Exploration
Ahmed Abdelhalim; Islam Abuelella; Shawky M Sakran; Said Mohamed Said
Abstract
Kharit basin is an interior Cretaceous rift basin hosted in a Precambrian basement complex of the Arabian-Nubian shield. Satellite images and potential geophysical data previously outlined the basin without a detailed field study. Kharit area is a remote and hyper-arid area; therefore, the application ...
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Kharit basin is an interior Cretaceous rift basin hosted in a Precambrian basement complex of the Arabian-Nubian shield. Satellite images and potential geophysical data previously outlined the basin without a detailed field study. Kharit area is a remote and hyper-arid area; therefore, the application of remote sensing is essential for completing the process of its geo-structural mapping. A multi-spectral optical dataset of the Landsat-8 and high-resolution images of Google Earth was integrated with the field investigation to classify the lithological units and define structures. That integration between analyzed satellite images and field investigations led to a geological map of a minimum scale of 1:50,000 for the lithological rock units and a maximum scale of up to 1:7000 for the structural mapping. The map shows an elongated NW-oriented rift basin filled by a thick deposit of Cretaceous sequences bounded from the east, west, and south by Proterozoic igneous and metamorphic rocks. Additionally, rift-related volcanic rocks were mapped along the western border fault system of the basin. The main mapped faults were delineated in three trends, NW-SE, WNW-ENE, and N-S, while several folds of NW orientations are developed as a normal drag of the main bounding faults. The Early Cretaceous extension along inherited Precambrian lineaments propagated this fault pattern and its associated folds. These structural elements configured the studied area architecture as several grabens with thick Cretaceous sequences.
Exploration
Abdallah Atef; Ahmed A. Madani; Adel A. Surour; Mokhles K. Azer
Abstract
This study reports the application of remote sensing data and knowledge-driven GIS modeling to provide favorability maps for gold and copper mineralized areas. The South Gabal Um Monqul (SGUM) and the Gabal Al Kharaza (GKZ) prospects located in the northern Eastern Desert of Egypt are the targets for ...
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This study reports the application of remote sensing data and knowledge-driven GIS modeling to provide favorability maps for gold and copper mineralized areas. The South Gabal Um Monqul (SGUM) and the Gabal Al Kharaza (GKZ) prospects located in the northern Eastern Desert of Egypt are the targets for the present study. Four thematic layers (lithology maps, old trenches buffer analysis, lineament density maps, and alteration zone maps) were prepared and used as inputs for a weighted overlay GIS model. Combined results from false color composite images, particularly the RGB parameters (PC2, PC1, and PC3) and the RGB parameters (MNF1, MNF2, and MNF3) classified the host rocks in both prospects. PCA-based extraction of lineaments was considered using line algorithm of PCI Geomatica. QuickBird band math (G+B), (R+G), and (G-B) for RGB was successful in delineating ancient workings within the mineralized zones. Old trenches layers were buffered to 20 m wide bands extending in all directions. Landsat-8 band ratios imagery (6/5 * 4/5, 6/7, and 6/2) in red, green, and blue (RGB) is potent in defining alteration zones that host gold and copper mineralizations. Acceptable scores of 30%, 30%, 20%, and 20% were assigned for the alteration zone maps, ancient workings buffer analysis, lithology maps and lineament density maps, respectively. Two favorability maps for mineralizations were generated for the SGUM and GKZ prospects. Validation of these maps and their potential application to detect new mineralization sites in the northern Eastern Desert were discussed.
Exploration
Mohammadjafar Mohammadzadeh; Majid Mahboubiaghdam; Moharram Jahangiri; Aynur Nasseri
Abstract
Most machine learning-monitored algorithms used to create mineral potential prediction maps require noise-free data to achieve high performance and reliable results. Unsupervised clustering methods are highly effective for uncovering a dataset’s hidden structures. Therefore, this study attempts ...
