Exploration
Kaustubh Sinha; Priyangi Sharma; Anurag Sharma; Kanwarpreet Singh; Murtaza Hassan
Abstract
In this expansive study, a thorough analysis of land subsidence in the Joshimath area has been conducted, exercising remote sensing (RS) and Geographic Information System (Civilians) tools. The exploration encompasses colourful pivotal parameters, including Annual Rainfall, Geology, Geomorphology, and ...
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In this expansive study, a thorough analysis of land subsidence in the Joshimath area has been conducted, exercising remote sensing (RS) and Geographic Information System (Civilians) tools. The exploration encompasses colourful pivotal parameters, including Annual Rainfall, Geology, Geomorphology, and Lithology, rounded by the integration of different indicators. Joshimath, a fascinating city nestled within the rugged geography of the Indian state of Uttarakhand, stands out for its unique geographical features and its vulnerability to environmental vulnerabilities. The disquisition is carried out with the backing of ArcMap software, a technical Civilians tool, while exercising data sourced from the recognized Indian Space Research Organisation (ISRO) and the National Remote seeing Centre (NRSC). This comprehensive approach aims to give inestimable perceptivity into the dynamic processes associated with land subsidence in the region, offering critical data for disaster mitigation strategies and sustainable land operation in the area. It's noteworthy that the region endured a significant case of land subsidence in late December 2022, emphasizing the punctuality and applicability of this study. This event not only emphasizes the urgency of comprehending land subsidence in Joshimath but also underscores the necessity for ongoing monitoring and mitigation sweats. The integration of these different data sources and logical ways promises to enhance the understanding of land subsidence dynamics and inform decision- makers in the pursuit of flexible and sustainable land use practices in Joshimath and other also vulnerable regions.
Exploration
Abdelrahem Khalefa Embaby; Ashraf Ismael; Faissal Ali; Hussin Farag; Bahaa Mousa; Sayed Gomaa; Mohamed Elwageeh
Abstract
The mineral resource estimation process necessitates a precise prediction of the grade based on limited drilling data. Grade is crucial factor in the selection of various mining projects for investment and development. When stationary requirements are not met, geo-statistical approaches for reserve estimation ...
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The mineral resource estimation process necessitates a precise prediction of the grade based on limited drilling data. Grade is crucial factor in the selection of various mining projects for investment and development. When stationary requirements are not met, geo-statistical approaches for reserve estimation are challenging to apply. Artificial Neural Networks (ANNs) are a better alternative to geo-statistical techniques since they take less processing time to create and apply. For forecasting the iron ore grade at El-Gezera region in El- Baharya Oasis, Western Desert of Egypt, a novel Artificial Neural Network (ANN) model, geo-statistical methods (Variograms and Ordinary kriging), and Triangulation Irregular Network (TIN) were employed in this study. The geo-statistical models and TIN technique revealed a distinct distribution of iron ore elements in the studied area. Initially, the tan sigmoid and logistic sigmoid functions at various numbers of neurons were compared to choose the best ANN model of one and two hidden layers using the Levenberg-Marquardt pure-linear output function. The presented ANN model estimates the iron ore as a function of the grades of Cl%, SiO2%, and MnO% with a correlation factor of 0.94. The proposed ANN model can be applied to any other dataset within the range with acceptable accuracy.
Exploration
Mobin Saremi; Saeed Yousefi; Mahyar Yousefi
Abstract
The Mineral Prospectivity Mapping (MPM) is a procedure of integrating various exploration data to identify promising areas for follow up mineral exploration programs. MPM facilitates identification of mineral deposit prospects through reducing search spaces for the purpose of mitigating cost and time ...
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The Mineral Prospectivity Mapping (MPM) is a procedure of integrating various exploration data to identify promising areas for follow up mineral exploration programs. MPM facilitates identification of mineral deposit prospects through reducing search spaces for the purpose of mitigating cost and time shortages. In this regard, geochemical anomaly maps constitute one of the most important evidential layers for MPM. In this research work, to produce an efficient geochemical evidential layer, the Staged Factor Analysis (SFA) method and Geochemical Mineralization Probability Index (GMPI) were performed on a dataset of 657 stream sediment samples. In addition to the mentioned maps, a layer of proximity to faults was used to efficiently identify the intended targets of copper hydrothermal deposits. The layers were then weighted and combined using logistic functions and the geometric average method. Based on the obtained results, the promising areas were found in three parts including western, central, and northern areas, which correspond to the faulted units of andesite, tuff, granite, and granodiorite intrusive masses. Finally, in order to evaluate the generated model, the prediction-area (P-A) plot was used, which shows the relative success of the generated map in specifying the desired exploration targets. The P-A plot showed that this model has a prediction rate of 64%. It seems that the proposed method by considering multi-element geochemical signatures and combination by another exploratory layer target the promising areas, those that are simultaneously present with other exploration evidence.
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
Jabar Habashi; Majid Mohammady Oskouei; Hadi Jamshid Moghadam
Abstract
The studied area located in eastern Iran shows a high potential for various mineralizations, especially copper due to its tectonic activity. Remote sensing data can effectively distinguish these areas because of the sparse vegetation. Therefore, in this study, the ASTER (Advanced Spaceborne Thermal Emission ...
