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

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

Application of the tectono-geochemistry method to mineral prospectivity mapping: a case study of the Gaosong tin-polymetallic deposit,Gejiu district,SW China

The tectono-geochemistry method as a lithogeochemical exploration tool is a process centric approach based on the premise that element migration and concentration within and adjacent to geological structures are controlled by large-scale dynamic earth processes that operate on a variety of scales to focus mass and energy flux. Here we present a case study of the Gaosong tin-polymetallic deposit, Gejiu district, SW China, illustrating how the tectono-geochemistry method can be applied to mineral prospectivity mapping. As a first step, 2216 fault rock samples collected within the Geiju district were assayed followed by a detailed examination of the assay results by means of factor analysis and multifractal singularity mapping. The main aims of these analyses were to (1) statistically explore the data with respect to specific element concentrations, associations and geochemical patterns by which the fault rock samples can be grouped, (2) better characterize the samples in terms of whether or not the rocks recorded any evidence for hydrothermal mineralization processes, and (3) identify any geochemical anomalies that may present vectors to buried ore. The results illustrate that element distribution patterns are greatly influenced by fault permeability and that NE-SW-striking faults are the most favorable in terms of concentration of ore elements. Factor analysis greatly assisted the identification and interpretation of, for example, element associations, and geochemical zonation patterns genetically related to intrusions. Recognition of singularities was of particular importance with respect to identifying and delineating primary fault tectono-geochemical anomalies that may be caused by buried ore. Of particular interest are low singularity values as these correspond with positive geochemical anomalies over known ore deposits in the Gejiu district. Anomalies delineated by local singularity exponents may represent undiscovered ore and, thus, warrant further exploration.     

Predictive mapping of prospectivity for orogenic gold,Giyani greenstone belt (South Africa)

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.     

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.     

Back-propagation neural network and support vector machines for gold mineral prospectivity mapping in the Hatu region,Xinjiang, China

Machine Learning technologies have the potential to deliver new nonlinear mineral prospectivity mapping (MPM) models. In this study, Back Propagation (BP) neural network Support Vector Machine (SVM) methods were applied to MPM in the Hatu region of Xinjiang, northwestern China. First, a conceptual model of mineral prospectivity for Au deposits was constructed by analysis of geological background. Evidential layers were selected and transformed into a binary data format. Then, the processes of selecting samples and parameters were described. For the BP model, the parameters of the network were 9–10???1; for the SVM model, a radial basis function was selected as the kernel function with best C?=?1 and γ = 0.25. MPM models using these parameters were constructed, and threshold values of prediction results were determined by the concentration-area (C-A) method. Finally, prediction results from the BP neural network and SVM model were compared with that of a conventional method that is the weight- of- evidence (W- of- E). The prospectivity efficacy was evaluated by traditional statistical analysis, prediction-area (P-A) plots, and the receiver operating characteristic (ROC) technique. Given the higher intersection position (74% of the known deposits were within 26% of the total area) and the larger AUC values (0.825), the result shows that the model built by the BP neural network algorithm has a relatively better prediction capability for MPM. The BP neural network algorithm applied in MPM can elucidate the next investigative steps in the study area.     

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.     

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.     

Relationship of paleosubduction regime and prospectivity of an epithermal gold field,coromandel, new zealand

The volcanic rocks of the Coromandel Peninsula, North Island, New Zealand, were formed during a late Miocene convergence of the Pacific and Indo-Australian plates. The consequence of varying convergence velocity at the subducting plate margin is tensional regimes and basement collapse between 17–21 Ma and again at 3 Ma. The regions of basement collapse may be expected to be more fractured and might be anticipated to allow the penetration of late-stage magmatic fluids more easily. The distribution of high- chloride hot springs in the region is consistent with this notion. Recent discovery of economic quantities of gold deposited in geothermal wells at Broadlands (Ohaaki) and Wairakei in the nearby Taupo volcanic zone suggests, by analogy, that Coromandel gold may have been similarly transported. If so, then gold mineralization prospectivity may be expected to vary in a way similar to the regional variation in the chloride content of hot springs. On this basis the northernmost Coromandel would appear to be even more highly mineralized than the southern portion, where most of the large producing mines have been sited to date.     

