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Hydrothermal alteration zones associated with porphyry copper deposit (modified after Lowell & Guilbert 1970). (A) Schematic cross section of hydrothermal alteration mineral zones, which consist of propylitic, phyllic, argillic and potassic alteration zones. (B) Schematic cross section of ores associated with each alteration zone.
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The Shahr-e-Babak region located in the Kerman metallogenic belt is one of the high potential segments of Urumieh–Dokhtar magmatic arc for porphyry copper and epithermal gold mineralization in the south of Iran. This high potential zone encompasses several porphyry copper deposits under exploitation, development and exploration stages. The aim of t...
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... rocks of copper mineralization are altered and genetically related to granitoid porphyry intrusions and adjacent to wall rocks ( Cooke et al. 2005;McInnes et al. 2005;Sillitoe 2010). Porphyry copper deposits often are associated with alteration halos such as potassic, phyllic, argillic, and pro- pylitic alteration zones (Figure 1; Lowell & Guilbert 1970). Recognizing hydrothermally altered rocks through remote sensing instruments have been widely and successfully used for the exploration of epithermal gold, porphyry copper, massive sulphide and uranium deposits around the world ( Pazand et al. 2013;Pour et al. 2014;Pournamdari et al. 2014aPournamdari et al. , 2014bPour & Hashim 2015a, 2015b, 2015cGabr et al. 2015;dos Reis Salles et al. 2016;Pour et al. 2016;Wang et al. 2017). ...
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... MNF band numbers contain much of spectral information, therefore, they are suitable for mapping altered minerals due to their unique spectral features enhanced in these bands. Figure 10 shows the first three new MNF components obtained from transformation as RGB colour composites. These three MNF bands contain the most useful spectral information of altered rocks and lithological units. ...
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... three MNF bands contain the most useful spectral information of altered rocks and lithological units. Yellow colour delineated target alteration zones from other lithologic units in FCC image (Figure 10). ...
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... and chrysocolla appeared at the surface, which is frequently observed and described by (Dimitrijević 1973;Zürcher et al. 2015). Outcrop views of different alteration zones of Parkam (Sara) prospect are shown in Figure 11 (A-E). The field study indicated that hydrothermal alteration zones around Parkam (Sara) prospect are widely extended in the region (Figure 11(A)). ...
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... views of different alteration zones of Parkam (Sara) prospect are shown in Figure 11 (A-E). The field study indicated that hydrothermal alteration zones around Parkam (Sara) prospect are widely extended in the region (Figure 11(A)). The phyllic zone is widespread in the centre and propylitic zone extended over pyroclastic rocks ( Figure 11 (B and C). ...
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... field study indicated that hydrothermal alteration zones around Parkam (Sara) prospect are widely extended in the region (Figure 11(A)). The phyllic zone is widespread in the centre and propylitic zone extended over pyroclastic rocks ( Figure 11 (B and C). The phyllic zone in an outer layer is well-developed and easy to distinguish around Parkam diorite-microdiorite body. ...
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... phyllic zone in an outer layer is well-developed and easy to distinguish around Parkam diorite-microdiorite body. The peripheral argillic zone is thin and mostly distributed in the southern part of the area (Figure 11(D)). The potassic zone rarely appears only within phyllic zone (Figure 11(E)). ...
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... peripheral argillic zone is thin and mostly distributed in the southern part of the area (Figure 11(D)). The potassic zone rarely appears only within phyllic zone (Figure 11(E)). ...
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... altered minerals were detected using X-ray diffraction (XRD) technique during laboratory analysis as following: (i) illite and hematite in phyllic zone; (ii) calcite, chlorite, epidote, limonite and quartz in prophylitic zone; and (iii) kaolinite and montmorillonite in argillic zones. Thin sections of rock samples were also analysed (Figure 12(A-C)). Clay minerals especially sericite kao- linite, epidote and chlorite are the most predominant minerals in the thin section samples collected from argillic, phyllic and propylitic zones of Parkam (Sara) prospect area. ...
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Citations
... Remote Sensing techniques were widely applied for classifying the different rock units and delineating hydrothermal alteration zones (Zhang et al. 2007;Asadi et al. 2016;Safari et al. 2018;Chattoraj et al. 2020;Shebl and Csámer 2021;Abd El-Wahed et al. 2021). Different multispectral images were used to distinguish hydrothermal alteration zones and their related ore deposits (Tommaso and Rubinstein 2007;Khademian et al. 2024). ...
