Hybrid support vector machine optimization model for inversion of tunnel transient electromagnetic method

Xiao Liang; Taiyue Qi; Zhiyi Jin; Wangping Qian; Xiao Liang; Taiyue Qi; Zhiyi Jin; Wangping Qian

doi:10.3934/mbe.2020221

Mathematical Biosciences and Engineering

2020, Volume 17, Issue 4: 3998-4017. doi: 10.3934/mbe.2020221

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Research article

Hybrid support vector machine optimization model for inversion of tunnel transient electromagnetic method

1.
Key Laboratory of Transportation Tunnel Engineering, Ministry of Education, Chengdu 610031, China
2.
School of Civil Engineering, Southwest Jiaotong University, Chengdu 610031, China

Received: 28 February 2020 Accepted: 26 May 2020 Published: 01 June 2020

The transient electromagnetic method (TEM) can effectively predict adverse geological conditions, and is widely used in underground engineering fields such as coal mining and tunneling. Accurate evaluation of adverse geological features is a crucial problem that requires urgent solutions. TEM inversion is an essential tool in solving such problems. However, the three-dimensional full-space detection of tunnels and its inversion are not sufficiently developed. Therefore, combining a least-squares support vector machine (LSSVM) with particle swarm optimization (PSO), this paper proposes a tunnel TEM inversion approach. Firstly, the PSO algorithm is adopted to optimize the LSSVM model, thus overcoming the randomness and uncertainty of model parameter selection. An orthogonal test method is adopted to optimize the initial parameter combination of the PSO algorithm, which further improves the accuracy of our PSO-LSSVM model. Numerical simulations are conducted to generate 125 sets of original data. The optimized PSO-LSSVM model is then used to predict certain values of the original data. Finally, the optimization model is compared with conventional machine learning methods, and the results show that the randomness of the initial parameters of the PSO algorithm has been reduced and the optimization effect has been improved. The optimized PSO algorithm further improves the stability and accuracy of the generalization ability of the model. Through a comparison of different machine learning methods and laboratory model tests, it is verified that the optimized PSO-LSSVM model proposed in this paper is an effective technique for tunnel TEM detection inversion.
- transient electromagnetic method,
- inversion method,
- hybrid support vector machine,
- particle swarm optimization
Citation: Xiao Liang, Taiyue Qi, Zhiyi Jin, Wangping Qian. Hybrid support vector machine optimization model for inversion of tunnel transient electromagnetic method[J]. Mathematical Biosciences and Engineering, 2020, 17(4): 3998-4017. doi: 10.3934/mbe.2020221

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Abstract

The transient electromagnetic method (TEM) can effectively predict adverse geological conditions, and is widely used in underground engineering fields such as coal mining and tunneling. Accurate evaluation of adverse geological features is a crucial problem that requires urgent solutions. TEM inversion is an essential tool in solving such problems. However, the three-dimensional full-space detection of tunnels and its inversion are not sufficiently developed. Therefore, combining a least-squares support vector machine (LSSVM) with particle swarm optimization (PSO), this paper proposes a tunnel TEM inversion approach. Firstly, the PSO algorithm is adopted to optimize the LSSVM model, thus overcoming the randomness and uncertainty of model parameter selection. An orthogonal test method is adopted to optimize the initial parameter combination of the PSO algorithm, which further improves the accuracy of our PSO-LSSVM model. Numerical simulations are conducted to generate 125 sets of original data. The optimized PSO-LSSVM model is then used to predict certain values of the original data. Finally, the optimization model is compared with conventional machine learning methods, and the results show that the randomness of the initial parameters of the PSO algorithm has been reduced and the optimization effect has been improved. The optimized PSO algorithm further improves the stability and accuracy of the generalization ability of the model. Through a comparison of different machine learning methods and laboratory model tests, it is verified that the optimized PSO-LSSVM model proposed in this paper is an effective technique for tunnel TEM detection inversion.

References

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