Temporal Geochemical Evolution of Neogene Magmatism in the Baguio Gold–Copper Mining District (Northern Luzon, Philippines)

Baguio, in the Central Cordillera of Northern Luzon, is a district that displays porphyry copper and epithermal gold mineralization, associated with Early Miocene–Pliocene–Quaternary calc‐alkaline and adakitic intrusions. Systematic sampling, K‐Ar dating, major and trace elements, and Sr, Nd, Pb isotopic analyses of fresh magmatic rocks indicate three magmatic pulses: an Early Miocene phase (21.2–18.7 Ma), a Middle–Late Miocene phase (15.3–8 Ma) and finally a Pliocene–Quaternary event (3–1 Ma). The first phase emplaced evolved calc‐alkaline magmas, essentially within the Agno Batholith complex, and is thought to be related to the westward‐dipping subduction of the West Philippine Basin. After a quiescence period during which the Kennon limestone was deposited, magmatic activity resumed at 15.3 Ma, in connection with the start of the subduction of the South China Sea along the Manila Trench. It emplaced first petrogenetically related and relatively unradiogenic low‐K calc‐alkaline lavas and intermediate adakites. Temporal geochemical patterns observed from 15.3 to 1 Ma include progressive enrichment in K and other large ion lithophile elements, increase in radiogenic Sr and Pb and corresponding decrease in radiogenic Nd. These features are thought to reflect the progressive addition to the Luzon arc mantle wedge of incompatible elements largely inherited from South China Sea sediments. The origin of the long quiescence period, from 8 to 3 Ma, remains problematic. It might represent a local consequence of the docking of the Zambales ophiolitic terrane to Northern Luzon. Then, magmatic activity resumed at 3 Ma, emplacing chemically diversified rocks ranging from low K to high K and including a large proportion of adakites, especially during the Quaternary (dacitic plugs). The authors tentatively relate this diversity to the development of a slab tear linked with the subduction of the fossil South China Sea ridge beneath the Baguio area.     

Data-driven mapping of avalanche release areas: a case study in South Tyrol, Italy

Avalanche hazard and risk mapping is of utmost importance in mountain areas in Europe and elsewhere. Advanced methods have been developed to describe several aspects of avalanche hazard assessment, such as the dynamics of snow avalanches or the intensity of snowfall to assume as a reference meteorological forcing. However, relatively little research has been conducted on the identification of potential avalanche release areas. In this paper, we present a probabilistic assessment of potential avalanche release areas in the Italian Autonomous Province of Bolzano, eastern Alps, using the Weights of Evidence and Logistic Regression methods with commonly available GIS datasets. We show that a data-driven statistical model performs better than simple, although widely adopted, screening criteria that were proposed in the past, although the complexity of observed release areas is only partly captured by the model. In the best case, the model enables predicting about 70 % of avalanches in the 20 % of area classified at highest hazard. Based on our results, we suggest that probabilistic identification of potential release areas could provide a useful aid in the screening of sites for subsequent, more detailed hazard assessment.     

First-level liquefaction hazard mapping of Laoag City,Northern Philippines

During the 1990 Luzon earthquake (M_s 7.8), the central part of Luzon Island, Philippines suffered much from liquefaction-related processes. Examination of inventories shows that the affected areas lie on certain geological environments that are characteristically vulnerable to liquefaction. Based on this local experience and the findings of earlier workers correlating geological setting with liquefaction susceptibility, a first-level map of liquefaction hazard for Laoag City, Northern Philippines, was produced. Distinct micro-geomorphological units were identified within the mainly fluvio-deltaic setting of the study area. The liquefaction susceptibility of each unit was then ranked as high, moderate, low or non-liquefiable, taking also the geomorphological evolution of the area into account. The geomorphological model of the fluvio-deltaic basin was tested against the results of the georesistivity survey carried out in this study. Moreover, compatibility of the liquefaction susceptibility map with historical liquefaction records supported the validity of the proposed ranking. The study showed that microzonation based on geomorphological criteria is indeed very useful in less-developed countries like the Philippines, where funds for a more rigorous determination of liquefaction potential are limited and not always available.     

