Predicting Gabal Gattar Uranium Content as a Function of Total Gamma-ray and Thorium Contents using an Artificial Neural Network in Northeastern Desert, Egypt

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 ۱۲۲۱ 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 (R۲) of about ۰.۹۹۹, a Root Mean Square Error (RMSE) equal to ۰.۱۱۵%, a Mean Relative Error (MRE) of -۰.۰۵%, and a Mean Absolute Relative Error (MARE) of ۰.۷۶%. Comparing the obtained results with the field investigation shows that the suggested ANN model performed well.

Keywords:

ANN , Uranium and Thorium concentrations , Total Gamma-ray , Modelling , Gattar area