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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]

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]

Producing an accurate and valid mineral prospectivity map is one of the most significant parts of mineral exploration studies. For this purpose, it is needed to obtain valid evidential layers and integrate them with an accurate methodology. Knowledge and data‐driven methods are two primary techniques applied to combine various evidential layers for mineral prospectivity mapping, of which each of them includes a variety of analytical techniques. In this study, in the first step, satellite data, aeromagnetic and airborne radiometric data, stream sediment geochemical data and geological data were applied to create valid remote sensing, geophysical, geochemical, lineaments and lithological evidential layers of the study area that are an essential factor in recognition porphyry copper mineralization, then in the second step, based on the known mineralization occurrences data, the evidential layers were weighted. Finally, these layers were integrated using fuzzy logic and index overlay methods in a combination of knowledge and data‐driven way. Validation of each layer was done using available data in the second step. The final mineral prospectivity map was evaluated, and the confirmation of this layer detected that the final mineral prospectivity map obtained from data‐driven multi‐index overlay method has a higher ore prediction rate of 76%, which identifies 24% of the area as potential zones for further exploration. [ABSTRACT FROM AUTHOR]

This study illustrates the application of a geometric average integration of aeromagnetic, radiometric and satellite imagery data over a region prone to Cu‐bearing mineralization at Chahargonbad area in Kerman province of Iran. Processing aeromagnetic, radiometric and Advanced Spaceborne Thermal Emission and Reflection Radiometer satellite data can provide exploratory insights about favourable zones in association with porphyry‐type ore occurrences, which can be synthesized through a combination of knowledge‐ and data‐driven approaches as a geometric average and be represented in a mineral prospectivity map. The existence of known deposits in a prospect region can facilitate the investigation of significant exploratory footprints extracted from airborne data by calculating each indicator layer's weight by plotting a prediction–area curve accompanied by a concentration–area fractal curve. Among various indicators, the most important ones are determined based on derived weights from the prediction–area plots to be synthesized in a single Cu favorability map. To fulfil this aim, indicator layers from airborne geophysics (magmatic bodies, magnetic lineaments and potassium radiometry) and remote‐sensing data (alterations such as argillic, phyllic, propylitic and iron oxide along with geological lineaments) were prepared and evaluated using the known porphyry Cu mineralization by the simultaneous plot of the concentration–area fractal model and the prediction–area curve to attain the ore prediction rate and the relevant occupied area of each map for weight assignment of indicators. The geometric average prospectivity model was applied to synthesize the leading indicators, and the result was compared with a multi‐class index overlay map. This study's significance lies in improvement of the performance of the mineral prospectivity/potential mapping after running a geometric average by a higher ore prediction rate of 79%, which has occupied 21% of the area as potential zones for further mining investigations. [ABSTRACT FROM AUTHOR]

The present study applies proposed hybrid mathematical frameworks, namely Data-driven Multi-Index Overlay Method (DMIO) and Best Worst Method-multi objective optimization by ratio analysis (BWM-MOORA) techniques, for generating robust predictive models enhancing mineral prospectivity analysis is demonstrated. These are used for (i) weighting multiple groups of evidence maps acquired from geological databases, such as remote sensing, geological, geochemical, and geophysical, and then (ii) integrating these weighted layers into robust predictive models to assess porphyry copper prospectivity. This case study of the Chahargonbad area is a district of the volcano-plutonic Urumieh–Dokhtar magmatic arc belt. Porphyry copper deposit (exploration and development) models were used for distilling geoscientific datasets that are available into a group of evidence-based layers. The BWM-MOORA method combines two separate processes in which BWM is applied to weight each criterion and sub-criteria, whereas the MOORA approach helps rank these attributes. Each layer is first converted to another space of [0,1] by the sigmoidal logistic function to perform the critical process of integrating each of the evidence layers. Then, a concentration-area fractal model was used to classify evidences into some geospatial populations. In addition, a prospectivity / mineral potential mapping (MPM) analysis is performed using the DMIO, a well-proven, and accurate data-based methodology, to verify the satisfactoriness and validation of the applied methodology. Prediction-area (P-A) and normalized density (Nd) analysis (plots) were performed as validation methods to estimate the reliability and evaluate the relative efficiency of the MPM models. Therefore, these detailed outcomes confirm the validity of the BWM-MOORA model (Nd = 3.17), which is slightly higher than the valid and proper model DMIO (Nd = 3) in determining the specific potential mineralization regions. • A hybrid MPM method. • Generating robust mineral prospectivity model. • Porphyry copper prospectivity mapping [ABSTRACT FROM AUTHOR]