一种基于GIS的矿产资源潜力评价的自动制图模型

在基于GIS技术的矿产资源评价工作中,矿产资源潜力评价的自动制图模型通常用来统计综合多源地学信息以便自动圈定成矿远景靶区。在本文中,笔者以人工智能研究领域中的一种不确定推理模型——确定性理论为基础,提出了一种新的矿产资源潜力评价的自动制图模型——合成有矿可信度模型。该模型可以根据研究区各种成矿有利和不利证据的空间分布图,统计生成对应于每一种证据的有矿可信度栅格图,然后,按照特定的有矿可信度合成规则,将所有的有矿可信度栅格图统计综合生成合成有矿可信度栅格图。以该图为依据,可以把研究区内合成有矿可信度相对较高的成矿远景区圈定出来。也可以生成研究区合成有矿可信度等值线图。应用该模型预测了新疆北部多拉纳萨依—阿舍勒地区的多金属成矿远景,并将预测结果与证据加权模型预测结果进行了比较,两种模型的预测结果基本相似,证明了该模型的有效性和实用性。     

一种基于图层综合的矿产资源潜力制图模型

提出了一种基于图层综合的矿产资源潜力自动制图模型。应用该模型生成矿产资源潜力分布图分三步完成:第一步,以每一种找矿标志的空间分布图为依据,生成相应的基本概率分配函数栅格图;第二步,统计综合基本概率分配函数栅格图;第三步,生成研究区矿产资源潜力分布图。利用新疆北部多拉纳萨依—阿舍勒地区的地质资料,比较了该模型与合成有矿可信度模型的找矿靶区圈定结果。两种模型的靶区圈定结果基本相同,证明证据理论模型是有效的和实用的。     

基于GIS技术的三江北段铜多金属成矿预测与评价

基于地质、地球物理、地球化学和遥感等多元地学信息的成矿预测与评价,能够优选和圈定成矿远景区,并为区域矿产资源量的估算提供依据,是国内外定量地学研究的重点和热点之一。文中选取西南三江北段为研究区,针对研究区铜多金属矿床具有海拔高、坡度大、近地表出露和矿区剥蚀环境发育等特征,在以往国内外学者普遍认同的控矿因素进行区域预测与评价的基础上,结合区域矿床的变化和保存条件研究内容,提出兼顾区域控矿、变化-改造、保存因素的成矿预测评价新认识。为了检验新认识,文中根据研究区成矿地质背景和已查明的24个铜多金属矿床,利用GIS技术提取铜多金属矿床赋存相关的29种多元信息,选取了证据权重法,开展基于控矿因素的20个因子和基于控矿、变化-改造和保存因素的29个因子两种区域铜多金属成矿预测与评价,编制成矿后验概率图,结合已知矿点和野外验证分析,认为基于控矿、变化-改造、保存因素的成矿预测结果优于单一的控矿因素预测结果:一方面剔除了冰川、水蚀或风化等近地表营力作用产生的非成矿异常区,另一方面提取了近地表赋存矿体的地层和岩体相关的成矿远景区。     

云南小江流域滑坡关键影响因子研究

确定诱发滑坡失稳的关键因素是滑坡研究的一个重要内容。采用不同影响因子图层进行危险性分区结果存在明显差异,这是由于第一因子对于滑坡变形失稳的贡献程度不同,即不同影响因子与滑坡的相关性不同。在进行滑坡灾害分析时,必须首先确定影响滑坡的关键因子以建立准确的统计分析模型。采用滑坡确定性系数的合并检验方法,在GIS中对云南小江流域进行了滑坡影响因子分析,并确定了影响滑坡的关键性因子。据此建立的多元统计分析预测模型经检验具有较高精度,要以为小江流域的灾害防治、规划建设提供科学依据。     

