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.     

Geology,geodynamics and orogenic gold prospectivity modelling of the Paleoproterozoic Kumasi Basin,Ghana, West Africa

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.     

Assessing the impact of conceptual mineral systems uncertainty on prospectivity predictions

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.     

Mapping mineral prospectivity using an extreme learning machine regression

In this research, we conduct a case study of mapping polymetallic prospectivity using an extreme learning machine (ELM) regression. A Quad-Core CPU 1.8 GHz laptop computer served as hardware platform. Almeida's Python program was used to construct the ELM regression model to map polymetallic prospectivity of the Lalingzaohuo district in Qinghai Province in China. Based on geologic, metallogenic, and statistical analyses of the study area, one target and eight predictor map patterns and two training sets were then used to train the ELM regression and logistic regression models. ELM regression modeling using the two training sets spends 61.4 s and 65.9 s; whereas the logistic regression modeling using the two training sets spends 1704.0 s and 1628.0 s. The four trained regression models were used to map polymetallic prospectivity. Based on the polymetallic prospectivity predicted by each model, the receiver operating characteristic (ROC) curve was plotted and the area under the curve (AUC) was estimated. The ROC curves show that the two ELM-regression-based models somewhat dominate the two logistic-regression-based models over the ROC performance space; and the AUC values indicate that the overall performances of the two ELM-regression-based models are somewhat better than those of the two logistic-regression-based models. Hence, the ELM-regression-based models slightly outperform the logistic-regression-based models in mapping polymetallic prospectivity. Polymetallic targets were optimally delineated by using the Youden index to maximize spatial association between the delineated polymetallic targets and the discovered polymetallic deposits. The polymetallic targets predicted by the two ELM-regression-based models occupy lower percentage of the study area (2.66–2.68%) compared to those predicted by the two logistic-regression-based models (4.96%) but contain the same percentage of the discovered polymetallic deposits (82%). Therefore, the ELM regression is a useful fast-learning data-driven model that slightly outperforms the widely used logistic regression model in mapping mineral prospectivity. The case study reveals that the magmatic complexes, which intruded into the Baishahe Formation of the Paleoproterozoic Jinshuikou Group or the Carboniferous Dagangou and Shiguaizi Formations, and which were controlled by northwest-western/east-western trending deep faults, are critical for polymetallic mineralization and need to be paid much attention to in future mineral exploration in the study area.     

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.     

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.     

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.     

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.     

A new linguistic quantifier knowledge-guided OWA approach for mineral prospectivity mapping: a case study of the Bavanat Region,Iran

The main purpose of this study is to introduce a geographic information system (GIS)-based, multi-criteria decision analysis method for selection of favourable environments for Besshi-type volcanic-hosted massive sulphide (VHMS) deposits. The approach integrates two multi-criteria decision methods (analytical hierarchy process and ordered weighted averaging) and theory of fuzzy sets, within a GIS environment, to solve the problem of big suggested areas and missing known ore deposits in favourable environment maps for time and cost reduction. We doubled the fuzzy linguistic variables’ significance as a method to apply the arrange weights that the analytical hierarchy process (AHP)-ordered weighted averaging (OWA) hybrid procedure depends on. Another aim of this work is to assist mineral deposit exploration by modelling existing uncertainty in decision-making. Both AHP and fuzzy logic methods are knowledge-based, and they are affected by decision maker judgments. We used data-driven OWA approach in a hybrid method for solving this problem. We applied a new knowledge-guided OWA approach on data with changing linguistic variables according to the mineral system for VHMS deposits. Additionally, we used a vector-based method combination, which increased the precision of results. Results of knowledge-guided OWA showed that all of the mines and discovered deposits have been predicted with 100% accuracy in half of the size of the suggested area. To summarize, results improved the selection of possible target sites and increased the accuracy of results as well as reducing the time and cost, which will be used for field exploration. Finally, the hybrid methods with a knowledge-guided OWA approach have delivered more reliable results compared to exclusively knowledge-driven or data-driven methods. The study proved that expert knowledge and processed data (information) are critical important keys to exploration, and both of them should be applied in hybrid methods for reaching reliable results in mineral prospectivity mapping.     