Fuzzy inference systems for prospectivity modeling of mineral systems and a case-study for prospectivity mapping of surficial Uranium in Yeelirrie Area,Western Australia

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.     

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.     

Machine learning predictive models for mineral prospectivity: An evaluation of neural networks,random forest,regression trees and support vector machines

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.     

Mapping of prospectivity and estimation of number of undiscovered prospects for lode gold,southwestern Ashanti Belt,Ghana

In the southwestern part of the Ashanti Belt, the results of fractal and Fry analyses of the spatial pattern of 51 known mines/prospects of (mostly lode) gold deposits and the results of analysis of their spatial associations with faults and fault intersections suggest different predominant structural controls on lode gold mineralisation at local and district scales. Intersections of NNE- and NW-trending faults were likely predominantly involved in local-scale structural controls on lode gold mineralisation, whilst NNE-trending faults were likely predominantly involved in district-scale structural controls on lode gold mineralisation. The results of the spatial analyses facilitate the conceptualisation and selection of spatial evidence layers for lode gold prospectivity mapping in the study area. The applications of the derived map of lode gold prospectivity and a map of radial density of spatially coherent lode gold mines/prospects results in a one-level prediction of 37 undiscovered lode gold prospects. The applications of quantified radial density fractal dimensions of the spatial pattern of spatially coherent lode gold mines/prospects result in an estimate of 40 undiscovered lode gold prospects. The study concludes finally that analysis of the spatial pattern of discovered mineral deposits is the key to a strong link between mineral prospectivity mapping and assessment of undiscovered mineral deposits.     

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.     

胶东玲珑金矿区碲化物形成条件及其对金富集过程的约束SCIEI北大核心CSCD

胶东地区探明金储量超过5000t,是我国最重要的金矿集区。玲珑金矿区位于胶东西北部招-平断裂北段,具有典型含金石英脉矿床的特征。矿区部分矿段产出富碲铋化物的明金矿石,是研究富碲铋化物金矿床成因和金超级富集机制的理想对象。通过显微岩相学观察、扫描电镜及电子探针分析,首次对胶东玲珑金矿区富碲铋化物明金矿石的矿物组合和形成机制进行了较为详细的研究。玲珑金矿区含明金矿石中的金矿物主要为含银自然金,成色整体较高,平均为894。共发现了五种与自然金共生的碲化物,分别是辉碲铋矿、碲铋矿、碲银矿、碲铋银矿和碲镍矿,其中辉碲铋矿含量最多。通过矿物共生组合研究及物理化学条件分析,确定成矿流体的碲逸度范围为-12.6相似文献   

河南小秦岭杨砦峪金矿床成矿流体特征

河南省杨砦峪金矿床位于华北克拉通南缘,是小秦岭地区大型的石英脉型金矿床。据野外观察,成矿过程经历了4个阶段：Ⅰ黄铁矿-石英脉阶段;Ⅱ石英-黄铁矿阶段;Ⅲ石英-多金属硫化物阶段;Ⅳ石英-碳酸盐阶段。包裹体岩相学、显微测温以及激光拉曼显微探针研究显示,该矿床为CO₂-H₂O-NaCl±CH₄流体体系,并且发生不混溶。从第Ⅰ成矿阶段到Ⅳ成矿阶段,流体包裹体的均一温度范围分别是307~407℃,270~320℃,225~272℃和166~226℃,呈现逐步降低的趋势;盐度w（NaCl_eq）平均值分别为7.3%,7.1%,9.0% 和6.4%。各阶段成矿压力为120~178 MPa,85~140 MPa,75~130 MPa和60~122 MPa,呈现逐渐降低的趋势。流体不混溶作用是导致杨砦峪矿质沉淀的重要原因,其诱因可能与控矿断裂由压扭转为张扭所导致的构造减压有关。     

