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Volume 15
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10.3390/min15090925
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Article

Inversion of Vertical Electrical Sounding Data Based on PSO-BP Neural Network

by
Yingjie Wang
1,2,*,
Guanwen Gu
1,3,4,*,
Ye Wu
1,3,4,
Shunji Wang
5,
Xingguo Niu
6,
Zhihe Xu
1,3,4,
Haoyuan He
1,3,4,
Xinglong Lin
1,5 and
Lai Cao
1
1
School of Earth Sciences and Engineering, Institute of Disaster Prevention, Sanhe 065201, China
2
Tianjin Survey Design Institute Group Co., Ltd., Tianjin 300191, China
3
Hebei Key Laboratory of Earthquake Dynamics, Sanhe 065201, China
4
Langfang Key Laboratory of Earth Exploration and Information Technology, Sanhe 065201, China
5
No. 1 Bureau of China Metallurgical Geology Bureau, Sanhe 065201, China
6
Inner Mongolia Nonferrous Geology and Mining (Group) Geophysical Exploration Co., Ltd., Hohhot 010010, China
*
Authors to whom correspondence should be addressed.
Minerals 2025, 15(9), 925; https://doi.org/10.3390/min15090925
Submission received: 6 July 2025 / Revised: 20 August 2025 / Accepted: 26 August 2025 / Published: 30 August 2025
(This article belongs to the Special Issue Application of Big Data Mining, Machine Learning and Artificial Intelligence in Geoscience, 2nd Edition)
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Abstract

To address the issues of traditional linear inversion methods, such as their dependence on initial models and the high computational cost of Jacobian matrix calculations, this study conducts inversion research on vertical electrical sounding data based on the backpropagation (BP) neural network combined with the Particle Swarm Optimization (PSO) algorithm. First, two-layer and three-layer horizontally layered geoelectric models were constructed to generate the sample data required for neural network training. Secondly, the PSO-BP neural network model was employed to perform test inversions. The inversion results demonstrate that both neural network methods can successfully invert apparent resistivity data into corresponding geoelectric model parameters, thereby validating the correctness of the PSO-BP neural network inversion approach. Finally, the PSO-BP neural network method was applied to training and inversion of field-measured apparent resistivity data. A comparison between the inversion results of the PSO-BP neural network and those of the conventional BP neural network revealed that the PSO-BP neural network yields superior inversion results. This further confirms the reliability, effectiveness, and practical applicability of the proposed inversion method. The work presented in this study provides a novel approach and perspective for the inversion of vertical electrical sounding data.
Keywords: BP neural network; inversion; PSO-BP neural network; vertical electrical sounding BP neural network; inversion; PSO-BP neural network; vertical electrical sounding

Share and Cite

MDPI and ACS Style

Wang, Y.; Gu, G.; Wu, Y.; Wang, S.; Niu, X.; Xu, Z.; He, H.; Lin, X.; Cao, L. Inversion of Vertical Electrical Sounding Data Based on PSO-BP Neural Network. Minerals 2025, 15, 925. https://doi.org/10.3390/min15090925

AMA Style

Wang Y, Gu G, Wu Y, Wang S, Niu X, Xu Z, He H, Lin X, Cao L. Inversion of Vertical Electrical Sounding Data Based on PSO-BP Neural Network. Minerals. 2025; 15(9):925. https://doi.org/10.3390/min15090925

Chicago/Turabian Style

Wang, Yingjie, Guanwen Gu, Ye Wu, Shunji Wang, Xingguo Niu, Zhihe Xu, Haoyuan He, Xinglong Lin, and Lai Cao. 2025. "Inversion of Vertical Electrical Sounding Data Based on PSO-BP Neural Network" Minerals 15, no. 9: 925. https://doi.org/10.3390/min15090925

APA Style

Wang, Y., Gu, G., Wu, Y., Wang, S., Niu, X., Xu, Z., He, H., Lin, X., & Cao, L. (2025). Inversion of Vertical Electrical Sounding Data Based on PSO-BP Neural Network. Minerals, 15(9), 925. https://doi.org/10.3390/min15090925

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