Exploration
Ahmed Mahmoud Abdelhameed; Maher Abdelateef El Amawy; Ayman Mahrous; Mohamed El-Khouly; Adel Fathy
Abstract
Hyperspectral imaging (HSI), combined with advanced machine learning algorithms (MLAs), has unlocked novel research opportunities and revolutionized geological mapping by enabling precise lithological classification. Accurately detailed geological mapping is one of the most essential requirements for ...
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Hyperspectral imaging (HSI), combined with advanced machine learning algorithms (MLAs), has unlocked novel research opportunities and revolutionized geological mapping by enabling precise lithological classification. Accurately detailed geological mapping is one of the most essential requirements for targeting mineralization. However, achieving comprehensive lithological mapping remains a challenge, hindering systematic mineral exploration. This work explores the use of PRISMA hyperspectral data and the Support Vector Machine (SVM) and Artificial Neural Network (ANN) algorithms to objectively map the Precambrian rock assemblages at the El Ineigi area in the Central Eastern Desert (CED) of Egypt. For this purpose, PRISMA data in HDF5 format were first pre-processed and subsequently transformed through principal component analysis (PCA). The processed spectral data were then combined with extensive fieldwork and previously existing geological maps and classified using SVM and ANN to achieve enhanced discrimination of the exposed rock units in the study area. Our results conclusively demonstrate the exceptional capability of PRISMA data for detailed lithological mapping. The SVM and ANN classification achieved remarkably high overall accuracy, successfully generating a robust geological map that clearly discriminates between various Neoproterozoic basement rock units in the El Ineigi area. Through the integration of diagnostic spectral signatures with field verification, we confidently identified all major mappable units, including metavolcanics, metagabbro-diorite complexes, younger granites, and Wadi deposits. The proposed integrated approach demonstrates superior performance compared to traditional mapping techniques, offering enhanced discrimination precision and operational efficiency. These findings strongly support the combined use of PRISMA hyperspectral data and MLAs for lithological mapping applications.
Exploitation
Yasar Agan; Turker Hudaverdi
Abstract
The purpose of this research work is to predict blast induced ground vibration in surface mine by using classical and machine learning algorithms. For the purpose of minimizing blast-induced ground vibration to acceptable levels, the level of vibration must be predicted. Blast-induced ground vibration ...
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The purpose of this research work is to predict blast induced ground vibration in surface mine by using classical and machine learning algorithms. For the purpose of minimizing blast-induced ground vibration to acceptable levels, the level of vibration must be predicted. Blast-induced ground vibration is defined peak particle velocity (ppv) in the ground. All data used to estimation were obtained by observing real blasting operations. After the measuring of the peak particle velocity, models of the prediction were created using independent site parameters. Most of the data is used to train the model, while remaining part is used for testing. The models were created using independent blasting parameters proportionally. Thus, more parameters are included in the models without complicating the models. A thorough validation process was conducted utilizing a diverse set of nine error criteria. Artificial intelligence models have been found to outperform traditional methods in predicting ground vibration. The mean absolute error values were found to be 1.42, 1.54, and 1.78 for ANFIS, GPR, and SVM, respectively. A similar situation is observed for other error criteria as well. ANFIS appears to be the most effective model for predicting ground vibration.
