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This research presents a case study that employs the Fuzzy Ordered Weighted Averaging (FOWA) method to develop mineral prospectivity/potential maps (MPM) for the Chahargonbad district in southeastern Iran. The primary objective of the study is to uncover intricate and concealed relationships between various evidence layers and known ore occurrences through a comprehensive analysis of multidisciplinary geospatial data. Consequently, thirteen evidence layers were meticulously derived from existing databases, encompassing geological, geochemical, geophysical, and remote sensing data, which were then integrated using the FOWA multi-criteria decision-making approach to delineate favorable zones for porphyry Cu mineralization. The FOWA methodology employs a diverse array of decision strategies to synthesize input geospatial evidence by incorporating multiple values for an alpha parameter. This parameter serves as the cornerstone of the algorithm, influencing experts' perspectives on MPM risk. The methodology generates seven mineral potential maps to identify the most suitable one(s). By considering a prediction-area plot for datadriven weight assignment to each evidence map, the hybrid FOWA outputs were scrutinized to pinpoint the most appropriate map for targeting significant Cu occurrences. The resulting synthesized evidence map indicates an ore prediction rate of 77%, with known Cu deposits primarily located within favorable zones occupying 23% of the entire district area. [ABSTRACT FROM AUTHOR]

In integrating geospatial datasets for mineral potential mapping (MPM), the uncertainty model of MPM can be inferred from the Dempster - Shafer rules of combination. In addition to generating the uncertainty model, evidential belief functions (EBFs) present the belief, plausibility, and disbelief of MPM, whereby four models can be simultaneously utilized to facilitate the interpretation of mineral favourability output. To investigate the functionality and applicability of the EBFs, we selected the Naysian porphyry copper district located on the Urmia - Dokhtar magmatic belt in the northeast of Isfahan city, central Iran. Multidisciplinary datasets-that are geochemical and geophysical data, ASTER satellite images, Quickbird, and ground survey-were designed in a geospatial database to run MPM. Implementing the Dempster law through the intersection (And) and union (OR) operators led to different MPM performances. To amplify the accuracy of the generated favourability maps, a combinatory EBFs technique was applied in three ways: (1) just OR operator, (2) just And operator, and (3) combination of And and OR operators. The plausibility map (as mineral favourability map) was compared to Cu productivity values derived from drilled boreholes, where the MPM accuracy of the hybrid method was higher than each operator. Of note, the success rate of the hybrid method validated by 21 boreholes was about 84%, and it demarcates high favourability zones occupying 0.67 km² of the studied area. [ABSTRACT FROM AUTHOR]

One of the specific features of porphyry copper (Cu) mineralization is the distinct occurrence of hydrothermal alteration zones, which can be mapped by processing various satellite images. Among free multispectral images, Landsat 8 OLI and ASTER data are known to be efficient in mapping different geological features, such as alteration zones and tectonic lineaments. This study aims to show the potential of these data types in mapping porphyry Cu mineralization by proposing a framework for employing and integrating different image processing methods. These methods include principal component analysis (PCA), spectral angle mapper (SAM), and matched filtering (MF) employed on these satellite images to map target al.teration zones. Moreover, PCA and directional filtering are applied to the ASTER dataset to enhance and map structural features. The results are evaluated and then combined to provide a potential map of Cu mineralization in the Chahargonbad area, located within the Urumieh-Dokhtar magmatic belt (UDMB) in Kerman province, Iran. The prediction-area plot and normalized density, which are data-driven methods, are used to assign the relative weight of each layer and evaluate them. Finally, using the calculated weights, data-driven multi-index overlay (DMIO) and fuzzy ordered weighted averaging (FOWA) methods are applied to combine the evidential layers. The potential mineralization maps created by the DMIO and FOWA provide a prediction rate of 80% and 82%, respectively. Furthermore, the accuracy of the integrated maps is investigated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curves. The AUC scores obtained from the ROC curves of DMIO and FOWA methods are 0.85 and 0.88, respectively, representing powerful positive spatial relationships with mineralization areas. Based on the results, the proposed framework can be applied to provide a potential map of porphyry Cu mineralization, particularly in arid regions, with reasonable accuracy. [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]

Identifying mineralization areas with an acceptable level of reliability is a complex matter demanding the implementation of supervised methods for mapping mineralization exploration aims. In this study, further collecting all available exploratory information and data of the region of study, their good processing and analysis, and then integrating them with an appropriate method, multi-criteria decision-making (MCDM) approaches were utilized to outranking porphyry Cu mineralization areas. This paper describes an acceptable procedure for obtaining evidential layers for recognizing porphyry Cu mineralization, assigning weight to the layers, and combining them. For this aim, first, a data-driven multi-class index overlay (DMIO) approach is applied as it is a proven method with proper results in mineral prospectivity mapping (MPM) to integrate evidential layers (criteria and sub-criteria emanated from geoscience data including geological, geochemical, remote sensing, and geophysical datasets). After that, two recent MCDM models, combined compromise solution (CoCoSo) and multi attributive ideal-real comparative analysis (MAIRCA), were used to prioritize the porphyry Cu potential areas producing MPM. Concentration-area (C–A) fractal method and prediction-area (P–A) plots by considering the areas of occurrence of the known mineralization and normalized density (N d) as traditional methods were applied for weighting and integration evidential layers and evaluation of the final maps in a data-driven manner. Consequently, verifying the verisimilitude of the MAIRCA prospectivity map (N d = 3.17) is similar to the DMIO map in delimiting the priority areas. The results of the CoCoSo model (N d = 2.7) show this methodology was also successfully applied in mapping the porphyry Cu mineralization areas in the study. • Application of GIS-based MCDM methods in porphyry Cu prospectivity mapping • Development of two new methods of outranking MCDM to produce MPM • Weighting and integration of all layers from geospatial data in a data-driven way • Obtaining valid and accurate MPM of porphyry Cu mineralization [ABSTRACT FROM AUTHOR]

Abstract: In this paper on mineral prospectivity mapping, a supervised classification method called Support Vector Machine (SVM) is used to explore porphyry-Cu deposits. Different data layers of geological, geophysical and geochemical themes are integrated to evaluate the Now Chun porphyry-Cu deposit, located in the Kerman province of Iran, and to prepare a prospectivity map for mineral exploration. The SVM method, a data-driven approach to pattern recognition, had a correct-classification rate of 52.38% for twenty-one boreholes divided into five classes. The results of the study indicated the capability of SVM as a supervised learning algorithm tool for the predictive mapping of mineral prospects. Multi-classification of the prospect for detailed study could increase the resolution of the prospectivity map and decrease the drilling risk. [Copyright &y& Elsevier]

Abstract: This paper describes the application of a well-known Multi Criteria Decision Making (MCDM) technique called Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE II) to explore porphyry copper deposits. Various raster-based evidential layers involving geological, geophysical, and geochemical geo-datasets are integrated to prepare a mineral prospectivity mapping (MPM). In a case study, thirteen layers of the Now Chun copper deposit located in the Kerman province of Iran are used to explore the region of interest. The PROMETHEE II technique is applied to produce the desired MPM, and the outputs are validated using twenty-one boreholes that have been classified into five classes. This proposed method shows a high performance when providing the MPM while reducing the cost of exploratory drilling in the study area. [Copyright &y& Elsevier]