Application of Direct Current Method and Seismic Wave Method in Advanced Detection of TBM Construction Tunnels

Abstract

Over the past decade, the application of Tunnel Boring Machines (TBMs) in tunnel construction has increased significantly. During the construction process, numerous unfavorable geological structures, especially water-conducting structures, are encountered. The commonly used Tunnel Seismic Prediction (TSP) method often cannot accurately interpret water-conducting features, while resistivity methods are sensitive to low-resistivity bodies, which are frequently associated with water channels. Due to the limited space and the surrounding pipe lining near the tunnel face, as well as the difficulty in drilling boreholes under TBM construction conditions, this paper proposes a novel electrode arrangement method that replaces rigid electrodes with flexible electrodes installed on the sidewalls. This approach overcomes the difficulty of deploying traditional electrodes downward in TBM tunnels. A simple direct current resistivity configuration was employed for field testing during the construction of the Guiyang Metro Line 3 TBM tunnel, and the results were compared with those from the Tunnel Seismic Prediction (TSP) method. The experimental results demonstrate that the improved DC resistivity method achieves high detection accuracy for water-conducting structures within a range of 30 m, showing strong consistency with the TSP detection results. This validates the feasibility and accuracy of the method, effectively addressing the challenges associated with traditional electrode deployment in TBM tunnels while compensating for the limited response of seismic methods to water-bearing structures. However, the effectiveness near the tunnel face remains suboptimal, with insufficient current distribution—an area requiring improvement, potentially by increasing forward current supply or further optimizing the electrode layout. Additionally, the study highlights the limitations of relying solely on a single advanced prospecting method. It suggests adopting an integrated approach, primarily based on seismic methods supplemented by electrical methods, to enable joint detection and interpretation, thereby minimizing the risk of accidents during construction.

1. Introduction

Since the beginning of the new century, domestic construction has advanced rapidly, with a significant volume of tunnel construction projects [1]. However, potential geological disaster risks such as water inflow and sudden water bursts can be encountered during tunnel excavation [2], and news reports of related disasters are frequent [3]. One of the main ways to guarantee the safety of tunnel construction is to use advanced detection technology [4].

Tunnel Boring Machines (TBMs) are one of the most widely used methods in tunnel construction today, accounting for 80% of total projects abroad, with an increasing proportion in domestic applications. Therefore, researching advanced detection technology in TBM tunnel construction has great practical value [5]. Complex equipment, which is included in the TBM construction process, is used to block the tunnel face by the cutterhead. Electromagnetic interference is significant, and the large size of the equipment leaves very limited space near the tunnel face, making advanced detection in TBM construction tunnels quite difficult. Consequently, advanced detection in TBM construction tunnels is currently a research hotspot [6].

The main methods currently applied in tunnel advanced detection include transient electromagnetic method, ground-penetrating radar [7], seismic methods, and direct current resistivity method [8]. The transient electromagnetic method is very sensitive to metals [9], but the complex electrical systems and metal structures within TBM tunnels can cause strong electromagnetic interference [10]. Ground-penetrating radar is limited by the heavy equipment [11] and narrow tunnel space, making it difficult to move and resulting in shallow detection depths [12]. The seismic wave method involves artificially generating seismic waves that propagate through the medium. When encountering interfaces with different impedance, reflection waves are generated and received by geophones. Through data analysis, the underground geological structure and resource distribution can be inferred [13]. Its advantages include being sensitive to interfaces in rocks and having a large exploration depth, but there is considerable underground reflection interference, and the detection results are not strongly correlated with water-conducting structures in the tunnel [14]. The direct current resistivity method primarily utilizes the electrical differences in geological bodies by establishing an artificial electric field to detect the geological environment [15,16], serving as a method for advanced forecasting [17]. The direct current resistivity method is sensitive to low resistivity and has certain advantages in detecting water-conducting structures [18,19].

