A tunnel advance detection system and method

Abstract

This invention belongs to the field of tunnel engineering or earthquake service technology, and discloses a tunnel advance detection system and method. The invention uses a detection unit to excite and acquire seismic wave signals, and utilizes the symmetrical structural characteristics of the sensor to separate the acquired signals into shear and longitudinal waves, obtaining wavefield information including shear wave component data and longitudinal wave component data projected onto three axes respectively. Based on the wavefield information, an inversion unit performs a full waveform inversion with geometric constraints introduced by migration imaging to obtain tunnel advance detection information. This invention can improve the accuracy of tunnel advance detection.

Description

Technical Field

[0001] This invention belongs to the field of tunnel engineering or earthquake service technology, and more specifically, relates to a tunnel advanced detection system and method. Background Technology

[0002] During tunnel construction, the heterogeneity and structural complexity of the strata ahead of the tunnel face are significant factors affecting tunneling safety. Advanced detection technology, through the acquisition and analysis of wavefield information, can predict the distribution of geological anomalies, thus providing a reference for the design of engineering support systems. Especially in intelligent tunneling projects such as full-face tunnel boring machines (TBMs), establishing high-precision advanced geological models has significant engineering value for controlling geological risks.

[0003] Signal analysis and dynamic imaging based on elastic wave theory are currently one of the important methods for obtaining parameters of the rock mass ahead. By recording the wave signals generated by excitation and extracting the polarization characteristics and phase information of particle vibrations, a wave velocity distribution image of the strata ahead can be reconstructed. With the improvement of high-performance computing capabilities, advanced detection has evolved from simple geometric kinematic travel-time analysis to the stage of quantitative reconstruction of strata parameters through full waveform matching, providing a high-precision analytical method for tectonic exploration under complex wavefield backgrounds.

[0004] However, existing advanced detection schemes mainly rely on mathematical transformations for wavefield separation. In complex transverse and longitudinal wave coupled fields, this post-processing method easily leads to a decrease in the component signal-to-noise ratio, making it difficult for the inversion objective function to accurately fit the disturbance response caused by the anomaly. Furthermore, due to the lack of effective geometric constraints, nonlinear problems in the inversion process often cause deviations in the wave velocity field reconstruction, limiting the accuracy of identifying the boundaries of geological anomalies. Therefore, how to achieve direct decoupling of wavefield components at the physical sensing level and improve the convergence accuracy of the inversion algorithm, thereby improving the accuracy of tunnel advanced detection, is a key technical challenge that urgently needs to be overcome in this field. Summary of the Invention

[0005] This invention provides a tunnel advance detection system and method, which solves the problem of low accuracy in existing tunnel advance detection technologies.

[0006] This invention provides a tunnel advanced detection system, comprising: a detection unit and an inversion unit; The detection unit is used to excite and acquire seismic wave signals, and uses the symmetrical structural characteristics of the sensor to separate the acquired signals into transverse and longitudinal waves to obtain wavefield information. The wavefield information includes transverse wave component data and longitudinal wave component data projected onto the three axes respectively. The inversion unit is used to perform full waveform inversion with geometric constraints introduced by migration imaging based on the wavefield information, so as to obtain tunnel advance detection information.

[0007] Preferably, the detection unit includes: a seismic wave excitation device, a seismic wave receiving device, and a signal processing device; The seismic wave excitation device is used to determine the dominant frequency of the seismic source excitation based on the depth of the detected target and to generate seismic wave signals; The seismic wave receiving device is used to receive seismic wave signals and obtain a high-fidelity signal; The signal processing device is used to perform time-difference-free parallel gain, transverse and longitudinal wave separation, and orthogonal projection processing on the fidelity signal to obtain the wave field information.

[0008] Preferably, the seismic wave receiving device includes: a sensing component; the sensing component consists of three orthogonally placed sensors, each sensor including a piezoelectric ceramic, an outer electrode, and an inner electrode; the outer electrode is disposed on the outer wall of the piezoelectric ceramic, and the four equally divided sections formed by the outer electrode serve as four positive electrodes; the inner electrode is disposed on the inner wall of the piezoelectric ceramic, and the entire section formed by the inner electrode serves as a common negative electrode; wires are welded to the common negative electrode and each of the positive electrodes, and the wires are connected to the signal processing device.

[0009] Preferably, the seismic wave receiving device further includes: a housing assembly; the housing assembly includes: a sensor cavity shell, an expansion block, a front wedge-shaped top block, a rear wedge-shaped top block, a connecting rod, a connecting rod outer sleeve, and a tightening mechanism; The sensor cavity shell is used to house the sensing component. The expansion block is wrapped around the outside of the sensor cavity shell. The expansion block has a segmented structure. The expansion block and the sensor cavity shell are in contact by the front wedge-shaped top block and the rear wedge-shaped top block through an inclined surface. The sensor cavity shell is connected to the connecting rod. The tail of the connecting rod is connected to the tightening mechanism. The outer sleeve of the connecting rod is connected to the rear wedge-shaped top block to fix the rear wedge-shaped top block. When the connecting rod is pulled by the tightening mechanism, the front wedge-shaped top block moves backward and together with the fixed rear wedge-shaped top block, squeezes the expansion block, causing the expansion block to expand radially and fit tightly against the channel.

