Tunnel blasting damage rapid measurement method and system based on wall-climbing robot

Abstract

The invention discloses a tunnel blasting damage rapid measurement method and system based on a wall-climbing robot, and the method comprises the following steps: S1, laying a three-dimensional optical fiber network, recording the coordinates of each section of optical fiber, and building a mapping relation between an optical fiber node and a space coordinate position; s2, constructing an initial three-dimensional wave velocity model of the tunnel rock mass; s3, correcting the velocity fields of the reflected wave and the winding wave to obtain an optimized wave velocity model; s4, exciting artificial seismic sources at a plurality of positions in the tunnel through a wall-climbing robot with an electromagnetic vibration exciter, and performing data acquisition by a DAS system through an optical fiber network; s5, processing the data acquired in the step S4, carrying out wavelength separation on the reflected wave and the diffracted wave, and respectively carrying out migration imaging on the reflected wave and the diffracted wave after classification; and S6, fusing imaging results of the reflected wave and the diffracted wave through a weight coefficient, and generating a comprehensive geologic model.

Description

Technical Field

[0001] The present invention relates to the technical field of tunnel engineering detection, and in particular to a method and system for rapid measurement of tunnel blasting damage based on a wall-climbing robot. Background Technique

[0002] At present, tunnel engineering is gradually extending towards deep and complex geological conditions. In a complex geological environment, the prevention and control of engineering disasters have become the key technical challenges for the safe and efficient construction of deep engineering. Tunnel engineering needs to frequently conduct geological structure detection during the construction and operation stages, so the demand for high-precision and high-efficiency detection technologies is becoming increasingly urgent.

[0003] Common tunnel surrounding rock damage detection technologies include ground penetrating radar, ultrasonic detection, and tomography using transmitted waves, etc. Ground penetrating radar is based on the principle of electromagnetic waves and is greatly affected by the steel bars inside the tunnel lining, making it difficult to detect the structural defects of the surrounding rock inside the lining. Ultrasonic detection is based on the fact that when elastic waves propagate in a rock medium, there is a correlation between wave velocity, amplitude, and spectral characteristics and the internal structural damage of the material. It has become one of the core methods for quantifying damage evolution in indoor rock loading tests. However, at the construction site with a complex rock mass structure, the propagation path of ultrasonic waves is easily affected by multiple scattering, resulting in a low signal-to-noise ratio and a complex wave velocity-stress relationship. Tomography using transmitted waves mainly inverses the rock mass velocity model through the travel time of transmitted waves, and its resolution is limited by the wavelength and ray coverage density: Since transmitted waves mainly reflect macroscopic velocity changes, when the defect structure characteristics are close to or smaller than the wavelength, the transmitted waves will be distorted due to diffraction effects and scattering, resulting in low sensitivity to micro-defects such as small-scale cracks and cavities inside the rock mass, and it is difficult to accurately image local anomalies such as inclined faults or independent cavities in complex rock masses.

[0004] Due to the limitations of the above detection technologies based on detection principles, for various geological structures such as faults, folds, joints, cavities, and fissures that often exist in complex geological structure areas, there are difficulties and challenges in different aspects for the detection of tunnel lining and surrounding rock structures.

[0005] There is a solution in the prior art that uses reflected waves to perform three-dimensional tomographic migration imaging on the tunnel surrounding rock structure: 10 geophones are arranged at 4 cross-sections at the rear two sidewalls and the arch crown of the tunnel in a cross plum blossom pattern as receiving points, and 12 seismic source points are symmetrically arranged on both sidewalls near the tunnel face in front of the tunnel. A sledgehammer is used to strike and excite seismic waves, and the migration and homing of reflection points are achieved through three-dimensional tomography to detect the seismic signals and geological body properties of the disaster-causing structures and disaster-causing water bodies in front of the tunnel face. However, seismic migration based on reflected waves is mostly used for imaging the formation interfaces of large-scale and continuous structures, and has a poor imaging effect on small-scale heterogeneous bodies such as cracks and karst caves that often exist in complex geological areas.

[0006] In addition, there is also a solution in the prior art, which is based on the basic principle of seismic diffraction scanning stacking migration imaging. Based on the common reflection surface element stacking technology, the diffracted waves in the seismic records are processed to obtain a seismic imaging profile with higher accuracy.

[0007] However, the above solutions are all based on traditional detection methods, where the positions of the seismic source and the receiving points are fixed, and the excitation position cannot be dynamically adjusted, resulting in a single propagation path of seismic waves, limited data coverage angle, and low redundancy. Therefore, it is necessary to design a new method for rapid measurement of tunnel blasting damage to solve the above problems. Summary of the Invention

[0008] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0009] A method for rapid measurement of tunnel blasting damage based on a wall-climbing robot, comprising the following steps:

[0010] S1. Layout a three-dimensional optical fiber network, record the coordinates of each section of the optical fiber, and establish a mapping relationship between the optical fiber nodes and the spatial coordinate positions;

[0011] S2. Construct an initial three-dimensional wave velocity model of the tunnel rock mass;

[0012] S3. Perform velocity spectrum analysis on the reflected waves and local correction on the diffracted waves respectively to obtain an optimized wave velocity model;

[0013] S4. Use a wall-climbing robot with an electromagnetic vibrator to excite artificial seismic sources at multiple positions in the tunnel, and the DAS system collects data through the optical fiber network;

[0014] S5. Process the data collected in step S4, separate the wave fields of the reflected waves and the diffracted waves, and then perform migration imaging on the reflected waves and the diffracted waves respectively;

[0015] S6. Generate a comprehensive geological model by fusing the imaging results of the reflected waves and the diffracted waves with weight coefficients.

