Prediction of Conductive Anomalies Ahead of the Tunnel by the 3D-Resitivity Forward Modeling in the Whole Space. (21st December 2021)

• Record Type: Journal Article
• Authors: Hu, Daiming; Tezkan, Bülent; Yue, Mingxin; Yang, Xiaodong; Wu, Xiaoping; Zhou, Guanqun
• Other Names: Xuan Dayang Academic Editor.
• Is Part Of: Geofluids. Volume 2021(2021)
• Publication Date: 2021-12-21
• Subjects: Hydrogeology -- Periodicals; Sedimentary basins -- Periodicals; Fluids -- Migration -- Periodicals; Groundwater flow -- Periodicals; Geothermal resources -- Periodicals; Fluid dynamics -- Periodicals; Earth -- Crust -- Periodicals; 551.49
• DOI: 10.1155/2021/7301311
• Languages: English
• ISSNs: 1468-8115

Abstract

Abstract : Water inrush in tunneling poses serious harm to safe construction, causing economic losses and casualties. The prediction of water hazards before tunnel excavations becomes an urgent task for governments or enterprises to ensure security. The three-dimensional (3D) direct current (DC) resistivity method is widely used in the forward-probing of tunnels because of its low cost and highly sensitive response to water-bearing structures. However, the different sizes of the tunnel will distort the distribution of the potential field, which causes an inaccurate prediction of water-bearing structures in front of the tunnels. Some studies have pointed out that the tunnel effect must be considered in the quantitative interpretation of the data. However, there is rarely a predicted model considering the tunnel effect to be reported in geophysical literature. We developed a predicted model algorithm by considering the tunnel effect for forward-probing in tunnels. The algorithm is proven to be feasible using a slab analytic model. By simulating a large number of models with different tunnel sizes, we propose an equation, which considers the tunnel effect and can predict the water-bearing structures ahead of the tunnel face. The Monte Carlo method is used to evaluate the quality of the predicted model by simulating and comparing 10, 000 random models. The results show that the proposed method is accurate to forecast the water-rich structures with small errors.