The direct current resistivity method for advanced detection has always been a research hotspot. Yue Jianhua, Liu Shucai, and others conducted detailed discussions on the impacts of tunnel space and surrounding media [20,21]. Huang Junge found that air cavities in tunnels could interfere with detection results, thus suggesting adjustments to the transmitter-receiver distance to reduce this interference [22]. Ruan Baiyao and others innovatively proposed direct current focus technology, providing new ideas for improving detection methods [23,24,25]. Liu Bin, Liu Shucai, and others successfully derived the equations for spherical water-bearing structures within the entire space current field and used the “comparison method” to remove the influence of tunnel cavity data [26]. Zhai Peihe and others demonstrated significant effects in probing the water-bearing properties of strata through examples [27]. Wang Yunbin proposed the in-hole resistivity method, arranging the detection electrodes within the borehole at the tunnel face. This method can effectively reduce the influence of interfering bodies in the tunnel, thereby obtaining more accurate detection date [28]. Yue Jianhua and others proposed achieving more comprehensive underground resistivity detection using measurement technologies of different scales, allowing for the acquisition of underground electrical characteristics at varying depths and ranges, which allows for the acquisition of underground electrical characteristics at different depths and ranges [29]. In 2002, Denis validated the reliability of the direct current resistivity method in tunnel environments by comparing borehole data from geotechnical pre-investigation with 2D resistivity images obtained near the tunnel boring machine [30]. In 2020, Bovi demonstrated through practical application that the DC resistivity method can effectively detect piping, noting that differences between soil layers trigger subsurface erosion, with permeable layers above impermeable strata appearing to initiate the process. Three-dimensional modeling of the pipes revealed the connectivity of the pipe network [31]. In 2021, Max conducted a laboratory study using a scaled TBM model equipped with a DC resistivity system in a simulated tunnel environment. The study showed that imaging ahead of the tunnel face is feasible using probes when excavation is paused [32].

This paper leverages the high sensitivity of the direct current resistivity method to low-resistivity structures such as water-conducting features and proposes an electrical identification technology. The main innovations are as follows: (1) By modifying the traditional electrode deployment method, the conventional electrodes are replaced with self-developed flexible electrodes, enabling the direct current resistivity method to be applied within the confined spaces of TBM construction tunnels. (2) It addresses the limitations of the currently popular Tunnel Seismic Prediction (TSP) method, which cannot accurately interpret unfavorable geological bodies in water-rich strata. The electrical detection technique improves prediction accuracy by electrically identifying such geological features. (3) The approach emphasizes seismic methods as the primary technique with resistivity methods as a supplementary tool. Conducting multi-method joint detection and interpretation can maximally reduce the potential risks and accidents encountered during construction.

2. Principle and Method

2.1. The Principle of the Direct Current Resistivity Method

In tunnel advanced detection, a stable current field is established in the entire space between the power supply electrodes A and B, while the measurement electrodes M and N are moved to measure the potential difference ΔU_MN. When there is an anomalous geological body ahead, a specific entire space current field will be generated. By analyzing the electric field distribution of the stable current field, geological information ahead can be inferred.

The basic equation for the tunnel direct current resistivity method can be reduced to the Poisson equation:

$$\nabla \cdot \left[\sigma \left(x,y,z\right)\nabla \phi \left(x,y,z\right)\right]=-I\delta \left(x-x_0\right)\delta \left(y-y_0\right)\delta \left(z-z_0\right)$$

(1)

where $\sigma$ represents the electrical conductivity of the underground medium $(S/m)$; $\varphi$ represents the potential $(V)$; $(x_0,y_0,z_0)$ represents the source coordinates; $I$ represents the current intensity $(A)$; $\delta (\cdot )$ represents the Dirac function.

The direct current resistivity method is situated in a stable current field throughout the entire space, so only Newman boundary conditions and third-type mixed boundary conditions need to be considered. Newman boundary conditions and third-type mixed boundary conditions are applied to the ground-air boundary and the boundary at infinity, respectively:

$$\left\{\begin{array}{l}\sigma {\displaystyle \frac{\partial \phi }{\partial n}}=0,\in {\mathrm{\Gamma }}_s\\ {\displaystyle \frac{\partial \phi }{\partial n}}+{\displaystyle \frac{\cos \theta }{r}}\phi =0,\in {\mathrm{\Gamma }}_{\infty }\end{array}\right.$$

(2)

where $\sigma$ represents the electrical conductivity of the underground medium $(S/m)$; $\phi$ represents the potential $(V)$; ${\displaystyle \frac{\partial \phi }{\partial n}}$ represents the normal derivative of the potential, $r$ represents the distance $(m)$; $\theta$ represents the angle $(°)$; ${\mathrm{\Gamma }}_s$ represents the ground-air boundary; ${\mathrm{\Gamma }}_{\infty }$ represents the boundary at infinity.

