Guidelines for Enhancing the Signature of Multi-element Mineralization Using Principal Component Analysis: Part 1—Monte Carlo Simulation
Principal component analysis (PCA) is a widely used method in geochemical data processing. The method can be useful to integrate variables associated with mineralization into a single component. In this paper, a Monte Carlo simulation is designed and applied to explore the performance of PCA under conditions controlled by four factors: the number of geo-objects (lithologic units), differences between geo-objects, the relationship between the variables and the number of variables. The results imply that: (1) more significant differences between geo-objects will result in less stable PC results; (2) more geo-objects make the result more robust; (3) variables with similar relationships help to stabilize the result; (4) more input variables do not always lead to a better result. These conclusions provide useful guidelines for using PCA to yield a targeted component like mineralization.
KeywordsPrincipal component analysis Monte Carlo simulation Data mining Geochemical exploration
We are thankful for the suggestions and the modifications from an anonymous reviewer and from Prof. Graeme Bonham-Carter. This research benefited from financial support from National Key R&D Program of China (2016YFC0600501), National Natural Science Foundation of China (Nos. 41430320 and 41602337) and a Chinese Geological Survey project (Minerals and Geological Prospecting on Shallow Covered Areas of Jinning, Inner Mongolia, No. DD20160045).
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