Evrişimsel Sinir Ağları Kullanarak Sondaj Karot Sandıklarının İncelenmesi ve Tenör Tahmini

Abstract

Mining companies have allocated approximately 10 billion USD for the exploration and investigation of new mines in 2018. Drilling has been a long-standing method for the exploration of underground mineral deposits. By using core samples obtained from drilling, information about the type, value, and quantity of minerals composing the rock can be obtained. In the mining industry, resource estimation and mineral analysis are of critical importance for improving operational efficiency, reducing costs, and ensuring sustainability. This study focuses on the examination of drilling core trays and ore grade estimation using Convolutional Neural Networks. An image segmentation and ore grade estimation application using the U-Net model has been developed for the evaluation of iron ore resources. The data used in this study were obtained from a database consisting of images extracted from drilling core trays. During the training process, U-Net models were trained to detect core samples and ore-bearing regions on the samples. The developed application performs ore grade calculations based on the core images and presents them as output. The aim of this study is to highlight the potential of modern data analysis techniques in the mining sector by providing an alternative method in the field of resource estimation and data analysis. It is expected that the findings of this thesis will contribute significantly, primarily to our country's mining industry, and inspire future research in the development of resource estimation techniques and the enhancement of operational efficiency.