[0016] Further, in step S2, it specifically includes the following steps:

[0017] S21. Measure the wave velocity of the tunnel lining material in the laboratory as the initial model reference value;

[0018] S22. Conduct laboratory tests on the borehole cores to obtain the density and wave velocity of different lithologies, and associate the core data with the position of the borehole spiral optical fiber to establish an initial vertical velocity gradient model;

[0019] S23. Divide the tunnel wall into multiple grids. The wall-climbing robot moves to the center point of each grid according to a preset path, and starts the electromagnetic vibrator to generate an artificial seismic source. After each excitation, the DAS system collects data through the optical fiber network and records the time when the first arrival wave reaches each optical fiber node as the first arrival travel time data;

[0020] S24. Adopt the synchronous iterative reconstruction algorithm to invert the large-scale velocity field of the rock mass and generate an initial three-dimensional wave velocity model.

[0021] Further, in step S24, when adopting the synchronous iterative reconstruction algorithm, the iterative formula is as follows:

[0022]

[0023] The convergence condition is:

[0024]

[0025] In the formula, v k is the three-dimensional wave velocity field model of the k-th iteration, and the initial wave velocity field v0 comes from the borehole core data and the calibration value of the lining material;

[0026] λ k is the adaptive step factor, i.e., the dynamic attenuation coefficient, which is used to balance the convergence speed and stability, λ0 is the initial step, and β is the attenuation rate;

[0027] Ω is the weight matrix, which is a diagonal matrix with dimensions of A×A, where A is the total number of rays, and any element is expressed as γ = 0.2 · borehole fracture density;

[0028] G is the ray path matrix, which is a sparse matrix with dimensions of A×B, where B is the total number of grids, and is used to describe the propagation path length of the ray in the grid. The element G ij is the path length of the i-th ray in the j-th grid. The ray path matrix G is generated by ray tracing based on the coordinates of the excitation point of the wall-climbing robot (10) and the optical fiber node;

[0029] Λ is the path normalization matrix, and the diagonal elements are the total path length ∑ j G ij of each ray, which is used to eliminate the influence of the ray path on the residual;

[0030] t obs is the first arrival travel time data measured by the DAS system, and ε is the convergence threshold;

[0031] When generating the initial three-dimensional wave velocity model, it specifically includes the following steps:

[0032] In the initial state, input the first arrival travel time data tobs Given the ray path matrix G and the initial wave velocity field v0, set the values of λ0, γ, γ and ε, and calculate the matrices Ω and Λ;

[0033] Calculate the residual Calculate the update amount Δv k = λ k ·Ω·G T ·Λ -1 ·r k , and iteratively update the wave velocity field v k+1 = v k +Δv k , if the convergence condition of Equation (2) is not satisfied, continue the iteration until the convergence condition of Equation (2) is satisfied, then output the current v k+1 as the initial three-dimensional wave velocity model and end the iteration.

[0034] Furthermore, in step S3, when correcting the velocity field of the reflected wave, the following steps are specifically included:

[0035] Based on the symmetric distribution of the excitation point and the receiving point, divide the reflected wave data into multiple common midpoint gathers. The common midpoint position is the center of the excitation point position of the wall-climbing robot and the receiving point position in the optical fiber network. Each common midpoint gather corresponds to a set of reflected signals in a specific area in front of the tunnel;

[0036] Calculate the stacking energy corresponding to each candidate velocity through velocity scanning, and select the velocity corresponding to the maximum energy as the optimal layer velocity of the current common midpoint. The reflection wave velocity spectrum analysis formula is:

[0037]

[0038] In the formula, V is the candidate velocity, that is, the layer velocity value to be evaluated, E(V) is the stacking energy corresponding to the candidate velocity V, used to evaluate the rationality of this velocity, M is the number of gathers at the common midpoint, N is the number of seismic traces in each common midpoint gather, A mn is the amplitude value of the nth seismic record in the mth common midpoint gather, directly measured by the DAS system, t mn is the arrival time of the nth seismic wave in the mth common midpoint gather, t0 is the two-way travel time for the wave to vertically incident on the reflection interface from the common midpoint and return, x n is the offset, that is, the horizontal distance between the source and the receiving point of the nth seismic record, δ is the impulse function, used to align the corrected waveform, and δ takes the value of 1 only when aligned in time, otherwise 0;

[0039] The convergence condition is: after the single-layer velocity is corrected, the change rate of the reflected wave stacking energy is less than the convergence threshold k, that is

[0040] The layer stripping method is used to optimize the velocity model from shallow to deep layers in sequence. The stacking energy is recalculated after each update until the velocity of each layer converges to generate the reflection wave calibration velocity field.