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Most machine learning-monitored algorithms used to create mineral potential prediction maps require noise-free data to achieve high performance and reliable results. Unsupervised clustering methods are highly effective for uncovering a dataset’s hidden structures. Therefore, this study attempts a combination of supervised and unsupervised methods employing training and testing data to generate a highly accurate potential map of the Sonajil copper-gold deposit located in the NW of Iran. Here, a semi-supervised Bayesian algorithm is used to map the mineral landscape. Initially, ten raster layers of exploratory features are prepared. Then based on the copper concentration, 27 exploratory drilled boreholes are divided into four classes, C1 to C4, and from each class, two boreholes are selected, and 100-meter buffering is performed around these boreholes to extract 1113 training data based on the behavioral pattern of boreholes and surface samples. Subsequently, the existing data is clustered using the FCM method, and the total dataset and the clustering data are entered into the Bayesian algorithm to evaluate the accuracy of the Bayesian classifier method across five distinct clusters. The results show increased average accuracy when using clustered data instead of whole data for MPM mapping. Notably, the Bayesian semi-supervised algorithm achieved an impressive accuracy rate of 96% when cluster five data is excluded. To validate the Bayesian semi-supervised method, boreholes data that is not used in training were employed, which confirm the credibility of generated MPM. Overall results highlight the value of the Bayesian semi-supervised algorithm in improving the accuracy and reliability of mineral prospectivity mapping via the application of the FCM clustering method that efficiently organize the data, enabling the Bayesian algorithm to evaluate the accuracy of the Bayesian classifier method across different clusters and providing a successful optimal result in detecting blind ores in areas without exploratory boreholes and delineating more mineralization targets in the Sonajil and adjoining areas.
Exploration
Ajay Kumar
Abstract
Land use (LU) classification based on remote sensing images is a challenging task that can be effectively addressed using a learning framework. However, accurately classifying pixels according to their land use poses a significant difficulty. Despite advancements in feature extraction techniques, the ...
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Land use (LU) classification based on remote sensing images is a challenging task that can be effectively addressed using a learning framework. However, accurately classifying pixels according to their land use poses a significant difficulty. Despite advancements in feature extraction techniques, the effectiveness of learning algorithms can vary considerably. In this study conducted in Talcher, Odisha, India, the researchers proposed the use of Artificial Neural Networks (ANNs) to classify land use based on a dataset collected by the Sentinel-2 satellite. The study focused on the Talcher region, which was divided into five distinct land use classes: coal area, built-up area, barren area, vegetation area, and waterbody area. By applying ANNs to the mining region of Talcher, the researchers aimed to improve the accuracy of land use classification. The results obtained from the study demonstrated an overall accuracy of 79.4%. This research work highlights the importance of utilizing remote sensing images and a learning framework to address the challenges associated with pixel-based land use classification. By employing ANNs and leveraging the dataset from the Sentinel-2 satellite, the study offers valuable insights into effectively classifying different land use categories in the Talcher region of India. The findings contribute to the advancement of techniques for accurate land use analysis, with potential applications in various fields such as urban planning, environmental monitoring, and resource management.
Exploration
Samaneh Barak; Ali Imamalipour; Maysam Abedi
Abstract
The Sonajil area is located in the east Azerbaijan province of Iran. According to studies on the geological structure, the region has experienced intrusive, subvolcanic, and extrusive magmatic activities, as well as subduction processes. As a result, the region is recognized for its high potential for ...
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The Sonajil area is located in the east Azerbaijan province of Iran. According to studies on the geological structure, the region has experienced intrusive, subvolcanic, and extrusive magmatic activities, as well as subduction processes. As a result, the region is recognized for its high potential for mineralization, particularly for Cu-Au porphyry types. The main objective of this research work is to utilize the fuzzy gamma operator integration approach to identify the areas with high potential for porphyry deposits. To carry out this exploratory approach, it is necessary to investigate several indicator layers including geological, remote sensing, geochemical, and geo-physical data. The analysis reveals that the northeastern and southwestern parts of the Sonajil region exhibit a greater potential for porphyry deposits. The accuracy of the resulting Mineral Potential Map (MPM) in the Sonajil region was evaluated based on data from 20 drilled boreholes, which showed an agreement percentage of 83.33%. Due to the high level of agreement, certain locations identified in the generated MPM were recommended for further exploration studies and drilling.
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.
Exploration
Vivek Sharma; Ravi Kumar Sharma; Pardeep Kumar
Abstract
In the present work, the empirical correlations between standard penetration test (SPT) N-values versus shear modulus (Gmax), and Peak Ground Acceleration (PGA) amplifications for sub-Himalayan district-Hamirpur, Himachal Pradesh (India) consisting of highly variable soil/rock strata at different ...
