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1.
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. 相似文献
2.
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. 相似文献
3.
This paper describes the geology and tectonics of the Paleoproterozoic Kumasi Basin, Ghana, West Africa, as applied to predictive mapping of prospectivity for orogenic gold mineral systems within the basin. The main objective of the study was to identify the most prospective ground for orogenic gold deposits within the Paleoproterozoic Kumasi Basin. A knowledge-driven, two-stage fuzzy inference system (FIS) was used for prospectivity modelling. The spatial proxies that served as input to the FIS were derived based on a conceptual model of gold mineral systems in the Kumasi Basin. As a first step, key components of the mineral system were predictively modelled using a Mamdani-type FIS. The second step involved combining the individual FIS outputs using a conjunction (product) operator to produce a continuous-scale prospectivity map. Using a cumulative area fuzzy favourability (CAFF) curve approach, this map was reclassified into a ternary prospectivity map divided into high-prospectivity, moderate-prospectivity and low-prospectivity areas, respectively. The spatial distribution of the known gold deposits within the study area relative to that of the prospective and non-prospective areas served as a means for evaluating the capture efficiency of our model. Approximately 99% of the known gold deposits and occurrences fall within high- and moderate-prospectivity areas that occupy 31% of the total study area. The high- and moderate-prospectivity areas illustrated by the prospectivity map are elongate features that are spatially coincident with areas of structural complexity along and reactivation during D4 of NE–SW-striking D2 thrust faults and subsidiary structures, implying a strong structural control on gold mineralization in the Kumasi Basin. In conclusion, our FIS approach to mapping gold prospectivity, which was based entirely on the conceptual reasoning of expert geologists and ignored the spatial distribution of known gold deposits for prospectivity estimation, effectively captured the main mineralized trends. As such, this study also demonstrates the effectiveness of FIS in capturing the linguistic reasoning of expert knowledge by exploration geologists. In spite of using a large number of variables, the curse of dimensionality was precluded because no training data are required for parameter estimation. 相似文献
4.
《地学前缘(英文版)》2022,13(6):101435
The past two decades have seen a rapid adoption of artificial intelligence methods applied to mineral exploration. More recently, the easier acquisition of some types of data has inspired a broad literature that has examined many machine learning and modelling techniques that combine exploration criteria, or ‘features’, to generate predictions for mineral prospectivity. Central to the design of prospectivity models is a ‘mineral system’, a conceptual model describing the key geological elements that control the timing and location of economic mineralisation. The mineral systems model defines what constitutes a training set, which features represent geological evidence of mineralisation, how features are engineered and what modelling methods are used. Mineral systems are knowledge-driven conceptual models, thus all parameter choices are subject to human biases and opinion so alternative models are possible. However, the effect of alternative mineral systems models on prospectivity is rarely compared despite the potential to heavily influence final predictions. In this study, we focus on the effect of conceptual uncertainty on Fe ore prospectivity models in the Hamersley region, Western Australia. Four important considerations are tested. (1) Five different supergene and hypogene conceptual mineral systems models guide the inputs for five forest-based classification prospectivity models model. (2) To represent conceptual uncertainty, the predictions are then combined for prospectivity model comparison. (3) Representation of three-dimensional objects as two-dimensional features are tested to address commonly ignored thickness of geological units. (4) The training dataset is composed of known economic mineralisation sites (deposits) as ‘positive’ examples, and exploration drilling data providing ‘negative’ sampling locations. Each of the spatial predictions are assessed using independent performance metrics common to AI-based classification methods and subjected to geological plausibility testing. We find that different conceptual mineral systems produce significantly different spatial predictions, thus conceptual uncertainty must be recognised. A benefit to recognising and modelling different conceptual models is that robust and geologically plausible predictions can be made that may guide mineral discovery. 相似文献
5.
A Mamdani-type fuzzy inference system for prospectivity modeling of mineral systems is described. The system is a type of knowledge-driven symbolic artificial intelligence that is transparent, intuitive and is easy to construct by geologists because they are built in natural language and use linguistic values. No examples are used for training the system and expert-opinions are incorporated indirectly in terms of objective mathematical functions, which reduce the possibility of over-emphasizing the known deposits usually used as training data. The cognitive reasoning of the exploration geologist is captured in explicit if–then type of statements written in natural language using linguistic values. Conditional dependencies in the exploration data sets are managed through the use of fuzzy operators. A case study for surficial uranium prospectivity modeling in the Yeelirrie area, Western Australia, is used to demonstrate the approach. In the output prospectivity map, the SE-NW trending Yeelirrie and E-W trending Hinkler's Well palaeochannels show high prospectivity, while other channels show very low prospectivity ranges. The known surficial uranium deposits fall in high prospectivity areas, although minor showings and anomalies in the southern part of the study area fall in low prospectivity areas. A comparison of the prospectivity model with the radiometric image shows that several channels showing high surface uranium concentrations in the NW and NE quadrants may not be prospective. 相似文献
6.
