模糊证据权方法在镇沅(老王寨)地区金矿资源评价中的应用

采用模糊证据权方法和GeoDASGIS技术开展了镇沅(老王寨)及其邻区的金矿资源潜力评价.分别采用GeoDASGIS软件提供的局部奇异性分析技术、S-A异常分解技术、主成分分析技术、证据权、模糊证据权等技术对相关地球化学元素进行了系统的处理和分析.应用主成分分析方法确定了可能的2种不同成矿类型,并采用主成分得分确定了组合异常点,在此基础上分别采用普通证据权和模糊证据权方法编制了成矿后验概率图,圈定了有利成矿地段.对比普通证据权方法与模糊证据权方法所得结果表明,模糊证据权方法可减小图层离散化造成的有用信息损失,提高预测结果精度.     

基于模糊预测对象的证据权方法及其在土地沙漠化评价中的应用

为了使在地理信息系统中被广泛用于点事件预测的证据权方法能对面事件进行评价和预测, 提出了一种新的基于模糊训练层的证据权方法.它是一种更广泛的证据权方法, 与普通证据权方法所不同的是, 它的训练层是模糊集合, 其取值是它的隶属度.通过适当的变换也可以把点训练层转换为模糊集合.因此, 该方法可以对面事件、点事件和线事件进行评价和预测.该方法可以处理训练层和证据层均为模糊集合的情况, 被称为双重模糊证据权方法.作为该方法的一个应用实例, 本文介绍毛乌素沙漠边缘的晋陕蒙地区土地沙漠化评价的应用实例.      

加权证据权模型和逐步证据权模型及其在个旧锡铜矿产资源预测中的应用

为了消除和减弱当证据层不满足条件独立性假设时对预测结果产生的影响, 提出了逐步证据权模型和加权证据权模型.加权证据权模型通过对logit模型进行修改, 对各个证据层给予一定的权重, 以调整由于证据层与其他证据层的条件相关性对模型的影响; 逐步证据权模型是将证据层按照一定的顺序逐步加入到模型中, 在加入到模型的过程中依次用已经获得的后验概率作为模糊训练层的方法.以个旧锡铜多金属矿产资源预测为例, 应用4种证据权模型的后验概率进行异常圈定, 结果表明两种新的模型对减弱证据层不满足条件独立性假设所产生的影响是有效的.      

基于GIS的证据权重法在黑龙江省西北部金矿成矿预测中的应用

在分析了黑龙江西北部地区金矿床成矿特征的基础上,建立了区域金矿找矿模型,然后提取了各类找矿信息,并在GIS中构成了与成矿有关的地层、构造、岩浆岩、遥感、物探、化探异常等11个证据图层。用证据权重模型统计综合证据图层生成研究区成矿后验概率图。按照后验概率相对大小把预测区成矿可能性划分成三级,圈定5个Ⅰ级成矿远景区,为在黑龙江省西北部进一步找矿奠定了基础。     

Application of Weights of Evidence to Mineral Potential Mapping of Yujiacun Ore Field in Northwest Yunnan Province, China

The multivariate information conprehensive processing technique is especially important at present to the digital mineral prospecting. However, the GIS-based weights of evidence have provided us with powerful tool for the quantitative assessment of mineral resource potential. In this paper, the mineralization model is established, based on the achievements made by previous researchers, to mend such deficiencies ad few references on ore fields in Yujiacun, Yunnan Province and the shortage of quantitative prediction and assessment of mineral resources. In addition, the weights of evidence are used to make a systematic quantitative prediction and assessment of mineral resources there, so that 2 mineral prospecting target areas of grade Ⅰ and 8 mineral prospecting target areas of grade Ⅱ are delineated, providing the further mineral resource exploration with the basis for the selection of mineral deposits.     

Artificial neural networks: A new method for mineral prospectivity mapping

A multilayer feed‐forward neural network, trained with a gradient descent, back‐propagation algorithm, is used to estimate the favourability for gold deposits using a raster GIS database for the Tenterfield 1:100 000 sheet area, New South Wales. The database consists of solid geology, regional faults, airborne magnetic and gamma‐ray survey data (U, Th, K and total count channels), and 63 deposit and occurrence locations. Input to the neural network consists of feature vectors formed by combining the values from co‐registered grid cells in each GIS thematic layer. The network was trained using binary target values to indicate the presence or absence of deposits. Although the neural network was trained as a binary classifier, output values for the trained network are in the range [0.1, 0.9] and are interpreted to indicate the degree of similarity of each input vector to a composite of all the deposit vectors used in training. These values are rescaled to produce a multiclass prospectivity map. To validate and assess the effectiveness of the neural‐network method, mineral‐prospectivity maps are also prepared using the empirical weights of evidence and the conceptual fuzzy‐logic methods. The neural‐network method produces a geologically plausible mineral‐prospectivity map similar, but superior, to the fuzzy logic and weights of evidence maps. The results of this study indicate that the use of neural networks for the integration of large multisource datasets used in regional mineral exploration, and for prediction of mineral prospectivity, offers several advantages over existing methods. These include the ability of neural networks to: (i) respond to critical combinations of parameters rather than increase the estimated prospectivity in response to each individual favourable parameter; (ii) combine datasets without the loss of information inherent in existing methods; and (iii) produce results that are relatively unaffected by redundant data, spurious data and data containing multiple populations. Statistical measures of map quality indicate that the neural‐network method performs as well as, or better than, existing methods while using approximately one‐third less data than the weights of evidence method.     