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... Another powerful technique of distinguishing iron ore mineralization is band ratioing, using the highest band re ectance pixel values as numerator and the lowest re ectance pixel One of the quickest and simple approaches is to represent band combination in red, green and blue as color composite (RGB) image, where the color nuance emphasizes the higher re ectance in particular band. The other very effective approach is the mathematical division of highest re ectance band pixel values as numerator with the lowest re ectance band pixel values as denominator (Safari et al., 2018). ...
... The use of Band ratios of Ferric iron (2/1), Ferric oxide (4/3) and Ferrous iron (5/4 + 1/2) of the ASTER imagery ensures the enhancement of iron ore bearing zones. This is due to the absorption of ferrous irons in some metasediments, exhibiting low re ectance in the visible and near-infrared Bands with increased re ectance towards Bands 4 and 5 (Safari et al., 2018). The ASTER dataset was analyzed and processed using ENVI 5.3™ and ArcMap 10.7™ softwares (www.esri.com). ...
Kogi State, Northcentral Nigeria has been known for its iron ore deposits and Kakanda village is one of those places that possess iron ore deposits. However, little information about the extent and locations of possible iron ore mineralized zone is available due to limited researches in that area. Hence, this study utilized aeromagnetic and Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) datasets for the delineation of Iron ore deposits in Kakanda Village and its environs. Enhancement techniques such as residual magnetic amplitude and analytic signal amplitude carried out on the aeromagnetic data revealed the concentration of iron ore deposits especially in the southern, eastern and western parts of the study area. This is consistent with signatures derived from other data enhancement techniques involving the total horizontal derivative, tilt derivative and 3D Euler deconvolution technique which were principally used to map structures guiding mineralization in the study area. Furthermore, analyses of the ASTER dataset using the true and false color composite and combination of band ratios indicate the occurrence of iron oxide and clay alterations relating to iron ore mineralization in the study area. It was observed that the signatures relating to iron ore mineralization of the aeromagnetic data and ASTER dataset are consistent with each other. The overlap of these signatures was used to produce the iron ore prospectivity map of the study area. The study showed that areas of delineated lineaments coincide with areas of iron ore mineralization. In the same vein, areas of dense lineaments coincide with areas of iron ore mineralization especially in the southern and eastern parts of the study area. The study area therefore concluded that mineralization in the study area is structurally controlled. Hence, further exploration activities should be focused on areas where there is an evident overlap of lineament and iron ore mineralization in the study area.
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... The resulting corrected scenes were stacked and mosaicked to obtain a single image for further processing for principal component analyses. In addition, Images produced by remote satellite sensors are frequently distorted because of cosmic impacts, including contortion brought on by variations in the calibration of the sensor Safari et al. 2018;Mamouch et al. 2022). Hence, radiometric evaluation and cosmic modifications were done to bands 1-8, with less emphasis being placed on band 2, which is not very important in the search of mineral deposits because most of the hydrothermal alteration minerals have their distinct signatures in the Visible Near Infrared (VNIR) and Shortwave Infrared (SWIR) regions of the electromagnetic spectrum (Osinowo et al. 2021). ...
In this paper, the knowledge-driven fuzzy AHP (FAHP) model was applied in the predictive prospectivity mapping of
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alization considered were heat source, gold/ligand source, structural control, and hydrothermal alteration. The proxies and
alternatives of the main criteria of the mineral system of orogenic gold mineralization were derived from geological, geo-
physical, and remote sensing datasets. Assignment of weights using the FAHP model indicates that heat source with weight
of 0.4318 has the highest contribution to orogenic gold mineralization in the study area. This was closely followed by gold/
ligand source (0.3669), structural control (0.1659) and hydrothermal alteration (0.0355). The integration of these criteria
using the multi-index overlay method produced the predictive mineral potential map (MPM) which was further classified
into six classes (background, very low, low, moderate, high, and very high potentials) using the concentration area (C–A)
fractal model. New major prospects of orogenic gold mineralization were delineated in the western and eastern flanks of the
study area. The validation of the produced MPM using 10 geochemical sampling points via the prediction area (P–A) plot
yielded 77% success rate indicating the model is suitable for predictive prospecting of orogenic gold mineralization. The
study concluded that the mineral system approach should be adopted in further research in similar geologic environments
for reliable potential mapping of any mineral deposits because it considers every “player” involved in mineralization.
