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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]

The concept of the "Exploration Information System" (EIS), first postulated by Yousefi et al. (2019), is now being realised and progressively implemented by various geological survey organisations with a particular focus on mineral prospectivity mapping (MPM) and exploration targeting. This Virtual Special Issue (VSI) of Applied Geochemistry showcases a collection of articles about new ideas and computational requisites designed to propel EIS from concept to reality, and which offer possible solutions to the challenges ahead. This Editorial not only serves to introduce the concept of EIS but also the contributions to this VSI, which was organized to showcase the current status quo of EIS-related research. The coherent set of contributions assembled in this VSI presents novel concepts and ideas by both mathematical modellers and geoscientists. [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]

Abstract Stream sediments remain the preferred geochemical sampling media for regional mineral exploration programs in most parts of the world where there is a well-defined drainage system. In Au exploration the analysis of either Bulk Leach Extractable Gold (BLEG) or acid-extractable Au in the −80# (<180 μm) stream sediment fraction are the two most common approaches. Western Turkey contains a variety of mineral deposit types and geological settings. Using a geochemical database derived mainly from company exploration programs, a study is made on the efficiency of singularity mapping technique in detecting the known mineral deposits or prospects. The composite media of samples are representative of sampling locations but also are extremely diluted. Therefore, recognition and mapping of significant geochemical anomalies of composite media still remains a challenge. In this study, geochemical assay results were subjected to multifractal analyses to detect gold geochemical anomalies, which are related to gold mineralization. In this regard, a singularity mapping technique was applied to enhance the single element geochemical signatures of BLEG sand −80# stream sediment data. Since the singularity mapping technique does not define threshold values for delineation of anomalies, number-size (N-S) and concentration-area (C-A) fractal models were applied to delimit the geochemical anomalies as target areas for further exploration. A hundred percent of the known gold deposits and prospects discovered in the study area are delineated by BLEG Au stream sediment anomalies by N-S and C-A multifractal modeling. Moreover, 89% of known gold prospects of the study area are delineated within the recognized anomalous zones of Au in −80# stream sediment data. The comparison of the delineated geochemical anomalies of BLEG and −80# stream sediment with those of rock chip samples demonstrated the efficiency of the applied methodology. Graphical abstract Image 1 Highlights • Bulk leach extractable gold and −80# stream sediment sampling was carried out. • Concentration-area and number-size fractal modeling were applied. • Singularity mapping method was applied to delineate geochemical anomalies. • Singularity method applied to −80# stream sediments detected 90% of gold prospects. • Singularity method applied to BLEG also detected 100% of known gold prospects. [ABSTRACT FROM AUTHOR]

• Enrichment and depletion in porphyry copper deposit is modeled. • Cosine Similarity method was used for targeting prospective areas. • Wunugetushan Cu–Mo deposit was used as reference. • The method take full advantage of available geochemical data. Recognizing distribution patterns of ore-related pathfinder elements requires an understanding of the corresponding ore-forming systems and may lead to the definition of more reliable exploration targets. Given that complex ore-forming processes in complex geological terrains can result in either enrichment or depletion of ore elements, simultaneous consideration of both processes in geochemical anomaly modelling would be advantageous for defining exploration targets. Here, we adapted the "cosine similarity measure" method for pattern recognition of ore-related geochemical anomalism to facilitate simultaneous modelling of element enrichment and depletion. We tested the performance of this new method on porphyry copper exploration data from the Manzhouli belt, China, and demonstrated that our method is highly efficient with respect to recognizing ore-related geochemical signatures and, thus, useful for "vectoring" towards porphyry copper deposits and defining more reliable geochemical exploration targets. [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]

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]

Diverse deposit-types or mineral systems form by diverse geological processes, so translation of knowledge about the controls of mineralization acquired from the 4D geological modeling into 2D spatial predictor maps is a major challenge for prospectivity analysis. In this regard, mathematical functions have been used to model the conceptual or perceived spatial relationships between geological variables and targeted type or system of mineralization. In this paper, due to the different models of spatial relationships between predictors and mineral deposits, we investigated the performance of different fuzzification functions to quantify the relationships. We demonstrated that various types of relationships between exploration features and a mineralization-type sought could be quantified using different fuzzification functions for prospectivity analysis. We illustrated the process of the prospectivity analysis by using a data set of orogenic gold deposits in Saqez-Sardasht Goldfield, Iran. Prospectivity modeling of orogenic gold mineralization in the study area showed that the NE-SW trending targets have priority for further prospecting of the deposits. [ABSTRACT FROM AUTHOR]

