关键词: 模糊熵, 特征权重, 隶属度, 样本可信度, 碳酸盐岩岩性识别

Abstract: The KNN classification model is a simple and direct inert classification algorithm, which is suitable for multi-classification problem and can be applied to complex lithology identification. The southeastern area of Sulige gas field is the research area in this study, and the lithology structure of this area is complex and diverse. Its identification is the focus of this research work. The traditional KNN method is easy to misjudge in the high degree of overlap, and the class domain with small sample size is easy to be misinterpreted. The edge of the sparse class is susceptible to interference, so that the result of classification is ineffective. In order to overcome the shortcomings, the KNN classification model based on fuzzy entropy is proposed, which is called Fuzzy Entropy-KNN（FE-KNN）. The FE-KNN classification model combines the traditional KNN with the fuzzy theory, discrimination different characteristics and different sample points. The accuracy of the classification is improved from 84.7% to 86.9%. FE-KNN provides a new idea of complex carbonate rock lithology identification.

Key words: fuzzy entropy, attribute weights, membership degree, sample confidence, carbonate lithology identification