[0010] Preferably, the signal processing device includes: a signal synchronous acquisition subunit, a wave field component separation subunit, and a global coordinate orthogonal projection subunit; The signal synchronization acquisition subunit includes a multi-channel signal amplifier and a signal acquisition card; the multi-channel signal amplifier is used to perform time-difference-free parallel gain processing on the fidelity signal; the signal acquisition card is equipped with a synchronization clock system to establish a unified time reference between the seismic wave excitation device and the seismic wave receiving device. The wave field component separation subunit is used to convert the received signal into a transverse wave separation signal and a longitudinal wave separation signal; The global coordinate orthogonal projection subunit is used to combine the wave incident angle and sensitivity correction coefficient to project the separated signal onto the orthogonal axis of the tunnel global coordinate system to obtain the projected component data.

[0011] Preferably, the inversion unit includes: a velocity background model construction module, a migration imaging structure extraction module, a migration-guided gradient correction module, and an evolution module; The velocity background model construction module is used to construct a velocity background model of the surrounding rock in front of the tunnel based on the travel time information in the wave field information. The offset imaging structure extraction module is used to perform reflected wave offset imaging on the separated wave field, obtain the spatial geometric image of the geological interface in front of the tunnel, extract the structural features of the anomaly, and construct a spatial weight matrix. The offset-guided gradient correction module is used to apply the spatial weight matrix to the gradient calculation of the full waveform inversion to obtain the corrected gradient. The evolution module is used to update the velocity background model step by step in different frequency ranges using the corrected gradient until the joint objective function containing both P-wave and S-wave residuals converges, and outputs a distribution map of strata parameters ahead of the tunnel.

[0012] Preferably, the corrected gradient is calculated using the following formula:

[0013] In the formula, For the corrected gradient, W struct This is the spatial weight matrix. G The gradient before correction. J The joint objective function; m For model parameters, take either P-wave velocity or S-wave velocity; It represents the Hadamardi (or Hadama) stack; The joint objective function is expressed as follows:

[0014] In the formula, α and β For balance coefficient, Observational data representing three-axis longitudinal waves, Simulation data representing triaxial longitudinal waves, Observational data representing three-axis shear waves, Simulation data representing a three-axis shear wave. λR ( m ) represents the regularization constraint term based on the offset image structure.

[0015] Preferably, the evolution module includes: a two-dimensional stratigraphic model construction submodule and a model fusion submodule; The two-dimensional stratigraphic model construction submodule is used to update the velocity background model step by step in different frequency ranges using the corrected gradient until the joint objective function converges to obtain the two-dimensional stratigraphic model. The model fusion submodule is used to obtain multiple two-dimensional inverted P-wave velocity models and two-dimensional inverted S-wave velocity models independently calculated along different measuring points along the tunnel based on the two-dimensional stratigraphic model; it is used to calculate the correlation coefficient and energy residual of each inversion result in the overlapping detection area generated by different measuring points in space; and it is used to construct a spatial weighting function based on the distance between the measuring point and the target area, the source excitation energy and the wave field illumination intensity, and use the spatial weighting function to perform weighted summation on multiple inversion results in the overlapping area to obtain a two-dimensional fused inversion profile with spatial continuity.

[0016] Preferably, the evolution module further includes: a three-dimensional stratigraphic model construction submodule; The three-dimensional stratigraphic model construction submodule is used to perform three-dimensional interpolation based on multi-axis detection and using the two-dimensional fusion inversion profile as a feature surface constraint to generate a three-dimensional stratigraphic model containing a three-axis P-wave velocity field and a three-axis S-wave velocity field.

[0017] On the other hand, the present invention provides a tunnel advance detection method, which is implemented using the above-mentioned tunnel advance detection system. The tunnel advance detection method includes the following steps: The seismic wave signal is excited and acquired by the detection unit, and the transverse and longitudinal waves are separated by the symmetrical structural characteristics of the sensor to obtain wavefield information. The wavefield information includes transverse wave component data and longitudinal wave component data projected onto the three axes respectively. Based on the wavefield information, the inversion unit performs full waveform inversion with geometric constraints introduced by offset imaging to obtain tunnel advance detection information.

