应用基于遗传算法的背向传播神经网络模型,由X射线荧光芯扫描仪对海洋沉积芯进行原地分析.

Application of a back propagation neural network model based on genetic algorithm to in situ analysis of marine sediment cores by X-ray fluorescence core scanner.

The use of core scanners to perform X-ray fluorescence (XRF) spectroscopic analysis can only obtain the intensities of the target elements, which is not conducive for application in marine geology research. In this paper, using a core scanner, in situ measurements were performed on 15 components: Al2O3, SiO2, K2O, CaO, TiO2, MnO, Fe2O3, V, Cr, Zn, Rb, Sr, Y, Zr, and Ba. We explored the feasibility of reducing the effect of interstitial water by normalizing the original intensity using the intensity of Ca and the ratio of coherent to incoherent and attempted to introduce a genetic algorithm-back propagation neural network model and use its nonlinear fitting capability to correct the matrix effect. The prediction precision of this method was 0.6-15.4%. The proposed method is suitable for the rapid analysis of major and minor components in marine sediment core samples, while taking full advantage of the high-resolution of the XRF core scanner.