Lithologic character identification based on QPSO-SVM

A novel support vector machine based on quantum particle swarm optimization (QPSO-SVM) is proposed for better solving formation lithologic character identification problem. An identification model for formation lithologic character is established using the data of actual well logging and lithologic profile by training the SVM, which is optimized by QPSO algorithm. The proposed method is applied to certain wells in Junggar Basin and the experimental results show it has higher identification precision, faster convergence speed and better generalization effect than BP neural network based approach.