2.2. The Principle of the TSP Method

The TSP method uses artificially generated seismic waves and, by analyzing the propagation characteristics of these waves (such as reflection, refraction, attenuation, etc.) in the rock mass, combined with signal processing techniques, infers the location and nature of geological anomalies ahead of the tunnel.

The relationship between the travel time $t$ of the reflected wave and the position of the reflection interface:

$$t=\sqrt{{\displaystyle \frac{{\left(2L\right)}^2+x^2}{v}}}$$

(3)

where $L$ represents the vertical distance from the seismic source to the reflection interface (the distance from the detection target to the tunnel face). $x$ represents the horizontal distance between the seismic source and the geophone. $v$ represents the seismic wave velocity in the rock mass (which can be calibrated using the direct wave velocity).

The reflection coefficient is defined as:

$$R={\displaystyle \frac{{\rho }_1{v}_1-{\rho }_2{v}_2}{{\rho }_1{v}_1+{\rho }_2{v}_2}}$$

(4)

where ${\rho }_1$ and ${\rho }_2$ represent the rock densities on both sides of the reflection interface; $v_1$ and $v_2$ represent the seismic wave propagation velocities $\left(m/s\right)$ on both sides of the reflection interface.

The time–frequency domain denoising method transforms the signal from the time domain to the time–frequency domain, making it easier to apply noise removal techniques related to both time and frequency. In the Short-Time Fourier Transform (STFT), according to the uncertainty principle of signal analysis, when the window function is a Gaussian-type function and the window area reaches its minimum, the S-transform can be expressed in the form of an inner product of $<s\left(\tau \right){\displaystyle \frac{\left|f\right|}{\sqrt{2\pi }}}{e}^{{\displaystyle \frac{{\left(t-\tau \right)}^2{f}^2}{2}}},e^{-i2\pi ft}>$:

$$S_{ST\left(t,f\right)}={\displaystyle \frac{\left|f\right|}{\sqrt{2\pi }}}{\int }_{-\infty }^{+\infty }s\left(\tau \right)e^{{\displaystyle \frac{{\left(t-\tau \right)}^2{f}^2}{2}}}{e}^{-i2\pi ft}d\tau$$

(5)

The expression for the lossless S-transform inverse transform (IST) is:

$$s\left(t\right)={\displaystyle {\int }_{-\infty }^{+\infty }\left[{\displaystyle {\int }_{-\infty }^{+\infty }{S}_{ST}\left(\tau ,f\right)d\tau }\right]}{e}^{i2\pi ft}df$$

(6)

In order to accurately locate the reflected waves from the tunnel face in space, Kirchhoff depth migration is used to image the geological interfaces of the measured rock mass, which helps determine the orientation of the geological interfaces.

The Kirchhoff integral formula is:

$$\phi \left(x_1,y_1,z_1,t\right)=-{\displaystyle \frac{1}{4\pi }}{\displaystyle \underset{Q}{∯}\left\{\left[\phi \right]\frac{\partial }{\partial n}\left(\frac{1}{r}\right)-\frac{1}{r}\left[\frac{\partial \phi }{\partial n}\right]-\frac{1}{vr}\frac{\partial r}{\partial n}\left[\frac{\partial \phi }{\partial t}\right]\right\}}dQ$$

(7)

In the formula, $\phi (x_1,y_1,z_1,t)$ represents the output wavefield; $\left[ \right]$ represents the delay position, where $\left[\phi \right]$ denotes the delay at point $\left(x,y,z\right)$ at time $t^{\prime }=t-r/v$; $n$ represents the outward normal of surface $Q$; $r$ is the distance from the input point to the output point; $v$ represents the root mean square velocity at the output point.

