Water-bearing structure in tunnel engineering based on high-precision three-dimensional advanced geological prediction technology

Abstract

Accurate prediction of concealed water-bearing structures ahead of tunnel excavation is a critical challenge for ensuring construction safety, controlling project costs and maintaining schedules. Conventional geophysical exploration methods often fail to satisfy engineering requirements under complex geological conditions due to limited accuracy and low resolution. This study aims to address the bottleneck of insufficient detection accuracy in single geophysical methods. To achieve this goal, a high-precision three-dimensional (3D) geological advance prediction technical framework is constructed and validated. The method systematically integrates advanced wavelet-based signal filtering and denoising techniques, efficient two-dimensional inversion algorithms and 3D visualization imaging. Of particular importance is that this study utilizes magnetic prospecting data as a multi-physical field joint constraint. It performs collaborative inversion with Transient Electromagnetic Method (TEM) data to overcome the multi-solution nature of single physical field interpretation. Validation in an actual tunnel engineering project demonstrates outstanding predictive performance: the root mean square error (RMSE) is reduced to 8%, the correlation coefficient (R) reaches 0.92, the signal-to-noise ratio (SNR) achieves 30 dB and spatial resolution is improved by approximately 30% compared with conventional two-dimensional inversion methods. The research results indicate that the proposed method significantly enhances the accuracy, reliability and detailed resolution capability of water-rich structure detection. It successfully overcomes the precision limitations of traditional geological prediction techniques. The novelty of this study lies in establishing a complete high-precision technical system from signal processing to multi-physical field comprehensive inversion. This provides a more reliable scientific basis for the safe construction of tunnels under complex geological conditions and surpasses the traditional prediction model that relies on a single physical field.