... Data from the ASTER satellite are the most widely used, due to their spectral qualities which include 14 bands (see classification): three bands in the VNIR, six in the SWIR and five in the TIR. These spectral qualities have been exploited by a number of researchers to generate specific alteration mineral distribution maps (Abrams and Yamaguchi, 2019;Amer et al., 2010Amer et al., , 2012Mars and Rowan, 2010;Pour et al., 2018;Qiu et al., 2006;Safari et al., 2018;Sheikhrahimi et al., 2019;Zhang et al., 2007;Zoheir and Emam, 2012). In addition to multispectral remote sensing, airborne radiometric data including potassium (%), equivalent thorium (ppm) and equivalent uranium (ppm) are also effective methods for mapping alteration, particularly potassic alteration associated with hydrothermal deposits (IAEA, 2003;Lowell and Guilbert, 1970;Zhang et al., 1998). ...
... In other studies, including the work by (Safari et al., 2018) a combination of Landsat and ASTER data was employed to characterize various deposits, such as the Shar-e-Babak deposit. Notably, the ASTER SWIR bands played a crucial role in detecting and mapping hydrothermal alteration zones due to their unique mineralogical information content. ...
The Sar-e-Châh-e-Shur region in eastern Iran is identified as having significant potential for diverse mineral deposits, particularly copper and iron mineralization, attributed to tectonic activities. Employing multispectral remote sensing data, including ASTER, Sentinel 2, and Landsat 8, proved advantageous for detecting alteration zones. The study utilized spectral ratios associated with porphyry copper-iron alterations, and advanced argillic-iron alterations to establish training data. In addition, hyperspectral data processing (Hyperion and Hymap) has also been used to further contribute to generating training classes. The high spectral accuracy of hyperspectral data enhanced the precision of multispectral data classification over a 2617 square kilometers area. Then, different classification algorithms were applied, and evaluation metrics such as the Kappa coefficient and overall accuracy were calculated using a confusion matrix to evaluate the performance of these classifications. In addition, to validate classifications, a search table was created based on 16 thin sections and Stream sediments analyses (univariate analysis or Single element analysis) for 574 samples. NS were calculated, providing quantitative measures of conformity and estimation. The class LAS-2-NN2 (representing advanced argillic alterations) attained the highest score of 3.5, demonstrating an 87.5% match. In general, results from hyperspectral data processing using neural network algorithms yielded a score of 2.43, signifying a 61% match with the geological map. As a conclusion, the study underscores the utility of combining multispectral and hyperspectral data for accurate mineral alteration mapping in resource-rich regions.
... Crosta approach (Crosta and Moore, 1989), a principal component analysis-based technique that used a combination of four bands 1, 4, 5, and 7 for hydroxyl-mineral discrimination and bands 1, 3, 4, and 5 for iron oxides minerals to analyse Landsat-7 data, was used to designate the zones of alteration. The Principal component analysis (PCA) technique was employed for two sets of band combinations; 2, 4, 5, 6 for iron-oxide minerals and bands 2, 5, 6, 7 for hydroxyl minerals of Landsat-8 to define the alteration zones (Crosta and Moore, 1990;Safari et al., 2018). Two sets of band combination 2, 4, 8, 11 and 2, 8, 11, 12 of Sentinel-2B were subjected to the same principal component analysis (PCA) techniques. ...
... Principal component analysis (PCA) approaches were used on two sets of band combinations: 2, 4, 8, 11 and 2, 8, 11, 12 (Crosta and Moore, 1990;Safari et al., 2018;Tangestani and Moore, 2001;Liu et al., 2011). PC4 demarcate iron-oxide minerals in bright pixels as seen in Bands 2 and 4, which have a high eigenvector value of (0.824) at band 2 and a negative eigenvector value of (-0.555) at band 4 in Table. 5. Whereas mapped hydroxyl minerals in dark pixels as shown in Table 6. ...
