Árpád Csámer

Bio: Árpád Csámer is an academic researcher from University of Debrecen. The author has contributed to research in topics: Geology & Advanced Spaceborne Thermal Emission and Reflection Radiometer. The author has an hindex of 2, co-authored 6 publications receiving 7 citations.

Multisource Data Analysis for Gold Potentiality Mapping of Atalla Area and Its Environs, Central Eastern Desert, Egypt

Abstract: In this research, airborne geophysical and remote sensing datasets were integrated for gold potentiality mapping (GPM) over the Atalla area in Central Eastern Desert, Egypt. Utilizing aeromagnetic data, detailed structural complexity maps were constructed using the center for exploration targeting (CET) procedure. Then, spectrometric gamma-ray data primarily located hydrothermally altered tracts with discriminating various rock units. The latter are precisely outlined by implementing various techniques (false-color composite (FCC), band ratio (BR), relative absorption band depth (RBD), directed principal component analysis (DPCA), and constrained energy minimization (CEM)) to ASTER, Sentinel 2 and ALOS PRISM datasets, with reference to the geological maps. The study exhibits that gold mineralization is structurally controlled by NW-SE direction. The findings of structural complexity and hydrothermal alteration (argillic, advanced argillic, phyllic, and propylitic) were used as weighted inputs for contouring gold potentiality. The resultant GPM accentuated five gold-promising zones; two are confirmed via locations of ancient gold mines, while the remaining three zones are strongly recommended for their gold potentiality.

Advanced land imager superiority in lithological classification utilizing machine learning algorithms

Abstract: Abstract Different types of remote sensing data are commonly used as inputs for lithological classification schemes, yet determining the best data source for each specific application is still unresolved, but critical for the best interpretations. In addition, various classifiers (i.e., artificial neural network (ANN), maximum likelihood classification (MLC), and support vector machine (SVM)) have proven their variable efficiencies in lithological mapping, yet determining which technique is preeminent is still questionable. Consequently, this study aims to test the potency of Earth observing-1 Advanced Land Imager (ALI) data with the frequently utilized Sentinel 2 (S2), ASTER, and Landsat OLI (L8) data in lithological allocation using the widely accepted ANN, MLC, and SVM, for a case study in the Um Salatit area, in the Eastern Desert of Egypt. This area has a recent geological map that is used as a reference for selecting training and testing samples required for machine learning algorithms (MLAs). The results reveal (1) ALI superiority over the most commonly used S2, ASTER, and L8; (2) SVM is much better than MLC and ANN in executing lithologic allocation; (3) S2 is strongly recommended for separating higher numbers of classes compared to ASTER, L8, and ALI. Model overfitting may negatively impact S2 results in classifying small numbers of targets; (4) we can significantly enhance the classification accuracy, to transcend 90% by blending different sensor datasets. Our new approach can help significantly in further lithologic mapping in arid regions and thus be fruitful for mineral exploration programs.

Fusion of Lineament Factor (LF) Map Analysis and Multifractal Technique for Massive Sulfide Copper Exploration: The Sahlabad Area, East Iran

Abstract: Fault systems are characteristically one of the main factors controlling massive sulfide mineralization. The main objective of this study was to investigate the relationship between fault systems and host lithology with massive sulfide copper mineralization in the Sahlabad area, South Khorasan province, east of Iran. Subsequently, the rose diagram analysis, Fry analysis, lineament factor (LF) map analysis and multifractal technique were implemented for geological and geophysical data. Airborne geophysical analysis (aeromagnetometric data) was executed to determine the presence of intrusive and extrusive masses associated with structural systems. Accordingly, the relationship between the formation boundaries and the fault system was understood. Results indicate that the NW-SE fault systems are controlling the lithology of the host rock for copper mineralization in the Sahlabad area. Hence, the NW-SE fault systems are consistent with the main trend of lithological units related to massive sulfide copper mineralization in the area. Additionally, the distance of copper deposits, mines and indices in the Sahlabad area with fault systems was calculated and interpreted. Fieldwork results confirm that the NW-SE fault systems are entirely matched with several massive sulfide copper mineralizations in the area. This study demonstrates that the fusion of lineament factor (LF) map analysis and multifractal technique is a valuable and inexpensive approach for exploring massive sulfide mineralization in metallogenic provinces.

