Using fuzzy logic in a Geographic Information System environment to enhance conceptually based prospectivity analysis of Mississippi Valley‐type mineralisation

Geographic Information Systems (GIS) provide an efficient vehicle for the generation of mineral prospectivity maps, which are products of the integration of large geological, geophysical and geochemical datasets that typify modern global‐scale mineral exploration. Conventionally, two contrasting approaches have been adopted, an empirical approach where there are numerous deposits of the type being sought in the analysed mature terrain, or a conceptual approach where there are insufficient known deposits for a statistically valid analysis. There are also a variety of potential methodologies for treatment of the data and their integration into a final prospectivity map. The Lennard Shelf represents the major Mississippi Valley‐type (MVT) province in Australia; however, there are only 13 deposits or major prospects known, making an empirical approach to prospectivity mapping impractical. Instead, a conceptual approach was adopted, where critical features that control the location of MVT deposits on the Lennard Shelf, as defined by widely accepted genetic models, were translated into features related to fluid pathways, depositional traps and fluid outflow zones, which can be mapped in a GIS and categorised as either regional or restricted diagnostic, or permissive criteria. All criteria were derived either directly from geological and structural data, or indirectly from geophysical and geochemical datasets. A fuzzy‐logic approach was adopted for the prospectivity analysis, where each interpreted critical feature of the conceptual model was assigned a weighting between 0 and 1 based on its inferred relative importance and reliability. The fuzzy‐logic method is able to cope with incomplete data, a common problem in regional‐scale exploration datasets. The data were best combined using the gamma operator to produce a fuzzy‐logic map for the prospectivity of MVT deposits on the southeastern Lennard Shelf. Five categories of prospectivity were defined. Importantly, from an exploration viewpoint, the two lowest prospectivity categories occupy ～90% and the highest two categories only 1.6% of the analysed area, yet eight of the 13 known MVT deposits lie in the latter and none in the former: i.e. all lie within ～10% of the area, despite the fact that deposit locations were not used directly in the analysis. The propectivity map also defines potentially mineralised areas in the central southeastern Lennard Shelf and the southern part of the Oscar Ranges, where there are currently no known deposits. Overall, the analysis demonstrates the power of fuzzy‐logic prospectivity mapping on a semi‐regional to regional scale, and emphasises the value of geological data, particularly accurate geological maps, in exploration for hydrothermal mineral deposits that formed late in the evolution of the terrain under exploration.     

Regional Exploration Targeting Model for Gangdese Porphyry Copper Deposits

An exploration targeting model for Gangdese porphyry copper deposit in Tibet, China, is constructed based on (i) the age of porphyry intrusions within Gangdese magmatic arc; (ii) the regional‐scale normal E–W, N–S and N–E striking faults; and (iii) comprehensive anomalously high concentrations of Cu‐Mo‐Au‐Ag‐Pb‐Zn. These targeting elements are derived from geological map and geochemical dataset, and are integrated by weights of evidence with the aid of geographic information system (GIS). The resulting prospectivity for porphyry copper deposits delineated by posterior probability demonstrates that the target areas extend along the Yaluzangbujiang River and contain the two large deposits, Qulong and Chongjiang, located in the eastern and central part of the Gangdese belt, respectively. These results indicate that the proposed exploration targeting model is a potential tool to map regional‐scale mineral prospectivity. The target areas with high values of favorability, especially where high concentrations of Cu‐Mo‐Au‐Ag‐Pb‐Zn are present, are the potential areas for finding undiscovered porphyry copper deposits.     

Porphyry,epithermal, and orogenic gold prospectivity of Argentina

This paper presents a review of the available information on the significant porphyry, epithermal, and orogenic gold districts in Argentina, including the tectonic, geological, and structural settings of large deposits or deposits that have been exploited in the past. Based on this review of the geology and mineralization, targeting models are developed for epithermal and orogenic gold systems, in order to produce GIS-based prospectivity models. Using publically available digital geoscience data, weights of evidence and fuzzy logic prospectivity maps were generated for epithermal and orogenic gold mineralization in Argentina. The results of the prospectivity mapping highlight existing gold deposits within known mineralized districts throughout Argentina, as well as other highly prospective areas with no known deposits within these districts. Additionally, areas within Argentina that have no known gold mineralization (based on publically available information) were highlighted as being highly prospective based on the models used.     

