Journal
COMPUTERS & GEOSCIENCES
Volume 123, Issue -, Pages 10-19Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.cageo.2018.11.003
Keywords
Kullback-Leibler divergence; Hierarchical cluster analysis; Geochemical exploration data; Geochemical pattern; Data mining
Funding
- National Key Research and Development Program of China [2016YFC0600501]
- National Natural Science Foundation of China [41430320, 41602337]
- Chinese Geological Survey project (Minerals and Geological Prospecting on Shallow Covered Areas of Jinning, Inner Mongolia) [DD20160045]
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available