GIS Predictive Model for Producing Hydrothermal Gold Potential Map Using Weights of Evidence Approach in Gengma Region, Sanjiang District, China

Gengma region, Sanjiang district is known to have some large-scale gold deposits. GIS predictive model for hydroghermal gold potential was carried out in this region using weights of evidence modeling technique. Datasets used include large-scale hydroghermal gold deposit records, geological, geophysical and remote sensing imagery. Based on the geological and mineral characteristics of areas with known gold occurrences in Sanjiang, several geological features were thought to be indicative of areas with potential for the occurrence of hydroghtermal gold deposits. Indicative features were extracted from geoexploration datasets for use as input in the predictive model. The features include host rock lithology, geologic structures, wallrock alteration and associated (volcanic-plutonic) igneous rocks. To determine which of the indicative geological features are important spatial predictors of area with potential for gold deposits, spatial analysis was done through the modeling method. The input maps were buffered and the optimum distance of spatial association for each geological feature was determined by calculating the contrast and studentized contrast. Five feature maps were converted to binary predictor patterns and used as evidential layers for predictive modeling. The binary patterns were integrated in two combinations, each of which consists of four patterns in order to avoid over prediction due to the effect of duplicate features in the two structural evidences. The two produced potential maps define almost similar favorable zones. Areas of intersections between these zones in the two potential maps placed the highest predictive favorable zones in the region.     

基于GIS和证据权重法的那仁宝力格地区铜多金属矿成矿预测

根据那仁宝力格地区主要铜多金属矿床成矿规律,利用GIS平台提取地、物、化、遥等多元地质找矿信息,在MRAS系统中开展了基于18个成、控矿要素的铜多金属矿成矿预测与评价。根据后验概率值大小编制了成矿后验概率等值线图,划分了4个等级的找矿条件和9个找矿远景区。结果表明,预测模型有效,近95%的已知矿床（点）位于D级及以上找矿有利地区。在众多成、控矿要素中,增隆昌组与阿古鲁沟组、构造信息、遥感组合异常、铁族元素地球化学组合异常等具有较大权重。新圈定的那仁宝力格找矿远景区和霍各乞南部找矿远景区是下一步研究区寻找新的铜多金属矿床的重点地区。     

基于GIS和证据权模型的克什克腾旗有色金属成矿预测与评价

以地质、地球物理勘探、地球化学勘探、遥感等手段获取的多源数据,经过发现、提取、转换和关联,并利用信息综合技术建立区域综合信息矿产预测模型,进行矿产资源定量评价,是当前矿产资源勘查领域信息找矿的重要途径。本文在对内蒙古赤峰市克什克腾旗地质背景与成矿规律进行研究的基础上,利用GIS技术进行区域成矿专题信息分析与提取,建立评...     

GIS矿产资源预测中的证据权重法

地理信息系统(GIS)技术的应用已成为提高矿产资源预测水平的重要途径。矿产资源预测的方法很多，证据权重法是其中的一种重要方法，它是基于二值图象的地学统计方法。基于GIS的证据权重法矿产资源预测是通过对与矿产资源相关的地学信息的叠加分析进行预测，其中每一种地学信息都作为预测的一个证据因子，而每一个证据因子对矿产资源预测的贡献由其权重值确定。基于GIS的证据权重法矿产资源预测的优点是数据驱动、易于编程实现，在金矿资源预测中已有很多成功的应用。     

