Kernel Principal Component Analysis (KPCA) in Electrical Facies Classification

In this study, in order to facies classification, the kernel principal component analysis (KPCA) feature extraction method is used 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 powerful 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 F۱ score criterion.The PCA method did not show a significant effect on 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 ۶% while using the KPCA feature extraction approach, increasing from ۵۲% to ۵۸% compared to the original well-log data.

کلیدواژه ها:

Electro-Facies ، Kernel Principal Component Analysis ، SVM ، Random forest ، Wire-line logs