[0041] Furthermore, in step S3, when correcting the velocity field of the diffraction wave, the specific steps are as follows:

[0042] The diffracted waves are separated by polarization angle threshold, the scattered signals with inclination angle greater than 30° are retained, and the interference of reflected waves is suppressed;

[0043] The diffraction wave travel time residual is calculated by the following formula to compare the measured diffraction wave travel time with the predicted travel time of the current velocity field:

[0044]

[0045] Where, t obs is the measured travel time of the diffraction wave measured by the DAS system, L ray is the propagation path length of the diffraction wave, calculated by ray tracing, V current is the local velocity of the current velocity field;

[0046] Based on regularized least squares inversion, if the diffraction wave travel time residual is greater than the preset threshold, it indicates local velocity anomaly. The local velocity anomaly area is updated, and the corrected local velocity field is used for reverse time migration imaging.

[0047] In step S3, after the correction of the velocity fields of the reflected wave and the diffracted wave is completed, the local velocity field after the diffraction wave correction is merged with the calibrated velocity field of the reflected wave to generate an optimized wave velocity model.

[0048] Furthermore, in step S4, specifically, based on the grid divided in step S23, the tunnel wall is divided into multiple finer grids, the wall-climbing robot moves to the center point of each grid according to the preset path, and the electromagnetic exciter is started to generate an artificial seismic source. After each excitation, the DAS system records the full waveform data of the optical fiber network, including direct waves, reflected waves and diffraction waves, and synchronizes the robot excitation time with the DAS system acquisition time through optical fiber timing technology.

[0049] Furthermore, in step S5, the following steps are specifically included:

[0050] First, the data is preprocessed to determine the arrival time of the first arrival wave by detecting the mutation point of the waveform amplitude, eliminating the delayed signal and non-seismic source interference, and performing data denoising and signal gain.

[0051] Then, the reflected wave and the diffracted wave are separated. The diffracted wave is separated based on the polarization angle threshold, and the scattered signal with an inclination angle greater than 30° is retained. The reflected wave retains the nearly vertical incident component, and the high-frequency diffracted wave component is filtered out in the frequency-wavenumber domain, retaining the low-frequency reflected wave signal;

[0052] When performing reflection wave migration imaging, according to the optimized wave velocity model obtained in step S3, using the Kirchhoff integral method, the reflection wave amplitude is weighted and superimposed on the imaging point according to travel time, as shown in the following formula:

[0053]

[0054] In the formula, A i is the reflection wave amplitude from the source point S i to the receiving point R i ; r si is the distance from the source point S i to the imaging point (x, z); r ri is the distance from the receiving point R i to the imaging point (x, z); V(x, z) is the calibrated velocity field;

[0055] Then, the velocity field is dynamically optimized through residual curvature analysis until the imaging of the reflection interface is clear;

[0056] When performing diffracted wave migration imaging, according to the optimized wave velocity model obtained in step S3, using the reverse time migration algorithm, through the interference imaging of the forward and reverse propagation of the full wave field, the asymmetric path of the diffracted wave is accurately captured, as shown in the following formula:

[0057]

[0058] In the formula, the forward propagation wave field is the wave field propagating forward from the source point, and the reverse propagation wave field is the wave field propagating backward from the receiving point;

[0059] After that, local velocity correction is performed. For the diffracted imaging blurred area, the local velocity anomaly is inversely calculated based on the travel time residual, and after updating, the migration imaging is performed again.

[0060] Furthermore, in step S6, when generating a comprehensive geological model by fusing the imaging results of the reflection wave and the diffracted wave through weight coefficients, as shown in the following formula:

[0061] I 最终 (x, z) = α·I 反射 (x, z) + β·I 绕射 (x, z) (7)

[0062] In the formula, α and β are weight coefficients, and α + β = 1.

[0063] On the other hand, the present invention provides a rapid measurement system for tunnel blasting damage based on a wall-climbing robot, which adopts the above-mentioned rapid measurement method for tunnel blasting damage based on a wall-climbing robot, and includes a DAS system and a wall-climbing robot;

[0064] The DAS system includes an optical fiber network and a demodulator. The optical fiber network is used to receive vibration signals, and the demodulator is used to extract relevant information by analyzing the waveforms of the vibration signals;

[0065] The wall-climbing robot includes a main body and a plurality of rotors, a plurality of driving wheels, a vacuum pump and an exciter mounted on the main body;

[0066] The rotors and the vacuum pump are respectively used to provide lift force and adsorption force. The plurality of driving wheels are used for the wall-climbing robot to walk on the tunnel wall, and the exciter is used to generate an artificial seismic source.

[0067] Further, the wall-climbing robot is further configured with a navigation module, a camera module and a data transmission module;

[0068] The navigation module is used to plan the movement path, the camera module is used to identify the lining quality of the tunnel surface, and the data transmission module is used to realize remote control and transmit the navigation information and video information to the mobile terminal in real time;

[0069] The optical fiber network includes circumferential optical fibers, longitudinal optical fibers and helically wound optical fibers;

[0070] The circumferential optical fibers are arranged at equal intervals along the circumferential direction of the tunnel wall to form a closed loop covering the entire cross-section of the tunnel;

[0071] The longitudinal optical fibers are arranged in parallel along the tunnel axis direction and are orthogonal to the circumferential optical fibers to form a grid;

[0072] When the helically wound optical fibers are arranged, first drill holes in the tunnel wall, and then bury the optical fibers into the drill holes in a helically wound manner.