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In the present work, the empirical correlations between standard penetration test (SPT) N-values versus shear modulus (Gmax), and Peak Ground Acceleration (PGA) amplifications for sub-Himalayan district-Hamirpur, Himachal Pradesh (India) consisting of highly variable soil/rock strata at different depths and across the terrain are evaluated. In the first stage, the N values obtained from SPTs are conducted in the field at 184 locations covering the studied area. The shear wave velocity for each soil profile of each borehole is calculated using the best available correlation in the literature. Further, the seismic response parameters are evaluated for these values using the ProShake software. Finally, the empirical relationships between maximum shear modulus and SPT value for different soil types are determined along with the ground motion amplifications. The amplification factor for Bhoranj sub-division varies from 1.40 to 2.60 and from 1.28 to 2.30, 1.20 to 2.10, 1.22 to 1.85, and 1.22 to 1.70 for Barsar, Nadaun, Hamirpur, and Sujanpur, respectively. The studied area consists of variable soil strata including clay, silt, sand, conglomerate, sandstone, and mixture thereof. The correlation between shear modulus and N value is coherent with already reported correlations for regular soils. The amplification factor reported for the sites plays an important role in planning infrastructure in the region. The correlations between maximum shear modulus (Gmax) and SPT value for hilly terrain comprising of highly complex geological formations such as mixed soil and fractured rocks presented in the study are not available in the research work carried out earlier.
Exploration
H. Sabeti; F. Moradpouri
Abstract
The geo-statistical simulation algorithms for continuous spatial variables have been used widely in order to generate the statistically-honored models. There are two main algorithms doing the continuous variable simulation, Sequential Gaussian Simulation (SGS) and Direct Sequential Simulation (DSS). ...
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The geo-statistical simulation algorithms for continuous spatial variables have been used widely in order to generate the statistically-honored models. There are two main algorithms doing the continuous variable simulation, Sequential Gaussian Simulation (SGS) and Direct Sequential Simulation (DSS). The main advantage of the DSS algorithm against the SGS algorithm is that in the DSS algorithm no Gaussian transformation of the original data is made. In this work, these two simulation algorithms are explained, and their applications to a 3D spatial dataset are deeply investigated. The dataset consists of the porosity values of 16 vertical wells extracted from an actual cube obtained by a seismic inversion process. One well data is excluded from the simulation process for the blind well test. Comparison between the histograms show that the histogram reproduction is slightly better for the SGS algorithm, although the population reproductions are the same for both SGS and DSS results. The DSS algorithm reproduce the mean of input data closer to the mean of well data compared to that of the SGS algorithm. Considering one realization from each simulation algorithm, the RMS error corresponding to all simulated cells against the real values is approximately equal for both algorithms. On the other hand, the error show a slightly less value when the mean of 100 realizations of the DSS result is considered.
Exploration
F. Mirsepahvand; M.R. Jafari; P. Afzal; M. A. Arian
Abstract
The goal of this research work is to recognize the metallic mineralization potential in the Ahar 1:100,000 sheet (NW Iran) using the remote sensing data based on determination of the alteration zones. This area is located in the Ahar-Arasbaran metallogenic zone as a significant metallogenic zone in Iran ...
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The goal of this research work is to recognize the metallic mineralization potential in the Ahar 1:100,000 sheet (NW Iran) using the remote sensing data based on determination of the alteration zones. This area is located in the Ahar-Arasbaran metallogenic zone as a significant metallogenic zone in Iran and Caucasus. In this research work, the Landsat-7 ETM+ and advanced space borne thermal emission and reflection radiometer (ASTER) multispectral remote sensing data was interpreted by the least square fit (LS-Fit), spectral angle mapper (SAM), and matched filtering (MF) algorithms in order to detect the alteration zones associated with the metallic mineralization. The results obtained by these methods show that there are index-altered minerals for the argillic, silicification, advanced argillic, propylitic, and phyllic alteration zones. The main altered areas are situated in the SE, NE, and central parts of this region.
Exploration
N. Mahvash Mohammadi; A. Hezarkhani
Abstract
Identification and mapping of the significant alterations are the main objectives of the exploration geochemical surveys. The field study is time-consuming and costly to produce the classified maps. Therefore, the processing of remotely sensed data, which provide timely and multi-band (multi-layer) data, ...
