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。     

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.      

混合模糊证据权模型在河北承德煤炭资源预测中的应用

应用模糊逻辑法、加权证据权法相结合的混合模糊证据权模型和GeoDASGIS技术开展了承德煤炭资源预测研究。采用模糊逻辑法对与煤炭矿床有关的证据层进行了系统的处理和分析,并在此基础上采用加权证据权方法编制了成矿后验概率图,最终划分出5个主要的找矿远景区。研究结果不仅对进一步开展预测区优选评价具有重要的参考意义,而且为混合知识驱动与数据驱动的混合预测模型提供了一种可借鉴的有效方法。     

专家证据权重法及其在东昆仑地区的应用

证据权重法是基于GIS矿产资源评价的主要方法之一,在东昆仑地区应用该方法后发现其结果和东昆仑地区的总体找(成)矿潜力实际情况不相符,研究后认为其原因是没有考虑东昆仑地区地质研究程度低、研究程度不均一且地质构造背景差异大等客观事实.为此,我们将东昆仑地区的基础地质、区域成矿规律及典型矿床研究等方面取得的认识("知识系统")和"证据权重法"结合起来,提出并建立了一个"专家证据权重法"矿产资源定量预测评价系统,该系统实际上相当于一个专家系统,其根据知识系统对各证据因子赋权重的过程体现了地质专家的思想.我们将该系统应用到东昆仑地区获得了满意的结果,这对其他研究程度低地区进行矿产资源评价工作具有重要的指导和借鉴意义.     

Deriving Optimal Exploration Target Zones on Mineral Prospectivity Maps

This paper describes a quantitative methodology for deriving optimal exploration target zones based on a probabilistic mineral prospectivity map. The methodology is demonstrated in the Rodalquilar mineral district in Spain. A subset of known occurrences of mineral deposits of the type sought was considered discovered and then used as training data, and a map of distances to faults/fractures and three band ratio images of hyperspectral data were used as layers of spatial evidence in weights-of-evidence (WofE) modeling of mineral prospectivity in the study area. A derived posterior probability map of mineral deposit occurrence showing non-violation of the conditional independence assumption and having the highest prediction rate was then put into an objective function in simulated annealing in order to derive a set of optimal exploration focal points. Each optimal exploration focal point represents a pixel or location within a circular neighborhood of pixels with high posterior probability of mineral deposit occurrence. Buffering of each optimal exploration focal point, based on proximity analysis, resulted in optimal exploration target zones. Many of these target zones coincided spatially with at least one occurrence of mineral deposit of the type sought in the subset of cross-validation (i.e., presumed undiscovered) mineral deposits of the type sought. The results of the study showed the usefulness of the proposed methodology for objective delineation of optimal exploration target zones based on a probabilistic mineral prospectivity map.     

加权证据权模型的应用与对比

证据权方法是目前最常用的信息综合方法之一,广泛应用于矿产资源定量预测与评价.然而,它要求变量间相互独立,地质上很难满足这一条件.如何削弱条件不独立对证据权预测结果的影响,已成为当前数学地球科学研究的热点.解决该问题的途径之一是对传统证据权模型进行校正,比如采取加权的方法对原证据权模型计算的证据权重进行修正,以便消除非条件独立性的影响.对近期提出的多种加权证据权模型进行了系统的对比研究,基于同样的应用实例和实验方案,对不同方法的应用效果进行了比较,结果表明,各种加权证据权模型均可不同程度地削弱证据图层条件不独立性的影响,其中,基于逻辑回归的加权证据权模型优于其他加权方法.      

Prospectivity for epithermal gold–silver deposits in the Deseado Massif,Argentina