[0018] One or more technical solutions provided in this invention have at least the following technical effects or advantages: This invention first excites and acquires seismic wave signals through a detection unit, and then uses the symmetrical structural characteristics of the sensor to separate the acquired signals into shear and p-waves, obtaining wavefield information (including shear wave component data and p-wave component data projected onto three axes respectively). Then, based on the wavefield information, an inversion unit performs a full waveform inversion with geometric constraints introduced by migration imaging to obtain tunnel advance detection information. In other words, this invention provides a seismic wave method for tunnel advance detection based on shear and p-wave separation. By utilizing the symmetrical structural characteristics of the sensor in the detection unit, this invention fundamentally solves the technical problem of mutual coupling and difficulty in pure separation of shear (S-wave) and p-wave (P-wave) signals in traditional advance detection. Through the geometric constraints of migration imaging, this invention can suppress the ambiguity of full waveform inversion and eliminate background noise interference, thereby improving the positioning accuracy and convergence speed of complex geological interfaces and anomalies. In summary, this invention can perform high-precision shear and p-wave separation detection, improve the accuracy of tunnel advance detection, and provide high-resolution physical parameter support for the identification and risk warning of adverse geological bodies ahead during tunnel construction. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the layout of a tunnel advance detection system provided in Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of the structure of a sensing component in a tunnel advance detection system provided in Embodiment 1 of the present invention; Figure 3 This is a schematic diagram of the structure of the outer shell assembly in a tunnel advance detection system provided in Embodiment 1 of the present invention; Figure 4 This is a schematic diagram of transverse and longitudinal wave signal separation; Figure 5 This is a schematic diagram of multi-level frequency scale decomposition; Figure 6 This is a schematic diagram of a three-dimensional geological model reconstruction. Detailed Implementation

[0020] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0021] Example 1: Example 1 provides a tunnel advance detection system, including: a detection unit and an inversion unit.

[0022] (1) Detection unit.

[0023] The detection unit is used to excite and acquire seismic wave signals, and uses the symmetrical structural characteristics of the sensor to separate the acquired signals into transverse and longitudinal waves to obtain wavefield information. The wavefield information includes transverse wave component data and longitudinal wave component data projected onto the three axes respectively.

[0024] (2) Inversion unit.

[0025] The inversion unit is used to perform full waveform inversion with geometric constraints introduced by migration imaging based on the wavefield information, so as to obtain tunnel advance detection information.

[0026] The following is a detailed explanation of each unit.

[0027] See Figure 1 The detection unit includes a seismic wave excitation device 31, a seismic wave receiving device 32, and a signal processing device 33. The signal processing device 33 mainly includes a multi-channel signal amplifier, a signal acquisition card, and a terminal connected in sequence (the terminal mainly performs the three-axis transverse and transverse-longitudinal wave data separation steps).

[0028] When conducting tunnel overdue detection, the detection unit is first deployed and installed. A mechanical tightening mechanism can be used to radially expand the seismic wave receiving device 32 in the tunnel sidewall hole, so as to achieve tight coupling between the seismic wave receiving device 32 and the hole. That is, the seismic wave receiving device 32 is coupled to the rock mass 1 through the pipe segment 2.

[0029] (1.1) Seismic wave excitation device.

[0030] The seismic wave excitation device 31 is used to determine the dominant frequency of the seismic source excitation based on the detected target depth and to generate seismic wave signals.

[0031] The dominant excitation frequency of the seismic wave excitation device 31 must be adapted to the detection depth.

[0032] In the formula, f It is the dominant frequency (Hz) of the earthquake source. V P It is the longitudinal wave velocity of the strata (m / s), which can be replaced by the rock wave velocity at the working face; D It is the depth (m) of the target being detected.

[0033] (1.2) Seismic wave receiving device.

[0034] The seismic wave receiving device 32 is used to receive seismic wave signals and obtain a high-fidelity signal.

[0035] Specifically, the seismic wave receiving device includes a sensing component 321 and a housing component 322.

[0036] (1.2.1) Sensing components.

[0037] See Figure 2The sensing component 321 consists of three orthogonally placed sensors. Each sensor includes a piezoelectric ceramic 3211, an outer electrode 3212, and an inner electrode 3213. The outer electrode 3212 is disposed on the outer wall of the piezoelectric ceramic 3211, and the four equally divided sections formed by the outer electrode 3212 serve as four positive electrodes. The inner electrode 3213 is disposed on the inner wall of the piezoelectric ceramic 3211, and the entire section formed by the inner electrode 3213 serves as a common negative electrode. The common negative electrode and each of the positive electrodes are respectively welded with a wire 3214, and the wire 3214 is connected to the signal processing device 33.

[0038] Based on the requirements of weak signal capture and high-fidelity dynamic response in the tunnel environment, the piezoelectric ceramic 3211 is preferably made of PZT-5H material. The outer electrode 3212 is formed by silver plating four equal sections on the outer wall of the piezoelectric ceramic 3211, and the inner electrode 3213 is formed by silver plating the entire inner wall of the piezoelectric ceramic 3211. A wire 3214 is welded to each section for measuring the voltage signal.

