基于LS-SVM的测井物性参数的预测方法

Forecast method of logging physical property parameters based on LS-SVM

支持向量机(SVM)是近年来发展起来的一种通用的机器学习方法,在小样本数据的拟合中已获得了很好的效果。采用新型的支持向量机——最小二乘支持向量机(LS-SVM)对孔隙度、渗透率和饱和度进行了预测,获得了满意的结果。该方法易于使用,很少受不确定性因素的影响,并具有较强的信息整合能力以及更高的预测准确性。

Support Vector Machine（SVM） is a general machine learning method in recent years,by which good results have been obtained in fitting of small samples.Using new Support Vector Machine--Least Square Support Vector Machine（KS-SVM） to predict porosity,permeability,saturation is satisfied.The method is prone to use,it is seldom affected by uncertain factor and has powerful conformity skill of information and higher veracity in forecast.