Exploration and prioritization of potentially mineralized areas by integrating different geoscientific datasets help to manage the time and costs of an exploration project. In this study, copper exploration and prioritization of explored areas were done on the Kerman copper belt, which was formed due to the subduction of the Arabian plate under the Iranian plate. After processing geoscientific layers (geology, hydrothermal alteration zones, stream sediment geochemistry, and airborne geophysics), the analytic hierarchy process (AHP) method was used to compute the weights of geoscientific layers by a decision-making group consisting of ten local experts in copper exploration. The weight of evidence (WofE) method was used to compare and assess the fairness of the AHP weights. The AHP weights were used for the multi-criteria optimization and compromise solution (VIKOR) method to integrate the geoscientific layers and create an AHP-VIKOR copper mineralization potential map. The “v” parameter of the VIKOR method was modified by replacing the concept of mathematical expectation value with the manual assignment. The real value-area (RV-A) fractal method was used to select optimum threshold values for different AHP-VIKOR copper mineralization potential classes. The accuracy of the classified AHP-VIKOR copper mineralized potential map was assessed by creating a confusion matrix and using existing mineralization locations. The average pixel values of the copper mineralization class were extracted and sorted to rank the very high potential areas. The AHP-VIKOR ranking results were compared with recorded ore grade and ore tonnage information of each copper deposition. The results demonstrated that the AHP ranks are roughly similar to the WofE ranks, except for lithology. An accuracy of 84% for classified copper mineralization potential map with the RV-A fractal method was obtained. The used expectation value for the “v” parameter of the VIKOR method showed that the decision-making group’s opinion was more important than the individual opinions (v = 0.6). The AHP-VIKOR method identified mineralization areas, especially porphyry deposits. Also, the outcome of the AHP-VIKOR ranking showed that the currently active mines, especially porphyry deposits, have the highest priority for mining. This study showed that the AHP-VIKOR approach helps explore and rank favorable areas with high copper ore grade and tonnage characteristics.

An accuracy assessment of a classified thematic map is critical for the success of a remote sensing project. The identification and quantification of accuracy sources for classified mineral maps derived from satellite images enable confident decisions to be made for further exploration operations. Nineteen rock samples were collected from the different lithological units of the hydrothermally altered Kuh Panj Cu porphyry occurrence within the south-eastern part of Iran. SPECIM hyperspectral imaging was applied to the rock samples, followed by X-ray diffraction (XRD) analysis to compare the SPECIM mineral maps. The SPECIM results were then interpreted for mineralogy and compositional mapping extracted from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images. The spectral angle mapper (SAM) and real value−area (RV−A) fractal methods were applied on SPECIM and ASTER images to interpret the mineralogy and derived classified map products. Two methods, including confusion matrix and one minus standard deviation over mean, were used to assess the accuracy of the classified SPECIM and ASTER derived mineral maps. Performing sensitivity studies were investigated, including the effect of spatially displacing ASTER mineral mapping and changing the SAM-derived threshold values on the accuracy of the ASTER mineral map. The sensitivity analyses demonstrated that selecting an optimum SAM-derived threshold value is more important than spatial displacement. Finally, accuracy sources were summarized in an accuracy budget table. The results demonstrated 89 and 88% accuracy for SPECIM and 88 and 92% accuracy for ASTER mineral maps via the confusion matrix and one minus standard deviation over mean methods, respectively. The accuracy budget helped to evaluate and compare all sources of accuracy with their accuracy values.

Mineral composition can be determined using different methods such as reflectance spectroscopy and X-ray diffraction (XRD). However, in some cases, the composition of mineral maps obtained from reflectance spectroscopy with XRD shows inconsistencies in the mineral composition interpretation and the estimation of (semi-)quantitative mineral abundances. We show why these discrepancies exist and how should they be interpreted. Part of the explanation is related to the sample choice and preparation; another part is related to the fact that clay minerals are active in the short-wave infrared, whereas other elements in the composition are not. Together, this might lead to distinctly different interpretations for the same material, depending on the methods used. The main conclusion is that both methods can be useful, but care should be given to the limitations of the interpretation process. For infrared reflectance spectroscopy, the lack of an actual threshold value for the H–OH absorption feature at 1900 nm and the poorly defined Al–OH absorption feature at 2443 nm, as well as for XRD, detection limit, powder homogenizing, and the small amount of montmorillonite below 1 wt.%, was the source of discrepancies.

Both the mineralogy and geochemistry of coal mine waste presents environmental and social challenges while simultaneously offering the potential source for recovery of metals, including critical raw materials (CRMs). Assessing these challenges and opportunities requires effective waste management strategies and comprehensive material characterization. This study deals with the integration of analytical data obtained from various portable sensor technologies. Infrared reflection spectroscopy (covering a wide wavelength range of 0.4 to 15 µm), and geochemical x-ray fluorescence (XRF) were utilized to differentiate between samples belonging to various geological lithologies and quantify elements of interest. Therefore, we developed a methodological framework that encompasses data integration and machine learning techniques. The model developed using the infrared data predicts the Sr concentration with a model accuracy of R² = 0.77 for the testing dataset; however, the model performances decreased for predicting other elements such as Pb, Zn, Y, and Th. Despite these limitations, the approach demonstrates better performance in discriminating materials based on both mineralogical and geochemical compositions. Overall, the developed methodology, enables rapid and in-situ determination of coal mine waste composition, providing insights into waste composition that are directly linked to potential environmental impact, and the possible recovery of economically valuable metals.