2.3. Three-Dimensional Inversion Algorithm for Tunnel Resistivity Method in Advanced Detection

The inversion algorithm uses the limited-memory quasi-Newton method, with the objective function defined as:

$${\mathrm{\Phi }}_{\mathrm{r}}={\left[d_r-F_r\left(m_r\right)\right]}^T{V_r}^{-1}\left[d_r-F_r\left(m_r\right)\right]+{\lambda }_1{\left(m_r-m_{r0}\right)}^T{L}^{T}L\left(m_r-m_{r0}\right)$$

(8)

In this formula, ${\mathrm{\Phi }}_r$ is the objective function for the DC resistivity method, ${\lambda }_1$ is the Lagrange multiplier, $m_r$ is the resistivity model vector; $m_{r0}$ is the prior resistivity model vector; $V_r$ is the data covariance matrix, $L$ is the model covariance matrix, which is the second-order partial derivative with respect to the grid width in each direction; $d_r$ is the apparent resistivity observation data vector; $F_r\left(\cdot \right)$ is the forward operator for the DC resistivity method. The specific inversion process is shown in Figure 1.

In the flowchart, $e$ is the residual vector between the observed data and the forward data; $g$ is the gradient of the objective function; $p$ is the search direction; $\alpha$ is the search step length.

Using the “Ratio Method” to Eliminate the Influence of the Tunnel Cavity on the Forward Response.

The primary target area for advance detection is the region ahead of the tunnel face. However, the presence of the tunnel cavity masks the apparent resistivity anomalies caused by water-bearing structures in front of the tunnel face. That is, the apparent resistivity anomalies include not only the influence of water-bearing structures ahead but also the effect of the tunnel cavity itself. These two influences cannot be distinguished based solely on the characteristics of the apparent resistivity anomalies. Therefore, the “ratio method” is employed to remove the influence of the tunnel cavity on the apparent resistivity anomalies. The formula is as follows:

$$\left\{\begin{array}{c}{\rho }_{\mathrm{c}}={\displaystyle \frac{{\rho }_a}{k}}\hfill \\ k={\displaystyle \frac{{\rho }_{tunnel}}{\rho }}\hfill \end{array}\right.$$

(9)

Among them, ρ_c is the apparent resistivity value after removing the influence of the tunnel cavity; ρ_a represents the apparent resistivity that needs to be corrected, that is, the actual observed value. k is the correction coefficient; ρ_tunnel refers to the apparent resistivity in underground space where only the tunnel and surrounding rock exist without any other structures. ρ represents the apparent resistivity value of the surrounding rock. To verify the effectiveness of the “ratio method”, after extensive tests, a tunnel model that is more in line with the actual situation was established. The tunnel is buried at a depth of 13.5–19.5 m, with a length of 39 m. A water-bearing structure with a resistivity of 6 Ωm and a geometric size of 9 m × 6 m × 6 m is designed 3 m in front of the tunnel face (6 m × 6 m), and the resistivity of the surrounding rock is 50 Ωm. A total of 12 field sources were designed, among which 3 were on the palm face. A total of 960 observation data were obtained. (−4.5, 3.0, 16.5) was selected as the power supply field source, and the apparent resistivity data was collected on the measurement line where it was located. The forward modeling results are shown. The ρ_a and ρ_tunnel curves almost coincide, which indicates that the apparent resistivity anomalies of the tunnel cavity almost cover all apparent resistivity anomalies, while the apparent resistivity anomalies caused by the water-bearing structure in front of the face are very weak. The ρ_c curve after removing the influence of the tunnel cavity is almost consistent with the resistivity ρ of the surrounding rock, which also demonstrates the view that the water-bearing structure causes a weak apparent resistivity anomaly (Figure 2).

Considering the above factors to eliminate the tunnel’s influence, the ratio method was integrated with the inversion algorithm. Inversion results were obtained by placing anomalous bodies at positions 4.5 m and 6 m, respectively, as shown in Figure 3 and Figure 4. Tests on synthetic data from theoretical models demonstrate that the inversion results after removing the tunnel cavity’s influence are superior to previous results and achieve the expected effectiveness. The dotted boxes in these two figures roughly indicate the location of the tunnel.

