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.
Jitendra Pandey; Dheeraj Kumar; Sumit kumar Chaudhari; Ajay Khalkho; Jai Krishna Pandey
Abstract
Detection and mapping of the Jharia coal mine fire through the integration of satellite-based observed data with ground thermography data have been used and described in this work. This assimilation has been achieved using three types of data set viz., Landsat satellite images, topographical area map, ...
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Detection and mapping of the Jharia coal mine fire through the integration of satellite-based observed data with ground thermography data have been used and described in this work. This assimilation has been achieved using three types of data set viz., Landsat satellite images, topographical area map, and ground temperature survey of different fire-affected sites of Jharia Coalfields (JCF). Thermal anomaly, as observed from the satellite imagery, is one of the most important characteristics of the coal fire detection process. It has been used as a prime indicator for the fire area's extent and intensity. Ground thermographic measurement has also been conducted to further substantiate the thermal anomaly. The obtained amalgamated data is plotted on topographical maps of different sites of JCF. The study reveals that around 70% of the total coal mines of JCF are in grip of either surface fire or sub-surface fire or both surface and sub-surface fire. About 93% of fires detected in the year 1988 were shifted to new locations or in a dormant condition, whereas the remaining about 7% of fires were still burning at the same locations mostly due to the shifting of these fires from the upper coal seam to the lower coal seam or vice versa. The temperature detected by satellite data was 10 to 15 times lower than the actual fire condition measured on the ground during field observation. The study concludes that the detection of several years long-standing fire conditions historical satellite data will be the best option to delineate the fire condition.
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.
Devraj Dhakal; Salad Omar Abdi; Kanwarpreet Singh; Abhishek Sharma
Abstract
The highway contributes significantly to human existence by providing safe, dependable, and cost-effective services that are environmentally friendly and promote economic progress. Highway projects need extensive planning to prevent work revisions, save time and cost, and increase job efficiency. Without ...
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The highway contributes significantly to human existence by providing safe, dependable, and cost-effective services that are environmentally friendly and promote economic progress. Highway projects need extensive planning to prevent work revisions, save time and cost, and increase job efficiency. Without a doubt, Highway transportation system must be constantly updated to keep up with technology breakthroughs, environmental change, and rising client needs. Incorporating Remote Sensing (RS) and Geographic Information Systems (GISs) has the potential to go beyond the limitations of RS, which typically collects information about the earth and its peripheries from space, and does not alter, analyze, calculate, query or display geographic engineering maps. Over the last few decades, the fusion of RS and GIS has shown promise, and the researchers are employing it in different stages of the Highway Planning and Development Process (HPDP) such as optimal route analysis, geometric design, operation and management, traffic modeling, accident analysis, and environmental impact analysis (noise pollutions, air pollutions). This paper gives an overall review of the use of RS and GIS on HPDP at various stages of their lifecycles.
Kamar Samir; Mohamed El-Sharkawi; Ahmed Niazy El-Barkooky; Mohamed Saleh Hassan Hammed
Abstract
The Precambrian rock assemblages of Umm Tawat area in the North Eastern Desert of Egypt have a distinctive ENE-trending exposure of Hammamat sediments (HS) between the Gebel Gattar granitic pluton and the volcanoclastic succession of Gebel El Dokhan. The present work applies the Landsat-8 data and image ...
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The Precambrian rock assemblages of Umm Tawat area in the North Eastern Desert of Egypt have a distinctive ENE-trending exposure of Hammamat sediments (HS) between the Gebel Gattar granitic pluton and the volcanoclastic succession of Gebel El Dokhan. The present work applies the Landsat-8 data and image processing techniques such as spectral signature, principal component analysis, decorrelation stretch, and band ratios to map the various Precambrian rock units and the lithofacies of the HS and their geological contacts. The recognized mappable units of this assemblage are fully identified by their spectral signature, field verification, lineament analysis, and petrographic description. The resultant high-resolution lithological map based on the maximum likelihood algorithm demonstrates ten fully discriminated mappable units of younger granitoid and HS lithofacies units besides the Dokhan volcanics and metagabro-diorite rock units. The identified five HS lithofacies units are brownish gray conglomerate and sandstone HSf1, a green conglomerate with dominant volcanic fragments HSf2, fine-grained sediments of graywacke and silty-mudstone HSf3, interbedded conglomerates and siltstone with uranium enrichments related to the intrusive contact HSf4, and thermally metamorphosed pelitic sediments HSf5. Remote sensing techniques have been applied for the first time to reveal detailed facies variation of the Hammamat sediments of Umm Tawat. Finally, the results aforementioned above are imported to the Arc GIS database to update the geologic map with precise rock unit boundaries.
Muhammad Ahsan M.; T. Celik; B. Genc
Abstract
The distribution of stream sediments is usually considered as an important and very useful tool for the early-stage exploration of mineralization at the regional scale. The collection of stream samples is not only time-consuming but also very costly. However, the advancements in space remote sensing ...
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The distribution of stream sediments is usually considered as an important and very useful tool for the early-stage exploration of mineralization at the regional scale. The collection of stream samples is not only time-consuming but also very costly. However, the advancements in space remote sensing has made it a suitable alternative for mapping of the geochemical elements using satellite spectral reflectance. In this research work, 407 surface stream sediment samples of the zinc (Zn) and lead (Pb) elements are collected from Central Wales. Five machine learning models, namely the Support Vector Regression (SVR), Generalized Linear Model (GLM), Deep Neural Network (DNN), Decision Tree (DT), and Random Forest (RF) regression, are applied for prediction of the Zn and Pb concentrations using the Sentinel-2 satellite multi-spectral images. The results obtained based on the 10 m spatial resolution show that Zn is best predicted with RF with significant R2 values of 0.74 (p < 0.01) and 0.7 (p < 0.01) during training and testing. However, for Pb, the best prediction is made by SVR with significant R2 values of 0.72 (p < 0.01) and 0.64 (p < 0.01) for training and testing, respectively. Overall, the performance of SVR and RF outperforms the other machine learning models with the highest testing R2 values.
Saeed Mojeddifar; Hojatollah Ranjbar; Hossain Nezamabadipour
Abstract
The main problem associated with the traditional approach to image classification for the mapping of hydrothermal alteration is that materials not associated with hydrothermal alteration may be erroneously classified as hydrothermally altered due to the similar spectral properties of altered and unaltered ...
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The main problem associated with the traditional approach to image classification for the mapping of hydrothermal alteration is that materials not associated with hydrothermal alteration may be erroneously classified as hydrothermally altered due to the similar spectral properties of altered and unaltered minerals. The major objective of this paper is to investigate the potential of a neuro-fuzzy system in overcoming this problem. The proposed system is applied to the northwestern part of the Kerman Cenozoic Magmatic Arc (KCMA), which hosts many areas of porphyry and vein-type copper mineralization. A software program based on an Adaptive Neuro-Fuzzy Inference System (ANFIS) was developed using the MATLAB ANFIS toolbox. The ANFIS program was used to classify Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) data based on the spectral properties of altered and unaltered rocks. The ANFIS result was then compared with other classified images based on artificial neural networks (ANN) and the maximum likelihood classifier (MLC). The verification of the results, based on field and laboratory investigations, revealed that the ANFIS method produces a more accurate map of the distribution of alteration than that obtained using ANN or MLC.