Depth Estimation of Sedimentary Sections and Basement Rocks in the Bornu Basin, Northeast Nigeria Using High-Resolution Airborne Magnetic Data

Abstract: This study involves the use of high-resolution airborne magnetic data to evaluate the thicknesses of sedimentary series in the Bornu Basin, Northeast Nigeria, using three depth approximation techniques (source parameter imaging, standard Euler deconvolution, and 2D GM-SYS forward modelling methods). Three evenly spaced profiles were drawn in the N-S direction on the total magnetic intensity map perpendicular to the regional magnetic structures. These profiles were used to generate three 2-D models. The magnetic signatures were visually assessed to determine the thickness of depo-centres and the position of intrusions. The thicknesses of sedimentary series based on source parameter imaging results are approximately ranged 286 to 615 m, 695 to 1038 m, and 1145 to 5885 m for thin, intermediate, and thick sedimentation, respectively. Similarly, the standard Euler deconvolution result shows thin (130 to 917 m), intermediate (1044 to 1572 m), and thick (1725 to 5974 m) sedimentation. The magnetic model of Profile 1, characterized by two major breaks, shows that the igneous intrusions and basement rocks are covered by sediments with thickness varying from 300 to <3500 m, while Profile 2 has a maximum estimated depth value of about 5000 m at the southern part. Furthermore, the Profile 3 model shows sediment thicknesses of 2500 and 4500 m in the northern and southern flanks of the profile, respectively. The maximum sediment thickness value from the various depth estimation methods used in this study correlate relatively well with each other. Furthermore, the anomalous depth zone revealed by the 2D forward models coincides with the locality of the thick sedimentation revealed by the source parameter imaging and standard Euler-deconvolution (St-ED) methods. The maximum depth values obtained from the various depth estimation methods used in this study correlated strongly with each other. The widespread occurrence of short-wavelength anomalies in the southern part of the study area as indicated by the jagged nature of the magnetic signature was validated by the analytic signal and upward-continuation results. Generally, it was observed that the southern part of the research area is characterized by thick sedimentation and igneous intrusions.

Advanced land imager superiority in lithological classification utilizing machine learning algorithms

Abstract: Abstract Different types of remote sensing data are commonly used as inputs for lithological classification schemes, yet determining the best data source for each specific application is still unresolved, but critical for the best interpretations. In addition, various classifiers (i.e., artificial neural network (ANN), maximum likelihood classification (MLC), and support vector machine (SVM)) have proven their variable efficiencies in lithological mapping, yet determining which technique is preeminent is still questionable. Consequently, this study aims to test the potency of Earth observing-1 Advanced Land Imager (ALI) data with the frequently utilized Sentinel 2 (S2), ASTER, and Landsat OLI (L8) data in lithological allocation using the widely accepted ANN, MLC, and SVM, for a case study in the Um Salatit area, in the Eastern Desert of Egypt. This area has a recent geological map that is used as a reference for selecting training and testing samples required for machine learning algorithms (MLAs). The results reveal (1) ALI superiority over the most commonly used S2, ASTER, and L8; (2) SVM is much better than MLC and ANN in executing lithologic allocation; (3) S2 is strongly recommended for separating higher numbers of classes compared to ASTER, L8, and ALI. Model overfitting may negatively impact S2 results in classifying small numbers of targets; (4) we can significantly enhance the classification accuracy, to transcend 90% by blending different sensor datasets. Our new approach can help significantly in further lithologic mapping in arid regions and thus be fruitful for mineral exploration programs.