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Identification and mapping of the significant alterations are the main objectives of the exploration geochemical surveys. The field study is time-consuming and costly to produce the classified maps. Therefore, the processing of remotely sensed data, which provide timely and multi-band (multi-layer) data, can be substituted for the field study. In this study, the ASTER imagery is used for alteration classification by applying two new methods of machine learning, including Random Forest and Support Vector Machine. The 14 band ASTER and 19 derivative data layers extracted from ASTER including band ratio and PC imagery, are used as training datasets for improving the results. Comparison of analytical results achieved from the two mentioned methods confirmed that the SVM model has sufficient accuracy and more powerful performance than RF model for alteration classification in the study area.
Exploration
V. Adjiski; D. Mirakovski; Z. Despodov; S. Mijalkovski
Abstract
Auxiliary ventilation of the blind development heading in underground mines is one of the most challenging work activities amongst mining underground operations. The auxiliary forcing ventilation system provides positive pressure, cooling, controlling gas layering, and removing diesel fumes and dust ...
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Auxiliary ventilation of the blind development heading in underground mines is one of the most challenging work activities amongst mining underground operations. The auxiliary forcing ventilation system provides positive pressure, cooling, controlling gas layering, and removing diesel fumes and dust levels from development headings, stopes, and services facilities. The effectiveness of the auxiliary forcing ventilation system depends upon many system variables. Currently, no scientific models and calculations are available that can be used to estimate the optimal distance from the outlet of the auxiliary forcing ventilation system to the development heading in underground mines that can provide the most efficient ventilation close to the face of the heading. In this work, scenarios are developed and simulated with a validated CFD model inside the ANSYS Fluent software. In each scenario, the system parameters such as dead zone, mean age of air, and face velocity are calculated, which are later used in the optimization process. By examining these parameters at the development heading zone, we can quantify the effectiveness of the ventilation system and confirm that the system design meets the government regulations. This work is carried out using the k-epsilon realizable turbulent model inside the ANSYS Fluent software.
Exploration
A. Habibnia; Gh. R. Rahimipour; H. Ranjbar
Abstract
Hanza region is located in the southern part of Urumieh–Dokhtar Metallogenic belt in southeastern Iran. This region includes six known porphyry copper deposits and it is considered as an ore- bearing region from geochemical point of view. The aim of this research is to examine effective processing ...
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Hanza region is located in the southern part of Urumieh–Dokhtar Metallogenic belt in southeastern Iran. This region includes six known porphyry copper deposits and it is considered as an ore- bearing region from geochemical point of view. The aim of this research is to examine effective processing techniques in the analysis of stream sediment geochemical datasets and ASTER satellite images. The processing methods have led to identification of eight new prospective areas. These methods are aimed at providing univariate geochemical maps. The stream sediment geochemical mapping for Cu and Mo was performed by the sample catchment basin approach. The results derived from this approach have been mapped in four classes associated with the first quartile, third quartile and threshold values obtained from Median Absolute Deviation method. False-colour composite and band ratio techniques were adopted as two well-known methods for the processing of an ASTER scene spanning the study area. Eight new targets for possible mineralization have been resulted from geochemical data analyses. Image processing techniques on the ASTER multispectral data have also revealed widespread hydrothermal alterations associated with the known porphyry copper deposits and the new prospects.
Exploration
M. Honarmand; H. Ranjbar; H. Shahriari; F. Naseri
Abstract
This research was performed with the objective of evaluating the accuracy of spectral angle mapper (SAM) classification using different reference spectra. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) digital images were applied in the SAM classification in order to map the ...
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This research was performed with the objective of evaluating the accuracy of spectral angle mapper (SAM) classification using different reference spectra. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) digital images were applied in the SAM classification in order to map the distribution of hydrothermally altered rocks in the Kerman Cenozoic magmatic arc (KCMA), Iran. The study area comprises main porphyry copper deposits such as Meiduk and Chahfiroozeh. Collecting reference spectra was considered after pre-processing of ASTER VNIR/SWIR images. Three types of reference spectra including image, USGS library, and field samples spectra were used in the SAM algorithm. Ground truthing and laboratory studies including thin section studies, XRD analysis, and VNIR-SWIR reflectance spectroscopy were utilized to verify the results. The accuracy of SAM classification was numerically calculated using a confusion matrix. The best accuracy of 74.01% and a kappa coefficient of 0.65 were achieved using the SAM method using field samples spectra as the reference. The SAM results were also validated with the mixture tuned matched filtering (MTMF) method. Field investigations showed that more than 90% of the known copper mineralization occurred within the enhanced alteration areas.
