Quantitative algorithm for airborne gamma spectrum of large sample based on improved shuffled frog leaping–particle swarm optimization convolutional neural network

In airborne gamma ray spectrum processing, different analysis methods, technical requirements, analysis models, and calculation methods need to be established. To meet the engineering practice requirements of airborne gamma-ray measurements and improve computational efficiency, an improved shuffled frog leaping algorithm–particle swarm optimization convolutional neural network (SFLA-PSO CNN) for large-sample quantitative analysis of airborne gamma-ray spectra is proposed herein. This method was used to train the weight of the neural network, optimize the structure of the network, delete redundant connections, and enable the neural network to acquire the capability of quantitative spectrum processing. In full-spectrum data processing, this method can perform the functions of energy spectrum peak searching and peak area calculations. After network training, the mean SNR and RMSE of the spectral lines were 31.27 and 2.75, respectively, satisfying the demand for noise reduction. To test the processing ability of the algorithm in large samples of airborne gamma spectra, this study considered the measured data from the Saihangaobi survey area as an example to conduct data spectral analysis. The results show that calculation of the single-peak area takes only 0.13 ~ 0.15 ms, and the average relative errors of the peak area in the U, Th, and K spectra are 3.11, 9.50, and 6.18%, indicating the high processing efficiency and accuracy of this algorithm. The performance of the model can be further improved by optimizing related parameters, but it can already meet the requirements of practical engineering measurement. This study provides a new idea for the full-spectrum processing of airborne gamma rays.