Formation of hydrothermal mineralization is related to the late stages of, or immediate following, orogenic compressional regimes that have been superimposed on arc magmatism. For instance, mineralization-related fluid flux mechanisms of porphyry Cu formation deal with how magmatic fluids are transferred from the corresponding source chambers to the environment of stock emplacement related to porphyry systems. In this paper, firstly, we review the imperative physicochemical processes of mineralizing fluid flux mechanisms, e.g., the function of intrusive, metalliferous compounds, and permeable structures, and then assemble them to translate the mechanism of forming focused fluid flux into 2D mappable criteria for mineral exploration targeting. According to the review and the ensuing knowledge and idea, we propose a simplifying approach by focusing on a geological point feature - inferred sites of strongly concentrated ore fluid flux - that represents a critical, if not the most critical, geological element in mineral exploration targeting. We demonstrate that this point feature can be derived as a mappable criterion and that there are stronger spatial and thermal relationships between the location of hydrothermal mineral deposits and this proposed point feature relative to the existing point and line features (e.g., intrusive contacts, fault density, fault intersections, and proximity to faults), which have been widely used in the procedure of prospectivity analysis for mineral exploration targeting. We illustrate the procedures of generating mineral exploration targets, proposed in this paper, using geochemical and geological data sets of porphyry Cu deposits of Iran. [Display omitted] • Function of hydrothermal mineralization-related focused fluid flux is discussed. • The function is translated into a newly-explored mappable exploration criterion. • The mappable criterion mitigates the problem of localizing hydrothermal mineralization. • Geochemical and geological data sets are applied in the modeling procedure. • Physicochemical ore-forming processes are discussed. [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]

Mineral potential targeting using geographical information system is an efficient technique to delimit a study area for further exploration of mineral deposits. This introduction presents an overview of the mineral potential modeling methods and future perspectives of research in the fields of target generation and summarizes the papers that have been incorporated into this Special Issue of the Journal of African Earth Sciences. [ABSTRACT FROM AUTHOR]

Two common problems affect integration of exploration criteria for mineral prospectivity mapping (MPM) in geographic information system (GIS): (a) stochastic error associated with sufficiency in number of known mineral occurrences (KMOs) used to estimate evidential weights and (b) systemic error associated with subjectivity of expert judgment applied to process, analyze, and assign weights to evidential data. In this paper we used logistic sigmoid (or S-shaped) function to transform continuous-value evidential data into logistic space without using KMOs as in data-driven MPM and without discretization of evidential data into classes by using arbitrary intervals based on expert judgment as in knowledge-driven MPM. We generated a prospectivity model using discretized evidential data as well. Then, we compared the prospectivity models generated using continuous- and discretized-value evidential data and demonstrated that the former is better model for selecting target areas for further exploration. [ABSTRACT FROM AUTHOR]

In mineral prospectivity mapping (MPM) logistic functions have been widely used to transform mineral exploration data or prospectivity values into the [0, 1] range to generate fuzzified evidential maps or to rank target areas as fuzzy prospectivity models. Recently researchers applied logistic functions to assign fuzzy weights of continuous-value spatial evidence. They assigned fuzzy weights to evidential features without using locations of known mineral occurrences (KMOs) as in data-driven MPM and without discretization of evidential values into some arbitrary classes as in knowledge-driven MPM to overcome exploration bias. However these methods suffer exploration bias resulting from expert judgments in defining slope ( s ) and inflection point ( i ) of the logistic function, which are defined by trial and error procedure. In this paper, the application of logistic transformation is demonstrated to assign continuous weights to evidential layers of geochemical and geological data. The weights were assigned without discretization of spatial evidence values and without using the locations of KMOs, while the i and s values of the logistic function were defined by a data-driven way. For this, we applied systems of equations including two equations and two unknown variables (i.e., i and s ). Thus by solving the system of equations the two unknown variables, i and s , were defined. [ABSTRACT FROM AUTHOR]

