Abstract: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.
柳成志, 滕立惠. 利用支持向量机识别松辽盆地火山岩岩性[J]. 地质与资源, 2014, 23(3): 288-291.
LIU Cheng-zhi, TENG Li-hui. ECOGNITION OF THE LITHOLOGY OF VOLCANIC ROCKS IN SONGLIAO BASIN BY SUPPORT VECTOR MACHINE. GEOLOGY AND RESOURCES, 2014, 23(3): 288-291.