance basin and freezing and thawing erosion in the high mountains. The total degraded area is 34,429.6 km², making up 37.5% of the land in the study area. Finally, land degradation in the source region of the Yellow River was evaluated according to changes in the physical structure and chemical component of soils, land productivity, secondary soil salt and water conditions.  相似文献   

6: Classification of VMS deposits: Lessons from the South Uralides     

Richard Herrington  Valeriy Maslennikov  Victor Zaykov  Igor Seravkin  Alexander Kosarev  Bernd Buschmann  Jean-Jacques Orgeval  Nicola Holland  Svetlana Tesalina  Paolo Nimis  Robin Armstrong 《Ore Geology Reviews》2005,27(1-4):203

VMS deposits of the South Urals developed within the evolving Urals palaeo-ocean between Silurian and Late Devonian times. Arc-continent collision between Baltica and the Magnitogorsk Zone (arc) in the south-western Urals effectively terminated submarine volcanism in the Magnitogorsk Zone with which the bulk of the VMS deposits are associated. The majority of the Urals VMS deposits formed within volcanic-dominated sequences in deep seawater settings. Preservation of macro and micro vent fauna in the sulphide bodies is both testament to the seafloor setting for much of the sulphides but also the exceptional degree of preservation and lack of metamorphic overprint of the deposits and host rocks. The deposits in the Urals have previously been classified in terms of tectonic setting, host rock associations and metal ratios in line with recent tectono-stratigraphic classifications. In addition to these broad classes, it is clear that in a number of the Urals settings, an evolution of the host volcanic stratigraphy is accompanied by an associated change in the metal ratios of the VMS deposits, a situation previously discussed, for example, in the Noranda district of Canada.Two key structural settings are implicated in the South Urals. The first is seen in a preserved marginal allochthon west of the Main Urals Fault where early arc tholeiites host Cu–Zn mineralization in deposits including Yaman Kasy, which is host to the oldest macro vent fauna assembly known to science. The second tectonic setting for the South Urals VMS is the Magnitogorsk arc where study has highlighted the presence of a preserved early forearc assemblage, arc tholeiite to calc-alkaline sequences and rifted arc bimodal tholeiite sequences. The boninitc rocks of the forearc host Cu–(Zn) and Cu–Co VMS deposits, the latter hosted in fragments within the Main Urals Fault Zone (MUFZ) which marks the line of arc-continent collision in Late Devonian times. The arc tholeiites host Cu–Zn deposits with an evolution to more calc-alkaline felsic volcanic sequences matched with a change to Zn–Pb–Cu polymetallic deposits, often gold-rich. Large rifts in the arc sequence are filled by thick bimodal tholeiite sequences, themselves often showing an evolution to a more calc-alkaline nature. These thick bimodal sequences are host to the largest of the Cu–Zn VMS deposits.The exceptional degree of preservation in the Urals has permitted the identification of early seafloor clastic and hydrolytic modification (here termed halmyrolysis sensu lato) to the sulphide assemblages prior to diagenesis and this results in large-scale modification to the primary VMS body, resulting in distinctive morphological and mineralogical sub-types of sulphide body superimposed upon the tectonic association classification.It is proposed that a better classification of seafloor VMS systems is thus achievable using a three stage classification based on (a) tectonic (hence bulk volcanic chemistry) association, (b) local volcanic chemical evolution within a single edifice and (c) seafloor reworking and halmyrolysis.  相似文献   

断代成矿单元研究划分 ----以陕西省成矿单元划分为例     

董王仓  李新林  陈丽蓉  王娅娅  白小鸟 《地质论评》2016,62(S1):207-208

随着地质找矿难度的日益加大,如何科学地圈定找矿靶区,已成为新时期矿产勘查部署及实现找矿突破的关键。成矿单元划分是区域成矿规律研究的基础性工作,在圈定找矿靶区、部署矿产勘查工作中起着重要作用。目前国内成矿单元划分的主要方法是以区域内成矿作用最强、矿床最多的构造旋回所形成的地质构造单元为基础,同时考虑其它构造旋回形成的矿床分布状况而进行成矿单元划分,即采用综合方法划分成矿单元（陈毓川等,2007.徐志刚等,2008.）。对于仅发育单一构造演化阶段或多数矿床形成于某一主要构造阶段的区域而言,上述划分思路和方案基本反映了成矿地质背景及矿产分布规律。但对于发育多个构造演化阶段或多旋回造山带的区域（如陕西省）而言,其矿产众多且形成于多个构造演化阶段,综合方法所划分的成矿区带则较难客观、清晰地反映其成矿背景及矿产分布规律。陕西省多年来的矿产勘查实践表明,成矿单元的划分应从本省多旋回构造演化、多阶段成矿作用的实际出发开展断代成矿单元划分,从而清晰地反映各构造演化阶段的成矿特征及矿床分布规律,以满足新时期地质找矿工作的需求。  相似文献   