[0039] The sensor in Example 1 is a single-axis transverse and longitudinal wave separation sensor. Example 1 utilizes the symmetry of the quarter-circular ring to simultaneously obtain P-wave and S-wave signals in one direction. By placing the sensors orthogonally along three axes, the three-axis transverse and longitudinal wave signals can be calculated. Example 1, employing the symmetrical structure of the aforementioned three-axis transverse and longitudinal wave separation sensor, enables direct decoupling of stress wave components at the hardware physical level.

[0040] (1.2.2) Housing assembly.

[0041] See Figure 3 The outer casing assembly 322 includes: a front wedge-shaped top block 3221, a sensor cavity shell 3222, an expansion block 3223, a rear wedge-shaped top block 3224, a connecting rod 3225, a connecting rod outer sleeve 3226, and a tightening mechanism 3227.

[0042] The main body of the outer shell assembly 322 is made of metal and adopts a coaxial nested structure. The sensor cavity shell 3222 is used to accommodate the sensor assembly 321. The expansion block 3223 is wrapped around the outside of the sensor cavity shell 3222. The expansion block 3223 has a segmented structure, and the inner surfaces of both ends of the expansion block 3223 are frustoconical so that it can expand radially when squeezed by the wedge-shaped top block on the inner surface. The expansion block 3223 and the sensor cavity shell 3222 are in contact by the front wedge-shaped top block 3221 and the rear wedge-shaped top block 3224 through inclined surfaces. The sensor cavity shell 3222 is connected to the connecting rod 3225 (e.g., threaded connection). The tail of the connecting rod 3225 is connected to the tightening mechanism 3227. The connecting rod outer sleeve 3226 is connected to the rear wedge-shaped top block 3224 to fix the rear wedge-shaped top block 3224. That is, the connecting rod outer sleeve 3226 plays a limiting role.

[0043] During operation, when the connecting rod 3225 in the middle is pulled by the tightening mechanism 3227, the front wedge-shaped top block 3221 moves backward and together with the fixed rear wedge-shaped top block 3224, squeezes the expansion block 3223, causing the expansion block 3223 to expand radially and fit tightly against the channel.

[0044] Example 1 introduces a segmented seismic wave receiver with full circumferential contact, which can ensure sufficient contact area between the seismic wave receiver and the channel while enabling sensor recycling, thus achieving tight coupling between the seismic wave receiver and the full circumference of the channel.

[0045] (1.3) Signal processing device.

[0046] The signal processing device is used to perform time-difference-free parallel gain, transverse and longitudinal wave separation, and orthogonal projection processing on the fidelity signal to obtain the wave field information.

[0047] Specifically, the signal processing device includes: a signal synchronous acquisition subunit, a wave field component separation subunit, and a global coordinate orthogonal projection subunit.

[0048] (1.3.1) Signal Synchronization Acquisition Subunit.

[0049] The signal synchronization acquisition subunit includes a multi-channel signal amplifier and a signal acquisition card, which are mainly used for parallel acquisition of the source wavelet and the high-fidelity piezoelectric signal from the triaxial receiver.

[0050] The multi-channel signal amplifier is used to perform time-difference parallel gain processing on the fidelity signal. Specifically, the multi-channel signal amplifier should not have fewer than 12 channels to ensure time-difference parallel gain processing on the 12 piezoelectric signals of the sensing component, ensuring that the amplitude-frequency characteristics of each channel remain highly consistent, thereby providing a fidelity signal with original phase information and amplitude ratio relationship for the subsequent wave field component separation subunit based on electrode algebra operations.

[0051] The signal acquisition card is equipped with a high-precision synchronous clock system to establish a unified time reference between the seismic wave excitation device and the seismic wave receiving device. By capturing the instantaneous phase of the seismic wave excitation in real time and using it as the zero point of global sampling, the source wavelet and the receiving signal are aligned on the time axis.

[0052] (1.3.2) Wave field component separation sub-unit.

[0053] The wave field component separation subunit is used to convert the received signal into a transverse wave separation signal and a longitudinal wave separation signal. That is, the wave field component separation subunit mainly uses the characteristics of the sensor's piezoelectric material and algebraic algorithms to initially separate the transverse and longitudinal wave components from the original signal.

[0054] See Figure 4 The wave field component separation subunit can convert the received signal into three-axis transverse and longitudinal wave data. Based on sensor characteristics, the following formula is used to... Figure 4 The received signal shown in (a) is processed into transverse and longitudinal wave data, resulting in the following: Figure 4 The transverse wave separation signal V shown in (b) is... s Longitudinal wave separation signal V p .

