Abstract：In the very long geological evolution history, earth surface has undergone multi-period and multistage geological processes, and the distribution of the same element in the same stratum differs or its material source might have come from different matrixes, which results in data histogram presenting a multimodal morphology. Multimodal separation is the basis of differentiating material sources, and helps improve geological interpretation level. The fact that geological data mostly obey the normal distribution (or logarithmic normal distribution) provides mathematical basis for multimodal separation. This paper explains the math principles of multiple matrix sample data separation and uses genetic algorithm as implementation approach to theoretically prove that genetic algorithm has a better effect on multiple matrix sample data separation, and its goodness of fit can be up to 95%. An analysis of practical examples shows that the genetic algorithm can correctly distinguish 2 kinds of lithology of the stratum, which suggests that it has practical significance in geological research.
