Exploration
Moslem Jahantigh; Hamid Reza Ramazi
Abstract
The present paper gives out data-driven method with airborne magnetic data, airborne radiometric data, and geochemistry data. The purpose of this study is to create a mineral potential model of the Shahr-e-Babak studied area. The studied area is located in the south-eastern of Iran. The various evidential ...
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The present paper gives out data-driven method with airborne magnetic data, airborne radiometric data, and geochemistry data. The purpose of this study is to create a mineral potential model of the Shahr-e-Babak studied area. The studied area is located in the south-eastern of Iran. The various evidential layers include airborne magnetic data, airborne radiometric data (potassium and thorium), lineament density map, cu geochemistry signature, and multi-variate geochemistry signature (PC1). High magnetic anomalies, lineament structures, and alteration zones (K/Th) were derived from airborne geophysics data. Geochemistry signatures (Cu and PC1) were derived from stream sediment data. The principal Component Analysis (PCA) as an unsupervised machine learning method and five evidential layers were used to produce a porphyry prospectivity model. As a result of this combination, mineral prospectivity model was produced. Then a plot of cumulative percent of the studied area versus pca prospectivity value was used to discrete high potential areas. Then to evaluate the ability of this MPM, the location of known cu indications was used. The results confirm an acceptable outcome for porphyry prospectivity modeling. Based on this model high-potential areas are located in south southwestern and eastern parts of the studied area.
Exploration
Khadijeh Validabadi Bozcheloei; Majid Hashemi Tangestani
Abstract
Evaporites are sediments that chemically precipitate due to the evaporation of an aqueous solution. Most evaporite formations, in addition to evaporite minerals, include detrital rocks such as mudstone, marl, and siltstone. Principal Component Analysis (PCA), Directed Principal Component Analysis (DPCA), ...
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Evaporites are sediments that chemically precipitate due to the evaporation of an aqueous solution. Most evaporite formations, in addition to evaporite minerals, include detrital rocks such as mudstone, marl, and siltstone. Principal Component Analysis (PCA), Directed Principal Component Analysis (DPCA), and Band Ratio methods were applied to Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) data for mapping the Gachsaran evaporite formation and distinguishing its lithological units in the Masjed Soleiman oil field, located in southwestern Iran. This oil field was the first recognized oil field in the Middle East. Colour composites of PCs 4, 5, and 2, as RGB images, effectively discriminated this formation from other sedimentary formations. The gypsum spectrum, resampled to the 9 band centres of ASTER, exhibited reflectance in bands 4 and 8 and absorption in bands 6 and 9. As a result, these bands were selected for DPCA application. PC4 effectively highlighted gypsum outcrops as bright pixels, while the band ratio 2/1 accentuated ferric iron, appearing as bright pixels, which correlated with the red marls. The results of this study demonstrate that ASTER image processing is a cost- and time-effective method that can be utilized for mapping evaporite formations and distinguishing their lithological units.
Exploration
Hossein Mahdiyanfar; Mirmahdi Seyedrahimi-Niaraq
Abstract
The primary purpose of this investigation is contamination mapping in surrounding areas of Irankuh Pb–Zn mine, located in central Iran, using an integrated approach of principal component analysis (PCA) with the Concentration-Area (C-A) and Power Spectrum-Area (S-A) fractal models. PCA categorized ...
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The primary purpose of this investigation is contamination mapping in surrounding areas of Irankuh Pb–Zn mine, located in central Iran, using an integrated approach of principal component analysis (PCA) with the Concentration-Area (C-A) and Power Spectrum-Area (S-A) fractal models. PCA categorized the 45 elements into eight principal components. Component 2, containing the toxic elements of Pb, Zn, As, Mn, Cd, and Ba, was identified as the contamination factor. This multivariate contamination factor was modeled using the C-A and S-A fractal methods (in spatial and frequency domains) to delineate pollution areas. Modeling of PCA data using the C-A fractal method showed four main populations for the contamination factors. Two populations with higher fractal dimensions are associated with contamination from mining activities or anthropogenic effects. Low fractal dimensions are considered the background population, which has not been affected or is less affected by these activities. Five geo-chemical populations were obtained for contamination factors using the S-A fractal modeling of PCA in the frequency domain. Therefore, various geo-chemical populations were achieved using geo-chemical filtering and two-dimensional inverse Fourier transformation. The geo-chemical populations related to classes 2, 3, and 4 containing intermediate frequency signals showed the pollution anomaly. The spatial distribution of pollutant geo-chemical signals exhibits excellent conformity with the mining operation limit and tailing dam location as pollutant sources. The results indicate that the elements Pb, Zn, Cd, and As have significant values in the surrounding soils rather than their concentrations in the earth’s crust. The results demonstrate that the S-A fractal models can more precisely delineate the environmental anomaly than the C-A fractal model, especially in intermediate frequency populations.
Environment
H. Mahdiyanfar
Abstract
Detection of deep and hidden mineralization using the surface geochemical data is a challenging subject in the mineral exploration. In this work, a novel scenario based on the spectrum–area fractal analysis (SAFA) and the principal component analysis (PCA) has been applied to distinguish and delineate ...
