Quantitative analysis of BP neural network on the relationships between ∑REE content and impact factors in Beibu Gulf

doi:10.3969/j.issn.1001-909X.2016.01.003

Abstract

Abstract: The distribution characteristics of ∑REE in the bottom sediments are influenced by many factors, so they are too difficult to analyze quantitatively. From qualitative analysis of the relationship between ∑REE content and its provenance, hydrodynamics, grain size and mineral distribution in Beibu Gulf, it is revealed that the main ∑REE composition is controlled by terrestrial rock. Both weaker hydrodynamics and the finer grain lead to the higher ∑REE content. Combined with the quantitative analysis of BP neural network which trained by the position of samples, gravel content, sand content, silt content, clay content and clay mineral content, the relative curves of individual impact factor with ∑REE content were achieved. These relative curves reveal the relationship between the ∑REE content and various impact factors, and the results are consistent with those of qualitative analysis. The method can study by itself, determine automatically and calculate quantitatively. It is helpful for identification of the impact related to each factor and ∑REE and to know how it is controlled the ∑REE distribution. So according to the curve variation and actual situation, environmental changes and geological evolution of the region can be inferred. This also provides a useful theoretical guidance for the analysis of the enrichment and dispersion for the rare earth elements.

Key words: REE, impact factors, quantitative analysis, BP neural network, Beibu Gulf

CLC Number:

P595

HU Hao, XU Dong, LONG Jiang-ping, ZHOU Meng-jia, TANG Bo, JIN Lu. Quantitative analysis of BP neural network on the relationships between ∑REE content and impact factors in Beibu Gulf[J]. Journal of Marine Sciences, 2016, 34(1): 18-26.

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