证据权模型中两种预测单元划分方式对比

证据权模型作为一种数据综合方法已被广泛应用于矿产资源定量预测与评价。在模糊证据权基础上,发展了基于地质单元思想的矢量证据图层构建和数据综合方法,并通过实例作具体阐述：它以矿点缓冲区图层作为训练图层,以各证据变量图层在空间上的叠置所形成的唯一地质单元作为评价对象,统一计算各个证据变量的证据权重,进而基于地质单元进行证据综合和后验概率成图。与基于栅格（或规则格网）的模型不同,基于矢量证据权模型以具有明确地质内涵的地质单元（而非规则网格单元）为预测单元,易于解释,并且消除了边界误差;相比基于规则格网划分所得到的成矿单元,以矿床（点）缓冲区作为训练对象,提高了已知矿点的代表性。实例表明：若预测单元大小为初始栅格大小整数倍,各缓冲等级平均面积计算误差为0.26%,否则面积平均误差达到6%;即使在预测单元大小为初始栅格大小整数倍情况下,矿点平均计算误差也达到4.78%。因此,基于地质单元思想的证据权预测单元划分方法在精度上优于基于栅格或规则格网方法。     

内蒙古新达来草原覆盖区钼铜多金属成矿预测

内蒙古新达来草原覆盖区位于西伯利亚板块东南大陆边缘晚古生代陆缘增生带,属古亚洲成矿域的二连—东乌旗铜钼铁晚古生代—中生代成矿带的一部分,找矿潜力巨大。该区被牧草和第四系大面积覆盖,基岩出露较差,找矿工作相对滞后。通过研究该地区区域成矿地质背景和典型矿床地质特征,分析控矿因素与找矿标志,构建综合信息预测模型,提取地质、地球化学、地球物理、遥感等找矿信息,借助GIS技术进行覆盖层“揭盖”和对找矿信息进行提取,以GeoDAS 矿产预测软件为平台,运用多重分形滤波技术区分复杂背景与异常,提取矿化弱异常,应用模糊证据权法进行信息综合集成,形成预测后验概率图,圈定找矿远景区并进行验证,探索内蒙古草原覆盖区内生金属矿床勘查与预测评价新途径。     

Use of Geomorphological Information in Indirect Landslide Susceptibility Assessment

The objective of this paper is to evaluate the importance of geomorphological expert knowledge in the generation of landslide susceptibility maps, using GIS supported indirect bivariate statistical analysis. For a test area in the Alpago region in Italy a dataset was generated at scale 1:5,000. Detailed geomorphological maps were generated, with legends at different levels of complexity. Other factor maps, that were considered relevant for the assessment of landslide susceptibility, were also collected, such as lithology, structural geology, surficial materials, slope classes, land use, distance from streams, roads and houses. The weights of evidence method was used to generate statistically derived weights for all classes of the factor maps. On the basis of these weights, the most relevant maps were selected for the combination into landslide susceptibility maps. Six different combinations of factor maps were evaluated, with varying geomorphological input. Success rates were used to classify the weight maps into three qualitative landslide susceptibility classes. The resulting six maps were compared with a direct susceptibility map, which was made by direct assignment of susceptibility classes in the field. The analysis indicated that the use of detailed geomorphological information in the bivariate statistical analysis raised the overall accuracy of the final susceptibility map considerably. However, even with the use of a detailed geomorphological factor map, the difference with the separately prepared direct susceptibility map is still significant, due to the generalisations that are inherent to the bivariate statistical analysis technique.     

Application of GIS to Geological Information Extraction

GEO-INFORMATION EXTRACTION USING GISTwo kinds of grid cells are used in the research area:oneis a regular grid cell,the other is an irregular one.A sampleis shown below to apply the regular grid cell to the extractionof geo-information.TABL E1　ATTRIBUTE OF ENTITY IN FIG.1ID area/ cm2 perimeter/ cm rock character time1 1 .5 5 4.88granite T2 1 .1 2 3 .93 metamorphic rock PTABLE 2　 ATTRIBUTE OF GRID CEL L IN FIG.2ID perimeter/ cm area/ cm2 cell number1 6.90 …     

青海北巴颜喀拉成矿带基于专家证据权重法锑金矿资源潜力评价

青海北巴颜喀拉成矿带虽然总体研究程度低,但显示出巨大的找矿潜力,急需寻求新的找矿突破。利用前人的地质、矿产、化探等资料,建立了青海北巴颜喀拉成矿带基于GIS的空间数据库。在此基础上重新建立了研究区锑金矿区域找矿模型,其中主要找矿标志包括金水系沉积物异常、锑水系沉积物异常、砷水系沉积物异常、重砂异常、NW－NWW向线性构造和巴颜喀拉山群。采用作者提出的专家证据权重法,对研究区首次进行了基于GIS下的专家证据权重法矿产资源评价工作。据此重新圈定找矿远景区和找矿靶区,将后验概率大于0.001的区域作为找矿远景区,将后验概率大于0.005的区域作为找矿靶区;并依据找矿靶区内预测含矿区域后验概率二重积分大小,将找矿靶区的找矿潜力级别由高到低分别划分为A 、B 、C 3级,其中A级6处,B级10处,C级13处。     