[0055]

[0056] In the formula, R i1 , R i2 , R i3 , R i4 It is received by the sensor. i The four signals corresponding to the axis and These represent the initial calculations. i Transverse and longitudinal wave signals of the shaft, i Take respectively x , y , z .

[0057] (1.3.3) Global coordinate orthogonal projection sub-unit.

[0058] The global coordinate orthogonal projection subunit is used to combine the wave incident angle and sensitivity correction coefficient to project the separated signal onto the orthogonal axis of the tunnel global coordinate system to obtain the projected component data, that is, to obtain high-purity component data.

[0059] The projected component data are represented as follows:

[0060] In the formula, and They are projected to i Shear wave component data and longitudinal wave component data of the axis. Is it a wave? i Angle of incidence in the axial plane ,in These are constant coefficients related to the sensor material parameters. k s It is the sensitivity correction coefficient, a constant coefficient related to the sensor and material parameters.

[0061] The inversion unit includes: a velocity background model construction module, a migration imaging structure extraction module, a migration-guided gradient correction module, and an evolution module.

[0062] (2.1) Velocity background model construction module.

[0063] The velocity background model construction module is used to construct a velocity background model of the surrounding rock in front of the tunnel based on the travel time information in the wave field information.

[0064] That is, the velocity background model construction module is mainly used to perform the following steps: Multi-parameter initial model establishment: Based on the travel time information of six wavefield components, the P-wave velocity of the surrounding rock ahead of the tunnel is constructed by first-arrival tomography or velocity analysis. V P ) and transverse wave velocity ( V S The initial background model.

[0065] (2.2) Offset imaging structure extraction module.

[0066] The offset imaging structure extraction module is used to perform reflected wave offset imaging on the separated wave field, obtain the spatial geometric image of the geological interface in front of the tunnel, extract the structural features of the anomaly, and construct a spatial weight matrix.

[0067] That is, the offset imaging structure extraction module is mainly used to perform the following steps: Structure extraction from migration imaging: During migration imaging, a forward simulation is performed using the wave equation to calculate the forward wave field. The acquired P-wave and S-wave component data are added to the normal stress and shear stress terms, respectively, as adjoint sources for adjoint simulation to calculate the adjoint wave field. The forward wave field and adjoint wave field are decomposed into P-wave and S-wave fields, respectively, and reflected wave migration imaging (e.g., RTM reverse time migration or Kirchhoff depth migration) is performed to obtain the spatial geometric image of the geological interface in front of the tunnel. The structural features of the anomaly are extracted using image processing algorithms, and a spatial weight matrix is ​​constructed, as shown below:

[0068] In the formula, It is a spatial weight matrix. This is an intensity image of the stratigraphic interface obtained from migration imaging; norm is a normalization factor. γ It is the gain factor, which determines the confidence level of the results from the migration imaging. For example, taking... γ When =5, the update step size of the anomaly boundary will be 6 times that of the background region.

[0069] (2.3) Offset-guided gradient correction module.

[0070] The offset-guided gradient correction module is used to apply the spatial weight matrix to the gradient calculation of the full waveform inversion to obtain the corrected gradient, and to suppress background noise interference by enhancing the interface weight.

[0071] The corrected gradient is calculated using the following formula:

[0072] In the formula, For the corrected gradient, W struct This is the spatial weight matrix. G The gradient before correction. J The joint objective function; m For model parameters, take either P-wave velocity or S-wave velocity; This represents the Hadamard product, which is the product of elements.

[0073] in, This refers to the weighted gradient that integrates migration tectonic information. This gradient uses a spatial weight matrix to focus model updates on the geological body boundaries, thereby correcting the search direction of the inversion. G It is the original sensitivity kernel function (gradient) calculated from the waveform residual.

[0074] The joint objective function is expressed as follows:

[0075] In the formula, α andβ For balance coefficient, Observational data representing three-axis longitudinal waves, Simulation data representing triaxial longitudinal waves, Observational data representing three-axis shear waves, Simulated data representing a three-axis shear wave; λR ( m ) is a regularization constraint term based on the offset image structure, which ensures that the physical parameter model derived from the inversion is consistent with the offset imaging results in terms of spatial morphology.

[0076] That is, the offset-guided gradient correction module mainly performs the following steps: During the full waveform inversion iteration process, forward simulation is performed using the wave equation to calculate the simulated received waveform and forward wave field. The residuals are obtained by subtracting the measured P-wave and S-wave component data obtained using the seismic wave receiving device. These residuals are then added to the normal stress term and shear stress term as adjoint sources for adjoint simulation to calculate the adjoint wave field. The forward wave field and adjoint wave field are decomposed into P-wave field and S-wave field, respectively, and inversion is performed to obtain the model update gradient. The spatial weight matrix is ​​applied to the gradient to correct the model update direction by increasing the update weight of the anomaly boundary region and suppressing the interference of the non-reflective background region.

