# Multi-Means Comprehensive Forecasting Technology for Water Inrush in Long Tunnels
## 6 Conclusions  
This study develops an integrated analytical method combining seismic reflection and seismoelectric effect for geological hazard and water intrusion detection during tunnel construction in Fuzhou. The methodology not only retrieves rock mass mechanical parameters, fault characteristics, and geological interface migration imaging over long distances through conventional seismic reflection but also quantifies the spatial distribution and saturation levels of water-bearing structures ahead of the tunnel face. This enables long-distance detection and early warning of adverse geological conditions, along with precise localization and sizing of water-bearing anomalies. Building upon long-range forecasting, transient electromagnetic theory and technology are implemented to delineate water body positions and scales, facilitating medium-distance water inrush risk assessment. For proximal detection, the ground-penetrating radar is employed to achieve high-resolution short-range monitoring. Collectively, these techniques establish a multi-scale detection framework integrating electromagnetic, seismic, and seismoelectric methods for comprehensive geological hazard and water inrush prediction in long tunnels. The proposed methodology was applied to a tunnel project in Fuzhou, demonstrating significant efficacy through field verification. The integration of seismic waves and seismoelectric effects achieved accurate long-distance localization of water inrush sources, while multi-method synergy enabled the precise identification of geological hazards and hydrodynamic risks during construction. This systematic approach provides critical technical support for safe tunnel construction, particularly in complex hydrogeological environments, by combining multi-physics detection capabilities with progressive uncertainty reduction mechanisms. According to the research, the conclusions are as follows:  
1. By integrating seismic reflection, seismoelectric effect, transient electromagnetic and ground penetrating radar methods, combined with advanced detection technologies and data analysis techniques, comprehensive detection and precise prediction of the distribution characteristics of adverse geological bodies and fissure water in front of the tunnel face at long, medium, and short distances have been achieved. 2. While this study establishes a pioneering multi-scale framework for geological hazard detection in tunnels, its transition from a case-specific solution to a universally applicable system demands addressing current technical and operational limitations. 3. Future efforts should prioritize interdisciplinary collaboration, algorithmic innovation, and large-scale validation to advance toward intelligent, real-time forecasting systems capable of adapting to the complexities of global tunneling projects. **Author Contributions:** Conceptualization, F.Y. ; methodology, W.Z. ; software, X.W. and J.C.; validation, H.X., Z.X. and G.W. ; formal analysis, X.W. ; investigation, J.Y. and H.X. ; resources, J.Y. and H.X. ; data curation, F.Y. ; writing--original draft preparation, Q.M. and F.Y. ; writing--review and editing, J.Y. ; supervision, J.Y. ; project administration, J.Y. and H.X. All authors have read and agreed to the published version of the manuscript. **Funding:** The authors gratefully acknowledge the financial support from the National Natural Science Foundation of China (42177168), the Natural Science Foundation of Hubei Province (2024 AFA068) and Project of Youth Innovation Promotion Association of Chinese Academy of Sciences (2021326). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy. **Conflicts of Interest:** Authors [PERSON] and [PERSON] were employed by PowerChina Chengdu Engineering Corporation Limited.