m.s. mirmohammadi

One of the most crucial steps in ore exploration is the recognition of geological patterns and features. These features contain mineralogy, lithology, alteration, rock texture, etc. This stage has always been associated with many challenges. Among the challenges of this stage, we can mention the time-consuming and costly nature of this stage and the need for high expertise and human resources to recognize these patterns and features. In recent years, deep learning and machine learning have been adopted in earth sciences. In this research, by using the architecture of U-net, ore and waste were separated, and the grade pattern was identified using the core box images. For this purpose, iron minerals were segmented using binary image segmentation, trial-and-error methods were used to optimize the network, and finally, the model's accuracy for identifying ore was 91%. The IoU metric was utilized for further evaluation; this metric is a suitable criterion for the final evaluation of the image segmentation model, which has reached 75% in recognition of iron ores. For the final evaluation of the obtained model, the grade outputs of the model and the XRF analysis results of one core were compared. The network error was evaluated at 9%, which shows the good accuracy of the obtained model according to the real data.