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The different methods for delineating favorable areas for mineral exploration utilize exploration criteria regarding targeted mineral deposits. The criteria are elicited according to conceptual model parameters of the targeted mineral deposits. The selection of indicator criteria, the evaluation of their comparative importance, and their integration are critical in mineral prospectivity modelling. In data-driven methods, indicator features are weighted using functions whereby the importance of certain indicator criteria may be ignored. In this paper, a datadriven method is described for recognizing and converting exploration criteria into quantitative coefficients representing favorability for the presence of the targeted mineral deposits. In this approach, all indicator features of the targeted mineral deposits are recognized and incorporated in the modelling procedure. The method is demonstrated for outlining favorable areas for a Mississippi valley-type fluorite deposit in an area, north of Iran. The method is developed by studying and modelling the geological characteristics of known mineral occurrences. The degree of prediction ability of each exploration criterion is quantified as a recognition coefficient, which can be used as a weight attributed to the criterion in mineral exploration targeting to outline favorable areas. [ABSTRACT FROM AUTHOR]

[Display omitted] • Majority voting ensemble technique is proposed for mineral prospectivity analysis. • Confidence index is proposed to mitigate uncertainty associated with prospectivity models. • The methods facilitate robust extraction of exploration targets. • The proposed approaches can be included in exploration information system (EIS). • The framework deals with problem solving in mineral exploration targeting. Various mineral prospectivity modelling (MPM) approaches are available for targeting mineral deposits, each method capable of predicting areas of high prospectivity. Given the diversity of MPM approaches, the modelled areas of high prospectivity can differ across different MPMs. However, rather than a negative, different MPM outputs can benefit mineral exploration targeting because each method has its advantages. Rather, the problem lies in the lack of consensus over how to best select and delimit mineral exploration targets from different MPM results. Here we aim to address the challenges outlined above whilst quantifying and mitigating the effects of inherent uncertainties. We first generate eleven different prospectivity models utilising deep learning, machine learning, fuzzy logic, and geometric average integration methods. Then, we adopt a majority voting ensemble technique to incorporate and combine the predictions of each prospectivity model. Next, we propose a confidence index designed to mitigate uncertainty associated with our multi-technique approach to MPM. The confidence index quantifies variation in prospectivity values for each cell of the MPM target area. The conjunction of a confidence index and majority voting model facilitates consistent and robust algorithm-driven extraction of exploration targets based on an ensemble of prospectivity models. [ABSTRACT FROM AUTHOR]

In mineral exploration programs, reducing uncertainty and increasing exploration success have always been challenging issues. To modulate the above-mentioned uncertainty and increase exploration accomplishment, integration, and prospectivity analysis techniques are used for mineral exploration targeting. This paper aims to model the mineral potential of porphyry copper deposits in the Jiroft region, Kerman province. To achieve this goal and overcome the aforementioned issues resulting from the operation of complex ore-forming geological processes, continuous weighting methods through logistic functions were used while training points and analyst's opinions were not contributed to the weighting procedure. Then, to generate exploration targets, the weighted layers were combined with three different integration methods namely, artificial neural network, geometric average, and fuzzy gamma operators. The comparison of the model obtained from the application of an artificial neural network with those obtained by the geometric average and the fuzzy gamma operators using prediction rate-area plots indicated that all the models have good overall performance and acceptable prediction rate. However, the performance of the artificial neural network model is slightly less than that of the other two models. Thus, the targets generated using the geometric average and fuzzy gamma operators are more reliable for planning further exploration programs. [ABSTRACT FROM AUTHOR]

Geochemical exploration data play a vital role in mineral prospectivity modelling (MPM) for discovering unknown mineral deposits. In this study, the improved spatially weighted singularity mapping (SWSM) method is used to improve the practice of identifying geochemical anomalies related to copper mineralization in the Sarduiyeh district, Iran. Then, the random forest algorithm (RF) and geometric average function (GA) are used to integrate the resulting geochemical predictor map with other predictor maps. As demonstrated by the high area under the curve (AUC) values, this approach can effectively delineate prospective areas with RF and GA. However, compared to the GA approach (AUC=0.78), the RF technique (AUC = 0.98) offers superior prediction capabilities due to its enhanced ability to capture spatial correlations between predictive maps and known mineral deposits. The proposed procedure, a hybrid of the improved SWSM and RF has outstanding predictive capabilities for identifying prospective areas. A case in point is the new, highly prospective areas identified in this study, which present priority targets for future exploration in the Sarduiyeh district. [ABSTRACT FROM AUTHOR]