Mapping mineral prospectivity for Cu polymetallic mineralization in southwest Fujian Province,China

In this study, both the fuzzy weights of evidence (FWofE) and random forest (RF) methods were applied to map the mineral prospectivity for Cu polymetallic mineralization in southwestern Fujian Province, which is an important Cu polymetallic belt in China. Recent studies have revealed that the Zijinshan porphyry–epithermal Cu deposit is associated with Jurassic to Cretaceous (Yanshanian) intermediate to felsic intrusions and faulting tectonics. Evidence layers, which are key indicators of the formation of Zijinshan porphyry–epithermal Cu mineralization, include: (1) Jurassic to Cretaceous intermediate–felsic intrusions; (2) mineralization-related geochemical anomalies; (3) faults; and (4) Jurassic to Cretaceous volcanic rocks. These layers were determined using spatial analyses in support by GeoDAS and ArcGIS based on geological, geochemical, and geophysical data. The results demonstrated that most of the known Cu occurrences are in areas linked to high probability values. The target areas delineated by the FWofE occupy 10% of the study region and contain 60% of the total number of known Cu occurrences. In comparison with FWofE, the resulting RF areas occupy 15% of the study area, but contain 90% of the total number of known Cu occurrences. The normalized density value of 1.66 for RF is higher than the 1.15 value for FWofE, indicating that RF performs better than FWofE. Receiver operating characteristics (ROC) were used to validate the prospectivity model and check the effects of overfitting. The area under the ROC curve (AUC) was greater than 0.5, indicating that both prospectivity maps are useful in Cu polymetallic prospectivity mapping in southwestern Fujian Province.     

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.     

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.     

新疆塔尔巴哈台-萨吾尔地区多重分形地球化学异常提取及成矿预测

塔尔巴哈台-萨吾尔地区位于中国新疆西北部,目前已发现若干处铜、金矿床,具有很好的成矿潜力。成矿定量预测方法常被用于综合成矿标志信息,进行成矿远景区的定量预测和评价。本文首先结合多重分形理论-奇异性指数模型进行地球化学异常提取,之后通过对区域成矿条件进行综合分析,基于地球化学异常以及构造、岩浆岩、地层与矿化的相关关系构建了塔尔巴哈台-萨吾尔地区铜-金成矿预测模型;研究进一步基于新近的找矿成果,以已知矿床和新近发现的矿化点信息作为依据,利用证据权重方法对研究区铜-金矿化的远景区进行了定量预测。预测结果显示出塔尔巴哈台-萨吾尔地区具有良好的找矿前景,区内存在多个新的成矿远景区,可作为新的找矿勘探的目标,开展进一步找矿勘查工作。     

GIS-based mineral potential modeling by advanced spatial analytical methods in the southeastern Yunnan mineral district,China

This paper demonstrates a modeling procedure of mineral potential mapping based on singularity theory, and further presents an idea to look into metallogeny of Sn–Cu polymetallic deposits in southeastern Yunnan mineral district, China by applying a localized regression method. Mineralization is a typical cascade process generally accompanied by irregular geological, geochemical and geophysical signatures. Singularity index as an efficient anomaly analytical tool helps to identify anomalies as well as characterize formation processes of these anomalies. In this study, the singularity-based mineral potential mapping method was utilized to characterize hydrothermal mineralization associated with magmatic, tectonic and sedimentary processes in this district. Based on the results, a mineral prospectivity model was constructed to delineate target areas. In addition to mineral prospectivity, controlling effects of geo-processes on mineralization are spatially non-stationary. Geographically-weighted regression analysis was thus employed to investigate these spatially-varied controlling effects and it has contributed to improve understanding to local metallogeny in the study area. Results of the spatial analysis presented can be used to guide following stages of mineral exploration in the district.     

