On the basis of SinoProbe,scientific drilling ZK01 of the Luzong ore district located in the Yangtze River basin of eastern China is an integrated geophysical logging study.This study aims at establishing the physical property of lithologic section and revealing the vertical distribution law of metals in the lower crust.For the purpose of detecting the lithologic distribution of the Luzong ore district and providing the information concerning the distribution of metallic ores and evaluation of reserves,the authors chose Support Vector Machine (SVM) to established automatic lithologic identification model for all of the wells.Three methods,i.e.,Grid Search (GS),Particle Swarm Optimization (PSO) and Genetic Algorithm (GA),were applied to find the best parameters C and γ.GA was the best method becasuse it took 34 seconds to obtain the best parameters as (151.9852,9.1105),and its accuracy was up to 98.6364%.Compared with BP neural network identification results,the GA-SVM model achieved better accuracy of 86.86%.The lithologic identification and automatic zonation results are similar to the core data and artificial lithologic section,and the rationality and feasibility of GA-SVM are verified.