The study area is the part of Lower Dir which is geographically situated in the NW of Khyber Pakhtunkhwaprovince, Pakistan. Geologically this area lies in the western part of the well-known Kohistan Island Arc. It representsthe best exposure to subduction-related island arc and has been regarded as the prominent metallogenic belt that hosts avariety of various minerals. The area has experienced various tectonic episodes in the past and altered the rockassemblages which developed the huge potential of copper and other precious base metals. To detect and monitor thesealteration zones, remote sensing techniques coupled with field observation were evaluated in the current study.Landsat-7, Landsat-8, and Sentinel-2B images were processed under the shelter of Principal component (Crostatechnique) analysis to demarcate the separate alteration zones. It was confirmed from the results that the signature ofthe mapped alteration zone reflects the ground truth observation of copper mineralization in the target area.Furthermore, remote sensing signatures were correlated with the petrographic details which also confirmed thesealteration zones. Spectrometry of the selected samples also delineates the same signature which is best fitted with theremote sensing data. From our current analysis, we suggest that a principal component technique in terms of medium tohigh-resolution remote sensing data is more beneficial for mineral exploration
... PCA transformation is a dimensionality-reduction method that decreases spectral redundancy in multiband datasets by creating new auxiliary bands (PCs), which are uncorrelated linear combination (eigenvector loadings) of the original bands (Masoumi et al. 2017;Noori et al. 2019;Sheikhrahimi et al. 2019). For example, the new PCs contain the unique contribution of eigenvector loadings for absorption and reflection bands of alteration mineral or mineral groups and they are used for elucidating different underlying geological processes (Grunsky 2010;Safari et al. 2018;Sheikhrahimi et al. 2019;El-Desoky et al. 2021). Usually, the first PC is associated with the largest explained variance while variances in each subsequent PC diminishes. ...
This study evaluates the potential for mineral prospectivity mapping (MPM) within the Kerkesha area, southwestern Eritrea, using remote sensing and geochemical data analysis. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) remote sensing data were used for mapping zones of hydrothermal alteration, while assessment of geologic structures was based on automated extraction of lineaments from a digital elevation model (DEM). Integration of these alteration and structural datasets with surface geochemical data was carried out to identify pathfinder elements associated with Au–Cu–Zn mineralization as well as to evaluate and delineate anomalous regions in this relatively underexplored region of the Arabia Nubia Shield (ANS). Specifically, the modeling approach for the extraction and interpretation of mineralization-related spectral footprints was the selective principal component analysis (SPCA), and the lineament features extracted from DEM derivatives were integrated with the soil geochemical data and modeled by principal component analysis (PCA). The results revealed a NE–SW trend of lineaments, delineated zones of hydrothermal alteration indicating the presence of multi-deposit type mineralization, and identified pathfinder elements. In addition, Au–Cu–Zn anomalous zones were extracted by one-class support vector machine (OCSVM) and the performance of such classification was validated by the area under the curve (AUC) and the Youden index computed from receiver operating characteristic (ROC) curves and the Kruskal–Wallis test. The results showed significance in differences between the anomalous and non-anomalous zones and existence of a relationship between known mineral deposits and predicted anomalies. The proposed MPM shows promising results for robust automated delineation and understanding of mineralization processes.
... Number of study areas References Sudan 1 [39], [40], [41] Namibia 1 [42], [43], [44] Argentina 4 [45], [46], [47] , [48], [49], [50], [51], [52], [53], [54], [55], [56] Iran 4 [57], [58], [59], [60], [61], [62], [63], [64], [65] , [66], [67], [68], [69], [70] Pakistan 3 [71], [72], [73], [74], [75], [76] , [77], USA 5 [71], [78], [79], [80], [81], [82], [83] [84], [85], [86], [87] , [88], [89] China Test area 1 [90], [91], [92] China Test area 2 [93], [94], [95] [99]. The performed indices include the OH-bearing altered minerals index (OHI = 7/6 × 4/6), the kaolinite index (KLI = 4/5 × 8/6), the alunite index (ALI = 7/5 × 7/8) and the calcite index (CLI = 6/8 × 9/8). ...
Mineral exploration is essential to ensure a sustainable supply of raw materials for modern living and the transition to green. It implies a series of expensive operations that aim to identify areas with natural mineral concentration in the crust of the Earth. The rapid advances in artificial intelligence and remote sensing techniques can help in significantly reducing the cost of these operations. Here, we produce a robust intelligent mineral exploration model that can fingerprint potential locations of porphyry deposits, which are the world's most important source of copper and molybdenum and major source of gold, silver, and tin. We present a deep learning pipeline for assessing multispectral imagery from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) with the objective of identifying hydrothermal alterations. Our approach leverages a Convolutional Neural Network (ConvNet) to analyze the high-resolution images, overcoming computational challenges through a patch-based strategy that involves an overlapping window for partitioning the images into fixed-size patches. Through the utilization of manually labeled patches for image classification and identification of hydrothermal alteration areas, our results demonstrate the remarkable ability of ConvNet to accurately detect hydrothermal alterations. The technique is adaptable for other ore deposit models and satellite imagery types, providing a revolution in satellite image interpretation and mineral exploration.