[0073] Compared with the prior art, the tunnel blasting damage rapid measurement method and system provided by the present invention use a three-dimensional optical fiber network to replace the traditional geophone (i.e., a point sensor) to receive the waveform data of the tunnel rock mass. The optical fiber network with a circumferential + longitudinal + drilled hole spiral layout overcomes the defect of insufficient radial sensitivity of the optical fiber. The high-density characteristic of DAS data can greatly improve the spatial resolution and effectively solve the problem of insufficient spatial coverage of the traditional geophone; and the wall-climbing robot is used to perform omnidirectional dynamic excitation on the tunnel wall. Compared with the traditional method of arranging a very small number of fixed seismic source points on the side wall of the tunnel, the introduction of the wall-climbing robot can realize dynamic excitation and full cross-section coverage of the excitation points of the tunnel. At the same time, combined with the three-dimensional optical fiber network, a high-density data coverage network can be formed and multi-angle data fusion can be realized, thereby greatly improving the accuracy of the wave velocity field model and the accuracy of the inversion data, and also being significantly superior to the traditional method in terms of economy and efficiency.

[0074] In addition, the traditional method relies on a single waveform for tunnel surrounding rock inversion migration imaging, and only supports the detection of large-scale structures or small-scale defects. In contrast, the present invention separates the reflected wave and the diffracted wave after receiving the full waveform data, performs migration imaging processing separately, and then superimposes the reflected wave imaging (large-scale structure) and the diffracted wave imaging (small-scale defect) to form a comprehensive geological model, which can take into account the imaging requirements of large-scale structures and small-scale inhomogeneous bodies, that is, macroscopic and microscopic imaging requirements, and significantly improve the detection comprehensiveness in complex geological areas. Description of the Drawings

[0075] Figure 1 Schematic diagram of a tunnel blasting damage rapid measurement system based on a wall-climbing robot provided by the present invention;

[0076] Figure 2 Schematic diagram of the optical fiber network in the tunnel and the wall-climbing robot;

[0077] Figure 3 Schematic diagram of the helically wound optical fiber and the protective sleeve;

[0078] Figure 4 Schematic diagram of the structure of the wall-climbing robot;

[0079] Figure 5 Schematic diagram of the flow of a tunnel blasting damage rapid measurement method based on a wall-climbing robot provided by the present invention.

[0080] Description of the Reference Numerals in the Drawings:

[0081] 10. Wall-climbing robot; 11. Main body; 12. Rotor; 13. Driving wheel; 14. Vacuum pump; 15. Vibrator; 16. Camera module; 20. Optical fiber network; 21. Circumferential optical fiber; 22. Longitudinal optical fiber; 23. Helically wound optical fiber; 24. Protective sleeve; 30. Demodulator; 40. Receiving point; 50. Boundary of abnormal geological body; 60. Ellipsoid. Detailed Embodiments

[0082] To make the technical means, creative features, achieved purposes and functions of the present invention easy to understand, the following further elaborates how the present invention is implemented in combination with specific embodiments.

[0083] The present invention provides a tunnel blasting damage rapid measurement method and system based on a wall-climbing robot. In a specific embodiment, referring to Figure 1 and Figure 2 as shown, the tunnel blasting damage rapid measurement system based on a wall-climbing robot includes a DAS (distributed fiber-optic acoustic sensing) system and a wall-climbing robot 10.

[0084] The DAS system includes an optical fiber network 20 and a demodulator 30. The optical fiber network 20 is used to receive vibration signals. Each small section of the optical fiber in the optical fiber network 20 can be regarded as an independent sensor for receiving vibration signals. The demodulator 30 can extract relevant information such as amplitude, frequency, velocity, and acceleration by analyzing the waveform of the vibration signals.

[0085] The optical fiber network 20 includes a circumferential optical fiber 21, a longitudinal optical fiber 22, and a helically wound optical fiber 23. The circumferential optical fiber 21 is arranged at equal intervals along the circumference of the tunnel wall to form a closed loop covering the entire cross-section of the tunnel. The longitudinal optical fiber 22 is arranged parallel to the tunnel axis direction and is orthogonal to the circumferential optical fiber 21 to form a grid. When the helically wound optical fiber 23 is arranged, first drill holes in the tunnel wall, and then bury the optical fiber into the holes in a helically wound manner at a certain angle. In addition, the outer layer of the optical fiber can be covered with a protective sleeve 24 to prevent construction damage. The structure composed of the helically wound optical fiber 23 and the protective sleeve 24 is as Figure 3 shown.

[0086] In this embodiment, the DAS system is based on the TGD-OFDR (Time-Gated Digital Optical Frequency Domain Reflectometry) technology and is used to record the direct wave signals (P-wave arrival times) at each monitoring point on the longitudinal optical fiber 22, the transverse optical fiber, and the helically wound optical fiber 23 in the optical fiber network, as well as to receive the reflected wave and diffracted wave signals from the surrounding rock area to be measured.

[0087] Figure 1 shows the basic principle of three-dimensional tomography of tunnel seismic waves. The wall-climbing robot 10 is excited at the illustrated position, which is the source point. Two receiving points 40 in the optical fiber network 30 are shown in the figure, and an abnormal geological body boundary 50 is also shown. It can be understood that the abnormal geological body boundary 50 in the figure can be replaced by any reflector or diffraction point. For any pair of source points and receiving points 40, the positions of the two combined with the reflector or diffraction point can define an ellipsoid 60.