Regional Probabilistic and Statistical Mineral Potential Mapping of Gold–Silver Deposits Using GIS in the Gangreung Area, Korea

The aim of the present study is to analyze relationships between epithermal Au‐Ag deposits of the hydrothermal type and related geological factors and integrate the relationships using probabilistic and statistical models in a geographic information system (GIS) environment. A variety of spatial geological data were compiled, evaluated and integrated to produce a map of potential Au and Ag deposits in the Gangreung area, Korea. This empirical approach assumes that all deposits shared a common genesis. The method consists of three main steps: (i) identification of spatial relationships; (ii) quantification of such relationships and (iii) integration of multiple quantified relationships. A spatial database containing Au and Ag deposits, topographic, geologic, geophysical and geochemical data was constructed using a GIS. The factors relating to 103 Au and Ag mineral deposits are the geological data such as lithology and fault structure, geochemical data including the abundance of Al, As, Ba, Ca, Cd, Co, conductivity, Cr, Cu, Eh, Fe, HCO3–, K, Li, Mg, Mn, Mo, Na, Ni, Pb, pH, Si, Sr, V, W, Zn, Cl^?, F^?, PO₄^3?, NO₂^?, NO₃^? and SO₄^2?, and geophysical data including Bouguer and magnetic anomalies. Using the constructed spatial database, the relationships between mineral deposit areas and 36 related factors are identified and quantified by probabilistic and statistical modeling; that is, likelihood ratio, weights of evidence and logistic regression. All the factors were combined to produce a map of the regional mineral potential using the overlay method in a GIS environment. The mineral potential map was then verified by comparison with known mineral deposits. The verification results give respective accuracies of 82.52%, 72.45% and 81.60% for the likelihood ratio, weights of evidence and logistic regression models, respectively. The mineral potential map can be used as a source of basic information for mineral resource development.     

虚拟现实和G IS系统集成方法分析

从三个角度论述虚拟现实(V irtua l R ea lity,VR)和地理信息系统(G IS)集成的必要性,通过对集成方法的归纳,提出4种集成方式,并详细介绍了各自的结构体系和具体特征。在此基础上划分了VR与G IS系统集成的4个阶段,分析当前系统集成现状,指出其发展趋势。同时探讨了VR和G IS系统集成中存在的关键问题,包括数据、虚拟景观技术、VRG IS网络化等,给出目前解决这些问题的一些建议和具体途径。     

基于证据加权模型的综合信息成矿预测

综合信息成矿预测是复杂高维非线性系统的建模与评价过程,通过识别和提取地、物、化、遥等多源地学观测数据中的综合致矿地质异常信息,并以此为依据进行成矿预测。成矿预测是在科学预测理论的指导下,应用地质成矿理论和数理统计方法对地质、物探、化探、遥感等信息进行充分挖掘,剖析成矿地质条件,总结成矿规律,建立综合信息成矿模型并应用于成矿预测,从而圈定和评价成矿远景区,为区域找矿工作部署和矿产资源开发利用的统筹规划提供科学依据。本次研究将证据加权模型引入到成矿预测研究中,通过建立和评价地质信息、化探信息、遥感蚀变信息、遥感线环形构造密度信息与已知矿化点的关系,然后用贝叶斯公式计算成矿后验概率,推出研究区成矿预测结果。研究结果表明:综合信息成矿模型预测结果符合研究区地质成矿规律,和已知成矿点吻合率达71.4%。     

专家证据权重法在矿产评价中的应用——以黑龙江省西北部金矿为例

由于黑龙江省西北部地区地质勘探程度低,而且研究区已知矿床（点）数目少,利用统计方法的证据权重法不能应用于该地区。为此,将黑龙江省西北部地区的基础地质、区域成矿规律、金矿床成矿特征等方面取得的认识建立了一个“专家知识库”,并与“证据权重法”结合起来,建立一个基于专家证据权重法的矿产资源定量评价系统。利用该系统对黑龙江省西北部金矿进行预测并取得了很好的效果,为在黑龙江省西北部进一步找矿奠定了基础。     

基于证据权法的伊尔岗地块金矿成矿预测

在分析澳大利亚伊尔岗地块金矿床成矿特征的基础上,建立了区域金矿找矿模型,提取各类找矿信息,在GIS中构成了与成矿有关的容矿岩石、构造、岩浆岩、物探、化探异常等6个证据图层。用证据权法统计综合证据图层生成研究区成矿后验概率图。按照后验概率的相对大小划分出3级成矿远景区,共圈定3个Ⅰ级成矿远景区,为澳大利亚的金矿预测提供了有效方法。