3. TBM Tunnel Direct Current Resistivity Advanced Observation and TSP Observation

3.1. Overview of the Detection Site

The experiment was conducted near Dayingpo Station, the twelfth section of Guiyang Rail Transit Line 3. The area is mainly composed of Jurassic and Cretaceous limestones and sandstones, with relatively complex stratigraphy containing a certain amount of clay and clastic rocks. The limestone layers may have a high groundwater level. The tunnel has an approximate diameter of 6.47 m, with segmental lining thickness of 35 cm. The vertical distance from the tunnel center to the ground surface is 16.8 m.

3.2. Direct Current Resistivity Experiment

3.2.1. Experimental Preparation

Rigid Electrodes:

Traditional electrodes require drilling holes. In tunnels, the presence of pipe lining makes drilling very inconvenient, and it is often impossible to drill into water-bearing strata.

Flexible Electrodes:

The cloth bag encapsulates the copper-plated metal spring electrode, copper-based metal salt crystals, and water-absorbing material. The copper-based metal salt crystals are filled around the copper-plated metal spring electrode. The electrode is pressed against the predetermined measurement point utilizing the pushing force exerted by the metal spring.

The soft electrode is primarily fabricated using bare copper wire, absorbent cotton, copper sulfate, and other materials. Its spiral section measures 8–10 cm in length, while the rear section is 20 cm long. It consists of copper-based metal salt crystals, a water-absorbing body, and other components. The main steps of the process are shown in Figure 5.

Outdoor tests were conducted, with extreme range tests carried out on both cement brick and soil surfaces to examine their grounding conditions.

As can be seen from Figure 6, the range between the soils at the test site is 25.4 mV, and the range between the non-polarized electrodes is 1.1 mV. The range between the electrodes is eliminated significantly.

As can be seen from Figure 7, the range between the cement bricks at the test site is 111.3.4 mV, and the range between the non-polarized electrodes is 1.3 mV. The range between the electrodes is eliminated significantly.

As shown in Figure 8, it is a comparison between soft electrodes and traditional rigid electrodes.

The electrical observation equipment is a self-developed device, mainly including electrodes, power supply, control console, transmitting device, acquisition device and voltage converter, etc. The equipment is shown in Figure 9. It has a delay of 100 ms, a power supply time of 2 s and a power-off time of 1 s. The primary field voltage is −6 V to +6 V, with an accuracy of ±1%. Self-compensation capability: −1 V to +1 V, effectively eliminating natural potential interference; Ultra-high input impedance: >30 MΩ, reducing signal attenuation and enhancing data fidelity; Strong compatibility: Adaptable to a power supply cycle of 4 to 16 s and supports flexible working modes; Intelligent delay control: The power-off delay time is adjustable from 50 to 1000 ms, and the signal acquisition window is optimized.

3.2.2. Field Construction Arrangement

Since the tunnel is surrounded by pipe lining, traditional electrode deployment methods that involve inserting into the ground cannot be used. Therefore, the electrodes are arranged on the grouting holes on the side walls of the pipe lining. By utilizing the pushing effect of the metal spring clips, the electrodes are pressed against the designated measurement points. The specific arrangement is illustrated in Figure 10.

A total of three observations were conducted, with the power supply electrodes and measurement electrodes shown in Figure 11. The positions of electrodes 1 to 15 in the first observation were consistent with the phases of electrodes 12 to 26 in the second and third observations; the second and third observations arranged 7 additional electrodes forward based on the first observation; electrodes 8 to 11 in the second and third observations were left vacant (this area corresponds to the TBM machine room location).

After arranging the measurement line, clip the electrodes and secure them to the corresponding positions on the segment using tape. The A electrode should be as close to the face as possible, generally installed in the nearest grouting hole to the face, and should be as deep as possible. The B electrode is typically set at a distance greater than 10 times that of the entire measurement line, usually over 200 m away. Connect the electrical measurement equipment and then plug the electrode measurement line into the corresponding ports on the device. Data is collected three times for each observation.

