A novel hierarchical clustering analysis method based on Kullback-Leibler divergence and application on dalaimiao geochemical exploration data

In this paper, we propose a new hierarchical clustering analysis method (HCA) that uses Kullback-Leibler divergence (D-KLS) of pairwise geochemical datasets of geo-objects (e.g., lithological units) as a measure of proximity. The method can reveal relationships among geo-objects based on geochemistry. This capability is verified through an application with geochemical exploration data from regolith that overlies the Dalaimiao region in China. D-KLSM and D-KLSC, two parts of D-KLS, respectively describe the differences on the mean and the differences on covariance and are also used as measures of proximity. D-KLSM characterizes rock type and D-KLSC. describes spatial relationships and component similarities between geo-objects. This contribution not only provides a tool that can reveal relationships between geo-objects based on geochemical data, but also reveals that D-KLS and its two parts can characterize geochemical differences from different perspectives. These measures hold promise in the enhancement of methods for recognizing geochemical patterns.