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The studied area located in eastern Iran shows a high potential for various mineralizations, especially copper due to its tectonic activity. Remote sensing data can effectively distinguish these areas because of the sparse vegetation. Therefore, in this study, the ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) multi-spectral data was used to recognize argillic, sericite, propylitic, and iron oxide alterations associated with copper mineralization. For this purpose, two categories (porphyry copper-iron and advanced argillic-iron) related alterations were considered to perform the classification of a 2617 square kilometer area using a neural network classification algorithm. To evaluate the accuracy of the classifier, the confusion matrix was computed, which provides overall accuracy and the kappa coefficient factors for assessing classification accuracy. As a result, 64.17% and 83.5% of overall accuracy, and 0.602 and 0.807 of the kappa coefficient were achieved for the advanced argillic alterations and porphyry copper categories, respectively. Ultimately, the validation of the classifications was carried out using the normalized score (NS) equation, employing quantitative criteria. Notably, the advanced argillic class emerged with the top normalized score of 2.25 out of 4, signifying a 56% alignment with the geological characteristics of the region. Consequently, this outcome has led to the identification of favorable areas in the central and northeastern parts of the studied area.
Exploration
Seyyed Saeed Ghannadpour; Morteza Hasiri; Hadi Jalili; Somayeh Talebiesfandarani
Abstract
The Zafarghand area (as a porphyry Cu deposit) is located in the northeast of Isfahan and southeast of Ardestan, which is a part of the Iran-Central structural zone, and more precisely, it is located in the Urmia-Dokhtar volcanic belt. In the porphyry Cu deposits exploration, identifying and determining ...
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The Zafarghand area (as a porphyry Cu deposit) is located in the northeast of Isfahan and southeast of Ardestan, which is a part of the Iran-Central structural zone, and more precisely, it is located in the Urmia-Dokhtar volcanic belt. In the porphyry Cu deposits exploration, identifying and determining the alteration zones is of special importance. The aim of the present study is to identify and highlight the alteration zones of Zafarghand area, with the help of the U-statistic method in the processing of ASTER sensor satellite images. Accordingly, considering the raster nature and digital form of satellite images, the digital number values of each pixel from the image matrices were considered as samples in a systematic network. Finally, the U spatial statistic algorithm was implemented as a moving window algorithm for determining anomaly samples in the set of digital number (DN) values of ASTER satellite image pixels. The results of this technique show that the application of the U-statistic method, considering its structural nature and neighboring samples in decision-making, has been successful and has proven to be very effective in determining the alteration zones in the Zafarghand area.
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
Eman M. Kamel; Mohamed S.H. Hammed; Osama E.A. Attia
Abstract
In the recent years, the use of ASTER and Landsat data have become prevalent for mapping different types of rock formations. Specifically, this study utilizes ASTER (L1B) and Landsat 8 (AOL) images to map outcrops of various gypsum facies in Ras Malaab area of west-central Sinai. These gypsum facies ...
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In the recent years, the use of ASTER and Landsat data have become prevalent for mapping different types of rock formations. Specifically, this study utilizes ASTER (L1B) and Landsat 8 (AOL) images to map outcrops of various gypsum facies in Ras Malaab area of west-central Sinai. These gypsum facies are part of a lithostratigraphic group called Ras Malaab, estimated to have been formed during the Miocene period. A range of image processing techniques was employed to create the final facies map including quartz and sulphate indices, composite image band combinations, band ratios, principal component analyses, decorrelation stretching, and SAM mapping followed by supervised classification. By using band combinations, mineral indices, and principal component analyses, sulphate minerals were distinguished from their surroundings. Additionally, decorrelation stretches and band ratios were used to differentiate between primary, secondary, faulted gypsum, anhydrite, and carbonates. The SAM rapid mapping algorithm was also an effective tool to distinguish between the main facies in the studied area and to differentiate between primary massive and bedded gypsum. The results of this study were summarized by creating a facies map of the area using supervised classification, which, in addition to petrographic studies, greatly aided in understanding the distribution of the different gypsum facies.
Exploration
Abdelrahem Khalefa Embaby; Sayed Gomaa; Yehia Darwish; Samir Selim
Abstract
This study aims to develop an empirical correlation model for estimating the uranium content of the G-V in the Gabal Gattar area, northeastern desert of Egypt, as a function of the thorium content and the total gamma rays. Using the recent MATLAB software, the effect of selecting tan-sigmoid as a transfer ...
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This study aims to develop an empirical correlation model for estimating the uranium content of the G-V in the Gabal Gattar area, northeastern desert of Egypt, as a function of the thorium content and the total gamma rays. Using the recent MATLAB software, the effect of selecting tan-sigmoid as a transfer function at various numbers of hidden neurons was investigated to arrive at the optimum Artificial Neural Network (ANN) model. The pure-linear function was investigated as the output function, and the Levenberg-Marquardt approach was chosen as the optimization technique. Based on 1221 datasets, a novel ANN-based empirical correlation was developed to calculate the amounts of uranium (U). The results show a wide range of uranium content, with a determination coefficient (R2) of about 0.999, a Root Mean Square Error (RMSE) equal to 0.115%, a Mean Relative Error (MRE) of -0.05%, and a Mean Absolute Relative Error (MARE) of 0.76%. Comparing the obtained results with the field investigation shows that the suggested ANN model performed well.
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
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.