[0077] (2.4) Evolution module.

[0078] The evolution module is used to update the velocity background model step by step in different frequency ranges using the corrected gradient until the joint objective function containing both P-wave and S-wave residuals converges, and outputs a distribution map of strata parameters ahead of the tunnel.

[0079] That is, the evolution module mainly performs the following steps: Frequency domain hierarchical iterative evolution: A scaling strategy from low frequency to high frequency is adopted, and the corrected gradient is used to update the frequency range step by step in different frequency intervals. V P and V S The model is updated until the objective function converges, and finally a high-resolution distribution map of the geological parameters in front of the tunnel is output.

[0080] like Figure 5As shown, taking the Ricker wavelet with a dominant frequency of 250 Hz as an example, its complete frequency band ranges from 0 to 800 Hz. Based on the frequency and energy characteristics of the signal, a half-octave band (growth factor 1.4) progressive strategy is adopted to divide the source and observation signals into graded frequency bands. A macroscopic velocity framework is constructed through the low-frequency base band (0 to 50 Hz), and cascade evolution is performed using the core frequency band with the most concentrated energy, so that the inversion spatial resolution increases stepwise with the wavelength scale. At the same time, the inversion upper limit is strictly limited by the physical sampling rate of the numerical simulation grid, ensuring that the energy across the entire frequency band achieves robust convergence from coarse-scale construction to fine physical parameters while maintaining numerical stability, and is updated step by step. V P and V S The model is updated until the objective function converges, and finally a high-resolution distribution map of the geological parameters in front of the tunnel is output.

[0081] Specifically, the evolution module includes a two-dimensional stratigraphic model construction submodule and a model fusion submodule.

[0082] (2.4.1) Two-dimensional stratigraphic model construction submodule.

[0083] The two-dimensional stratigraphic model construction submodule is used to update the velocity background model step by step in different frequency ranges using the corrected gradient until the joint objective function converges, thereby obtaining the two-dimensional stratigraphic model.

[0084] (2.4.2) Model fusion submodule.

[0085] The model fusion submodule mainly performs the following steps: Subspace inversion result extraction: Based on the two-dimensional stratigraphic model, multiple two-dimensional inverted P-wave velocity models and two-dimensional inverted S-wave velocity models are obtained by independently calculating different measuring points along the tunnel; Overlapping region analysis: For the overlapping detection areas generated by different measuring points in space, calculate the correlation coefficient and energy residual of each inversion result in the overlapping area; Spatial weighted fusion calculation: Based on the distance between the measuring point and the target area, the source excitation energy and the wave field illumination intensity, a spatial weight function is constructed, and the spatial weight function is used to perform weighted summation on multiple inversion results in the overlapping area to obtain a two-dimensional fusion inversion profile with spatial continuity.

[0086] The spatial weighting function is expressed as follows: ω ( h , v ), h and v These represent the horizontal and vertical coordinates within the two-dimensional profile. The merged profile parameters are expressed as follows:

[0087] In the formula, V ( h , v ) represents the fused profile parameters; ω k ( h , v ) is the spatial weight corresponding to the k-th measuring point, and its value decreases as the detection depth increases; V k ( h , v ) is the first k The inversion parameters are obtained from n measurement points, where n is the total number of measurement points.

[0088] In addition, in a preferred embodiment, the evolution module may further include a three-dimensional stratigraphic model construction submodule.

[0089] (2.4.3) Three-dimensional stratigraphic model construction submodule.

[0090] The three-dimensional stratigraphic model construction submodule is used to perform three-dimensional interpolation based on multi-axis detection and using the two-dimensional fusion inversion profile as a feature surface constraint to generate a three-dimensional stratigraphic model containing a three-axis P-wave velocity field and a three-axis S-wave velocity field.

[0091] Specifically, the three-dimensional stratigraphic model construction submodule mainly performs the following steps: Feature surface constraint extraction: Extract key structural feature parameters such as velocity abrupt change interfaces, fault strikes, and stratigraphic dip angles from the two-dimensional fusion inversion results; Three-dimensional non-uniform interpolation: Using two-dimensional fused inversion profiles as constraints, three-dimensional kriging interpolation is adopted, and characteristic parameters are used as directional constraints to map two-dimensional stratigraphic parameters of different planes to the three-dimensional spatial grid of the stratigraphy. 3D stratigraphic model reconstruction: By combining prior geological information, the interpolated gaps are smoothed to ultimately generate a 3D stratigraphic model containing both 3D P-wave velocity fields and 3D S-wave velocity fields. (See [link to relevant documentation]). Figure 6 .

[0092] It should be noted that when performing 3D stratigraphic modeling, the input data should include the fusion inversion results obtained from no fewer than three non-coplanar probe axes; when there are fewer than three non-coplanar probe axes, the accuracy of the 3D stratigraphic model is lower.

