利用自组织特征映射神经网络和K-means聚类算法挖掘区域化探数据中的地质信息

区域化探数据包含丰富的地质信息,从区域化探数据中挖掘出这些信息,对于区域地质研究具有重要意义.笔者提出了一种利用自组织特征映射网络和K-means聚类算法挖掘区域化探数据中地质信息的方法,将标准化之后的元素含量数据作为模型输入值,通过自组织神经网络进行聚类,再通过K-means算法进行二次聚类,从聚类结果中分析其中包含的地质信息.以英格兰西南部某区水系沉积物区域化探数据为例,进行实例研究以检验该方法的实际效果.实例结果表明:①利用该方法得出的聚类结果图很好地响应了地质体的空间分布,可用于推断地质体的分布特征;②地质信息隐藏在每个聚类类型的地球化学特征之中,通过对这些特征进行分析和解释,可以挖掘出其中所包含的信息;③基于SOM网络和K-means聚类的区域化探数据挖掘方法是一种有效的地质信息获取方法,对于传统区域地质研究可以起到补充和增强的作用.

Self-organizing feature map neural network and K-means algorithm as a data excavation tool for obtaining geological information from regional geochemical exploration data

Regional geochemical data contain abundant geological information.The excavation of useful information from regional geochemical data is of important significance for the study of regional geology.In this paper,a model based on the self-organizing feature map and K-means algorithm is applied as a data excavation tool to discover hidden geological information from regional geochemical exploration data.For each data point,the raw data of each element is transformed by data normalization as the input value of the model.By means of SOM clustering and K-means clustering as the second step,the input data points can be divided into different groups,and then geological information can be acquired by analyzing the clustering results.Stream sediment survey data from southwest England is used as an example to test the performance of this model.The case study results demonstrate that:First,the clustering maps generated by the model agree well with the geological spatial distribution pattern.Accordingly,they can be used to predict the spatial distribution of geological bodies;Second,geological information is concealed in each cluster member.By analyzing and interpreting these geochemical characteristics,the geological information concealed in geochemical data can be discovered;Third,regional geochemical data excavation approach based on SOM network and K-means clustering is an effective geological information acquisition method,which can be used as a supplementary and strengthening way for conventional regional geology research.