Delimiting exploration targets using geochemical exploration data can be a challenging issue when different geochemical signatures represent the same deposit-type sought. In this regard, fuzzy operators have been used to integrate different geochemical evidence layers into a single model for generating target areas. In this paper, a GIS-based expected value function was adapted to integrate different geochemical evidence layers into a stronger geochemical signature for delimiting exploration targets. Then, the expected value function and fuzzy operators were compared. The comparison demonstrated that the former is more efficient than the later for generating a stronger geochemical evidence layer. The higher efficiency of the expected value function is because it simultaneously uses the value of all input variables and their relative importance in the process of integration. The proposed approach was evaluated by using a lithogeochemical data set for prospecting porphyry-Cu deposits in Jiroft area, Kerman province, southeast of Iran, as a case study. [ABSTRACT FROM AUTHOR]

Abstract: In stream sediment geochemical exploration, anomalies can be recognized by ‘undiluting’ concentrations of indicator elements in every stream sediment sample catchment basin (SCB) as a function of topographic, geomorphologic, and geologic factors. However, this SCB modeling, like contour mapping, of stream sediment geochemical anomalies depends on sampling locations and sampling density. These arbitrary aspects of stream sediment sampling can render SCB or contour mapping of stream sediment anomalies inefficient. In this paper, instead of evaluating the relative exploration importance of each sample per SCB, we evaluated the relative exploration importance of samples per natural drainage catchment basin (DCB). Accordingly, we developed a new fuzzy weighting scheme for each DCB based on the distribution of anomalous and background samples in each DCB. In this new approach of weighted drainage catchment basin (WDCB) mapping of stream sediment geochemical anomalies, individual DCBs are given fuzzy weights representing their relative importance for prospecting the deposit-type sought. Hence, a map of WDCB can be used directly as a geochemical evidence layer in fuzzy-based mineral prospectivity mapping. In this regard, we demonstrated that the prediction rate of prospectivity map obtained by using WDCB approach with respect to known mineral deposit occurrences is higher than that of prospectivity map obtained by using a SCB or contour map. [Copyright &y& Elsevier]

Mineral exploration targeting is a highly complex decision-making task. Two key risk factors, the quality of exploration data and robustness of the underlying conceptual targeting model, have a strong impact on the effectiveness of this decision-making. Geographic information systems (GIS) can be used not only for compiling, integrating, interrogating and interpreting diverse exploration data, but also for targeting by employing powerful mathematical algorithms, an approach that is commonly referred to as mineral potential modelling or mineral prospectivity mapping (MPM). Here, we pose and examine key aspects around the question of " how can we get better at mineral exploration targeting using GIS? " We do this by (1) reviewing the fundamental aspects of MPM, (2) identifying significant deficiencies of MPM, and (3) discussing possible solutions to alleviating or eliminating these deficiencies. In particular, we discuss how these deficiencies can be overcome by adopting an intelligence amplification system , such as the recently proposed exploration information system (EIS) for translating critical ore-forming processes into spatially predictive criteria (i.e., predictor maps and spatial proxies) and improving decision-making in mineral exploration targeting. [Display omitted] • Geographic information systems (GIS) are related to exploration information system (EIS). • The current status of study in GIS-based mineral exploration targeting is reviewed. • Use and abuse of mineral deposit models in mineral exploration targeting are discussed. • Mineral system-based exploration targeting using EIS is discussed. • Future research directions are provided. [ABSTRACT FROM AUTHOR]