3.3. TSP Experiment

3.3.1. Experimental Preparation

The necessary equipment includes: a DOSO shield tunneling advanced geological forecasting instrument produced by Beijing Skweco Geophysical Information Technology Co., Ltd. (Beijing, China) (Equipment ID: SN:24020412), six three-component velocity seismic geophones, sensor connection cables, a hammer trigger, external trigger selection, trigger lines, and steel stakes. The geophones have a sensitivity of 500 mV/g, a frequency range of 1 to 2000 Hz, and a lateral sensitivity of ≤5%, magnetic iron wall coupling, 32 K sample points, a sampling interval of 0.02 ms, and external trigger selection.

3.3.2. On-Site Construction Arrangement

We conducted the TSP measurement experiment on the Guiyang Metro Line 3 at the same time. The tunnel face mileage is the same as that of the electrical method observation. The geophones were arranged starting 10 m from the tunnel face, with each geophone attached to the pipe wall. A geophone was placed every 1.5 m, with a total of six geophones. A shot point was placed 4.5 m away from the last geophone. The position of the first observation of electrode A in the electrical method is the same as this location. After connecting the wires, the hammering measurements were performed, and the data was repeatedly collected three times.

For this project, the measurement line was chosen between the control room on the left side of the tunnel and the tail of the shield machine. Geophones and seismic source points were arranged at the 7 o’clock direction of the corresponding segment in that interval, and the observation system is arranged according to the layout shown in Figure 12.

4. Data Results and Discussion

4.1. Electrical Method Observation Inversion

Figure 13 shows the inversion results, with the central coordinates of the tunnel face at (37,217, 0, 0). The results indicate that the low-resistivity structure in front of the tunnel is not very apparent, and no low-resistivity structures have been found, leading to the initial assumption that there is likely no water-conducting structure. Additionally, a high conductor was detected on the side wall of the tunnel (where the electrodes were laid), with an extension range in three directions: X (37,250, 37,267), Y (0, −9.6), and Z (−3, 1). Furthermore, there is a high-resistivity structure to the left of the high conductor. The tunnel has an extension range in three directions: X (37,227, 37,235), Y (−13.6, −3.2), and Z (−1, 1), where there is a significant possibility of a water-conducting structure, along with nearby low-resistivity structures. The specific situation still needs to be assessed in conjunction with whether there are moist or leaking conditions at this location during ongoing construction.

4.2. TSP Observation Inversion

Figure 14 shows the original waveform collected from the TSP of the tunnel, which includes the XYZ three-component waveforms, with an interval of 1.5 m. From the figure, it can be observed that the arrival of the reflected waves is clear, with minimal acoustic interference and a relatively high signal-to-noise ratio.

Figure 15 illustrates the variation in the normalized amplitude of reflected waves with frequency before and after filtering the TSP-DOSO collected data. The main frequency is concentrated below 700 Hz. The bandpass filter is set to a frequency band of 10–700 Hz, with 700–2000 Hz serving as the transition band. After filtering, signals with frequencies <10 Hz and >1000 Hz are eliminated, and signals in the 700–1000 Hz range are suppressed.

As shown in Figure 16, The time and frequency corresponding to the red curve in the time–frequency spectrum represent the transmitted time–frequency spectrum, which is designed to reduce acoustic and surface waves, thus enhancing the signal-to-noise ratio of the data.

Figure 17 shows the longitudinal and transverse wave profiles in the x-z plane. From 37,120 to 37,160, the longitudinal wave first increases and then decreases, while the transverse wave shows the same pattern. From 37,160 to 37,200, the longitudinal wave first decreases and then slightly increases, whereas the transverse wave first decreases, then increases, and subsequently decreases again. From 37,200 to 37,240, both the longitudinal and transverse waves decrease, and from 37,240 to 37,270, both the longitudinal and transverse waves increase.

Based on the velocity distribution, it is inferred that there are a total of 9 reflection interfaces, as shown in Figure 18.