Previous prospectivity modelling for epithermal Au–Ag deposits in the Deseado Massif, southern Argentina, provided regional-scale prospectivity maps that were of limited help in guiding exploration activities within districts or smaller areas, because of their low level of detail. Because several districts in the Deseado Massif still need to be explored, prospectivity maps produced with higher detail would be more helpful for exploration in this region.We mapped prospectivity for low- and intermediate-sulfidation epithermal deposits (LISEDs) in the Deseado Massif at both regional and district scales, producing two different prospectivity models, one at regional scale and the other at district-scale. The models were obtained from two datasets of geological evidence layers by the weights-of-evidence (WofE) method. We used more deposits than in previous studies, and we applied the leave-one-out cross validation (LOOCV) method, which allowed using all deposits for training and validating the models. To ensure statistical robustness, the regional and district-scale models were selected amongst six combinations of geological evidence layers based on results from conditional independence tests.The regional-scale model (1000 m spatial resolution), was generated with readily available data, including a lithological layer with limited detail and accuracy, a clay alteration layer derived from a Landsat 5/7 band ratio, and a map of proximity to regional-scale structures. The district-scale model (100 m spatial resolution) was generated from evidence layers that were more detailed, accurate and diverse than the regional-scale layers. They were also more cumbersome to process and combine to cover large areas. The evidence layers included clay alteration and silica abundance derived from ASTER data, and a map of lineament densities. The use of these evidence layers was restricted to areas of favourable lithologies, which were derived from a geological map of higher detail and accuracy than the one used for the regional-scale prospectivity mapping.The two prospectivity models were compared and their suitability for prediction of the prospectivity in the district-scale area was determined. During the modelling process, the spatial association of the different types of evidence and the mineral deposits were calculated. Based on these results the relative importance of the different evidence layers could be determined. It could be inferred which type of geological evidence could potentially improve the modelling results by additional investigation and better representation.We conclude that prospectivity mapping for LISEDs at regional and district-scales were successfully carried out by using WofE and LOOCV methods. Our regional-scale prospectivity model was better than previous prospectivity models of the Deseado Massif. Our district-scale prospectivity model showed to be more effective, reliable and useful than the regional-scale model for mapping at district level. This resulted from the use of higher resolution evidential layers, higher detail and accuracy of the geological maps, and the application of ASTER data instead of Landsat ETM + data. District-scale prospectivity mapping could be further improved by: a) a more accurate determination of the age of mineralization relative to that of lithological units in the districts; b) more accurate and detailed mapping of the favourable units than what is currently available; c) a better understanding of the relationships between LISEDs and the geological evidence used in this research, in particular the relationship with hydrothermal clay alteration, and the method of detection of the clay minerals; and d) inclusion of other data layers, such as geochemistry and geophysics, that have not been used in this study.     

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.     

A comprehensive VIKOR method for integration of various exploratory data in mineral potential mapping

The central Iranian volcanic-sedimentary belt in Kerman province of Iran that is located within the Urumieh-Dokhtar magmatic arc zone is chosen to integrate diverse evidential layers for mineral potential mapping. The studied area has high potential of mineral occurrences especially porphyry copper, and the prepared potential maps aim to outline new prospect zones for further investigation. Two evidential layers including the downward continued map and the analytic signal of filtered magnetic data are generated to be used as geophysical plausible traces of porphyry copper occurrences. The low values of the resistivity layer acquired from airborne frequency domain electromagnetic data are also used as an electrical criterion in this study. Four remote sensing evidential layers including argillic, phyllic, propylitic, and hydroxyl alterations are extracted from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images in order to map the altered areas associated with porphyry copper deposits. The Enhanced Thematic Mapper Plus (ETM+) images are used as well to prepare iron oxide layer. Since potassic alteration is generally the mainstay of copper ore mineralization, the airborne potassium radiometry data is used to explore both phyllic and potassic alteration. Finally, the geochemical layers of Cu/B/Pb/Zn elements and the main geochemical component responsible for ore mineralization extracted from principal component analysis are included in the integration process to prepare final potential maps. The conventional and the extended version of VIKOR method (as a well-known algorithm in multi-criteria decision making problems) produced two mineral potential maps, and the results were compared with the ones acquired from prevalent methods of the index overlay and fuzzy logic operators of sum and gamma. The final mineral potential maps based upon desired geo-data set indicate adequately matching of high potential zones with previous working and active mines of copper deposits.     

