Kernel Principal Component Analysis (KPCA) in Electrical Facies Classification
نویسندگان
1 Ph.D. Candidate, Petroleum, Mining, and Materials Engineering Department, Islamic Azad University, Central Tehran Branch, Tehran, Iran
2 Professor, Institute of Geophysics, University of Tehran, Iran
3 Associate Professor, Petroleum, Mining, and Materials Engineering Department, Islamic Azad University, Central Tehran Branch, Tehran, Iran
doi
10.22050/ijogst.2023.360469.1653چکیده
This study uses the kernel principal component analysis (KPCA) feature extraction method for facies classification to extract new features from the measured well logs. After applying the principal component analysis (PCA) and KPCA feature extraction approaches, the classification was made using three robust classifiers: multilayer perceptron neural network (MLP), support vector machine (SVM), and random forest (RF). Finally, the predicted results for the test data that were not included in the training process were evaluated with the F1 score criterion. The PCA method did not significantly affect the classification performance due to the nonlinear structure of the facies. Our results show that the KPCA improves the performance of facies classification. Compared with the conventional approach based on well-log data, our new approach improves the classification accuracy for each classifier algorithm. In the RF results, the classification accuracy has increased by about 6%, while using the KPCA feature extraction approach raises classification accuracy from 52% to 58% compared to the original well-log data.