Regional prospectivity analysis for hydrothermal-remobilised nickel mineral systems in western Victoria,Australia

Fuzzy logic mineral prospectivity modelling was performed to identify camp-scale areas in western Victoria with an elevated potential for hydrothermal-remobilised nickel mineralisation. This prospectivity analysis was based on a conceptual mineral system model defined for a group of hydrothermal nickel deposits geologically similar to the Avebury deposit in Tasmania. The critical components of the conceptual model were translated into regional spatial predictor maps combined using a fuzzy inference system. Applying additional criteria of land use restrictions and depth of post-mineralisation cover, downgrading the exploration potential of the areas within national parks or with thick barren cover, allowed the identification of just a few potentially viable exploration targets, in the south of the Grampians-Stavely and Glenelg zones. Uncertainties of geological interpretations and parameters of the conceptual mineral system model were explicitly defined and propagated to the final prospectivity model by applying Monte Carlo simulations to the fuzzy inference system. Modelling uncertainty provides additional information which can assist in a further risk analysis for exploration decision making.     

A Comparison of Modified Fuzzy Weights of Evidence,Fuzzy Weights of Evidence,and Logistic Regression for Mapping Mineral Prospectivity

Weights of evidence and logistic regression are two of the most popular methods for mapping mineral prospectivity. The logistic regression model always produces unbiased estimates, whether or not the evidence variables are conditionally independent with respect to the target variable, while the weights of evidence model features an easy to explain and implement modeling process. It has been shown that there exists a model combining weights of evidence and logistic regression that has both of these advantages. In this study, three models consisting of modified fuzzy weights of evidence, fuzzy weights of evidence, and logistic regression are compared with each other for mapping mineral prospectivity. The modified fuzzy weights of the evidence model retains the advantages of both the fuzzy weights of the evidence model and the logistic regression model; the advantages being (1) the predicted number of deposits estimated by the modified fuzzy weights of evidence model is nearly equal to that of the logistic regression model, and (2) it can deal with missing data. This method is shown to be an effective tool for mapping iron prospectivity in Fujian Province, China.     

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.     

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.     

From 2D to 3D: Prospectivity modelling in the Taupo Volcanic Zone,New Zealand

A 2D prospectivity model of epithermal gold mineralisation has been completed over the Taupo Volcanic Zone (TVZ), using the weights of evidence modelling technique. This study was used to restrict a 3D geological interpretation and prospectivity model for the Ohakuri region. The TVZ is commonly thought of as a present-day analogue of the environment in which many epithermal ore deposits, such as in the Hauraki Goldfield, Coromandel Volcanic Zone, are formed. The models utilise compiled digital data including historical exploration data, geological data from the Institute of Geological and Nuclear Sciences Ltd. Quarter Million Mapping Programme, recent Glass Earth geophysics data and historic exploration geochemical data, including rock-chip and stream sediment information. Spatial correlations between known deposits and predictive maps are determined from the available data, which represent each component of the currently accepted mineral system model for epithermal gold. The 2D prospectivity model confirms that the TVZ has potential for gold mineralisation. However, one of the weaknesses of this weights of evidence model is that the studies are carried out in 2D, with an approximation of 3D provided by geophysical and drilling data projected to a 2D plane. Consequently, a 3D prospectivity model was completed over the Ohakuri area, constrained by the results of the 2D model and predictive maps. The 3D model improved the results allowing more effective exploration targeting. However, the study also highlighted the main issues that need to be resolved before 3D prospectivity modelling becomes standard practise in the mineral exploration industry. The study also helped develop a work flow that incorporates preliminary 2D spatial data analysis from the weights of evidence technique to more effectively restrict and develop 3D predictive map interpretation and development.     