基于逻辑回归模型和确定性系数的崩滑流危险性区划

崩滑流是崩塌、滑坡和泥石流地质灾害的总称。本文根据逻辑回归模型和贵州省崩滑流地质灾害发生的确定性系数CF,统计贵州省内崩滑流发生概率与其影响因子之间的函数关系; 并利用GIS技术编制贵州省崩滑流地质灾害危险性区划图。首先根据影响因子子集中已发崩滑流灾害面积和影响因子子集面积来计算崩滑流地质灾害发生的确定性系数CF; 其次将灾害是否发生作为因变量,影响因子子集发生崩滑流地质灾害的确定性系数CF作为自变量,应用逻辑回归模型统计分析它们之间的函数关系; 然后利用GIS技术计算研究区内各独立属性单元发生崩滑流地质灾害的概率p,按p值10等分标准将研究区划分为10个危险性等级区,并绘制贵州省崩滑流地质灾害危险性区划图; 最后用已发崩滑流地质灾害的分布数据来检验危险性区划的效果。研究结果表明:本文根据逻辑回归模型和崩滑流地质灾害发生的确定性系数CF,将贵州省分为Ⅰ～Ⅹ的10个崩滑流地质灾害危险性等级区与实际情况基本符合,能够良好地反映贵州省境内发生崩滑流地质灾害的难易程度。     

单因素分析多因素综合作图法——定量岩相古地理重建

笔者倡导并持续采用单因素分析多因素综合作图法这一定量的岩相古地理学方法论。单因素是能独立地反映某地区、某地质时期、某沉积层段沉积环境某些特征的因素,它的有无或含量的多少均可独立地反映该地区、该层段沉积环境的某些特征,如沉积环境水体的深浅、能量大小、性质等。某沉积层段的厚度、岩石类型、结构组分、矿物成分、化学成分、化石及其生态组合等，均可作为单因素。单因素分析多因素综合作图可分三个步骤：第一，是对各剖面尤其是各基干剖面进行认真的地层学和定量岩石学研究，取得各种第一手的定性和定量资料，尤其是定量资料，了解各剖面各沉积层段的沉积环境特征。第二，在已取得的各剖面的定量资料中，按要求的作图单位，选择出那些能独立地反映其沉积环境特征的因素，即单因素；并把全区各剖面各作图单位的各种单因素的百分含量都统计出来，作出各种相应的单因素图，主要是等值线图。这些单因素图可以从不同的侧面定量地反映该地区该沉积层段的沉积环境。这就是单因素分析。第三，把这些定量的单因素图叠加起来，并结合该地区该沉积层段的其他定量和定性资料，去粗取精，去伪存真，全面分析，综合判断，即可编制出该地区该沉积层段的定量岩相古地理图。这就是多因素综合作图。这一方法论的核心是定量，即以各剖面的定量单因素资料为基础，从各单因素图的分析入手，再通过各单因素图的叠加和综合分析判断，最后作出定量的岩相古地理图。在这种岩相古地理图中，各古地理单元的确定都有确切的定量资料和单因素图为依据。定量是笔者等用单因素分析多因素综合作图法编制出的岩相古地理图的最大特色。这就使岩相古地理图和岩相古地理学发展到了定量的阶段。这在古地理学中是个重大的进展。     

浅覆盖区多源数据融合整合的技术方法

多源数据的整合融合技术是在数字化填图平台上开发的一项新技术。工作方法是：将已有的地质矿产、地球物理、地球化学、遥感等多源数据进行计算机整合处理后,提取相关的地质信息,将多源数据与地质数据整合,建立多源数据库,形成多源叠加分析成果图,应用于浅覆盖区的地质填图。     

Computer-composite mapping for geologists

A computer program for overlaying maps has been tested and evaluated as a means for producing geologic derivative maps. Four maps of the Sugar House Quadrangle, Utah, were combined, using the Multi-Scale Data Analysis and Mapping Program, in a single composite map that shows the relative stability of the land surface during earthquakes. Computer-composite mapping can provide geologists with a powerful analytical tool and a flexible graphic display technique. Digitized map units can be shown singly, grouped with different units from the same map, or combined with units from other source maps to produce composite maps. The mapping program permits the user to assign various values to the map units and to specify symbology for the final map. Because of its flexible storage, easy manipulation, and capabilities of graphic output, the composite-mapping technique can readily be applied to mapping projects in sedimentary and crystalline terranes, as well as to maps showing mineral resource potential.     