Ore deposits are diverse with much of their diversity attributable to the complex interplay of ore-forming processes with a variety of geological environments, over a range of scales and both in space and time. This diversity makes it difficult for geoscientists to accurately predict the location of undiscovered ore deposits. Improving our understanding of the processes that are critical to ore deposit formation would help us to hone our predictive capabilities. However, this task is difficult to achieve as we cannot observe these genetic processes first-hand and different parameters and ingredients are important at different scales. Modelling offers a means of simulating and analysing ore-forming processes and their mappable expressions. This knowledge can then be used to build a predictive model by translating key process components into spatial proxies that can be mapped or recognized in mineral exploration data. Modelling and analysis of ore-forming processes are therefore critical for the future success of mineral exploration. Currently underutilized in exploration targeting, the application of statistical and mathematical concepts can help steer geoscientists towards a better understanding of the complex geological processes critical in the formation of mineral deposits and, ultimately, improved exploration success rates. This editorial article presents a brief introduction to the main concepts that support a collection of articles published in a virtual special issue (VSI) of Ore Geology Reviews entitled " Spatial modelling and analysis of ore-forming processes in mineral exploration targeting ". The articles examine three critical themes: (1) Translating the expressions of ore-forming processes and critical parameters of mineral systems into mappable spatial proxies; (2) identifying mineral deposit footprints through geochemical and geophysical data analysis; and (3) targeting and improving the discovery chance of mineral deposits by way of spatial data analysis. [ABSTRACT FROM AUTHOR]

• A new "information system" is proposed for mineral exploration targeting. • The system is called "exploration information system" (EIS). • EIS uses "mineral system" concepts in conjunction with GIS. • Ore-forming processes are classified into pre-, syn-, and post-mineralization parts. • EIS aims to automated translation of ore-forming processes into spatial proxies. The advent of modern data collection and storage technologies has brought about a huge increase in data volumes with both traditional and machine learning tools struggling to effectively handle, manage and analyse the very large data quantities that are now available. The mineral exploration industry is by no means immune to this big data issue. Exploration decision-making has become much more complex in the wake of big data, in particular with respect to questions about how to best manage and use the data to obtain information , generate knowledge and gain insight. One of the ways in which the mineral exploration industry works with big data is by using a geographic information system (GIS). For example, GIS platforms are often used for integration, interrogation and interpretation of diverse geoscience and mineral exploration data with the goal of refining and prioritising known and identifying new targets. Here we (i) briefly discuss the importance of carefully translating conceptual ore deposit models into effective exploration targeting maps, (ii) propose and describe what we term exploration information systems (EIS): a new idea for an information system designed to better integrate the conceptual mineral deposit model (i.e., the critical and constituent processes of the targeted mineral system) with data available to support exploration targeting, and (iii) discuss how best to categorise mineral systems in an EIS as scale-dependent subsystems to form mineral deposits. Our vision for the future use of EIS in exploration targeting is one whereby the mappable ingredients of a targeted mineral system are translated and combined into a set of weighted evidence (or proxy) maps automatically, resulting in an auto-generated mineral prospectivity map and a series of ranked exploration targets. We do not envisage the EIS replacing human input and ingenuity; rather we envisage the EIS as an additional tool in the exploration toolbox and as an intelligence amplifying system in which humans are making use of machines to achieve the best possible results. [ABSTRACT FROM AUTHOR]

This paper uses support vector machine (SVM), back propagation neural network (BPNN), and Multivariate Regression Analysis (MLA) methods to predict the gold in the Dalli deposit situated in the central province of Iran. After analyzing the data, the dataset was prepared. Subsequently, through comprehensive statistical analyses, Au was chosen as the output element for modelling, while Cu, Al, Ca, Fe, Ti, and Zn were considered input parameters. Then, the dataset was divided into two groups: training and testing datasets. For this purpose, 70% of the datasets were randomly entered into the data process, while the remaining data were assigned for the testing stage. The correlation coefficients for SVM, BPNN, and MLA were 94%, 75%, and 68%, respectively. A comparison of these coefficients revealed that all used methods successfully predicted the actual grade of Au. However, the SVM was more reliable and accurate than other methods. Considering the sensitivity of the gold data and the small number in the exploratory database, the results of this research are used to prepare the main layer in the mineral prospectivity mapping (MPM) of gold in 2 and 3D. [ABSTRACT FROM AUTHOR]