[0093] Example 1 introduces a spatial weight matrix obtained from migration imaging to guide the gradient of the full waveform inversion, integrates the geometric constraint depth into the physical parameter iteration process, and uses an adaptive fusion mechanism based on spatial illumination intensity and distance weight to fuse data from overlapping detection areas of multiple measurement points. Combined with anisotropic interpolation of non-coplanar multi-axis, it can achieve a refined reconstruction from two-dimensional profiles to three-dimensional stratigraphic models.

[0094] In summary, Example 1 can achieve the separation of transverse and longitudinal waves of seismic waves and perform stratigraphic inversion, providing high-resolution physical parameter support for the identification of adverse geological bodies ahead during tunnel construction.

[0095] Example 2: Example 2 provides a tunnel advance detection method, implemented using the tunnel advance detection system as described in Example 1. The tunnel advance detection method provided in Example 2 includes the following steps: S1. Seismic wave signals are excited and acquired through the detection unit, and the transverse and longitudinal waves are separated by the symmetrical structural characteristics of the sensor to obtain wavefield information. The wavefield information includes transverse wave component data and longitudinal wave component data projected onto the three axes respectively. S2. Based on the wave field information, the inversion unit performs full waveform inversion with geometric constraints introduced by offset imaging to obtain tunnel advance detection information.

[0096] For example, when a tunnel advance detection system includes a three-dimensional geological model construction submodule, the tunnel advance detection method can be understood to mainly include the following steps: (1) Excite and collect seismic wave signals to obtain triaxial detection data; (2) Based on the triaxial sounding data, triaxial transverse and longitudinal wave sounding data are obtained by using the transverse and longitudinal wave conversion method; (3) Based on the three-axis transverse and longitudinal wave detection data, the six data items are divided into a unified frequency domain interval; (4) Based on each data item, add it separately to the inversion model, and perform full waveform inversion of two-dimensional migration imaging constraints step by step from low to high frequency domain intervals; (5) The two-dimensional inversion results obtained from the corresponding data of different measurement points are weighted and fused; (6) Based on the two-dimensional fusion results, three-dimensional interpolation is performed to obtain a three-dimensional stratigraphic model.

[0097] Since the steps in the tunnel advance detection method provided in Embodiment 2 correspond to the functions of each unit in the tunnel advance detection system provided in Embodiment 1, Embodiment 2 can be understood by referring to the description of Embodiment 1, and will not be repeated here.

[0098] Finally, it should be noted that the above specific embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to examples, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A tunnel advanced detection system, characterized in that, include: Detection unit and inversion unit; The detection unit is used to excite and acquire seismic wave signals, and uses the symmetrical structural characteristics of the sensor to separate the acquired signals into transverse and longitudinal waves to obtain wavefield information. The wavefield information includes transverse wave component data and longitudinal wave component data projected onto the three axes respectively. The inversion unit is used to perform full waveform inversion with geometric constraints introduced by migration imaging based on the wavefield information, so as to obtain tunnel advance detection information.

2. The tunnel advanced detection system according to claim 1, characterized in that, The detection unit includes: a seismic wave excitation device, a seismic wave receiving device, and a signal processing device; The seismic wave excitation device is used to determine the dominant frequency of the seismic source excitation based on the depth of the detected target and to generate seismic wave signals; The seismic wave receiving device is used to receive seismic wave signals and obtain a high-fidelity signal; The signal processing device is used to perform time-difference-free parallel gain, transverse and longitudinal wave separation, and orthogonal projection processing on the fidelity signal to obtain the wave field information.

3. The tunnel advanced detection system according to claim 2, characterized in that, The seismic wave receiving device includes: a sensing component; the sensing component consists of three orthogonally placed sensors, each sensor including a piezoelectric ceramic, an outer electrode, and an inner electrode; the outer electrode is disposed on the outer wall of the piezoelectric ceramic, and the four equally divided sections formed by the outer electrode serve as four positive electrodes; the inner electrode is disposed on the inner wall of the piezoelectric ceramic, and the entire section formed by the inner electrode serves as a common negative electrode; wires are welded to the common negative electrode and each of the positive electrodes, and the wires are connected to the signal processing device.

4. The tunnel advanced detection system according to claim 3, characterized in that, The seismic wave receiving device further includes: a housing assembly; the housing assembly includes: a sensor cavity shell, an expansion block, a front wedge-shaped top block, a rear wedge-shaped top block, a connecting rod, a connecting rod outer sleeve, and a tightening mechanism; The sensor cavity shell is used to house the sensing component. The expansion block is wrapped around the outside of the sensor cavity shell. The expansion block has a segmented structure. The expansion block and the sensor cavity shell are in contact by the front wedge-shaped top block and the rear wedge-shaped top block through an inclined surface. The sensor cavity shell is connected to the connecting rod. The tail of the connecting rod is connected to the tightening mechanism. The outer sleeve of the connecting rod is connected to the rear wedge-shaped top block to fix the rear wedge-shaped top block. When the connecting rod is pulled by the tightening mechanism, the front wedge-shaped top block moves backward and together with the fixed rear wedge-shaped top block, squeezes the expansion block, causing the expansion block to expand radially and fit tightly against the channel.