基于模糊C-均值算法粗糙集理论的云模型在岩爆等级评价中的应用

岩爆等级评价具有模糊性和不确定性,而粗糙集理论的云模型对处理模糊性和不确定性问题具有独特优势,由此提出了基于模糊C均值（简称FCM）算法粗糙集的云模型理论在岩爆等级评价中的新模型。该模型选用岩石单轴抗压强度 、洞室围岩最大的切向应力 、岩石单轴抗拉强度 和岩石弹性能量指数Wet作为岩爆等级评价因子,依据岩爆分级标准计算各评价因子隶属于不同岩爆等级的云数字特征。同时,以国内外40例岩爆工程为研究对象,运用基于FCM算法的粗糙度理论进行因子属性重要性评价,计算各评价因子权重。根据正向正态云发生器,得到待评样本的综合确定度,由最大综合确定度判定岩爆级别。研究表明：该模型的评价结果与实际情况基本一致,具有一定的可行性,为岩爆预测提供了一种新的研究方法与思路。     

资源潜力评价中典型矿床与区域矿产编图思路讨论

根据新一轮全国矿产资源潜力评价工作需要,首先按矿产预测类型选择典型矿床编制典型矿床成矿要素图-典型矿床预测要素图-典型矿床成矿模式图和预测模型图,然后再按预测研究工作区范围编制区域地质建造构造矿产图-区域成矿要素图-区域预测要素图-区域成矿模式图和预测模型图。讨论了有关图件的编制思路,并提出了图中突出显示成矿要素和预测要素的方法。     

基于RBF神经网络的矿产资源潜力预测模型

提出一种基于RBF神经网络的矿产资源潜力制图模型。应用该模型生成矿产资源潜力分布图分三步完成：第一步,以找矿标志的空间分布图和已知矿点空间分布图为依据,提取训练样本;第二步,根据训练样本构建RBF矿产资源潜力制图模型;第三步,生成矿产资源潜力分布图。笔者以新疆北部阿尔泰多金属成矿带为研究区,比较了该模型与合成有矿可信度等模型的找矿靶区圈定结果。两种模型的靶区圈定结果基本相同,证明了RBF矿产资源潜力制图模型的有效性。     

参数化设计方法在地矿图件计算机辅助编绘中的应用

应用参数化设计方法对ＣＡＤ基础软件进行二次开发,可使地矿点源信息系统更能适应复杂地矿图件的编绘工作;以线型制作和图案填充为便说明,经设计的具体实现途径是用参数化表达图元内部特征和建立图元间关联关系的数据库;应用参数化设计方法进行图件编绘程序开发,关键在于建立正确的灵堂模型和灵活地应用数据库技术。     

奇异性理论在个旧锡铜矿产资源预测中的应用: 综合信息集成与靶区圈定

在致矿弱异常提取和复合异常分解的基础上, 进行多元信息综合和集成, 绘制成矿后验概率图是矿床资源预测的基本过程.以个旧锡矿为例, 介绍一种新的信息集成模型和后验概率图的应用方法.结果表明, 个旧锡铜矿床分布受多个控矿要素控制, 包括地球化学异常、岩体、有利岩性以及构造条件等.通过证据权所提供的空间相关性统计量可以定量确定控矿要素的最佳控矿距离, 并以此为依据形成二态信息图层.对每个图层的叠加可看作一次找矿信息的累积和更新, 因此整个信息图层的集成过程可以看作多次信息叠加过程(multiplicative cascade process).由此绘制的后验概率图具有自相似性、奇异性和分形谱系, 空间分布服从多重分形统计分布.因此, 后验概率图的绘制可以作为致矿地质异常圈定的信息综合和集成方法.      

