Isotope tracer methods were used to determine flow paths, recharge areas, and relative age for groundwater in the Ameriyat Al- Falluja Al-Anbar governorate. The isotope of surface water and groundwater in the Ameriyat Al- Falluja area was assessed using a stable isotope technique. Data stable isotope parameters (2H and 18O) for three surface water and five groundwater samples were detected. The comparison of hydrogen and oxygen isotope compositions between groundwater and Euphrates River water demonstrated that the composition of the hydrogen and oxygen isotopes from Euphrates River matched that of the local meteoric water. This indicated that rainfall is the primary source of the river water. Environmental Isotopes results show that the surface water and groundwater originates from direct leakage through permeable beds. There is an interaction between Euphrates and groundwater wells in the study area except in the W1 and W5 wells sites.
Shear and compressional wave velocities, coupled with other petrophysical data, are vital in determining the dynamic modules magnitude in geomechanical studies and hydrocarbon reservoir characterization. But, due to field practices and high running cost, shear wave velocity may not available in all wells. In this paper, a statistical multivariate regression method is presented to predict the shear wave velocity for Khasib formation - Amara oil fields located in South- East of Iraq using well log compressional wave velocity, neutron porosity and density. The accuracy of the proposed correlation have been compared to other correlations. The results show that, the presented model provides accurate
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The current issues in spam email detection systems are directly related to spam email classification's low accuracy and feature selection's high dimensionality. However, in machine learning (ML), feature selection (FS) as a global optimization strategy reduces data redundancy and produces a collection of precise and acceptable outcomes. A black hole algorithm-based FS algorithm is suggested in this paper for reducing the dimensionality of features and improving the accuracy of spam email classification. Each star's features are represented in binary form, with the features being transformed to binary using a sigmoid function. The proposed Binary Black Hole Algorithm (BBH) searches the feature space for the best feature subsets,
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