5. The tunnel advanced detection system according to claim 2, characterized in that, The signal processing device includes: a signal synchronous acquisition subunit, a wave field component separation subunit, and a global coordinate orthogonal projection subunit; The signal synchronization acquisition subunit includes a multi-channel signal amplifier and a signal acquisition card; the multi-channel signal amplifier is used to perform time-difference-free parallel gain processing on the fidelity signal; the signal acquisition card is equipped with a synchronization clock system to establish a unified time reference between the seismic wave excitation device and the seismic wave receiving device. The wave field component separation subunit is used to convert the received signal into a transverse wave separation signal and a longitudinal wave separation signal; The global coordinate orthogonal projection subunit is used to combine the wave incident angle and sensitivity correction coefficient to project the separated signal onto the orthogonal axis of the tunnel global coordinate system to obtain the projected component data.

6. The tunnel advanced detection system according to claim 1, characterized in that, The inversion unit includes: a velocity background model construction module, a migration imaging structure extraction module, a migration-guided gradient correction module, and an evolution module; The velocity background model construction module is used to construct a velocity background model of the surrounding rock in front of the tunnel based on the travel time information in the wave field information. The offset imaging structure extraction module is used to perform reflected wave offset imaging on the separated wave field, obtain the spatial geometric image of the geological interface in front of the tunnel, extract the structural features of the anomaly, and construct a spatial weight matrix. The offset-guided gradient correction module is used to apply the spatial weight matrix to the gradient calculation of the full waveform inversion to obtain the corrected gradient. The evolution module is used to update the velocity background model step by step in different frequency ranges using the corrected gradient until the joint objective function containing both P-wave and S-wave residuals converges, and outputs a distribution map of strata parameters ahead of the tunnel.

7. The tunnel advanced detection system according to claim 6, characterized in that, The corrected gradient is calculated using the following formula: In the formula, For the corrected gradient, W struct This is the spatial weight matrix. G The gradient before correction. J The joint objective function; m For model parameters, take either P-wave velocity or S-wave velocity; It represents the Hadamardi (or Hadama) stack; The joint objective function is expressed as follows: In the formula, α and β For balance coefficient, Observational data representing three-axis longitudinal waves, Simulation data representing triaxial longitudinal waves, Observational data representing three-axis shear waves, Simulation data representing a three-axis shear wave. λR ( m ) represents the regularization constraint term based on the offset image structure.

8. The tunnel advanced detection system according to claim 6, characterized in that, The evolution module includes: a two-dimensional stratigraphic model construction submodule and a model fusion submodule; The two-dimensional stratigraphic model construction submodule is used to update the velocity background model step by step in different frequency ranges using the corrected gradient until the joint objective function converges to obtain the two-dimensional stratigraphic model. The model fusion submodule is used to obtain multiple two-dimensional inverted P-wave velocity models and two-dimensional inverted S-wave velocity models independently calculated along different measuring points along the tunnel based on the two-dimensional stratigraphic model; it is used to calculate the correlation coefficient and energy residual of each inversion result in the overlapping detection area generated by different measuring points in space; and it is used to construct a spatial weighting function based on the distance between the measuring point and the target area, the source excitation energy and the wave field illumination intensity, and use the spatial weighting function to perform weighted summation on multiple inversion results in the overlapping area to obtain a two-dimensional fused inversion profile with spatial continuity.

9. The tunnel advanced detection system according to claim 8, characterized in that, The evolution module also includes: a three-dimensional stratigraphic model construction submodule; The three-dimensional stratigraphic model construction submodule is used to perform three-dimensional interpolation based on multi-axis detection and using the two-dimensional fusion inversion profile as a feature surface constraint to generate a three-dimensional stratigraphic model containing a three-axis P-wave velocity field and a three-axis S-wave velocity field.

10. A method for advanced tunnel detection, characterized in that, The tunnel advance detection method, implemented using the tunnel advance detection system as described in any one of claims 1 to 9, comprises the following steps: The seismic wave signal is excited and acquired by the detection unit, and the transverse and longitudinal waves are separated by the symmetrical structural characteristics of the sensor to obtain wavefield information. The wavefield information includes transverse wave component data and longitudinal wave component data projected onto the three axes respectively. Based on the wavefield information, the inversion unit performs full waveform inversion with geometric constraints introduced by offset imaging to obtain tunnel advance detection information.

Images