Hybrid outranking of geospatial data: Multi attributive ideal-real comparative analysis and combined compromise solution

Identifying mineralization areas with an acceptable level of reliability is a complex matter demanding the implementation of supervised methods for mapping mineralization exploration aims. In this study, further collecting all available exploratory information and data of the region of study, their good processing and analysis, and then integrating them with an appropriate method, multi-criteria decision-making (MCDM) approaches were utilized to outranking porphyry Cu mineralization areas. This paper describes an acceptable procedure for obtaining evidential layers for recognizing porphyry Cu mineralization, assigning weight to the layers, and combining them. For this aim, first, a data-driven multi-class index overlay (DMIO) approach is applied as it is a proven method with proper results in mineral prospectivity mapping (MPM) to integrate evidential layers (criteria and sub-criteria emanated from geoscience data including geological, geochemical, remote sensing, and geophysical datasets). After that, two recent MCDM models, combined compromise solution (CoCoSo) and multi attributive ideal-real comparative analysis (MAIRCA), were used to prioritize the porphyry Cu potential areas producing MPM. Concentration-area (C–A) fractal method and prediction-area (P–A) plots by considering the areas of occurrence of the known mineralization and normalized density (N_d) as traditional methods were applied for weighting and integration evidential layers and evaluation of the final maps in a data-driven manner. Consequently, verifying the verisimilitude of the MAIRCA prospectivity map (N_d = 3.17) is similar to the DMIO map in delimiting the priority areas. The results of the CoCoSo model (N_d = 2.7) show this methodology was also successfully applied in mapping the porphyry Cu mineralization areas in the study.     

Incorporating conceptual and interpretation uncertainty to mineral prospectivity modelling

Prospectivity analyses are used to reduce the exploration search space for locating areas prospective for mineral deposits.The scale of a study and the type of mineral system associated with the deposit control the evidence layers used as proxies that represent critical ore genesis processes.In particular,knowledge-driven approaches(fuzzy logic)use a conceptual mineral systems model from which data proxies represent the critical components.These typically vary based on the scale of study and the type of mineral system being predicted.Prospectivity analyses utilising interpreted data to represent proxies for a mineral system model inherit the subjectivity of the interpretations and the uncertainties of the evidence layers used in the model.In the case study presented,the prospectivity for remobilised nickel sulphide(NiS)in the west Kimberley,Western Australia,is assessed with two novel techniques that objectively grade interpretations and accommodate alternative mineralisation scenarios.Exploration targets are then identified and supplied with a robustness assessment that reflects the variability of prospectivity value for each location when all models are considered.The first technique grades the strength of structural interpretations on an individual line-segment basis.Gradings are obtained from an objective measure of feature evidence,which is the quantification of specific patterns in geophysical data that are considered to reveal underlying structure.Individual structures are weighted in the prospectivity model with grading values correlated to their feature evidence.This technique allows interpreted features to contribute prospectivity proportional to their strength in feature evidence and indicates the level of associated stochastic uncertainty.The second technique aims to embrace the systemic uncertainty of modelling complex mineral systems.In this approach,multiple prospectivity maps are each generated with different combinations of confidence values applied to evidence layers to represent the diversity of processes potentially leading to ore deposition.With a suite of prospectivity maps,the most robust exploration targets are the locations with the highest prospectivity values showing the smallest range amongst the model suite.This new technique offers an approach that reveals to the modeller a range of alternative mineralisation scenarios while employing a sensible mineral systems model,robust modelling of prospectivity and significantly reducing the exploration search space for Ni.     

Three-dimensional mineral prospectivity modeling for targeting of concealed mineralization within the Zhonggu iron orefield,Ningwu Basin,China

The Zhonggu iron orefield is one of the most important iron orefields in China, and is located in the south of the Ningwu volcanic basin, within the middle and lower Yangtze metallogenic belt of eastern China. Here, we present the results of new 3D prospectivity modeling that enabled the delineation of areas prospective for exploration of concealed and deep-seated Baixiangshan-type mineralization and Yangzhuang-type mineralization within the Zhonggu orefield; both of these deposits are Kiruna-type Fe-apatite deposits but are hosted by different formations within the Ningwu Basin. The modeling approach used during this study involves 3 steps: (1) combining available geological and geophysical data to construct 3D geological models; (2) generation of 3D predictive maps from these 3D geological models using 3D spatial analysis and 3D geophysical methods; (3) combining all of the 3D predictive maps using logistic regression to create a prospectivity map. This approach integrates a large amount of available geoscientific data using 3D methods, including 3D geological modeling, 3D/2D geophysical methods, and 3D spatial analysis and data integration methods. The resulting prospectivity model clearly identifies highly prospective areas that not only include areas of known mineralization but also a number of favorable targets for future mineral exploration. The 3D prospectivity modeling approach showcased within this study provides an efficient way to identify camp-scale concealed and deep-seated exploration targets and can easily be adapted for regional- and deposit- scale targeting.     

