Abstract: When using river geomorphology to study tectonic deformation, it is often difficult to distinguish the same level geomorphology in areas with severe weathering. In this paper, we take the geomorphologic surfaces of the Qingyijiang river basin as an example and try to distinguish the geomorphic surfaces by the sediment features that make up them. In order to distinguish different geomorphic surfaces, the traditional particle-size analysis method, SOFM network method and system clustering analysis method are taken to classify 29 samples from different geomorphic surfaces. The classification results of the three methods are different to a certain extent. We analyzed and compared the classification results of the three methods in detail. The results show that the traditional particle size analysis method, SOFM network method and cluster analysis method all can distinguish the geomorphic surface of different genesis, besides, they also can distinguish low-level terraces(T₁, T₂)and high-level terraces(T₃, T₄)for different grades of river terraces. Furthermore, the results also show that SOFM network method and cluster analysis method can make a certain distinction for the low-level terraces(T₁, T₂), while the traditional particle size analysis method is difficult to distinguish them.
In addition, we analyzed and compared the three methods from the classification results, the results presentation, the operation process, and the error transmission. The results suggest that the advantages and disadvantages of the three methods are obvious. From the perspective of the classification results, the three methods all can distinguish the river terraces and alluvial fans and can make certain discrimination for different levels of river terraces. From the presentation of the results, the result of SOFM network is simple and clear. From the operation process, the traditional particle-size analysis method is relatively cumbersome, and the SOFM network method and the cluster analysis method are relatively simple to operate. From the perspective of error transmission, the traditional particle-size analysis method calculates the partial particle size feature value of the sample, which has a certain loss for the particle size distribution information of the whole sample. The error of the clustering analysis method has cumulative features and the influence exists consistently. The classification results of the SOFM network are independent of each other, which effectively avoids the problem of such error transmission of clustering analysis method.
Overall, the classification results of the SOFM network method are simple and clear, the operation is simple, and the error is small. It has stronger adaptability to identifying different levels of different geomorphic surfaces. The results of this study will provide a simple and effective means for distinguishing different levels of geomorphic surfaces.

Key words: geomorphic surfaces, sedimentary characteristics, particle size analysis, Self-Organizing Feature Map(SOFM), artificial neural network, system clustering analysis

摘要： 利用河流地貌面研究构造变形需要区分不同级地貌面，这对于侵蚀、风化严重的地区是比较困难的。为此，文中尝试利用组成地貌面的沉积物特征对其进行区分，并以青衣江流域地貌面为实例，对采集于不同地貌面的29个样本分别采用传统粒度分析、自组织特征映射（SOFM）网络分析及系统聚类分析3种方法进行分类。结果表明：传统粒度分析方法、SOFM网络方法及聚类分析方法都能够区分不同成因的地貌面，同时对于成因相同的不同级河流阶地，能够区分低级阶地（T₁、T₂）和高级阶地（T₃、T₄）。对于低级阶地（T₁、T₂），SOFM网络方法和聚类分析方法能够进行一定的区分，而传统粒度分析方法的效果较差。整体而言，SOFM网络方法操作简单，分类结果清晰直白、误差较小，对于识别不同级地貌面具有更强的适应性。这一研究结果将为区分不同级地貌面提供一种简单、有效的手段。

关键词: 地貌面, 沉积特征, 粒度分析, 自组织人工神经网络, 系统聚类分析