[0088] In this embodiment, as Figure 4 shown, the wall-climbing robot 10 includes a main body 11 and a plurality of rotors 12, a plurality of driving wheels 13, a vacuum pump 14, and an exciter 15 installed on the main body 11. The rotors 12 and the vacuum pump 14 are respectively used to provide lift and adsorption forces. The plurality of driving wheels 13 are used for the wall-climbing robot 10 to walk on the tunnel wall, and the exciter 15 is used to generate an artificial seismic source.

[0089] Furthermore, the wall-climbing robot 10 is also equipped with a navigation module, a camera module 16, and a data transmission module. The navigation module is used to plan the movement path and accurately detect the blind area of tunnel excitation. The camera module 16 is used to identify the quality of the tunnel surface lining, improve the accuracy of defect discrimination, and can also be used for the discrimination of the quality of the surrounding rock surface. The data transmission module is used to achieve remote control and transmit the navigation information and video information to the mobile terminal in real time.

[0090] Reference Figure 5 As shown, the present invention also provides a method for rapid measurement of tunnel blasting damage based on a wall-climbing robot, comprising the following steps:

[0091] S1, laying out a three-dimensional optical fiber network 20, recording the coordinates of each optical fiber segment, and establishing a mapping relationship between optical fiber nodes and spatial coordinate positions;

[0092] S2, constructing an initial three-dimensional wave velocity model of the tunnel rock mass;

[0093] S3, respectively correcting the velocity fields of the reflected wave and the bypass wave to obtain an optimized wave velocity model;

[0094] S4, using a wall-climbing robot 10 with an electromagnetic vibrator 15 to excite artificial seismic sources at multiple locations in the tunnel, and the DAS system collects data through an optical fiber network 20;

[0095] S5, processing the data collected in step S4, and performing wavelength separation on the reflected wave and the diffracted wave, and performing offset imaging on the reflected wave and the diffracted wave after classification;

[0096] S6. The imaging results of the reflection wave and the diffraction wave are fused through weight coefficients to generate a comprehensive geological model.

[0097] In a specific embodiment:

[0098] In step S1, specifically, when laying out the three-dimensional optical fiber network 20, the optical fibers are laid out at equal intervals along the circumference of the tunnel wall to form a closed loop, covering the entire cross-section of the tunnel; multiple optical fibers are laid out in parallel along the axial direction of the tunnel, forming a grid orthogonal to the circumferential optical fiber 21; holes are drilled in the tunnel wall, and the optical fibers are buried in the holes in a spiral winding manner at a certain angle to form a three-dimensional optical fiber network 20, which effectively solves the problem of the optical fiber being insensitive to radial vibration. A special coupling agent such as epoxy resin is then used to fix the optical fiber to avoid loosening, and the outer layer is covered with a protective sleeve 24 to prevent construction damage. Furthermore, the optical fiber connectivity can be detected by a TGD-OFDR reflectometer to ensure that the optical fiber network 20 has no breakpoints. Finally, a total station is used to measure and record the coordinates of each section of optical fiber, and a mapping relationship between the optical fiber node and the spatial coordinate position is established.

[0099] In step S2, when constructing the initial three-dimensional wave velocity model of the tunnel rock mass, the following steps are specifically included:

[0100] S21, laboratory measurement of tunnel lining material wave velocity as the initial model benchmark value;

[0101] S22, laboratory testing is performed on the drill core to obtain the density and wave velocity of different lithologies, and the core data is correlated with the position of the spiral optical fiber in the drill hole to establish an initial vertical velocity gradient model;

[0102] S23. Divide the tunnel wall into multiple grids. Coarse grids can be used for division, such as grids of 5m×5m, to improve the calculation efficiency. The wall-climbing robot 10 moves to the center point of each grid according to the preset path, starts the electromagnetic vibrator 15 to generate an artificial seismic source. After each excitation, the DAS system collects data through the optical fiber network 20, records the time when the first arrival wave reaches each optical fiber node, and uses it as the first arrival travel time data.

[0103] S24. Adopt the simultaneous iterative reconstruction algorithm to invert the large-scale velocity field of the rock mass and generate an initial three-dimensional wave velocity model.

[0104] Further, in step S24, when adopting the simultaneous iterative reconstruction algorithm, the iterative formula is as follows:

[0105]

[0106] The convergence condition is:

[0107]

[0108] In the formula, v k is the three-dimensional wave velocity field model of the k-th iteration. The initial wave velocity field v0 comes from the borehole core data and the calibration value of the lining material.

[0109] λ k is the adaptive step factor, that is, the dynamic attenuation coefficient, which is used to balance the convergence speed and stability. λ0 is the initial step, and β is the attenuation rate.

[0110] Ω is the weight matrix, which is a diagonal matrix of dimension A×A. A is the total number of rays, that is, the number of ray paths between all excitation points and receiving points. Any element is expressed as γ = 0.2 · borehole fracture density;

[0111] G is the ray path matrix, which is a sparse matrix of dimension A×B. B is the total number of grids, which is used to describe the propagation path length of the ray in the grid. The element G ij is the path length of the i-th ray in the j-th grid. The ray path matrix G is generated by ray tracing based on the coordinates of the excitation points of the wall-climbing robot (10) and the optical fiber nodes.

[0112] Λ is the path normalization matrix, and the diagonal elements are the total path length ∑ j G ij of each ray, which is used to eliminate the influence of the ray path on the residual.