Techniques for GIS-based multicriteria decision-making (GIS-MCDM), like mineral prospectivity mapping (MPM), are concerned with combining information from several criteria into a single evaluation model to solve certain problems in the fields of geosciences. In this paper, we introduce the geometric average method for MPM, as a GIS-MCDM approach, and demonstrate its advantage over the expected value MPM method. The comparative analysis shows that the geometric average MPM method yields better prediction of mineral prospectivity and it overcomes the limitation of the expected value method in terms of using spatial evidence values with the same unit. [ABSTRACT FROM AUTHOR]

There are methods of mineral prospectivity mapping whereby, besides assignment of weights to classes of evidence in an evidential map, every evidential map is also given a weight based on expert opinion. In this regard, evaluating the relative importance of every evidential map derived from particular spatial data sets is a highly subjective exercise and the assignment of meaningful weights to evidential maps usually involves a trial-and-error procedure. In this paper, we used a prediction–area (P–A) plot and normalized density to estimate weights of every evidential map. The method of P–A plot is a data-driven way, rather than using expert opinion, to evaluate and weight evidential maps. [ABSTRACT FROM AUTHOR]

Complexities of geological processes portrayed as certain feature in a map (e.g., faults) are natural sources of uncertainties in decision-making for exploration of mineral deposits. Besides natural sources of uncertainties, knowledge-driven (e.g., fuzzy logic) mineral prospectivity mapping (MPM) is also plagued and incurs further uncertainty in subjective judgment of analyst when there is no reliable proven value of evidential scores corresponding to relative importance of geological features that can directly be measured. In this regard, analysts apply expert opinion to assess relative importance of spatial evidences as meaningful decision support. This paper aims for fuzzification of continuous spatial data used as proxy evidence to facilitate and to support fuzzy MPM to generate exploration target areas for further examination of undiscovered deposits. In addition, this paper proposes to adapt the concept of expected value to further improve fuzzy logic MPM because the analysis of uncertain variables can be presented in terms of their expected value. The proposed modified expected value approach to MPM is not only a multi-criteria approach but it also treats uncertainty of geological processes a depicted by maps or spatial data in term of biased weighting more realistically in comparison with classified evidential maps because fuzzy membership scores are defined continuously whereby, for example, there is no need to categorize distances from evidential features to proximity classes using arbitrary intervals. The proposed continuous weighting approach and then integrating the weighted evidence layers by using modified expected value function, described in this paper can be used efficiently in either greenfields or brownfields. [ABSTRACT FROM AUTHOR]

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]

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]

Abstract: Integration of stream sediment geochemical data with other types of mineral exploration data, especially in knowledge-driven mineral potential mapping (MPM), is a challenging issue. In this regard, multivariate analyses (e.g., factor analysis) are generally used to extract significant anomalous geochemical signature of the mineral deposit-type sought. In this study, we used stepwise factor analysis to generate a geochemical mineralization probability index (GMPI) through a new approach to create stream sediment geochemical evidential maps. GMPI is a weight that can be mapped, and hence, can be used as an evidential map in MPM. Using stepwise factor analysis enhances recognition of anomalous geochemical signatures, increases geochemical anomaly intensity and increases the percentage of the total explained variability of data. With the GMPI, we developed a new data-driven fuzzification technique for (a) effective assignment of weights to stream sediment geochemical anomaly classes, and (b) improving the prediction rate of mineral potential maps and consequently increasing exploration success. Furthermore, the predictive capacity of each stream sediment geochemical sample for prospecting the deposit-type sought upstream of its location can be evaluated individually using GMPI. In addition, the GMPI can be used efficiently in knowledge-driven MPM as a new exploratory data analysis tool to generate a weighted evidential map in less explored areas. In this paper, we successfully demonstrated the application of GMPI to generate a reliable geochemical evidential map for porphyry-Cu potential mapping in an area in Kerman province, southeast of Iran. [Copyright &y& Elsevier]

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]