As shown in Figure 19, At the same location, TSP measurements were conducted along the line, and the obtained P-wave velocity distribution map is shown above. The black pipe represents the tunnel, with no low velocities between 37,120 m and 37,160 m. From 37,160 m to 37,200 m, the velocity first decreases, then increases, and then decreases again, which may indicate a fractured zone. It can be roughly judged that there is no water-conducting structure ahead of the tunnel. On the left side of the tunnel, from 37,225 m to 37,240 m, the wave velocity is relatively low, indicating a significant drop, which may suggest the presence of a water-conducting structure. Meanwhile, from about 37,250 m to 37,267 m on the left side of the tunnel, the wave velocity is very high, possibly indicating the presence of high-density geological bodies such as rock.

4.3. Data Comparison and Analysis

A comprehensive comparison between the DC resistivity method and the TSP method shows good consistency in their results. Both methods suggest that there is basically no water-conducting structure ahead of the tunnel. They also suggest that the probability of a water-conducting structure and a high-resistance structure existing on the left side is higher. The resistivity distribution correlates well with the low and high speeds of the P-wave, with similar locations, and both methods can reflect the information about the geological body’s location and state ahead, which verifies the feasibility of these methods. Compared to the TSP method, the DC resistivity method is more sensitive to water-conducting structures, which is an advantage. However, the detection range in front of the tunnel is relatively shallow, with a detection accuracy of 30 m, and the effect is not very good, which requires further exploration. As presented in

Table 1. Comparison Table between TSP Method and DC Resistivity Method.

Property	TSP Method	DC Resistivity Method
Equipment	Sercel equipment: 6 three-component velocity geophones, sensor cables, hammer trigger, external trigger selection, trigger cable, steel rod, etc.	Self-developed equipment: Electrodes, cables, power supply, control unit, transmission device, acquisition unit, voltage converter, etc.
Principle	Seismic wave propagation	Electromagnetic field theory
Acquisition Method	Hammer impact method	Three-pole array
Sensitivity	Relatively low sensitivity; weak response to subtle anomalies	Capable of detecting minor resistivity changes; more sensitive to gradual variations in water content and slight changes in porosity
Specificity	Velocity layers, interfaces, geological structures	Higher specificity in detecting water-related features
Detection Depth	Deep (can exceed 100 m)	Shallow (approximately 30 m)
Accuracy	Relatively high	Relatively high, but suffers from more prominent multi-solution issues, often requiring prior information

, a general comparison of several key characteristics between the Direct Current (DC) resistivity method and the Tunnel Seismic Prediction (TSP) method is outlined.

5. Conclusions

(1)

Experimental results demonstrate that this method exhibits certain effectiveness in the forward prediction of geological structures ahead. By innovating the traditional electrode arrangement—replacing conventional electrodes with self-developed soft electrodes and adopting a sidewall layout—the challenges associated with electrode deployment in TBM tunnels have been effectively addressed. Compared to domestic and international approaches such as drilling or stop-and-probe devices, this method offers simpler deployment, stronger on-site applicability, and faster detection speed, while maintaining considerable effectiveness. These advantages warrant further exploration and development.

(2)

Currently, the TSP (Tunnel Seismic Prediction) advance detection method is widely used. However, when encountering water-rich strata, TSP has limitations in accurately interpreting unfavorable geological structures. By comparing results obtained from the TSP method with those from the DC resistivity method, the feasibility and accuracy of the DC resistivity method were validated. The DC resistivity method can detect minor resistivity variations, making it more sensitive to gradual changes in water content and subtle porosity variations. It exhibits higher specificity in detecting water-bearing structures, thereby compensating for the interpretive limitations of TSP in water-rich formations. By utilizing electrical detection technology to identify such geological features based on electrical properties, prediction accuracy is significantly improved.

(3)

Due to the use of a simplified setup in this experiment, the effectiveness near the tunnel face was suboptimal, with insufficient current distribution—an area requiring improvement. Potential solutions include increasing the forward current supply or further optimizing the electrode layout. The results also demonstrate the limitations of relying solely on a single advance prediction method. We recommend adopting an integrated approach, primarily based on seismic methods supplemented by electrical methods, to enable joint detection and interpretation, thereby minimizing the risk of accidents during construction.