Chatham Rise nodular phosphate — Modelling the prospectivity of a lag deposit (off-shore New Zealand): A critical tool for use in resource development and deep sea mining

After almost five decades of episodic exploration, feasibility studies are now being completed to mine the deep-water nodular phosphate deposit on the central Chatham Rise. Weights of evidence (WofE) and fuzzy logic prospectivity models have been used in these studies to help in mapping of the exploration and resource potential, to constrain resource estimation, to aid with geotechnical engineering and mine planning studies and to provide background geological data for the environmental consent process. Prospectivity modelling was carried out in two stages using weights of evidence and fuzzy logic techniques. A WofE prospectivity model covering the area of best data coverage was initially developed to define the geological and environmental variables that control the distribution of phosphate on the Chatham Rise and map areas where mineralised nodules are most likely to be present. The post-probability results from this model, in conjunction with unique conditions and confidence maps, were used to guide environmental modelling for setting aside protected zones, and also to assist with mine planning and future exploration planning. A regional scale fuzzy logic model was developed guided by the results of the spatial analysis of the WofE model, elucidating where future exploration should be targeted to give the best chance of success in expanding the known resource. The development work to date on the Chatham Rise for nodular phosphate mineralisation is an innovative example of how spatial data modelling techniques can be used not only at the exploration stage, but also to constrain resource estimation and aid with environmental studies, thereby greatly reducing development costs, improving the economics of mine planning and reducing the environmental impact of the project.     

Evidential belief functions for data-driven geologically constrained mapping of gold potential,Baguio district,Philippines

A data-driven application of the theory of evidential belief to map mineral potential is demonstrated with a redefinition of procedures to estimate evidential belief functions. The redefined estimates of evidential belief functions take into account not only the spatial relationship of an evidence with the target mineral deposit but also consider the relationships among the subsets of spatial evidences within a set of evidential data layer. Proximity of geological features to mineral deposits is translated into spatial evidence and evidential belief functions are estimated for the proposition that mineral deposits exist in a test area. The integrated maps of degrees of belief for the proposition that mineral deposits exist in a test area is classified into a binary mineral potential map. For the Baguio district (Philippines), the binary gold potential map delineates (a) about 74% of the training data (i.e., locations of large-scale gold deposits) and (b) about 64% of the validation data (i.e., locations of small-scale gold deposits). The results demonstrate the usefulness of a geologically constrained mineral potential mapping using data-driven evidential belief functions to guide further surficial exploration work in the search for yet undiscovered gold deposits in the Baguio district. The results also indicate the usefulness of evidential belief functions for mapping uncertainties in the geologically constrained integrated predictive model of gold potential.     

Combining fractal analysis of mineral deposit clustering with weights of evidence to evaluate patterns of mineralization: Application to copper deposits of the Mount Isa Inlier, NW Queensland, Australia

The clustering of mineral occurrences and their spatial associations with particular geological features are critical aspects of mineral distributions for exploration and understanding ore genesis. Variations in the degree of clustering of mineral occurrences or geological features can be measured by fractal dimensions, obtained from a shifting box counting method. Spatial associations between mineral occurrences and geological features can be quantified by the weights of evidence (WofE) method using the contrast value, which increases with the strength of the spatial relationship. A new method is proposed to evaluate mineral occurrence distributions by combining the power of fractal analysis of clustering with the WofE approach. The method compares the correlation between the variation in degree of clustering of mineral occurrences and a geological feature in a study area, with the contrast value of the same feature. The possible outcomes can be simplified into four scenarios, depending on whether the correlation in variation of clustering and the contrast are high or low, respectively. Each outcome has specific exploration implications. If either a high correlation in variation of clustering or a high contrast value is obtained, the geological feature can be used for exploration targeting.The integrated fractal and WofE approach is applied to copper occurrences in the Proterozoic Mount Isa Inlier, NW Queensland, Australia, which hosts large numbers of copper deposits (1,869 occurrences), including the world class Mount Isa copper deposit. Variation in clustering of copper occurrences has a positive correlation with variation in clustering of fault bends (R = 0.823), fault intersections (R = 0.862) and mafic rocks (R = 0.885). WofE results indicate that the copper occurrences are spatially associated with fault intersections and bends and with mafic rocks. Analyses were carried out separately for the two major lithostratigraphic sequences in the Inlier, the Eastern and Western Successions. The Western Succession copper occurrences are apparently more clustered than those of the Eastern Succession, which may reflect a lower degree of exploration and/or geological factors. The association of copper occurrences with mafic rocks compared with fault bends and intersections is greater in the Eastern Succession, which may reflect genetic factors. Correlations in the variation of clustering of mineral occurrences and geological features have a linear relationship with the contrast values, and the spatial association between all geological features and copper occurrences constitute high correlation/high contrast cases. The linear relationship suggests that the geological features that control the clustering of the copper occurrences could be the same features that control their localization.     