In this study, fractal/multifractal modeling methods have been applied for preliminary hydrothermal Ni prospection using a set of stream sediment geochemical data of Ahangaran district, which is an arid terrain in eastern Iran. The study area has a complex geological and morphological setting, which is dominated by the presence of aeolian sands. Cretaceous ophiolitic rocks hosting nickel endowment are influenced by extensive listwanitic alteration. Hydrothermal nickel mineralization occurs as veinlets and disseminated forms within the listwanite units. Among the analyzed elements, Ni, Cr and, Co have been recognized as being associated with listwanitic alteration and Ni mineralization, through robust factor analysis of compositional data. The regional background patterns of these mineralization-related elements were delineated via the concentration – area (C-A) fractal modeling method. Different multifractal characteristics of the selected elements were recognized using the multifractal spectrum f ( α ) curves and different multifractal indices. The enrichment and depletion patterns of the related elements in different parts of the study area were also identified using the local singularity mapping technique. The application of local singularity mapping manifested different geochemical patterns that were not recognized via the C-A fractal modeling method. The superiority of the local singularity mapping technique in delineating geochemical populations over the C-A fractal modeling method, was revealed by quantification of the correlation between geochemical anomalies and geological evidence of the mineralization. [ABSTRACT FROM AUTHOR]

Weighting and synthesizing exploration evidence criteria for mineral prospectivity mapping (MPM) are affected by complexity and ambiguity of ore mineralization processes. In this regard, fuzziness could facilitate the modeling of such vague processes for MPM. Furthermore, imprecise selection of the exploration criteria to be used in MPM has negative influence on the efficiency of the generated prospectivity models. In this paper, of various exploration criteria, a coherent set of exploration features were recognized by using the distance distribution analysis. Then, the application of cosine amplitude-based similarity procedure was adapted as a data-driven fuzzy logic approach for predictive mapping of porphyry-Cu prospectivity in Arasbaran metallogenic zone, NW Iran. In addition, a conventional data-driven fuzzy prospectivity model was generated for comparison purpose. Comparison of the two models demonstrated the superiority of the cosine amplitude-based fuzzy procedure for MPM. [ABSTRACT FROM AUTHOR]

The spectrum–area (S–A) fractal model is a powerful tool for decomposition of complex anomaly patterns of gridded geochemical data. Ordinary moving average interpolation techniques are commonly being used for gridding geochemical data; however, these methods suffer from two major drawbacks of (1) ignoring the locally high values and (2) smoothing the interpolated surface. Multifractal moving average interpolation methods have been developed to overcome the shortcomings of ordinary moving average methods. This study seeks to compare two sets of multifractal and ordinary gridded geochemical data using success rate curves and applies the S–A fractal model to decompose anomalous geochemical patterns. A set of stream sediment geochemical data in Ahar area, NW Iran, was used as a case study. Then, a mineralization-related multi-element geochemical signature was gridded by ordinary and multifractal approaches and considered for further analyses. The S–A fractal method was applied to decompose anomaly and background components of the resultant multi-element geochemical signature. Exploration targets were delimited and further evaluated using two bivariate statistical procedures of Student's t-value and normalized density index. The results revealed that (a) application of multifractal gridded data enhances the predicting ability of geochemical signatures, (b) application of S–A fractal model on multifractal gridded data allows for superior discrimination of geochemical anomalies, and (c) the multi-element geochemical anomalies in the Ahar area related to porphyry-Cu deposits were properly delineated through sequence application of multifractal interpolation and S–A fractal model. [ABSTRACT FROM AUTHOR]

Recognition of significant geochemical signatures and separation of geochemical anomalies from background are critical issues in interpretation of stream sediment data to define exploration targets. In this paper, we used staged factor analysis in conjunction with the concentration-number (C–N) fractal model to generate exploration targets for prospecting Cr and Fe mineralization in Balvard area, SE Iran. The results show coexistence of derived multi-element geochemical signatures of the deposit-type sought and ultramafic-mafic rocks in the NE and northern parts of the study area indicating significant chromite and iron ore prospects. In this regard, application of staged factor analysis and fractal modeling resulted in recognition of significant multi-element signatures that have a high spatial association with host lithological units of the deposit-type sought, and therefore, the generated targets are reliable for further prospecting of the deposit in the study area. [ABSTRACT FROM AUTHOR]

