基于遗传算法优化的支持向量机在岩性识别中的应用

Application of Support Vector Machine in lithology identification based on genetic algorithm optimization

为提高测井岩性识别的精度,本文结合乌夏地区岩芯资料和测井数据,总结该地区砂砾岩测井响应特征,优选出声波、自然伽马、密度、中子孔隙度和电阻率等5条测井曲线参数作为训练和测试样本,通过遗传算法挑选出最佳的支持向量机核函数参数σ和惩罚因子C,建立支持向量机岩性识别模型。结果表明该模型实际数据预测总体符合率为81.6%,在识别准确率上与传统测井识别砂砾岩岩性方法相比都有明显提升。

To improve the accuracy of lithology identification on well logs, the data of cores and well logs are used to summarize the logging response of the glutenite in Wuxia area. Five well logs including AC, CNL, DEN, GR and RXO are selected for the testing. The SVM lithology identification model is built by optimizing the SVM kernel parameter σ and penalty factor C with genetic algorithm. The result shows that the actual data forecasting coincidence rate of the model is 81.6% generally, and the accuracy of GA - SVM is obviously improved, compared with traditional identification methods of glutenite.