Enhanced Geological Prediction for Tunnel Excavation Using Full Waveform Inversion Integrating Sobolev Space Regularization with a Quadratic Penalty Method

TL;DR

This paper introduces a novel frequency domain full waveform inversion method with Sobolev space regularization and a quadratic penalty, significantly improving non-destructive geological prediction accuracy for tunnel excavation amidst complex structures and noise.

Contribution

It develops a new inversion approach combining a penalty method and Sobolev space regularization to enhance robustness and resolution in geological predictions for tunnel excavation.

Findings

Outperforms traditional Tikhonov and total variation regularization methods.

Provides high-resolution inversion results even with noisy data.

Demonstrates robustness in complex faulted tunnel models.

Abstract

In the process of tunnel excavation, advanced geological prediction technology has become indispensable for safe, economical, and efficient tunnel construction. Although traditional methods such as drilling and geological analysis are effective, they typically involve destructive processes, carry high risks, and incur significant costs. In contrast, non-destructive geophysical exploration offers a more convenient and economical alternative. However, the accuracy and precision of these non-destructive methods can be severely compromised by complex geological structures, restrictions on observation coverage and environmental noise. To address these challenges effectively, a novel approach using frequency domain full waveform inversion, based on a penalty method and Sobolev space regularization, has been proposed to enhance the performance of non-destructive predictions. The proposed method constructs a soft-constrained optimization problem by restructuring the misfit function into a combination of data misfit and wave equation drive terms to enhance convexity. Additionally, it semi-extends the search space to both the wavefield and the model parameters to mitigate the strong nonlinearity of the optimization, facilitating high-resolution inversion. Furthermore, a Sobolev space regularization algorithm is introduced to flexibly adjust the regularization path, addressing issues related to noise and artefacts to enhance the robustness of the algorithm. We evaluated the performance of the proposed full waveform inversion using several tunnel models with fault structures by comparing the results of the enhanced method with those of traditional least-squares-based Tikhonov regularization and total variation regularization full waveform inversion methods. The verification results confirm the superior capabilities of the proposed method as expected.