Diversity and complexity of geological processes in different areas result in different element associations for prospecting a certain mineral deposit-type sought. In this regard, because element associations are affected by the diversity of geological characteristics of different areas, it is important to analyze and recognize significant geochemical signatures that represent the deposit-type sought. This paper aims to recognize significant multi-element geochemical signatures of porphyry-Cu deposits in the Noghdouz area, Iran, using stream sediment data. For this, we used factor analysis and two modeling methods of geochemical anomalies, sample catchment basin and contour map. Then, to recognize significant geochemical signatures of the deposit-type sought and evaluate the anomaly mapping methods, we adapted prediction-area (P-A) plot, normalized density, and success rate curve. By using these processes, we recognized the best geochemical signature of the deposit-type sought in the study area. The proposed methods in this paper can efficiently be used in other areas to recognize significant geochemical signatures of different types of mineral deposits. [ABSTRACT FROM AUTHOR]

Dispersion pattern of geochemical elements in stream sediment data of a study area is affected by several factors, e.g., geological and geomorphological characteristics of the area. In this paper, we demonstrated recognition of efficient and inefficient mono-elemental geochemical signatures in a study area and exclusion of inefficient elements is worthwhile for increasing the probability of exploration success. For this, we adapted prediction-area (P-A) plot, normalized density and success-rate curve as tools that evaluate the ability of geochemical signatures in prediction of undiscovered mineral deposits and in delimiting exploration targets. After identification of efficient and inefficient elements, we combined efficient indicator elements to generate an effective prospectivity model. To illustrate the procedure we used a stream sediment data set for prospecting porphyry-Cu deposits in the Noghdouz area, Iran. [ABSTRACT FROM AUTHOR]

Identification of geochemical anomalies using singularity theory is a topic of interest in the field of mineral exploration. The ordinary singularity mapping technique does not consider the critical role of geological structures in the ore-forming environment. Therefore, models of spatially weighted singularity have been developed through the incorporation of fault systems, which might play an important role in ore-forming processes, and thus, are key factors in spatial analysis of mineralization indicators. In this study, we proposed a strengthened singularity mapping technique that was spatially enhanced and weighted by mineralization-efficient fault systems. For this, we applied distance distribution analysis to distinguish "efficient" (linked to mineralization) and "inefficient" (not linked to mineralization) fault systems, and then, used the former to generate the strengthened spatially weighted singularity models of geochemical indicator elements. The rationale is that faults with different orientations are the result of various geological processes, of which only some are linked to the ore-forming processes. We also integrated the strengthened models of geochemical anomalies by using random forest (RF) and principal component analysis (PCA) techniques to produce a stronger geochemical clue. Comparison of the integration results demonstrated that the former, a strengthened anisotropic geochemical singularity modeling technique proposed in this paper through the incorporation of efficient fault systems and RF method, is superior to the later, an existing anisotropic geochemical singularity modeling technique through the incorporation of all faults and PCA method. Data related to porphyry copper mineralization in Sarduiyeh district, Iran, was used to illustrate the procedure applied. [Display omitted] • Efficient fault systems are incorporated by distance-distribution analysis. • A strengthened anisotropic geochemical singularity is produced. • Spatially weighted singularity mapping technique was improved. • Random forest algorithm can robustly integrate anisotropic geochemical signatures. [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]

Background: Postoperative pain is a usual phenomenon that can cause some problems such as increased blood pressure, increased oxygen use, an increased risk of myocardial infarction (MI). Like any other medical procedure, orthopedic surgery is associated with pain and complications. Recently, doxepin has been used as a tricyclic anti depressant (TCA) to decrease pain. Therefore, we designed this study to evaluate the effects of doxepin on the postoperative pain following lower limb orthopedic surgery. Methods: This randomized double blind placebo controlled trial was performed on 102 lower limb orthopedic patients in three groups of 34. The first two group received 50 and 100 mg doxepin while the last group received placebo. Patients aged between 18 and 65 years. Data was collected by a standard questionnaire and analyzed by analysis of variance (ANOVA) and chi-square test in SPSS. Findings: No significant differences were observed between the three groups in terms of pain intensity before or after surgery (P = 0.58). The only significant difference was seen between the first (50 mg doxepin) and placebo groups during the first 24 hours after surgery (P = 0.25). The groups were not different in demographic characteristics (age, gender, or education), either. In addition, received doses of morphine, and the frequency of nausea, vomiting, itchiness, and other side effects were not significantly different between groups. Conclusion: According to our results, doxepin was not effective on pain reduction after lower limb orthopedic surgery. We therefore hypothesize doxepin to have more effects on chronic pains than acute pains. [ABSTRACT FROM AUTHOR]

