Akademik Veri Yönetim Sistemi
Applied Sciences, cilt.15, sa.22, ss.1-21, 2025 (SCI-Expanded, Scopus)
Hydrothermal alteration processes, including silicification, sericitization, carbonatization, chloritization, and epidotization, serve as critical indicators in the exploration of precious metal deposits. The identification of these alterations traditionally relies on expert petrographic analysis of thin sections, a method that is time-intensive and prone to subjective interpretation. Although the automated classification of rock types from thin section images using machine learning (ML) and deep learning (DL) techniques has gained increasing attention, the classification of specific hydrothermal alteration types remains underexplored. This study evaluates the performance of four deep convolutional neural network (CNN) architectures—DenseNet121, ResNet50, VGG16, and InceptionV3—for classifying these five alteration types from thin section images. A new dataset comprising 5000 high-resolution thin section images (1000 per alteration type) was developed and used to train and evaluate the models under four optimization algorithms: Adam, RMSprop, SGD, and Adadelta. Among these, the DenseNet121 model achieved the highest performance, attaining accuracy and F1-score values of 1.00 with both RMSprop and Adam optimizers, while the InceptionV3 model recorded the shortest training time at 662 s. The results demonstrate that CNN-based approaches can effectively automate the classification of hydrothermal alteration types, offering a fast, consistent, and objective alternative to traditional methods. This study highlights the potential of deep learning techniques to enhance geological exploration through the accurate and efficient identification of hydrothermal alteration minerals.