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.     

Vectorial fuzzy logic: A novel technique for enhanced mineral prospectivity mapping,with reference to the orogenic gold mineralisation potential of the Kalgoorlie Terrane,Western Australia

Modern exploration is a multidisciplinary task requiring the simultaneous consideration of multiple disparate geological, geochemical and geophysical datasets. Over the past decade, several research groups have investigated the role of Geographic Information Systems as a tool to analyse these data. From this research, a number of techniques has been developed that allow the extraction of exploration‐relevant spatial factors from the datasets. The spatial factors are ultimately condensed into a single prospectivity map. Most techniques used to construct prospectivity maps tend to agree, in general, as to which areas have the lowest and highest prospectivities, but disagree for regions of intermediate prospectivity. In such areas, the prospectivity map requires detailed interpretation, and the end‐user must normally resort to analysis of the original datasets to determine which conjunction of factors results in each intermediate prospectivity value. To reduce this burden, a new technique, based on fuzzy logic principles, has been developed for the integration of spatial data. Called vectorial fuzzy logic, it differs from existing methods in that it displays prospectivity as a continuous surface and allows a measure of confidence to be incorporated. With this technique, two maps are produced: one displays the calculated prospectivity and the other shows the similarity of input values (or confidence). The two datasets can be viewed simultaneously as a three‐dimensional perspective image in which colour represents prospectivity and topography represents confidence. With the vectorial fuzzy logic method, factors such as null data and incomplete knowledge can also be incorporated into the prospectivity analysis.     

宁芜盆地白象山矿区人工神经网络 三维成矿定位预测研究

本文基于三维地质环境,综合白象山矿区积累的地质资料和物探成果,首先开展三维地质建模工作,详细刻画了白象山矿区的三维地质结构;在三维地质模型基础上,利用三维空间分析手段对三维控矿因素进行定量挖掘,提取了多种三维控矿因素;最后采用人工神经网络方法进行三维成矿定位预测。预测结果显示,人工神经网络三维成矿定位预测能很好的定位出已知矿体,同时显示,在已知矿体北部及东部的深边部具有较高的成矿概率,可作为开展进一步找矿勘探的靶区。因此,人工神经网络三维成矿定位预测对于白象山矿区的应用是有效的,可服务于新老矿区的深边部三维成矿定位预测,同时可为隐伏矿、盲矿的成矿预测和优选靶区提供定量、定位新的方法和途径。     

Receiver operating characteristics (ROC) as validation tool for prospectivity models — A magmatic Ni–Cu case study from the Central Lapland Greenstone Belt,Northern Finland

A prospectivity model for magmatic Ni–Cu deposits was created by integrating spatially referenced geophysical and geochemical datasets based on a simple and practical exploration model. The study area is the Central Lapland Greenstone Belt, Northern Fennoscandian Shield, Finland. Magmatic nickel deposits are related to rock types that are typically characterized by local magnetic and gravity anomalies. These deposit types can also be a source of nickel, copper and cobalt anomalies in the overlying glacial till cover. This straightforward exploration criterion was translated into a fuzzy logic prospectivity model. The model validation is an essential step in justifying the validity of the prospectivity model. This was accomplished by using receiver operating characteristics (ROC) technique. We used the known Ni–Cu occurrences and deposits as true positive cases and other deposit type locations or random points as true negative cases in the validation process. It appears that the ROC technique provides a robust model validation and optimization technique, providing that suitable validation data exists.