Selection of non-deposit sites is a challenging issue affecting the application of supervised algorithms for modeling mineral exploration targets. For this, equal number of deposit and non-deposit sites has been widely applied for training purposes. In this paper, we investigated the effect of changes in the number of non-deposit sites on the effectiveness of exploration targeting models while the number of deposit sites is constant. The results obtained demonstrated that exploration targeting models are affected by the ratio of non-deposit and deposit sites. Thus, balancing between the number of deposit and non-deposit sites is an efficient way to produce more-reliable exploration targets when supervised algorithms are applied for modeling. The idea of this research came from the fact that mineralization is a rare event, and therefore, in a region of interest number of non-deposit sites is much more than that of deposit events. To illustrate the procedure proposed, we used an exploration dataset of porphyry Cu mineralization in Chahargonbad area, SE Iran. A sequence application of self-organizing map and multilayer perceptron neural network algorithm was applied to better illustration of the changing effects of the number of non-deposit sites on the ensuing exploration targeting models. • Counting of deposit and non-deposit sites is discussed for training purpose. • The number affects the ensuing exploration targeting models. • Making a balance between the two types of training data yields significant results. • Self-organizing map clustering approach leads to define non-deposit sites. [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]

In this paper, an application of a knowledge-driven mineral prospectivity mapping (MPM) approach so-called “the evidential belief functions (EBFs) using Dempster-Shafer's rule of combination” is proposed. This technique is used to weight and integrate a large scale exploration dataset in order to localize prospects for definition of further exploration drilling sites. In this study, exploration datasets of Seridune copper deposit in the Kerman province, SE Iran used for the methodology. In this regard, geophysical evidence layers extracted from interpretation of magnetic and electrical surveys, geological evidence layers derived via the geological datasets (i.e. lithology, fault and alteration), and geochemical evidence maps were generated and integrated for MPM. Furthermore, various MPM approaches including outranking, index overlay and fuzzy logic methods were examined for comparison with the introduced method. To evaluate and compare the efficiency of the methods, the productivity of the drilled boreholes (Cu concentration multiplied by its ore thickness along each drilled borehole) was used to validate the generated prospectivity models. The results showed higher efficiency of the Dempster-Shafer's model in comparison with the prospectivity models generated using other MPM approaches. [ABSTRACT FROM AUTHOR]

Generation of enhanced and augmented evidence maps corresponding to operation of ore-forming geological processes improves mineral exploration success. Structural lineaments have a substantial impact on such processes. Therefore, precise recognition of the lineaments facilitates mineral exploration targeting toward undiscovered mineralized zones. The paper aims 1) to recognize and analyze structural lineaments, especially those which are spatially and genetically associated with orogenic gold mineral system, through analysis of a multi-disciplinary geospatial dataset including aeromagnetic geophysics, the Advanced Spaceborne Thermal Emission, and Reflection Radiometer imagery and Digital Elevation Model data and 2) to generate an efficient structural evidence layer for exploration targeting. The methods applied are edge enhancement filters with line detection algorithms. The detection algorithm, Hough Transform, and total horizontal derivative of upward continued filter and its tilt derivative) were utilized to extract structural lineaments. Evaluation of the spatial association of known Au mineralization in the study area with the recognized structural features demonstrated that abundance, density, and trend of such features strongly control the localization of Au mineralization. In addition, band ratio, principal component analysis, spectral angle mapper, and spectral feature fitting were applied to map and generate mineralization-related alteration layer. The evidence layers were then integrated through the conventional TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method to delineate reliable exploration targets. Image 1 • An enhanced structural evidence map is generated. • The structural features are responses to the corresponding ore-forming processes. • Application of the structural layer results in more-reliable exploration targets. [ABSTRACT FROM AUTHOR]