Fuzzy analytical hierarchy process and GIS for predictive Cu porphyry potential mapping: a case study in Ahar–Arasbaran Zone (NW, Iran)

Selection of potential areas for mineral exploration is a complex process and needs many diverse criteria. Combining analytic hierarchy process (AHP) modeling with geographic information system (GIS) provides an effective means for studies of mineral potential mapping evaluation. Fuzzy AHP is an extension of conventional AHP and by using fuzzy theory is obtained the advantage rather AHP method. In this paper to provide, potential mapping for Cu porphyry mineralization used fuzzy AHP and GIS in the Ahar–Arasbaran areas, several criteria, such as geology, geochemical and geophysical data, alteration, and faults were used. Each criterion was evaluated with the aid of fuzzy AHP and mapped by GIS. The method allowed a mixture of quantitative and qualitative information with group decision. The results and its validation demonstrate the acceptable outcomes for copper porphyry exploration.     

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的证据权模型在秦岭～松潘金矿潜力预测中的应用

数据驱动的证据权法被用来进行金矿潜力制作。为了确定秦岭~松潘金矿的潜力区,需利用地质、地球化学、地球物理等数据。数据采集、图形处理、空间分析都是在GIS平台上进行的。预测结果表明,证据权法在综合不同空间数据上是有效的,最终的预测图件圈出了最有利的矿化区,可用于进一步勘查研究。     

Data- and knowledge-driven mineral prospectivity maps for Canada's North

Data- and knowledge-driven techniques are used to produce regional Au prospectivity maps of a portion of Melville Peninsula, Northern Canada using geophysical and geochemical data. These basic datasets typically exist for large portions of Canada's North and are suitable for a “greenfields” exploration programme. The data-driven method involves the use of the Random Forest (RF) supervised classifier, a relatively new technique that has recently been applied to mineral potential modelling while the knowledge-driven technique makes use of weighted-index overlay, commonly used in GIS spatial modelling studies. We use the location of known Au occurrences to train the RF classifier and calculate the signature of Au occurrences as a group from non-occurrences using the basic geoscience dataset. The RF classification outperformed the knowledge-based model with respect to prediction of the known Au occurrences. The geochemical data in general were more predictive of the known Au occurrences than the geophysical data. A data-driven approach such as RF for the production of regional Au prospectivity maps is recommended provided that a sufficient number of training areas (known Au occurrences) exist.     

Exploration feature selection applied to hybrid data integration modeling: Targeting copper-gold potential in central Iran

A Sugeno-type fuzzy inference system is implemented in the framework of an adaptive neural network to map Cu–Au prospectivity of the Urumieh–Dokhtar magmatic arc (UDMA) in central Iran. We use the hybrid “Adaptive Neuro Fuzzy Inference System” (ANFIS; Jang, 1993) algorithm to optimize the fuzzy membership values of input predictor maps and the parameters of the output consequent functions using the spatial distribution of known mineral deposits. Generic genetic models of porphyry copper–gold and iron oxide copper–gold (IOCG) deposits are used in conjunction with deposit models of the Dalli porphyry copper–gold deposit, Aftabru IOCG prospect and other less important Cu–Au deposits within the study area to identify recognition criteria for exploration targeting of Cu–Au deposits. The recognition criteria are represented in the form of GIS predictor layers (spatial proxies) by processing available exploration data sets, which include geology, stream sediment geochemistry, airborne magnetics and multi-spectral remote sensing data. An ANFIS is trained using 30% of the 61 known Cu–Au deposits, prospects and occurrences in the area. In a parallel analysis, an exclusively expert-knowledge-driven fuzzy model was implemented using the same input predictor maps. Although the neuro-fuzzy analysis maps the high potential areas slightly better than the fuzzy model, the well-known mineralized areas and several unknown potential areas are mapped by both models. In the fuzzy analysis, the moderate and high favorable areas cover about 16% of the study area, which predict 77% of the known copper–gold occurrences. By comparison, in the neuro-fuzzy approach the moderate and high favorable areas cover about 17% of the study area, which predict 82% of the copper–gold occurrences.     