Classification of forest land attributes using multi-source remotely sensed data     

《International Journal of Applied Earth Observation and Geoinformation》2016

The aim of the study was to (1) examine the classification of forest land using airborne laser scanning (ALS) data, satellite images and sample plots of the Finnish National Forest Inventory (NFI) as training data and to (2) identify best performing metrics for classifying forest land attributes. Six different schemes of forest land classification were studied: land use/land cover (LU/LC) classification using both national classes and FAO (Food and Agricultural Organization of the United Nations) classes, main type, site type, peat land type and drainage status. Special interest was to test different ALS-based surface metrics in classification of forest land attributes. Field data consisted of 828 NFI plots collected in 2008–2012 in southern Finland and remotely sensed data was from summer 2010. Multinomial logistic regression was used as the classification method. Classification of LU/LC classes were highly accurate (kappa-values 0.90 and 0.91) but also the classification of site type, peat land type and drainage status succeeded moderately well (kappa-values 0.51, 0.69 and 0.52). ALS-based surface metrics were found to be the most important predictor variables in classification of LU/LC class, main type and drainage status. In best classification models of forest site types both spectral metrics from satellite data and point cloud metrics from ALS were used. In turn, in the classification of peat land types ALS point cloud metrics played the most important role. Results indicated that the prediction of site type and forest land category could be incorporated into stand level forest management inventory system in Finland.  相似文献   

APG:A novel python-based ArcGIS toolbox to generate absence-datasets for geospatial studies     

Seyed Amir Naghibi  Hossein Hashemi  Biswajeet Pradhan 《地学前缘(英文版)》2021,12(6):90-104

One important step in binary modeling of environmental problems is the generation of absence-datasets that are traditionally generated by random sampling and can undermine the quality of outputs.To solve this problem,this study develops the Absence Point Generation(APG)toolbox which is a Python-based ArcGIS toolbox for automated construction of absence-datasets for geospatial studies.The APG employs a frequency ratio analysis of four commonly used and important driving factors such as altitude,slope degree,topographic wetness index,and distance from rivers,and considers the presence locations buffer and density layers to define the low potential or susceptibility zones where absence-datasets are gener-ated.To test the APG toolbox,we applied two benchmark algorithms of random forest(RF)and boosted regression trees(BRT)in a case study to investigate groundwater potential using three absence datasets i.e.,the APG,random,and selection of absence samples(SAS)toolbox.The BRT-APG and RF-APG had the area under receiver operating curve(AUC)values of 0.947 and 0.942,while BRT and RF had weaker per-formances with the SAS and Random datasets.This effect resulted in AUC improvements for BRT and RF by 7.2,and 9.7％from the Random dataset,and AUC improvements for BRT and RF by 6.1,and 5.4％from the SAS dataset,respectively.The APG also impacted the importance of the input factors and the pattern of the groundwater potential maps,which proves the importance of absence points in environmental bin-ary issues.The proposed APG toolbox could be easily applied in other environmental hazards such as landslides,floods,and gully erosion,and land subsidence.  相似文献   

Landslide susceptibility zonation method based on C5.0 decision tree and K-means cluster algorithms to improve the efficiency of risk management   总被引：1，自引：0，他引：1  

Zizheng Guo  Yu Shi  Faming Huang  Xuanmei Fan  Jinsong Huang 《地学前缘(英文版)》2021,12(6):243-261

Machine learning algorithms are an important measure with which to perform landslide susceptibility assessments,but most studies use GIS-based classification methods to conduct susceptibility zonation.This study presents a machine learning approach based on the C5.0 decision tree(DT)model and the K-means cluster algorithm to produce a regional landslide susceptibility map.Yanchang County,a typical landslide-prone area located in northwestern China,was taken as the area of interest to introduce the proposed application procedure.A landslide inventory containing 82 landslides was prepared and subse-quently randomly partitioned into two subsets:training data(70％landslide pixels)and validation data(30％landslide pixels).Fourteen landslide influencing factors were considered in the input dataset and were used to calculate the landslide occurrence probability based on the C5.0 decision tree model.Susceptibility zonation was implemented according to the cut-off values calculated by the K-means clus-ter algorithm.The validation results of the model performance analysis showed that the AUC(area under the receiver operating characteristic(ROC)curve)of the proposed model was the highest,reaching 0.88,compared with traditional models(support vector machine(SVM)=0.85,Bayesian network(BN)=0.81,frequency ratio(FR)=0.75,weight of evidence(WOE)=0.76).The landslide frequency ratio and fre-quency density of the high susceptibility zones were 6.76/km2 and 0.88/km2,respectively,which were much higher than those of the low susceptibility zones.The top 20％interval of landslide occurrence probability contained 89％of the historical landslides but only accounted for 10.3％of the total area.Our results indicate that the distribution of high susceptibility zones was more focused without contain-ing more"stable"pixels.Therefore,the obtained susceptibility map is suitable for application to landslide risk management practices.  相似文献