Çok etikeli dengesizlik verileri ile litolojiyi sınıflandırmak için derin öğrenme yöntemlerinin kullanılması

To anticipate and detect lithologies in a variety of surveys, geologists can save operational expenses and uptime by utilising deep learning methodologies and applications. Accurate data processing and scientific research using data gathered in different geological areas are made possible by this. The four lithologies data in the present research were analysed and classified using multi-class imbalance issues and high dimensionality. One of the biggest issues facing modern data analysis is the imbalance in data classification. Particularly when combined with other challenging factors like the existence of overlapping class distributions, and data imbalance can have a significant impact on the accuracy of classification. When there are several classes involved, mutual imbalance relationships between them exacerbate the situation, making its influence more evident. Furthermore, the high dimensionality issue may result in overfitting and increased computational complexity, both of which may impair classification efficiency. Recursive Feature Elimination (RFE) is used to find the most valuable predictive features, while Synthetic Minority Oversampling (SMOTE) is used to resample the data. Using hybrid multi-class DL system unbalanced learning approach is our solution to solving these issues. Finally, by offering precise categorization and quick responses about the interpretation of data collected in many study regions, we think that our innovations might contribute to the advancement of geological research.