[0113] t obs is the first arrival travel time data measured by the DAS system, and ε is the convergence threshold.

[0114] When generating the initial three-dimensional wave velocity model, the following steps are specifically included:

[0115] In the initial state, input the first arrival wave travel time data t obs , the ray path matrix G and the initial wave velocity field v0, set the values of λ0, β, γ and ε, and calculate the matrices Ω and Λ; in this embodiment, take the initial step size λ0 = 1.0, the attenuation rate β = 0.05, γ = 0.1, ε = 1%;

[0116] Calculate the residual Calculate the update amount Δv k = λ k ·Ω·G T ·Λ -1 ·r k , iteratively update the wave velocity field v k+1 = v k +Δv k , if the convergence condition of Equation (2) is not satisfied, continue to iterate until the convergence condition of Equation (2) is satisfied, then output the current v k+1 As the initial three-dimensional wave velocity model, end the iteration.

[0117] In step S3, correct the velocity fields of the reflected wave and the diffracted wave respectively to obtain an optimized wave velocity model. The reflected wave imaging depends on the velocity model of the large-scale layered structure. If the initial velocity field error is large, it will cause the position deviation or imaging blur of the reflection interface, so the interlayer velocity optimization is required. The diffracted wave is generated by small-scale defects such as fissures and cavities, and its propagation path is affected by local velocity anomalies. The traditional velocity field (based on the reflected wave or the first arrival wave) may ignore local velocity mutations, resulting in diffracted wave imaging blur or positioning deviation, so targeted correction is required.

[0118] Furthermore, when correcting the velocity field of the reflected wave, the following steps are specifically included:

[0119] Based on the symmetric distribution of the excitation point and the receiving point, divide the reflected wave data into multiple common midpoint (CMP) gathers. The common midpoint position is the center of the excitation point position of the wall-climbing robot 10 and the receiving point 40 position in the optical fiber network 20. Each common midpoint gather corresponds to a set of reflection signals in a specific area in front of the tunnel;

[0120] Calculate the stacking energy corresponding to each candidate velocity through velocity scanning, and select the velocity corresponding to the maximum energy as the optimal layer velocity of the current common midpoint. The reflection wave velocity spectrum analysis formula is:

[0121]

[0122] Wherein, V is the candidate velocity, i.e., the layer velocity value to be evaluated, and the scanning range is usually ±20% of the initial velocity. E(V) is the stacking energy corresponding to the candidate velocity V, which is used to evaluate the rationality of this velocity. M is the number of traces in the common midpoint gather, and N is the number of seismic traces included in each common midpoint gather (i.e., the received signals with different offsets). A mn is the amplitude value of the nth seismic record in the mth common midpoint gather, which is directly measured by the DAS system. t mn is the arrival time of the seismic wave of the nth trace in the mth common midpoint gather. t0 is the two-way travel time for the wave to vertically incident from the common midpoint to the reflection interface and return. x n is the offset, i.e., the horizontal distance between the source and the receiver of the nth seismic record. δ is the impulse function, which is used for alignment correction of the waveform. δ takes the value of 1 only when aligned in time, otherwise it is 0.

[0123] The convergence condition is that after the single-layer velocity is corrected, the change rate of the reflected wave stacking energy is less than the convergence threshold k, that is

[0124] The layer stripping method is adopted to optimize the velocity model from shallow to deep layer by layer. After each update, the stacking energy is recalculated until the velocities of each layer converge, and a reflected wave calibration velocity field is generated.

[0125] The reflected wave velocity spectrum analysis corrects the background velocity error of the layered structure and ensures the accurate imaging of large-scale structures such as faults and rock interfaces.

[0126] On the other hand, when correcting the velocity field of diffracted waves, the specific steps are as follows:

[0127] Separate diffracted waves through the polarization angle threshold, retain the scattering signals with dip angles greater than 30°, and suppress the reflected wave interference;

[0128] Calculate the travel time residual of diffracted waves through the following formula to compare the measured travel time of diffracted waves with the predicted travel time of the current velocity field:

[0129]

[0130] Wherein, t obs is the measured travel time of diffracted waves measured by the DAS system, L rag is the propagation path length of diffracted waves, which is obtained through ray tracing. V current is the local velocity of the current velocity field;

[0131] Based on the regularized least squares inversion, if the travel time residual of diffracted waves is greater than the preset threshold, it indicates local velocity anomaly. Update the local velocity anomaly area and use the corrected local velocity field for reverse time migration imaging.

[0132] In step S3, after completing the correction of the velocity fields of the reflected wave and the diffracted wave, the locally corrected velocity field of the diffracted wave is fused with the calibrated velocity field of the reflected wave to generate an optimized wave velocity model.

[0133] In step S4, the wall-climbing robot 10 with the electromagnetic vibrator 15 is used to excite artificial seismic sources at multiple positions in the tunnel, and the DAS system collects data through the optical fiber network 20.

[0134] Specifically, based on the grids divided in step S23, the tunnel wall is divided into a plurality of finer grids, such as 1m×1m grids. The wall-climbing robot 10 moves to the center point of each grid according to a preset path, starts the electromagnetic vibrator 15 to generate an artificial seismic source. After each excitation, the DAS system records the full waveform data of the optical fiber network 20, including the direct wave, the reflected wave and the diffracted wave, and synchronizes the excitation time of the robot and the acquisition time of the DAS system through the optical fiber timing technology.