Exploration targeting for orogenic gold deposits in the Granites-Tanami Orogen: Mineral system analysis,targeting model and prospectivity analysis

A major challenge for mineral exploration geologists is the development of a transparent and reproducible approach to targeting exploration efforts, particularly at the regional to camp scales, in terranes under difficult cover where exploration and opportunity costs are high. In this study, a three-pronged approach is used for identifying the most prospective ground for orogenic gold deposits in the Paleoproterozoic Granite-Tanami Orogen (GTO) in Western Australia.A key input to the analyses is the recent development of a 4D model of the GTO architectural evolution that provides new insights on the spatio-temporal controls over orogenic gold occurrences in the area; in particular, on the role of pre-mineralization (pre-1795 Ma) D_GTOE–D_GTO1–D_GTO2 architecture in localization of gold deposits and the spatial distribution of rock types in 3D. This information is used to build up a model of orogenic gold minerals system in the area, which is then integrated into the three mutually independent but complementary mineral prospectivity maps namely, a concept-driven “manual” and “fuzzy” analysis; and a data-driven “automated” analysis.The manual analysis involved: (1) generation of a process-based gold mineral systems template to aid target selection; (2) manual delineation of targets; (3) manual estimation of the probability of occurrence of each critical mineralization process based on the available information; and (4) combining the above probabilities to derive the relative probability of occurrence of orogenic gold deposits in each of the targets. The knowledge-based Geological Information System (GIS) analysis attempts to replicate the expert knowledge used in the manual approach, but queried in a more systematic format to eliminate human heuristic bias. This involves representing the critical mineralization processes in the form of spatial predictor maps and systematically querying them through the use of a fuzzy logic model to integrate the predictor maps and to derive the western GTO orogenic gold prospectivity map. The data-driven ‘empirical’ GIS analysis uses no expert knowledge. Instead it employs statistical measures to evaluate the spatial associations between known deposits and predictor maps to establish weights for each predictor layer then combines these layers into a predictive map using a Weights of Evidence (WofE) approach.Application of a mineral systems approach in the manual analysis and the fuzzy analysis is critical: potential high value targets identified by these approaches in the western GTO lie largely under cover, whereas traditional manual targeting is biased to areas of outcrop or sub-crop amenable to direct detection technology such as exploration geochemistry, and therefore towards areas that are data rich.The results show the power of combining the three approaches to prioritize areas for exploration. While the manual analysis identifies and employs human intuition and can see through incomplete datasets, it is difficult to filter out human bias and to systematically apply to a large region. The fuzzy method is more systematic, and highlights areas that the manual analysis has undervalued, but lacks the intuitive power of the human mind that refines the target by seeing through incomplete datasets. The empirical WoE method highlights correlations with favorable host stratigraphy and highlights the control of an early set of structures potentially undervalued in the knowledge driven approaches, yet is biased due to the incomplete nature of exploration datasets and lack of abundant gold deposits due to the extensive cover.The results indicate that the most prospective areas for orogenic gold in western GTO are located in the central part of the study area, largely in areas blind to previous exploration efforts. According to our study, the procedure to follow should be to undertake the analyses in the following order: manual prospectivity analysis, followed by the conceptual fuzzy approach, followed by the empirical GIS-based method. Undertaking the manual analysis first is important to prevent explorationists from being biased by the automated GIS-based outputs. It is however emphasized that all of the prospectivity outputs from these three methods are possible, and they should not be treated as ‘treasure maps’, but instead, as decision-support aids. Therefore, a final manual prospectivity analysis redefined by the mutual consideration of output from all of the methods is required.The strategy employed in this study constitutes a new template for best-practice in terrane- to camp-scale exploration targeting that can be applied to different terranes and deposit types, particularly in terranes under cover, and provides a step forward in managing uncertainty in the exploration targeting process.