Probabilistic Fuzzy Logic Modeling: Quantifying Uncertainty of Mineral Prospectivity Models Using Monte Carlo Simulations

Significant uncertainties are associated with the definition of both the exploration targeting criteria and computational algorithms used to generate mineral prospectivity maps. In prospectivity modeling, the input and computational uncertainties are generally made implicit, by making a series of best-guess or best-fit decisions, on the basis of incomplete and imprecise information. The individual uncertainties are then compounded and propagated into the final prospectivity map as an implicit combined uncertainty which is impossible to directly analyze and use for decision making. This paper proposes a new approach to explicitly define uncertainties of individual targeting criteria and propagate them through a computational algorithm to evaluate the combined uncertainty of a prospectivity map. Applied to fuzzy logic prospectivity models, this approach involves replacing point estimates of fuzzy membership values by statistical distributions deemed representative of likely variability of the corresponding fuzzy membership values. Uncertainty is then propagated through a fuzzy logic inference system by applying Monte Carlo simulations. A final prospectivity map is represented by a grid of statistical distributions of fuzzy prospectivity. Such modeling of uncertainty in prospectivity analyses allows better definition of exploration target quality, as understanding of uncertainty is consistently captured, propagated and visualized in a transparent manner. The explicit uncertainty information of prospectivity maps can support further risk analysis and decision making. The proposed probabilistic fuzzy logic approach can be used in any area of geosciences to model uncertainty of complex fuzzy systems.     

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.     

Data-driven predictive mapping of gold prospectivity,Baguio district,Philippines: Application of Random Forests algorithm

The Random Forests (RF) algorithm has recently become a fledgling method for data-driven predictive mapping of mineral prospectivity, and so it is instructive to further study its efficacy in this particular field. This study, carried out using Baguio gold district (Philippines), examines (a) the sensitivity of the RF algorithm to different sets of deposit and non-deposit locations as training data and (b) the performance of RF modeling compared to established methods for data-driven predictive mapping of mineral prospectivity. We found that RF modeling with different training sets of deposit/non-deposit locations is stable and reproducible, and it accurately captures the spatial relationships between the predictor variables and the training deposit/non-deposit locations. For data-driven predictive mapping of epithermal Au prospectivity in the Baguio district, we found that (a) the success-rates of RF modeling are superior to those of weights-of-evidence, evidential belief and logistic regression modeling and (b) the prediction-rate of RF modeling is superior to that of weights-of-evidence modeling but approximately equal to those of evidential belief and logistic regression modeling. Therefore, the RF algorithm is potentially much more useful than existing methods that are currently used for data-driven predictive mapping of mineral prospectivity. However, further testing of the method in other areas is needed to fully explore its usefulness in data-driven predictive mapping of mineral prospectivity.     

Mineral systems approach applied to GIS-based 2D-prospectivity modelling of geological regions: Insights from Western Australia

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.     

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.     

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.     

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.     

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.     

三维成矿预测数据整合过程不确定性研究——以宁芜盆地钟姑矿田为例

基于多元地学大数据的三维成矿预测方法是开展深部找矿预测的新方法和新手段,也是当前成矿预测领域的研究热点之一。然而,大数据具有高维、混杂、非精确等特点,其分析处理过程面临多重不确定性。多元地学大数据整合是三维成矿预测的最终环节,其存在的不确定性将直接作用于预测结果,影响进一步的找矿应用和风险评估。本文以宁芜盆地钟姑矿田为例,从大数据思维出发,定量分析和度量预测要素和数学模型在数据整合过程中存在的不确定性及对三维成矿预测结果的影响。结果显示,断裂构造、背斜轴部等预测要素的不确定性对三维成矿预测结果的影响最为强烈;数据整合模型中,较之Logistic回归模型和证据权重模型,神经网络模型可能具有更高的不确定性程度。进一步工作可通过增强上述预测要素的可靠性和有效性、采用更多的数据整合模型进行更为全面的不确定性分析和评价,以获得更为可靠的三维成矿预测成果,从而降低成矿预测和找矿勘探风险。     

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.