A case study for the integration of predictive mineral potential maps

This study aims to elaborate on the mineral potential maps using various models and verify the accuracy for the epithermal gold (Au) — silver (Ag) deposits in a Geographic Information System (GIS) environment assuming that all deposits shared a common genesis. The maps of potential Au and Ag deposits were produced by geological data in Taebaeksan mineralized area, Korea. The methodological framework consists of three main steps: 1) identification of spatial relationships 2) quantification of such relationships and 3) combination of multiple quantified relationships. A spatial database containing 46 Au-Ag deposits was constructed using GIS. The spatial association between training deposits and 26 related factors were identified and quantified by probabilistic and statistical modelling. The mineral potential maps were generated by integrating all factors using the overlay method and recombined afterwards using the likelihood ratio model. They were verified by comparison with test mineral deposit locations. The verification revealed that the combined mineral potential map had the greatest accuracy (83.97%), whereas it was 72.24%, 65.85%, 72.23% and 71.02% for the likelihood ratio, weight of evidence, logistic regression and artificial neural network models, respectively. The mineral potential map can provide useful information for the mineral resource development.     

Integration of GIS,AHP and TOPSIS for earthquake hazard analysis

Worldwide, earthquakes and related disasters have persistently had severe negative impacts on human livelihoods and have caused widespread socioeconomic and environmental damage. The severity of these disasters has prompted recognition of the need for comprehensive and effective disaster and emergency management (DEM) efforts, which are required to plan, respond to and develop risk mitigation strategies. In this regard, recently developed methods, known as multi-criteria decision analysis (MCDA), have been widely used in DEM domains by emergency managers to greatly improve the quality of the decision-making process, making it more participatory, explicit, rational and efficient. In this study, MCDA techniques of the Analytical Hierarchical Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), integrated with GIS, were used to produce earthquake hazard and risk maps for earthquake disaster monitoring and analysis for a case study region of Küçükçekmece in Istanbul, Turkey. The five main criteria that have the strongest influence on the impact of earthquakes on the study region were determined: topography, distance to epicentre, soil classification, liquefaction and fault/focal mechanism. AHP was used to determine the weights of these parameters, which were also used as input into the TOPSIS method and GIS (ESRI ArcGIS) for simulating these outputs to produce earthquake hazard maps. The resulting earthquake hazard maps created by both the AHP and TOPSIS models were compared, showing high correlation and compatibility. To estimate the elements at risk, population and building data were used with the AHP and TOPSIS hazard maps for potential loss assessment; thus, we demonstrated the potential of integrating GIS with AHP and TOPSIS in generating hazard maps for effective earthquake disaster and risk management.     

空间信息技术的发展及其在地球科学中的应用

空间信息技术有两个分支：其一是空间图形信息技术,即地理信息系统（ＧＩＳ）;其二是地质统计学。两种技术在本质上是相通的。ＧＩＳ的核心是建立图形元素的拓扑关系与建立空间数据库与属性数据库的联动关系。正是立足于这两个重要关系,使对千变万化的地学图形信息进行灵活高效的编辑、检索与显示成为可能。在迄今为止所有的计算机应用系统中,ＧＩＳ无疑将在地学中产生最重要的影响。地质统计学是一种空间数字处理技术,它是传统统计学的空间版本。用包含多元统计学的传统统计方法研究地质问题存在严重的缺陷,因为传统统计学忽略变量的空间存在,而地质过程具有强烈的空间性。地质统计学恰恰是立足于变量的空间关系发展起来的统计科学,因此它特别适用于地球科学。正如空间图形与空间数字可以相互转换一样,两种空间信息技术也存在内在的关系。利用ＧＩＳ与地质统计学这两种空间信息技术,使我们能不断地在空间图形与空间数字这两种状态间穿越。空间信息技术为地质学家在进行矿产资源预测、物化遥数据分析、地质环境评价、地质灾害评价与矿产资源管理等方面提供了强有力的工具。空间信息技术特别适合于地球科学,它不仅能大幅度地提高地球科学对图形的编制和研究的效率和水平,而且也将转变传统的?     

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.     

