Recognition of Oil Shale Based on LIBSVM Optimized by Modified GeneticAlgorithm

摘要

In order to improved the speed, accuracy and generalization of oil shale recognition model with log dada, consideringparameters of traditional SVM were chosen by experience, a LIBSVM recognition model with optimized parameterswas proposed based genetic algorithm. First of all, all the samples data were processed to double type asLIBSVM tool needing, and the best normalization way was chosen through comparing different accuracies of variousnormalization ways. Secondly, the fitness value was calculated by the traditional LIBSVM. Finally, parameters C and gwere optimized by genetic algorithm according the fitness value. The optimized LIBSVM oil shale recognition model wasapplied in northern Qaidam basin to identify oil shale, the results show that optimized recognition model is a tool of bettergeneralization ability and the recognition accuracy reaches as much as 97.2806%. According to the popularization effectsin the well area of same geology background, this optimized LIBSVM model is the best for now.