Application of an LSTM model based on deep learning through X-ray fluorescence spectroscopy

Abstract

BackgroundTraditional X-ray fluorescence spectrum analysis has the limitations of poor accuracy of the characteristic peak counting rate and shadow peak.PurposeThis study aims to propose a long and short term memory (LSTM) neural network model based on deep learning for the loss correction of the characteristic peak count rate and shadow peak.MethodsFirstly, a LSTM neural network model based on deep learning was proposed to estimate accurately the amplitudes of nuclear pulse signals by learning samples. Then, a convolutional neural network (CNN) with unique convolutional kernel structure was introduced to deal with the challenges of large sample size of the nuclear pulse signal and the low training efficiency of the model by extracting the sample features layer by layer, thereby effectively reducing the number of samples and the complexity of model training. Finally, a series of offline nuclear pulse sequences of powdered iron ore samples were used to generate the dataset required for model training. Among the 64 000 entries in this dataset, 44 800 were used as training sets, 12 800 were used as validation sets, and the remaining 6 400 were used as testing sets.ResultsThe trained CNN-LSTM model saves considerable training time, overcomes the defects of local convergence of traditional methods, and accurately estimates the parameters of input pulse under different degrees of distortion. Results show that the accuracy rate of the training and verification sets is greater than 99%. An analysis of the count repair results reveals that the average value of the correction ratio of the three shadow peaks, that is, the correction ratio of the depth learning model trained in this study to the count loss derived from the distorted pulses, is 91.52%.ConclusionsThe CNN-LSTM model can effectively correct the shadow peaks derived from the amplitude loss of distorted pulses and improve the accuracy of the characteristic peak count rate in X-ray fluorescence spectra. The model is shown to have high application value for the field of X-ray fluorescence spectroscopy.

Keywords

• deep learning
• long and short term memory
• x-ray fluorescence spectroscopy
• convolutional neural network