Enhanced geological prediction for tunnel excavation using full waveform inversion Integrating Sobolev space regularization with a quadratic penalty method

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, we propose a novel frequency-domain full waveform inversion approach that incorporates a penalty method and Sobolev space regularization 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 synthetic 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 effectiveness of the proposed method is validated by the experimental results, which demonstrate that it consistently achieves superior performance compared to traditional approaches.