基于支持向量机的Q_3黄土孔隙微观结构研究  

Research on mesoscopic pore of loess based on image processing of SVM

作　　者：谷天峰[1] 王家鼎[1] 郭乐[1] 李开超[1] 

机构地区：[1]西北大学地质系/大陆动力学国家重点实验室,西安710069

出　　处：《水文地质工程地质》2010年第6期102-106,共5页Hydrogeology & Engineering Geology

基　　金：国家自然科学基金(40972193);陕西省教育厅自然科学专项(09JK745);西北大学大陆动力学国家重点实验室科技部专项经费(BJ091350)

摘　　要：为了解决利用黄土SEM图像提取孔隙参数的问题,将孔隙区域的提取问题转化为孔隙像素点的分类问题,通过把整个图像的像素点分为孔隙点和骨架点,从而实现孔隙的提取。利用这种方法,对潼关Q3黄土震陷试验前后的微结构图像进行处理,得到了动力作用前后的孔隙分布图,并计算了孔隙面积、面孔隙度、孔隙分形维数等参数,结果表明:震陷试验后大孔隙数量减小,中、小孔隙数量有所增加,震陷主要是由大孔隙破坏所致。By applying the SVM theory in a quantitative analysis of loess SEM images,this paper puts forward a new method for calculating soil microstructure parameters,which convert pore zone distinguishment to pore pixel dot assortment.Q3 loess SEM images before and after seismic subsidence test are processed by using the above method and parameters such as pore numbers,pore areas,surface porosity,pore fractal dimension and pore distribution graph are achieved.The results show that after test macropores decrease in areas,mesopores and fine pores increase in numbers,and seismic subsidence is mainly induced by macropore failure.

关 键 词：黄土微观结构 图像处理 支持向量机 孔隙分类 

分 类 号：P642.131[天文地球—工程地质学] TU411.92[天文地球—地质矿产勘探]