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Detection of deep and hidden mineralization using the surface geochemical data is a challenging subject in the mineral exploration. In this work, a novel scenario based on the spectrum–area fractal analysis (SAFA) and the principal component analysis (PCA) has been applied to distinguish and delineate the blind and deep Mo anomaly in the Dalli Cu–Au porphyry mineralization area. The Dalli mineral deposit is located on the volcanic–plutonic belt of Sahand–Bazman in the central part of Iran. The geochemical data was transformed to the frequency domain using the Fourier transformation, and SAFA was applied for classification of geochemical frequencies and detection of geochemical populations. The very low-frequency signals in the fractal method were separated using the low-pass filter function and were interpreted using PCA. This scenario demonstrates that the Mo element has an important role in the mineralization phase in the very low-frequency signals that are related to the deep mineralization; it is an important innovation in this work. Then the Mo geochemical anomaly has been mapped using the inverse Fourier transformation. This research work shows that the high-power spectrum values in SAFA are related to the background elements and the deep mineralization. Two exploratory boreholes drilled inside and outside the deep Mo anomaly area properly confirm the results of the proposed approach.
Exploitation
H. Aryanmehr; M. Hosseinjanizadeh; M. Honarmand; F. Naser
Abstract
In this work, we focus on investigating the Quickbird and Landsat-8 datasets for mapping hydrothermal and gossans alterations in reconnaissance porphyry copper mineralization in the Babbiduyeh area. This area is situated in the Central Iranian Volcano-sedimentary Complex, where large copper deposits ...
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In this work, we focus on investigating the Quickbird and Landsat-8 datasets for mapping hydrothermal and gossans alterations in reconnaissance porphyry copper mineralization in the Babbiduyeh area. This area is situated in the Central Iranian Volcano-sedimentary Complex, where large copper deposits like Sarcheshmeh as well as numerous occurrences of copper exist. The alteration zones are discriminated by implementation of band ratio and principal component analysis on the Quickbird and Landsat-8 datasets. The image processing results are evaluated by field surveys, X-ray diffraction (XRD), microscopic thin section, and spectroscopic studies of field samples as well as the 1:100000 Sarduiyeh and 1:5000 Babbiduyeh geological maps. In addition, the spectral characterizations of the samples are analyzed by visual inspection, and the PIMAView, SAMS, and ViewSpecpro software programs. The combined spectroscopic measurements, XRD analyses, and petrographic studies revealed mineral assemblages typical of the phyllic, phyllic-supergen, propylitic, argillic, and gossan alterations. The results obtained from image processing and analysis of field samples illustrated examples of effects of iron oxides and hydroxides on the surface of phyllic and argillic alterations. Hence, it can be concluded that the areas discriminated in Quickbird as gossans correspond to the phyllic and argillic alteration areas.
Exploitation
H. Shahi
Abstract
Discrimination of the blind and dispersed mineralization deposits is a challenging problem in geochemical exploration. The frequency domain (FD) of the surface geochemical data can solve this important issue. This new exploratory information can be achieved using the interpretation of FD of geochemical ...
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Discrimination of the blind and dispersed mineralization deposits is a challenging problem in geochemical exploration. The frequency domain (FD) of the surface geochemical data can solve this important issue. This new exploratory information can be achieved using the interpretation of FD of geochemical data, which is impossible in spatial domain. In this research work, FD of the surface geochemical data is analyzed to decompose the complex geochemical patterns related to the mineral deposits. In order to identify the dispersed mineralization zone in the Chichakloo Pb–Zn deposit, a newly developed approach is proposed based on the coupling of two-dimensional Fourier transform (2DFT) and principal component analysis (PCA). The surface geochemical data is transferred to FD using 2DFT, and two low-pass filters are designed and performed on FD. Then the PCA method is employed on these frequency bands (FBs) separately. This proposed scenario desirably illustrates the relationship between the low frequencies in the surface geochemical distribution map (GDM) and the deep deposits. The informations obtained from the detailed exploration and the exploration drillings such as boreholes confirm the results obtained from this method. This new combined approach is a valuable data-processing tool and pattern-recognition technique in geochemical explorations. This approach is quite inexpensive compared to the traditional exploration methods.
H. Shahi; R. Ghavami Riabi; A. Kamkar Ruhani; H. Asadi Haroni
Abstract
In this research work, the frequency domain (FD) of surface geochemical data was analyzed to decompose the complex geochemical patterns related to different depths of the mineral deposit. In order to predict the variation in mineralization in the depth and identify the deep geochemical anomalies and ...
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In this research work, the frequency domain (FD) of surface geochemical data was analyzed to decompose the complex geochemical patterns related to different depths of the mineral deposit. In order to predict the variation in mineralization in the depth and identify the deep geochemical anomalies and blind mineralization using the surface geochemical data for the Dalli Cu-Au porphyry deposit, a newly developed approach was proposed based on the coupling Fourier transform and principal component analysis. The surface geochemical data was transferred to FD using Fourier transformation and high and low pass filters were performed on FD. Then the principal component analysis method was employed on these frequency bands separately. This new combined approach demonstrated desirably the relationship between the high and low frequencies in the surface geochemical distribution map and the deposit depth. This new combined approach is a valuable data-processing tool and pattern-recognition technique to identify the promising anomalies, and to determine the mineralization trends in the depth without drilling. The information obtained from the exploration drillings such as boreholes confirms the results obtained from this method. The new exploratory information obtained from FD of the surface geochemical distribution map was not achieved in the spatial domain. This approach is quite inexpensive compared to the traditional exploration methods.