改进的加权证据权模型及其在个旧锡铜矿产资源预测中的应用

为了探讨新的加权系数估计方法对于消除或减弱证据层不满足条件独立性假设时对预测结果的影响, 对加权证据权模型的加权系数估计方法进行了新的探讨,尝试用顺序估计法估计加权系数.加权系数的顺序估计法是将加权证据权模型与基于模糊预测对象的证据权模型相结合,将证据层按照一定顺序逐步加入到加权证据权模型中,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对证据层加入到模型的顺序进行修正,并通过条件相关系数的方法估计加权系数.分别以1组多元正态分布模拟数据和个旧锡铜多金属矿产资源预测为例,比较了多种模型的后验概率,结果表明加权证据权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.      

Data fusion and porphyry copper prospectivity models,southeastern Arizona

Mineral exploration programs commonly use a combination of geological, geophysical and remotely sensed data to detect sets of optimal conditions for potential ore deposits. Prospectivity mapping techniques can integrate and analyse these digital geological data sets to produce maps that identify where optimal conditions converge. Three prospectivity mapping techniques – weights of evidence, fuzzy logic and a combination of these two methods – were applied to a 32,000 km² study area within the southeastern Arizona porphyry Cu district and then assessed based on their ability to identify new and existing areas of high mineral prospectivity. Validity testing revealed that the fuzzy logic method using membership values based on an exploration model identified known Cu deposits considerably better than those that relied solely on weights of evidence, and slightly better than those that used a combination of weights of evidence and fuzzy logic. This led to the selection of the prospectivity map created using the fuzzy logic method with membership values based on an exploration model. Three case study areas were identified that comprise many critical geological and geophysical characteristics favourable to hosting porphyry Cu mineralisation, but not associated with known mining or exploration activity. Detailed analysis of each case study has been performed to promote these areas as potential targets and to demonstrate the ability of prospectivity modelling techniques as useful tools in mineral exploration programs.     

The use of the weights-of-evidence modeling technique to predictive porphyry Cu potential mapping in Ahar–Arasbaran zone,Iran

The weights-of-evidence is a data-driven method that provides a simple approach to integration of diverse geo-data set information. In this study, we will use weights-of-evidence to build a model for predicting tracts in the Ahar–Arasbaran zone of Urumieh-Dokhtar orogenic belt (northwestern Iran) that are favorable for porphyry copper deposits. Weights of evidence are a data-driven method requiring known deposits and occurrences that are used as training points in the evaluated area. This zone hosts two major porphyry Cu deposits (The Sarcheshmeh deposit contains 450 million tonnes of sulfide ore with an average grade of 1.13 % Cu and 0.03 % Mo and Sungun deposit, which has 500 million tonnes of sulfide reserves grading 0.76 % Cu and 0.01 % Mo), and a number of subeconomic porphyry copper deposits are all associated with Mid- to Late Miocene diorite/granodiorite to quartz-monzonite stocks. Five evidential layers including geology, alteration, geochemistry, geophysics, and faulting are chosen for potential mapping. Weight factors were determined based on the applied method to generate last mineral prospectivity map. The studied area reduces to less than 11.78 %, while large zones are excluded for further studies. This result represents a significant area reduction and may help to better focus on mineral exploration targeting porphyry copper deposits in the Ahar–Arasbaran zone.     

A hybrid AHP-VIKOR approach for prospectivity modeling of porphyry Cu deposits in the Varzaghan District,NW Iran

Identifying highly favorable areas related to a particular mineralization type is the main objective of mineral prospectivity modeling (MPM). The northwestern portion of Ahar-Arasbaran porphyry copper belt (AAPCB) is situated within the Urumieh-Dokhtar magmatic belt (UDMB). Because of owning many worthwhile Cu-Mo and Cu-Au porphyry deposits, this area is entitled to incorporate diverse spatial evidence layers for the MPM. In this paper, a hybrid AHP-VIKOR, as an improved knowledge-driven MPM procedure has been proposed for integration of various exploration evidence layers. For this, the AHP is used to calculate important weights of spatial criteria while the VIKOR is applied to outline ultimate prospectivity model. Six effective spatial evidence layers pertaining to the Varzaghan District are selected: (1) multi-elemental geochemical layer of Cu-Mo-Bi-Au; (2) remotely sensed data of argillic, phyllic, and iron oxide alteration layers; and (3) geological and structural layers of Oligo-Miocene intrusions and fault. In addition, a fuzzy prospectivity model (γ?=?0.9) is implemented to assess the AHP-VIKOR approach. Two credible validation methods comprising normalized density index and success rate curve are adapted for quantitative evaluation of predictive models and enhancing the probability of exploration success. The achieved results proved the higher accuracy of the AHP-VIKOR model compared with the fuzzy model in delimiting the favorable areas.     