[0135] In step S5, the data collected in step S4 is processed, and the wavelength separation of the reflected wave and the diffracted wave is performed. After classification, migration imaging is respectively performed on the reflected wave and the diffracted wave. Specifically, it includes the following steps:

[0136] First, the data is preprocessed. The arrival time of the first arrival wave is determined by detecting the mutation points of the waveform amplitude, the delayed signals and non-seismic source interferences are removed, and data denoising and signal gain are performed;

[0137] Then, the separation of the reflected wave and the diffracted wave is carried out. The diffracted wave is separated based on the polarization angle threshold, the scattering signals with an inclination angle greater than 30° are retained, the reflected wave retains the nearly vertically incident component, and the high-frequency diffracted wave component is filtered out in the frequency-wavenumber domain, and the low-frequency reflected wave signal is retained;

[0138] When performing the migration imaging of the reflected wave, according to the optimized wave velocity model obtained in step S3, the Kirchhoff integral method is used to weight and superimpose the reflected wave amplitude to the imaging point according to the travel time, as shown in the following formula:

[0139]

[0140] In the formula, A i is the reflected wave amplitude from the source point S i to the receiving point R i ; r si is the distance from the source point S i to the imaging point (x, z); r ri is the distance from the receiving point R i to the imaging point (x, z); V(x, z) is the calibrated velocity field;

[0141] Then, the velocity field is dynamically optimized through residual curvature analysis until the imaging of the reflection interface is clear.

[0142] When performing diffracted wave migration imaging, according to the optimized wave velocity model obtained in step S3, the reverse time migration algorithm is adopted, and through the forward and backward propagation interference imaging of the full wave field, the asymmetric path of the diffracted wave is accurately captured, as shown in the following formula:

[0143]

[0144] In the formula, the forward propagation wave field is the wave field propagating forward from the source point, and the backward propagation wave field is the wave field propagating backward from the receiving point;

[0145] Thereafter, local velocity correction is performed. For the diffracted imaging blurred area, the local velocity anomaly is inversely inferred based on the travel time residual, and after updating, the migration imaging is performed again.

[0146] In step S6, the imaging results of the reflected wave and the diffracted wave are fused through the weight coefficients to generate a comprehensive geological model. As shown in the following formula:

[0147] I 最终 (x, z) = α · I 反射 (x, z) + β · I 绕射 (x, z) (7)

[0148] In the formula, α and β are weight coefficients, α + β = 1. It is advisable to take α = 0.7 and β = 0.3, and the values of α and β can be dynamically adjusted according to the signal-to-noise ratio.

[0149] In summary, for the tunnel blasting damage rapid measurement method and system based on the wall-climbing robot provided by the present invention, the three-dimensional fiber optic network 20 is used to replace the traditional geophone (i.e., point sensor) to receive the tunnel rock mass waveform data. The fiber optic network 20 with circumferential + longitudinal + borehole spiral layout overcomes the defect of insufficient radial sensitivity of the fiber optic, and the high-density characteristic of the DAS data can greatly improve the spatial resolution, effectively solving the problem of insufficient spatial coverage of the traditional geophone; and the wall-climbing robot 10 is used to perform all-round dynamic excitation on the tunnel wall. Compared with the traditional method of arranging a very small number of fixed source points on the tunnel side wall, the introduction of the wall-climbing robot 10 can achieve dynamic excitation and full cross-section coverage of the excitation points in the tunnel. At the same time, combined with the three-dimensional fiber optic network 20, a high-density data coverage network can be formed and multi-angle data fusion can be realized, thereby greatly improving the accuracy of the wave velocity field model and the accuracy of the inversion data, and also being significantly superior to the traditional method in terms of economy and efficiency.

[0150] In addition, the traditional method relies on a single waveform for tunnel surrounding rock inversion migration imaging, which only supports the detection of large-scale structures or small-scale defects. In contrast, the present invention separates the reflected wave and the diffracted wave after receiving the full waveform data, performs migration imaging processing separately, and then superimposes the reflected wave imaging (large-scale structure) and the diffracted wave imaging (small-scale defect) to form a comprehensive geological model, which can take into account the imaging requirements of large-scale structures and small-scale inhomogeneous bodies, that is, macroscopic and microscopic imaging requirements, and significantly improve the detection comprehensiveness in complex geological areas.

[0151] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the purpose and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A rapid measurement method for tunnel blasting damage based on a wall-climbing robot, characterized in that, It includes the following steps: S1. Deploy a three-dimensional fiber optic network (20), record the coordinates of each fiber segment, and establish a mapping relationship between fiber optic nodes and spatial coordinate positions; S2. Construct an initial three-dimensional wave velocity model of the tunnel rock mass; S3. Conduct velocity spectrum analysis on the reflected waves and perform local correction on the diffracted waves respectively to obtain an optimized wave velocity model; S4. Use a wall-climbing robot (10) with an electromagnetic exciter (15) to generate artificial seismic sources at multiple positions in the tunnel, and the DAS system collects data through the fiber optic network (20); S5. Process the data collected in step S4, separate the wave fields of the reflected waves and diffracted waves, and then perform migration imaging on the reflected waves and diffracted waves respectively; S6. Generate a comprehensive geological model by fusing the imaging results of the reflected waves and diffracted waves with weight coefficients.

