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海底沉积物中稀土元素的分布特征受很多影响因子的影响,很难定量分析。北部湾沉积物稀土元素(ΣREE)与物源、水动力、沉积物粒度和粘土矿物百分比等关系定性分析显示,本区的ΣREE的物源主要由陆源岩石控制,弱水动力和细粒度都对应较高含量的ΣREE。结合北部湾海底沉积物的位置、砾石含量、砂含量、粉砂含量、粘土含量和粘土矿物含量训练出来的BP神经在控制变量的情况下定量分析它们与ΣREE的关系,获得单个影响因子与ΣREE的关系曲线。这些关系曲线揭示了北部湾沉积物中稀土元素与各影响因子的联系,所获得的结果与定性分析的结果基本一致,该方法能够通过自主学习,自动判断并定量计算,有助于识别每一个因子对稀土元素含量影响的大小,是如何控制ΣREE的分布,从而根据曲线的变化规律结合实际情况去推断区域的环境变化及地质演变,对稀土元素的富集和分散提供有益的理论指导。
The distribution characteristics of rare earth elements in seabed sediments are affected by many influencing factors and are difficult to quantitatively analyze. The qualitative analysis of REE (ΣREE) in the Beibu Gulf and its provenance, hydrodynamic force, sediment grain size and clay mineral percentage shows that the source of ΣREE in this area is mainly controlled by terrestrial rocks, with weak hydrodynamic force and fine grain size Higher content of ΣREE. Based on the control variables, the BP neural networks trained with the location, gravel content, sand content, silt content, clay content and clay mineral content of Beibu Gulf sediments were quantitatively analyzed for their relationship with ΣREE, and the single influencing factors and ΣREE The relationship curve. The relationship curves reveal the relationship between rare earth elements in the sediments of Beibu Gulf and various influencing factors. The results obtained are basically the same as the results of qualitative analysis. The method can be automatically judged and quantified through autonomous learning, which helps identify each The influence of the factors on the content of rare earth elements is how to control the distribution of ΣREE, so as to infer the environmental change and geological evolution of the region according to the changing law of the curve and provide practical theoretical guidance for the enrichment and dispersion of rare earth elements.