基于计算机X射线断层术与扫描电镜图像的黄土微结构定量分析  

Quantitative analysis of loess microstructure based on CT and SEM images

作　　者：王慧妮[1,2] 倪万魁[1] 

机构地区：[1]长安大学地质工程与测绘学院,西安710054 [2]湖北省基础地理信息中心,武汉430071

出　　处：《岩土力学》2012年第1期243-247,254,共6页Rock and Soil Mechanics

基　　金：冻土工程国家重点实验室基金项目(No.SKLFSE200702)

摘　　要：以湿陷性黄土的电镜扫描(SEM)和三轴CT扫描试验为基础,针对CT图像分辨率较低、难以实现土微结构精确量化的缺陷,通过对不同放大倍数的SEM图像进行图像分析,并从其中选择标准训练样本,利用训练样本对CT图像进行监督分类,从而达到定量化分析土的微结构的目的。通过比较CT图像基于自身灰度分级和基于SEM训练样本两种不同方法进行监督分类,结果表明基于SEM训练样本的CT图像监督分类,可以更好地量化监测黄土大孔隙、团粒、黏土集粒和矿物颗粒在固结剪切过程中的变化规律,从而为土的微结构研究提供了新的视角。Because of the low resolution of CT images, it is difficult to quantize the loess microstructure accurately. Therefore, in this paper, SEM images are associated with triaxial CT images for the investigating of the microstructure of the collapsible loess. Firstly, the reasonable training samples are extracted from a large number of SEM images with different magnifications. The supervised classification of the CT images is carried out based on these training samples. For a comparison, supervised classification under gray classification of CT images is also made. The results show that： supervised classification of CT images based on SEM images exhibits better performance on the quantization monitoring of the change rule for loess microstructure. It is shown the high quality for monitoring larger pores, aggregates, clay aggregate particles and closed mineral substance of the loess sample during the triaxial shear test. It is believed that this supervised classification based on SEM images could provide a new sight for researching loess microstructure.

关 键 词：黄土微结构 CT图像 SEM图像 训练样本 监督分类 

分 类 号：TU444[建筑科学—岩土工程]