ECOGNITION OF THE LITHOLOGY OF VOLCANIC ROCKS IN SONGLIAO BASIN BY SUPPORT VECTOR MACHINE

Using the method of support vector machine (SVM), with selection of characteristic elements, an identification method for the lithology of volcanic rocks is established to distinguish the basaltic, andesitic, trachytic, dacitic and rhyolitic volcanic rocks. By learning and prediction of the volcanic rock samples from the Songliao Basin, the average recognition rate for volcanic rocks reaches to 95% and more, showing that the SVM obtain a good result in the identification of volcanic rock component.