Recognition of significant ore-forming processes, which control mineralization, improves the efficiency of mineral prospectivity modeling. In this study, controlling processes of skarn copper mineralization in Varzaghan district, northwestern Iran, were distinguished by a series of spatial and numerical analyses comprising point pattern, fractal, fry and distance distribution methods. The recognized processes were then translated to a set of exploration criteria of the deposits in the area. Based on the accomplished exploration criteria, two data-driven models of skarn copper prospectivity were generated using logistic regression and random forest techniques. The comparison of two generated models demonstrated that the targets derived by the latter technique were more reliable for further exploration than those created by the former one. [ABSTRACT FROM AUTHOR]

Converting geochemical exploration data into information relevant to descriptive statistics of indicator elements facilitates gaining the knowledge of spatial and genetic relationships between mineralization and elemental dispersion patterns. This allows obtaining insights into alternatives to exploration strategies aiming at vectoring toward undiscovered mineral deposit sites. In this regard, shaping geochemical data for better understanding of their underlying patterns and extraction of information about ore deposition-related anomalies are challenging issues. To better extraction of the information from the geochemical data, we adapted the "information value" concept to quantify the significance of geochemical anomaly classes as spatial proxies of mineral deposits. To illustrate and evaluate the procedure proposed, a dataset of porphyry-Cu exploration was used to delineate exploration targets in the Baft district, Kerman province, Iran. Through this, geochemical models of information values were created for ore deposition-related elements. Then, the geochemical models were combined to produce an information value-based multi-element geochemical signature aiming at utilizing the prediction ability of every indicator element in a single model. Through the combination, spatial patterns of geochemical signals around mineral deposits are revealed that makes it easier to explore undiscovered deposit sites. We also applied fuzzy operators, as common and alternative practices, to integrate values of the same indicator elements for comparison purpose. The results demonstrated that the model of information value-based multi-element geochemical signature is an efficient geochemical proxy, over those geochemical models generated by the application of fuzzy operators, for exploration targeting. • Information value (IV) concept is adapted for geochemical anomaly modeling. • The IV is applied to introduce enhanced geochemical signatures. • The enhanced geochemical model is a derived multi-element indicator. • The IV facilitates converting geochemical data to exploration insight. [ABSTRACT FROM AUTHOR]

• An efficient geochemical evidence layer was generated. • The layer was used as a response to ore deposition processes. • The layer was applied for mineral exploration targeting. Recognition of mineralization-related geochemical footprints and modeling their multi-element dispersion patterns are important aspects to consider when "vectoring" toward undiscovered ore deposits. The collection, analysis, and interpretation of stream sediment geochemical data together make an exploration method that has proven to be successful at the district scale mineral exploration targeting. Identifying the possible sources of stream sediment geochemical anomalies and mapping evidence (i.e., footprints) of the underlying ore-forming processes, however, are not trivial tasks. This is because stream sediment samples represent transported material reflecting the entire geology upstream from the sample locations. Furthermore, indicator element distribution patterns are commonly strongly affected by local factors such as regolith, topographic gradient, vegetation density, and/or climate. Therefore, there is a need for finding a better geochemical anomaly separation method regarding the nature of geochemical data obtained from the area sampled. The main objectives of this study were (1) evaluating and comparing the spatial U-statistic and concentration–area fractal modeling methods of anomaly identification, both amenable to spatial analysis, for recognizing geochemical footprints of porphyry copper mineralization, and (2) measuring their performance in the context of district-scale exploration targeting in an area located in southeast Iran. Subsequently, the methods were first evaluated on a dataset of element contents to decompose anomalous populations. Finally, the geochemical model that proved more efficient with respect to predicting the known mineral deposits was integrated with additional evidence maps to delineate exploration targets. Our evaluation of the resulting geochemical targeting model demonstrated that the targets derived from this method are robust and worthy of follow-up exploration. [ABSTRACT FROM AUTHOR]