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 总被引：2，自引：2，他引：0
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。 相似文献
7.
Emmanuel J. M. Carranza 《Resource Geology》2011,61(1):30-51
This paper proposes that the spatial pattern of known prospects of the deposit‐type sought is the key to link predictive mapping of mineral prospectivity (PMMP) and quantitative mineral resource assessment (QMRA). This proposition is demonstrated by PMMP for hydrothermal Au‐Cu deposits (HACD) and by estimating the number of undiscovered prospects for HACD in Catanduanes Island (Philippines). The results of analyses of the spatial pattern of known prospects of HACD and their spatial associations with geological features are consistent with existing knowledge of geological controls on hydrothermal Au‐Cu mineralization in the island and elsewhere, and are used to define spatial recognition criteria of regional‐scale prospectivity for HACD. Integration of layers of evidence representing the spatial recognition criteria of prospectivity via application of data‐driven evidential belief functions results in a map of prospective areas occupying 20% of the island with fitting‐ and prediction‐rates of 76% and 70%, respectively. The predictive map of prospective areas and a proxy measure for degrees of exploration based on the spatial pattern of known prospects of HACD were used in one‐level prediction of undiscovered mineral endowment, which yielded estimates of 79 to 112 undiscovered prospects of HACD. Application of radial‐density fractal analysis of the spatial pattern of known prospects of HACD results in an estimate of 113 undiscovered prospects of HACD. Thus, the results of the study support the proposition that PMMP can be a part of QMRA if the spatial pattern of discovered prospects of the deposit‐type sought is considered in both PMMP and QMRA. 相似文献
8.
GIS-based 3D prospectivity mapping: A case study of Jiama copper-polymetallic deposit in Tibet,China
This paper reports a deposit-scale GIS-based 3D mineral potential assessment of the Jiama copper-polymetallic deposit in Tibet, China. The assessment was achieved through a sequential implementation of metallogenic modelling and 3D modelling of geology, geochemistry and prospectivity. A metallogenic model for the Jiama deposit and 3D modelling workflow were used to construct multiple 3D layers of volumetric and triangular mesh models to represent geology, geochemistry and ore-controlling features in the study area. A GIS-based 3D weights-of-evidence analysis was then used to estimate the subsurface prospectivity for Cu (Mo) orebodies in the area, which led to the identification of three prospective deep-seated exploration targets. Additionally, the geochemical modelling indicates three potential fluid flow pathways based on the 3D zonation of major geochemical elements and their ratios, particularly the Zn/Pb ratios, which support the results of the weights of evidence model. 相似文献
9.
10.
GIS-based 2D prospectivity modelling of three greenfield geological regions of Western Australia, namely, the West Arunta Orogen, West Musgrave Orogen and Gascoyne Province, was implemented for a range of deposit types including orogenic and intrusion-related gold, volcanic sediment-hosted base-metal sulfides, magmatic nickel–copper and magmatic platinum group element sulfides, iron-oxide copper gold, tin–tungsten, igneous and metamorphic related rare earth elements, surficial uranium and unconformity-related uranium.Conceptual mineral systems models were generated to identify the targeting criteria. The inputs to the models were the spatial proxies derived from 1:100,000 to 1:500,000 scale public domain data. The results showed similar prospectivity patterns for all of the targeted deposit types except sediment-hosted uranium and surficial uranium deposit types. Once a favourable geodynamic architecture is established, it can sustain different mineral systems and produce diverse deposit types depending on the nature of ligands in the source regions and physical–chemical environment in the trap regions through repeated reactivation in the subsequent geological history. A model is proposed to explain the formation of different deposit types at different stages of tectonic evolution of a province. The implication for GIS-based 2D prospectivity modelling at the scale of geological region is that the prospectivity model may not be deposit type specific. Further, prospectivity modelling should be carried out sequentially at progressively finer scales (regional- to district- to camp-scale), using only the targeting criteria that are relevant at the specific scale to delineate targets for specific deposit types. 相似文献
11.
