Application of dual-tree complex wavelet transform in advanced geological prediction of the tunnel section in Lalin of Sichuan-Tibet Railway

YANG Dan; LI Wei; WEI Yong-Liang; SONG Bin

doi:10.11720/wtyht.2021.0296

2021 Vol. 45, No. 6

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Article navigation > Geophysical and Geochemical Exploration > 2021 > 45(6): 1504-1511

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YANG Dan, LI Wei, WEI Yong-Liang, SONG Bin. 2021. Application of dual-tree complex wavelet transform in advanced geological prediction of the tunnel section in Lalin of Sichuan-Tibet Railway. Geophysical and Geochemical Exploration, 45(6): 1504-1511. doi: 10.11720/wtyht.2021.0296

Citation:

YANG Dan, LI Wei, WEI Yong-Liang, SONG Bin. 2021. Application of dual-tree complex wavelet transform in advanced geological prediction of the tunnel section in Lalin of Sichuan-Tibet Railway. Geophysical and Geochemical Exploration, 45(6): 1504-1511. doi: 10.11720/wtyht.2021.0296

Application of dual-tree complex wavelet transform in advanced geological prediction of the tunnel section in Lalin of Sichuan-Tibet Railway

1. Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China;
2. China Railway Academy Co., Ltd., Chengdu 610032, China

Received Date: 24 May 2021

Revised Date: 20 December 2021

Publish Date: 21 December 2021

Abstract

Abstract

The Sichuan-Tibet Railway features extremely complex geological conditions and construction environment, which greatly disturb the advanced geological forecast signals of ground penetrating radar (GPR). Therefore, it is necessary to adopt more effective signal noise reduction technology to conduct signal processing. With the tunnel section in Lalin of the Sichuan-Tibet railway as an example, this study applies the DTCWT to the advanced geological prediction of tunnels. Firstly, GPR signals were decomposed to some coefficients using the dual-tree complex wavelet transform (DTCWT), which were collected for the advanced geological prediction. Then high-frequency coefficients were shrunk for denoising based on four threshold processing selection rules (i.e., Sqtwolog, rigrsure, Heursure, and Minimaxi) combined with four thresholding schemes (i.e., hard, soft, firm shrinkage, nonnegative garrote shrinkage). Then signals were reconstructed using Wavelet coefficients through DTCWT inversion after the threshold shrinking. The wavelet denoising effects were assessed by calculating the normalized root-mean-square error (NRMSE), signal-to-noise ratio (SNR), and energy ratio (P). It is found that the best denoising effects can be obtained using the rigrsure threshold selection method combined with nonnegative garrote shrinkage and that the recognition accuracy of advanced geological forecast can be significantly improved accordingly.
- advanced geological prediction of tunnel /
- ground-penetrating radar /
- dual-tree complex wavelet transform /
- denoising /
- Sichuan-Tibet Railway

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References

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