Recognition of Oil Shale Based on LIBSVM Optimized by Modified GeneticAlgorithm

Abstract

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