有利因素相关法在松潘地区“东北寨式”金矿资源评估中的应用

据“东北寨式”金矿的成矿模式,结合松潘地区的成矿条件、地球化学异常评价以及资料水平和工作程度等,选择“有利因素相关法”对本区内334资源量进行了远景预测,取得了很好的结果。该方法实际上可归入“矿床模拟估计法”中,其主要原理是：通过对已知对象的研究,阐明控矿因素(地质变量)与矿产资源量之间的关系,建立一定矿床类型的矿产储量与地质条件之间的定量关系(定量预测模型)。将其移植到地质条件类似的预测区应用时,只要预测区上获得预测模型中相同的地质变量及其相同的数据类型值,代入模型,就可估算预测区内的矿产资源量。预测结果,在松潘地区,“东北寨式”金矿可望增长的3341外资源量约为81吨,3342资源量约65．9吨,为下步在该区部署地质工作提供了依据。     

贵州罗甸玉勘查评价方法探讨-以官固矿床为例

贵州罗甸玉属于高档软玉,具有稀缺性和极高的经济价值,但其勘查评价方法受到矿体特殊产状和厚度薄的综合因素制约,不适用于固体矿产勘查工作规范。通过前期勘查实践和方法研究,新提出在勘查工作中以含矿体作为基本填图单元应用于图面表达具有显著的合理性和可操作性,在资源评价工作中采用系数调整法校正资源储量,可以解决软玉矿勘查评价难的问题,进而完善软玉矿的勘查评价体系。     

Application and verification of fuzzy algebraic operators to landslide susceptibility mapping

The aim of this study was to apply and to verify the use of fuzzy logic to landslide susceptibility mapping in the Gangneung area, Korea, using a geographic information system (GIS). For this aim, in the study, a data-derived model (frequency ratio) and a knowledge-derived model (fuzzy operator) were combined. Landslide locations were identified by changing the detection technique of KOMPSAT-1 images and checked by field studies. For landslide susceptibility mapping, maps of the topography, lineaments, soil, forest, and land cover were extracted from the spatial data sets, and the eight factors influencing landslide occurrence were obtained from the database. Using the factors and the identified landslide, the fuzzy membership values were calculated. Then fuzzy algebraic operators were applied to the fuzzy membership values for landslide susceptibility mapping. Finally, the produced map was verified by comparing with existing landslide locations for calculating prediction accuracy. Among the fuzzy operators, in the case in which the gamma operator (λ = 0.975) showed the best accuracy (84.68%) while the case in which the fuzzy or operator was applied showed the worst accuracy (66.50%).     

Suitability analysis for topographic factors in loess landslide research: a case study of Gangu County,China

Loess Plateau is one of the ecologically fragile regions in China. It is one of the slippery strata of which landslides often developed. The formation and development of landslides are mainly affected by various natural environments, triggering factors, the vulnerability of landslide-bearing bodies, and topography has a controlling effect on landslides and determines landslide distribution. As important environmental elements, the selection and reclassification of topographic factors are the basis for loess landslide vulnerability map. In this study, our research suggests an effective workflow to select and analyze the topographic factors in the loess landslides. Nine hazard-formative environmental factors [e.g., slope, aspect, slope shape (SS), slope of slope (SOS), slope of aspect (SOA), surface amplitude (SA), surface roughness (SR), incision depth (ID) and elevation variation coefficient (EVC)] are prepared for landslide suitability analysis. The models of certainty factor, sensitivity index and correlation coefficient are combined to select and analyze the suitability of these factors. Four topographic factors (i.e., slope, SOS, SS and SR) were ultimately selected to carry out the landslide vulnerability mapping with other factors. Our results showed that most of the landslides were located in medium and high classes and accounting for 75.3%, and these places also coincided with higher economies and intense human activities. Our research also suggested that in situ measurements are necessary to determine how to reclassify these topographic factors and how many grades these topographic factors divided, which would further improve the reliability of landslide vulnerability map for the decision makers to deal with the possible future landslides in terms of safety and human activities.     