深部水热型地热潜力区的GIS预测模型——以土耳其西安纳托利亚地区为例

开发地热资源有着很大的不确定性和很高的经济风险,需要建立可靠的数学预测模型以确定潜在地热区。通过对地震震中、断层、布格重力异常、磁异常和红外遥感5个与地热密切相关的因素进行整合,建立了地热潜力区的数学预测模型;并以土耳其西安纳托利亚地区为例,对模型的优劣性进行分析评价。运用指数叠加模型和证据权重模型分别建立地热潜力区图,预测成功指数表明证据权重模型的预测结果更为准确。其中,中高潜力区总面积为26 529 km²,占总面积的31.14%,包含39个地热点,占总地热点的50.65%。预测结果显示潜在地热区位于Ayd?n,Denizli,Manisa和Bal?kesir,其中前2个地区的地热已经被开发利用。本研究可以在地热勘探阶段确定潜在地热区,降低经济风险。     

Contributions of secular changes in the regional evolution of ore deposits to predictive mineral exploration: A case study of the Zhongtiao Mountain Cu metallogenic area,southern North China Craton

The Zhongtiao Mountain is located in the southern part of the North China Craton. The area experienced multi-stage tectono-magmatic events during the Precambrian, including Neoarchean-Early Paleoproterozoic (2550–2350 Ma) crustal growth, Paleoproterozoic (2350–1850 Ma) rifting–subduction–accretion–collision, and Early Mesoproterozoic (1800–1750 Ma) extension. The geological events contributed to a major copper mineral system in the region. Here we evaluate the processes of the mineral system, such as the source of metals, migration pathways, the formation of trap zones, and the deposition of metal in an attempt to establish a mineral accumulation evaluation model for regional ore prospecting. A three-step process has been proposed in this study as follows. (i) Determining the spatial and temporal distribution of the essential elements and processes of the mineral system to understand the most critical ore-controlling factors. (ii) Translating the ore-forming processes into mappable features and quantitative extraction of the information on mineralization using Geographic Information System (GIS) technology to establish a mineral accumulation evaluation model. They were treated as evidence layers for weight-of-evidence (WofE) analysis. (iii) Utilizing the weighted values of the evidence layers to create a posterior probability map. Based on the posterior probabilities, four mineral accumulation horizons were finally delineated for the Zhongtiao Mountain, which are considered to provide important guidelines for further ore exploration and study.     

Fuzzy logic mineral potential mapping for copper exploration using multi-disciplinary geo-datasets,a case study in seridune deposit,Iran

This paper describes the application of the knowledge-based fuzzy logic method to integrate various exploratory geo-dataset in order to prepare a mineral prospectivity map (MPM) for copper exploration. Different geophysical layers which are derived from the magnetic and the electrical surveys, along with the ones extracted from the background geology (i.e., lithology, fault and alteration) and geochemical data are incorporated in such process. Seridune copper deposit located in the Kerman province of Iran is the case study to delineate its high potential zones of Cu-bearing mineralization for drilling additional boreholes. Four layers from the magnetic data involving upward continuation, analytic signal, reduced to pole and pseudo gravity are assigned in the multi-disciplinary geo-dataset to locate the intrusive complexes responsible for Cu mineralization. The apparent resistivity, chargeability and sulfide factor layers acquired from geo-electrical data are also included in the final preparation of MPM. Then the normalized weights of seven geophysical, three geological and one geochemical evidential layers as main criteria are determined based upon the knowledge of expert decision makers. Fuzzy operators (i.e., Sum and Gamma) are applied to integrate these exploratory features. To evaluate the performance and applicability of the approach, the productivity of the drilled boreholes (Cu concentration multiplied by ore thickness) are used to validate the produced MPMs. It is shown that an optimum correlation coefficient of 0.86 exists between the MPM values and Cu productivity criterion along drilled boreholes.