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

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Abstract

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.

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Data Availability

The data that support the findings of this study are openly available in Science Data Bank at https://www.doi.org/10.57760/sciencedb.j00186.00121 and https://cstr.cn/31253.11.sciencedb.j00186.00121

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Acknowledgements

We thank the Sichuan Provincial Key Laboratory of Applied Nuclear Techniques in Geosciences and CDUT Team 203 for their English language review. We thank Professors Liang-Quan Ge, David Cohen, Sheng-Qing Xiong, and Si-Chun Zhou for their support.

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Authors and Affiliations

  1. College of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Chengdu, 610000, China

    Fei Li, Xiao-Fei Huang, Yue-Lu Chen, Tang Wang, Feng Cheng, Guo-Qiang Zeng & Mu-Hao Zhang

  2. Applied Nuclear Technology in Geosciences Key Laboratory of Sichuan Province, Chengdu, 610000, China

    Fei Li, Feng Cheng, Guo-Qiang Zeng & Mu-Hao Zhang

  3. Airborne Survey and Remote Sensing Center of Nuclear Industry, Shijiazhuang, 050000, China

    Bing-Hai Li

  4. Hebei Key Laboratory of Airborne Detection and Remote Sensing Technology, Shijiazhuang, 050000, China

    Bing-Hai Li

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  1. Fei Li
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Contributions

All authors contributed to the study conception and design. The algorithm proposal and verification, raw data, and analysis were performed by Fei Li, Xiao-Fei Huang, Bing-Hai Li, Feng Cheng, Guo-Qiang Zeng and Mu-Hao Zhang. The first draft of the manuscript was written by Fei Li, Xiao-Fei Huang, Yue-Lu Chen, and Tang Wang, and all authors commented on the previous versions of the manuscript. All the authors have read and approved the final version of the manuscript.

Corresponding authors

Correspondence to Feng Cheng or Guo-Qiang Zeng.

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The authors declare that they have no competing interests.

Additional information

This work was supported by the National Natural Science Foundation of China (No. 42127807), Natural Science Foundation of Sichuan Province (Nos. 23NSFSCC0116 and 2022NSFSC12333), and the Nuclear Energy Development Project (No. [2021]-88).

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Li, F., Huang, XF., Chen, YL. et al. Quantitative algorithm for airborne gamma spectrum of large sample based on improved shuffled frog leaping–particle swarm optimization convolutional neural network. NUCL SCI TECH 34, 112 (2023). https://doi.org/10.1007/s41365-023-01265-5

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