Regional groundwater productivity potential mapping using a geographic information system (GIS) based artificial neural network model

An artificial neural network model (ANN) and a geographic information system (GIS) are applied to the mapping of regional groundwater productivity potential (GPP) for the area around Pohang City, Republic of Korea. The model is based on the relationship between groundwater productivity data, including specific capacity (SPC) and its related hydrogeological factors. The related factors, including topography, lineaments, geology, and forest and soil data, are collected and input into a spatial database. In addition, SPC data are collected from 44 well locations. The SPC data are randomly divided into a training set, to analyse the GPP using the ANN, and a test set, to validate the predicted potential map. Each factor??s relative importance and weight are determined by the back-propagation training algorithms and applied to the input factor. The GPP value is then calculated using the weights, and GPP maps are created. The map is validated using area under the curve analysis with the SPC data that have not been used for training the model. The validation shows prediction accuracies between 73.54 and 80.09?%. Such information and the maps generated from it could serve as a scientific basis for groundwater management and exploration.     

A preliminary application of weights of evidence in gold exploration in Xiong-er Mountain Region, He-Nan Province

Weights of evidence modeling for combining indicator patterns in mineral resource evaluation is based on an application of Bayes' rule. Two weights are defined for each indicator pattern and Bayes' rule is applied repeatedly to combine indicator patterns. If all patterns are conditionally independent with respect to deposits, the logit of the posterior probability can be calculated as the sum of the logit of the prior probability plus the weights of the overlay patterns. The information to be integrated for gold exploration in Xiong-er Mountain Region comes from a geological map, an interpreted map of a Thematic Mapper (TM) image, and the locations of known gold deposits. Favorable stratigraphic units, structural control factors, and alteration factors are considered. The work was conducted on an S600 I²S image-processing system. FORTRAN programs were developed for creating indicator patterns, statistical calculations, and pattern integration. Six indicator patterns were selected to predict mineral potential. They are conditionally independent according to pairwiseG ² tests, and an overall chi-square test. The potential area predicted using the 32 known deposits generally coincides with the prospect areas determined by geological fieldwork.     

Landslide susceptibility mapping using a modified decision tree classifier in the Xanthi Perfection,Greece

The objective of this study was to validate the outcomes of a modified decision tree classifier by comparing the produced landslide susceptibility map and the actual landslide occurrence, in an area of intensive landslide manifestation, in Xanthi Perfection, Greece. The values that concerned eight landslide conditioning factors for 163 landslides and 163 non-landslide locations were extracted by using advanced spatial GIS functions. Lithological units, elevation, slope angle, slope aspect, distance from tectonic features, distance from hydrographic network, distance from geological boundaries and distance from road network were among the eight landslide conditioning factors that were included in the landslide database used in the training phase. In the present study, landslide and non-landslide locations were randomly divided into two subsets: 80 % of the data (260 instances) were used for training and 20 % of the data (66 instances) for validating the developed classifier. The outcome of the decision tree classifier was a set of rules that expressed the relationship between landslide conditioning factors and the actual landslide occurrence. The landslide susceptibility belief values were obtained by applying a statistical method, the certainty factor method, and by measuring the belief in each rule that the decision tree classifier produced, transforming the discrete type of result into a continuous value that enabled the generation of a landslide susceptibility belief map. In total, four landslide susceptibility maps were produced using the certainty factor method, the Iterative Dichotomizer version 3 algorithm, the J48 algorithm and the modified Iterative Dichotomizer version 3 model in order to evaluate the performance of the developed classifier. The validation results showed that area under the ROC curves for the models varied from 0.7936 to 0.8397 for success rate curve and 0.7766 to 0.8035 for prediction rate curves, respectively. The success rate and prediction curves showed that the modified Iterative Dichotomizer version 3 model had a slightly higher performance with 0.8397 and 0.8035, respectively. From the outcomes of the study, it was induced that the developed modified decision tree classifier could be efficiently used for landslide susceptibility analysis and in general might be used for classification and estimation purposes in spatial predictive models.