The optimization of kernel function and its parameters for SVM in well-logging

Support Vector Machine (SVM) is an effective method for reservoir layer classification. However, its classification performance is influenced by many factors. In this paper, we study how the main factors affect classification accuracy of SVM on oil data. We collect two well-logging dataset, use k-Cross Validation and Grid Search to find the optimal SVM parameters, and compare the classification performance of different kernel function. The experiment results show that the type of kernel function affects classification rate most and Polynomial performs best.