We present a mineral systems approach to predictive mapping of orogenic gold prospectivity in the Giyani greenstone belt (GGB) by using layers of spatial evidence representing district-scale processes that are critical to orogenic gold mineralization, namely (a) source of metals/fluids, (b) active pathways, (c) drivers of fluid flow and (d) metal deposition. To demonstrate that the quality of a predictive map of mineral prospectivity is a function of the quality of the maps used as sources of spatial evidence, we created two sets of prospectivity maps — one using an old lithologic map and another using an updated lithological map as two separate sources of spatial evidence for source of metals/fluids, drivers of fluid flow and metal deposition. We also demonstrate the importance of using spatially-coherent (or geologically-consistent) deposit occurrences in data-driven predictive mapping of mineral prospectivity. The best predictive orogenic gold prospectivity map obtained in this study is the one that made use of spatial evidence from the updated lithological map and spatially-coherent orogenic gold occurrences. This map predicts 20% of the GGB to be prospective for orogenic gold, with 89% goodness-of-fit between spatially-coherent inactive orogenic gold mines and individual layers of spatial evidence and 89% prediction-rate against spatially-coherent orogenic gold prospects. In comparison, the predictive gold prospectivity map obtained by using spatial evidence from the old lithological map and all gold occurrences has 80% goodness-of-fit but only 63% prediction-rate. These results mean that the prospectivity map based on spatially-coherent gold occurrences and spatial evidence from the updated lithological map predicts exploration targets better (i.e., 28% smaller prospective areas with 9% stronger fit to training gold mines and 26% higher prediction-rate with respect to validation gold prospects) than the prospectivity map based on all known gold occurrences and spatial evidence from the old lithological map. 相似文献
12.
Renguang ZUO 《Resource Geology》2011,61(3):296-303
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. 相似文献
13.
隐伏矿体三维成矿定量预测是以多元地学大数据为基础的矿产预测新技术与方法。本文基于隐伏矿体三维成矿定量预测的实际需要及相关方法步骤,采用集成二次开发的方式,设计实现了一套可在三维环境下基于大数据开展定量化成矿预测工作的软件系统。本文阐述了系统的总体架构及开发方式,并对系统中各个功能模块的设计及实现过程进行详细阐述。系统融合了当前三维成矿定量预测研究的最新方法及成果,内含数据库管理、三维地球物理正演、三维空间分析以及三维预测评价等功能模块,能够对多元地学大数据进行集成和分析预测。为了验证系统的适用性和有效性,该系统被应用于长江中下游成矿带钟姑矿田三维成矿定量预测研究,相关成果表明系统的建立不但能够深化和发展三维成矿预测理论,也为新时期基于大数据的隐伏矿体找矿勘探工作提供了新方法及有力工具。 相似文献
14.
N. S. Bortnikov A. V. Volkov A. L. Galyamov I. V. Vikent’ev V. V. Aristov A. V. Lalomov K. Yu. Murashov 《Geology of Ore Deposits》2016,58(2):83-103
Seven main ore-forming systems—porphyry and epithermal; orogenic related to granitic intrusions; magmatic ultramafic; volcanic-hosted massive sulfide and volcanic–sedimentary; sedimentary basins; related to alkaline magmatic activity; and placers and weathering mantles—are sources of high-tech critical metals. The following promising types of ore deposits containing high-tech critical metals as by-products are recognized: Cu–Mo porphyry, Fe–Cu–Au and Pb–Zn skarn, base-metal epithermal, volcanic-hosted massive sulfide, base-metal stratiform, various tin deposits, and placers containing rare metals including REE. The mineral resources of critical metals in Russia are compared with those known in other countries. The contents of high-tech critical metals in ores of some noble-metal deposits of the Russian Northeast are reported. It is shown that the subsurface of Russia possesses considerable mineral resource potential for hightech critical metals, which allows new enterprises to be created or production of operating enterprises to increase. 相似文献
15.
西藏铁格隆南铜(金)矿床三维模型分析与深部预测 总被引：1，自引：1，他引：0
铁格隆南铜(金)矿床是近年来在班公湖-怒江成矿带西段多龙矿集区新发现的超大型Cu(Au-Ag)矿床。本文针对铁格隆南矿区深部找矿问题,以现代成矿地质理论和多元地学信息综合分析技术为支撑,以构建矿床找矿模型为指导,依托数据库技术、3S技术、三维建模与可视化技术及地质统计学理论与方法,开展基于矿产地质、地球物理、地球化学等成矿条件及找矿标志的三维地质实体建模与矿化异常三维空间重构,将铁格隆南矿床的预测评价研究拓展到三维空间,揭示了区内成矿地质特征、地球化学及地球物理异常表征,据此探讨了矿床的成因及矿体分布特征。并在此基础上,开展了矿区的地质-地球化学-地球物理综合信息分析与预测评价,以期减少单一信息多解性和成矿条件不确定性,为铁格隆南矿区深部找矿工作提供参考。研究结果表明:在地质找矿模型指导下,基于深部成矿空间三维结构重构基础上的三维地质、地球物理、地球化学异常信息提取与综合分析,可以有效的识别成矿地质体和矿致异常信息,实现深部矿产资源靶区空间定位预测,为深部找矿预测研究提供了新思路。综合分析结果显示铁格隆南矿床深部找矿潜力巨大。 相似文献
16.