模糊逻辑法在秦岭-松潘成矿区金矿潜力预测中的应用

在介绍模糊逻辑的基本理论、详细分析秦岭-松潘地区金成矿规律的基础上,应用ArcView GIS及其扩展模块Arc-SDM对研究区地质、地球化学、地球物理等数据进行处理,提取预测变量。已知的金矿（点）被用来构造研究区隶属度函数,确定各预测变量的隶属度大小。根据不同预测变量与金矿的相关性大小,选择不同的模糊算子进行综合,生成控矿因子。最后应用伽马算子,综合控矿因子,得到最终的秦岭-松潘金矿潜力图。根据金矿潜力图和模糊逻辑模型的检验结果,说明模糊逻辑可以对空间数据进行有效的综合,预测潜力图件中圈出的有利矿化区,可用于进一步勘查研究。     

全国固体矿产资源区划数据库建设

为建立全国固体矿产资源成矿远景区划数据库,使全国性资源评价规划工作计算机化,也为在新一轮国家矿产资源潜力评价中引入现代新技术新方法打下重要基础,文章应用GIS技术管理空间数据的有效手段,建立了基于GIS的区划空间数据库,以助于区划地质成果资料的充分、有效利用,开发的数据库可以在新一轮全国成矿远景区划中使用.     

基于GIS空间分析进行多源信息成矿预测——以东天山土屋-延东铜矿床预测为例

GIS空间分析在区域地质多源信息综合分析中,具有十分重要的作用.本文介绍了GIS空间分析的特征及应用于区域地质找矿的两种方法,即统计分析和叠加分析.参与多源信息成矿预测的信息图层,以东天山地区为例,选取找矿有利岩性、遥感断裂影响带(Buffer区)、地球化学异常、地质异常及已知矿(点)床的分布等6种控矿因素作为多源成矿预测的空间信息数据源.在此基础上,利用GIS的空间分析功能进行多源信息的综合分析,预测出3级成矿有利地区.     

矿产资源领域三项基本国情调查成果数据的综合集成与应用

全国矿产资源潜力评价、全国矿产资源储量利用现状调查、全国矿业权实地核查是我国在矿产资源领域开展的三项全国性基本国情调查项目,形成了多于10 Tb的地质资料数据。为了促进地质资料共享,提升为找矿突破战略行动、企事业单位及社会大众的多方服务能力,分析了具有典型海量、异构特性的三项调查成果数据集成需求,提出了“地质信息一张图”的构建思想,分别从数据集成、应用集成两个角度对成果的共享与应用进行了研究。通过服务接口提供地质数据资源、计算资源,使用各类客户端完成三项成果数据的在线访问、分析、融合展示、专题图生成等业务。提出了在线按需提供多种地质资料产品、网上提交需求而离线加工的两种新型地质资料服务模式。以金矿、铁矿的区域性成果数据为研究对象,初步搭建了三大专项集成与共享试验平台,基于GIS编程实现了原型系统,实现了跨专项的数据分析、各种图件自动生成等功能,证明了方案的可操作性,为我国后续开展三大专项的全国性集成工作、国土资源“一张图”建设等提供了有价值的参考。     

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。     

整装勘查信息系统应用

以服务整装勘查业务管理与研究、支撑整装勘查区进展与成果展示为目标,基于GIS开发了以地质、矿产地、工作程度、矿业权、物探、化探、遥感、潜力评价成果等空间数据为基础的整装勘查信息系统。以大湖塘钨(铜)多金属矿整装勘查区为例,对系统的功能进行了应用与验证。案例应用结果表明: 系统界面友好,功能齐全明了,易学习易操作,可将各类数据组合叠加浏览,提供"一张图"的数据空间展示,用户可通过在全国地图上直观地浏览各整装勘查区矿产勘查进展,建立矿产勘查进展空间分布格局,还可以借助系统完成统计分析、专题图件制作等,极大提高了整装勘查业务管理和研究的效率。     

Potential groundwater recharge zones within New Zealand

Water resources in New Zealand are not evenly distributed across the country which makes it difficult to adequately allocate the use of water resources in every basin. Groundwater is a fundamental water resource in New Zealand for agricultural, industrial and domestic use. Detailed knowledge regarding groundwater recharge potential is a pre-requisite for sustainable groundwater management, including the assessment of its vulnerability to contamination by pollutants. In this study, a comprehensive GIS approach was used to map the potential groundwater recharge zones across New Zealand. National data sets of lithology, slope, aspect, land use, soil drainage and drainage density were converted to raster data sets with a spatial resolution of 500 m × 500 m and superimposed to derive groundwater potential zones. The resultant maps demonstrate that the potential is low in urban and mountainous areas, such as the Southern Alps, whereas the highest potential can be found in regions with large lakes and in the lower elevation plains areas, where Quaternary sediments prevail. The resulting maps can be used to identify areas of high nutrient leaching in zones where high groundwater recharge potential exists.     

