中文摘要：

对沉积颗粒大小、形态特征的量化和解释，是沉积动力条件分析和古地理重建的重要内容之一。图像法作为粒度分析的标准，长期受制于样本量小、效率低等困难。基于动态图像法的新型粒度粒形分析系统可在几分钟内获取多达n×l0^7个颗粒的形状和大小信息，为沉积环境重建和动力条件分析提供了新的机会。本文基于两组样品（青藏高原的湖滩砂和黄河中游的河流砂）的动态图像粒度粒形数据，定量分析和对比了湖滩与河流两种环境沉积颗粒的粒度、粒形特征及其异同。结果表明，样品组成颗粒的球度与均值粒度之间整体上存在明显的反相关，球度一粒度曲线可大致划分为斜率不等的三段：占据颗粒绝大多数的中段（C．S．）球度适中，斜率最小；细端（F．T．）球度大，斜率也大；粗端（C．T）球度小，斜率小且不稳定。结合天然沉积颗粒的形状和密度特征，本文认为这些不同斜率的段落可能源于沉积颗粒密度效应和形状效应导致的粒度分异。对比研究表明，湖滩砂与河流砂在粒形一粒度曲线形态与分段特征方面均存在明显差异，两组样品在不同粒形参数（球度-长宽比-凹凸度）三角图和不同球度分段所对应颗粒体积百分比（c．T．-C．S．-F．T．）三角图中均能完全分离。这些事实表明基于动态图像分析的粒度粒形分析可望成为一种潜在的第四纪环境重建工具，在环境研究中有助于对天然沉积物物源特征以及风化、侵蚀、搬运和堆积过程的认识和理解。

英文摘要：

Quantification and explanation of sediment grain size and shape is an important aspect of sedimentary dynamic analysis and paleogeographic reconstruction. Development of image analysis method, as a standard of grain size analysis, is limited by sampling size and time consuming for long. Now data of millions of grains could be achieved within a few minutes based on quick image achieving and processing technology, supplying potential challenge for knowing and understanding of sediment environment and dynamic condition. In this study, measurement of grain size and shape was performed on two groups of sands, one group （tl - tS） of lakeshore sediments is collected from beach ridges of the northern bank of the Lake Tangra Yumco, the Tibetan Plateau, and another group （wl- w7） of floodplain sediments from drilling core on the left bank of the Yellow River floodplain near the city Wuhai, Inner Mongolia, using dynamic image analysis system. Several size and shape parameters of each grain, such as D （ diameter of equal projection-area circle） , S（sphericity） , Raspect（ aspect ratio） and ψc（convexity） , are defined and calculated in a special database, and then relations between grain size and shape, and differences of grain size and shape between these two environments were discussed. Average size of all samples is middle （ 1 - 24） or fine sand （2-3Ф） , sorting is 0.49-0.66. The ranges of sphericity, aspect ratio and convexity are 0.90-0.91, 0.71-0.76 and 0.90-0.94, respectively. Grains for each sample are divided into 17 groups according to different sphericity interval, and average grain size is calculated for all groups of sphericity interval. The resulting sphericity-size curves show that mean grain size decrease with increase of sphericity, and that the sphericity-diameter is characterized as ＂three segments＂. The fine tails （S〉0.94） of two groups of samples have steep slope and rapid convergent with increasing sphericity ; the central segments have only gentle slope ; the c