Machine learning algorithms (MLAs) such us artificial neural networks (ANNs), regression trees (RTs), random forest (RF) and support vector machines (SVMs) are powerful data driven methods that are relatively less widely used in the mapping of mineral prospectivity, and thus have not been comparatively evaluated together thoroughly in this field.The performances of a series of MLAs, namely, artificial neural networks (ANNs), regression trees (RTs), random forest (RF) and support vector machines (SVMs) in mineral prospectivity modelling are compared based on the following criteria: i) the accuracy in the delineation of prospective areas; ii) the sensitivity to the estimation of hyper-parameters; iii) the sensitivity to the size of training data; and iv) the interpretability of model parameters. The results of applying the above algorithms to epithermal Au prospectivity mapping of the Rodalquilar district, Spain, indicate that the RF outperformed the other MLA algorithms (ANNs, RTs and SVMs). The RF algorithm showed higher stability and robustness with varying training parameters and better success rates and ROC analysis results. On the other hand, all MLA algorithms can be used when ore deposit evidences are scarce. Moreover the model parameters of RF and RT can be interpreted to gain insights into the geological controls of mineralization. 相似文献
17.
The Zijinshan high-sulfidation epithermal Cu–Au deposit is located in the Zijinshan ore field of South China, comprising porphyry–epithermal Cu–Au–Mo–Ag ore systems. The Cu ore body is more than 1000 m thick and is characterized by an assemblage of digenite–covellite–enargite–alunite. Digenite is the dominant Cu-bearing mineral, which makes this deposit unique, although the mechanisms of digenite formation remain controversial. To elucidate the genesis of digenite, this paper presents the Cu isotopic compositions of Cu-sulfides in the Zijinshan high-sulfidation Cu–Au deposit. The Cu isotopic values (65Cu relative to NIST 976) of all samples range from −2.97‰ to +0.34‰, and most values fall in a narrow range from −0.49‰ to +0.34‰, which is similar to the Cu isotopic signature of typical porphyry systems. Copper isotope ratios of each mineral decrease with increasing depth, a trend that is also typical of porphyry deposits. The variation tendency of δ65Cu values between sulfides is consistent with the sequence of mineral formation. These observations suggest that the Cu-sulfides in the Zijinshan Cu–Au deposit have a hypogene origin. 相似文献
18.
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. 相似文献
19.
Emmanuel John M. Carranza 《Resource Geology》2010,60(2):129-149
Wildcat modelling of mineral prospectivity has been proposed for greenfields geologically-permissive terranes where mineral targets have not yet been discovered but a geological map is available as a source of spatial data of predictors of mineral prospectivity. This paper (i) revisits the initial way of assigning wildcat scores (Sc) to predictors of mineral prospectivity and (ii) proposes an improvement by transforming Sc into improved wildcat scores (ISc) by using a logistic function. This was shown in wildcat modelling of prospectivity for low-sulphidation epithermal-Au (LSEG) deposits in Aroroy district (Philippines). Based on knowledge of characteristics of and controls on LSEG mineralization in the Philippines, the spatial predictors of LSEG prospectivity used in the study are proximity to porphyry plutonic stocks, faults/fractures and fault/fracture intersections. The Sc and ISc of spatial predictors are input separately to principal components analysis to extract a favourability function that can be interpreted as a wildcat model of LSEG prospectivity. The predictive capacity of the wildcat model of LSEG prospectivity based on the ISc of geological predictors is roughly 70% higher than that of the wildcat model of LSEG prospectivity based on the Sc of geological predictors. A slight increase of predictive capacity of wildcat modelling of LSEG prospectivity is also achieved when the ISc of geological predictors are integrated with the ISc of geochemical anomalies, but not with the Sc of geochemical anomalies. The proposed improvement is significant because if the study district were a greenfields exploration area, then a wildcat model of LSEG prospectivity based on the old wildcat methodology would have caused several LSEG targets to be missed. 相似文献
20.
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. 相似文献