Abstract: Aiming at the problem of poor adaptability of soil background estimation algorithm after parameter determination, we  proposed a soil background estimation algorithm based on radial basis function (RBF) neural network model, which could effectively improve
 the background deduction effect and element quantitative accuracy of energy-dispersive X-ray fluorescence detection for soil. We first  analyzed the commonly used background estimation models, and proposed an algorithm model based on improved RBF neural network for the deduction effect and problem of continuous peak-stripping method and wavelet transform on background estimation， then we theoretically proved the validity of the algorithm model and  applied it to the actual soil energy-dispersive X-ray fluorescence detection system to detect the national standard soil samples, and the quantitative detection of heavy metal elements such as Cr,Zn and As was analyzed in depth. The experimental results show that the soil background estimation algorithm can better extract the energy eigenvalues of elements, reduce the influence of background on the characteristic peaks and mass fractions of elements, and effectively improve the quantitative accuracy of soil elements.

Key words: energy dispersive X-ray fluorescence detection, soil element analysis, radial basis function neural network model, background estimation