A new approach for forest fire risk modeling using fuzzy AHP and GIS in Hyrcanian forests of Iran

The presented research was performed in order to model the fire risk in a part of Hyrcanian forests of Iran. The fuzzy sets integrated with analytic hierarchy process (AHP) in a decision-making algorithm using geographic information system (GIS) was used to model the fire risk in the study area. The used factors included four major criteria (topographic, biologic, climatic, and human factors) and their 17 sub-criteria. Fuzzy AHP method was used for estimating the importance (weight) of the effective factors in forest fire. Based on this modeling method, the expert ideas were used to express the relative importance and priority of the major criteria and sub-criteria in forest fire risk in the study area. The expert ideas mean was analyzed based on fuzzy extent analysis. Then, the fuzzy weights of criteria and sub-criteria were obtained. The major criteria models and fire risk model were presented based on these fuzzy weights. On the other hand, the spatial data of 17 sub-criteria were provided and organized in GIS to obtain the sub-criteria maps. Each sub-criterion map was converted to raster format and it was reclassified based on risk of its classes to fire occurrence. Then, all sub-criteria maps were converted to fuzzy format using fuzzy membership function in GIS. The fuzzy map of each major criterion (topographic, biologic, climatic, and human criteria) was obtained by weighted overlay of its sub-criteria fuzzy maps considering to major criterion model in GIS. Finally, the fuzzy map of fire risk was obtained by weighted overlay of major criteria fuzzy maps considering to fire risk model in GIS. The actual fire map was used for validation of fire risk model and map. The results showed that the fuzzy estimated weights of human, biologic, climatic, and topographic criteria in fire risk were 0.301, 0.2595, 0.2315, and 0.208, respectively. The results obtained from the fire risk map showed that 38.74% of the study area has very high and high risk for fire occurrence. Results of validation of the fire risk map showed that 80% of the actual fires were located in the very high and high risk areas in fire risk map. It can show the acceptable accuracy of the fire risk model and map obtained from fuzzy AHP in this study. The obtained fire risk map can be used as a decision support system for predicting of the future fires in the study area.     

基于GIS和单元簇的模糊逻辑推理及其在区域矿产预测中的应用

论述了 GIS支持下的一种基于“单元簇”概念和模糊逻辑推理的多元地学信息综合分析方法及其在区域矿产预测中的应用。针对以往矿产定量预测中的单元划分方法对空间信息利用不足的问题 ,用单元的空间组合 (“单元簇”)代替单元作为定量类比的基本单位 ,从而能较充分地利用地质变量的局部空间结构信息 ;将单元作为 GIS区图元 ,利用 GIS空间分析功能实现对单元及单元簇的管理和操作 ;建立两个层次即变量对单元和组成单元对单元簇的模糊推理规则 ,经两次模糊推理计算出所有未知单元的找矿有利度 ,为进一步圈定找矿远景区提供了基础 ,并以新疆康古尔塔格地区金矿预测实例说明了其应用效果     

基于GIS空间分析进行多源信息成矿预测

GIS空间分析在区域地质多源信息综合分析中，具有十分重要的作用。作者在本文中介绍了GIS空间的分析特征及应用于区域地质找矿的两种方法，即统计分析和叠加分析。结合兰坪盆地的区域地质特征，作者选取了遥感线性体密度异常、频度异常、遥感环形影像异常、地球化学异常、地球物理异常、地质异常及已知矿（点）床的分布等七种控矿因素作为多源成矿预测的空间信息数据源。在此基础上，利用了GIS的空间分析功能进行多源信息的综合分析，预测出三级成矿有利的区域。     

应用GIS研究矿产资源潜力--以云南澜沧江流域为例

应用地理信息系统(GIS)技术,深入地研究了云南澜沧江流域成矿的建造和构造,划分出不同级别的地质异常单元和有利成矿的断层影响带;分析了化探异常与相关矿床的耦合度和出现率;系统地总结了已有矿点资料,建立了矿产资源潜力评价空间分析模型.在上述研究基础上作出该地区有色、稀有、贵金属资源潜力图,对资源潜力作出评价.