2. The rapid measurement method for tunnel blasting damage based on a wall-climbing robot according to claim 1, characterized in that In step S2, it specifically includes the following steps: S21. Measure the wave velocity of the tunnel lining material in the laboratory as the initial model reference value; S22. Conduct laboratory tests on the borehole cores to obtain the density and wave velocity of different lithologies, associate the core data with the positions of the borehole spiral optical fibers, and establish an initial vertical velocity gradient model; S23. Divide the tunnel wall into multiple grids, the wall-climbing robot (10) moves to the center point of each grid according to a preset path, starts the electromagnetic exciter (15) to generate an artificial seismic source, and after each excitation, the DAS system collects data through the fiber optic network (20) and records the arrival time of the first arrival wave at each fiber optic node as the first arrival wave travel time data; S24. Use the simultaneous iterative reconstruction algorithm to invert the large-scale velocity field of the rock mass and generate an initial three-dimensional wave velocity model.

3. The rapid tunnel blasting damage measurement method based on a wall-climbing robot according to claim 2, wherein, In step S24, when using the simultaneous iterative reconstruction algorithm, the iterative formula is as follows: The convergence condition is: where v k is the three-dimensional wave velocity field model for the k-th iteration, and the initial wave velocity field v0 comes from borehole core data and calibration values of lining materials; λ k is the adaptive step size factor, i.e., the dynamic attenuation coefficient, which is used to balance the convergence speed and stability. λ0 is the initial step size, and β is the attenuation rate. Ω is the weight matrix, which is a diagonal matrix with dimensions A×A, where A is the total number of rays, and any element is expressed as γ = 0.2 · borehole fracture density; G is a ray path matrix, which is a sparse matrix with dimensions of A×B. B is the total number of grids and is used to describe the propagation path length of rays in the grids. The element G ij is the path length of the i-th ray in the j-th grid. The ray path matrix G is generated by ray tracing based on the coordinates of the excitation point of the wall-climbing robot (10) and the optical fiber nodes; A is a path normalization matrix, and the diagonal elements are the total path lengths Σ of each ray j G ij , which is used to eliminate the influence of ray paths on the residuals; t obs is the first arrival travel time data measured by the DAS system, and ε is the convergence threshold; When generating the initial three-dimensional wave velocity model, it specifically includes the following steps: In the initial state, input the first arrival travel time data \(t\). obs Given the ray path matrix \(G\) and the initial wave velocity field \(v_0\), set the values of \(\lambda_0\), \(\beta\), \(\gamma\) and \(\varepsilon\), and calculate the matrices \(\Omega\) and \(\Lambda\). Calculate the residual Calculate the update amount Δv k = λ k ·Ω·G T ·Λ -1 ·r k , and iteratively update the wave velocity field v k+1 = v k + Δv k , if the convergence condition of Equation (2) is not satisfied, continue the iteration until the convergence condition of Equation (2) is satisfied, then output the current v k+1 As the initial three-dimensional wave velocity model, end the iteration.

4. The rapid measurement method for tunnel blasting damage based on a wall-climbing robot according to claim 3, characterized in that, In step S3, when correcting the velocity field of the reflected waves, it specifically includes the following steps: Based on the symmetric distribution of the excitation points and receiving points, divide the reflected wave data into multiple common midpoint gathers. The common midpoint position is the center of the excitation point position of the wall-climbing robot (10) and the receiving point position in the fiber optic network (20). Each common midpoint gather corresponds to a set of reflected signals in a specific area in front of the tunnel; Calculate the stacking energy corresponding to each candidate velocity through velocity scanning, and select the velocity corresponding to the maximum energy as the optimal layer velocity of the current common midpoint. The reflected wave velocity spectrum analysis formula is: Wherein, V is the candidate velocity, i.e., the layer velocity value to be evaluated, E(V) is the stacking energy corresponding to the candidate velocity V, which is used to evaluate the rationality of this velocity, M is the number of traces in the common midpoint gather, N is the number of seismic traces in each common midpoint gather, A mn is the amplitude value of the nth seismic record in the mth common midpoint gather, which is directly measured by the DAS system, t mn is the arrival time of the seismic wave of the nth trace in the mth common midpoint gather, t0 is the two-way travel time for the wave to vertically incident from the common midpoint to the reflection interface and return, x n is the offset, i.e., the horizontal distance between the source and the receiver of the nth seismic record, δ is the impulse function, which is used for aligning the corrected waveform, and δ takes the value of 1 only when it is aligned in time, otherwise it is 0; The convergence condition is that after the single-layer velocity correction, the change rate of the superposition energy of the reflected wave is less than the convergence threshold κ, that is Adopt the layer stripping method to optimize the velocity model from shallow to deep in turn. Recalculate the stacking energy after each update until the velocities of each layer converge to generate a reflected wave calibrated velocity field.

5. The rapid measurement method for tunnel blasting damage based on a wall-climbing robot according to claim 4, characterized in that, In step S3, when correcting the velocity field of the diffracted waves, the specific steps are as follows: Separate the diffracted waves through the polarization angle threshold, retain the scattering signals with an inclination angle greater than 30°, and suppress the interference of the reflected waves; Calculate the diffracted wave travel time residual through the following formula to compare the measured travel time of the diffracted wave with the predicted travel time of the current velocity field: where t obs is the measured diffraction wave travel time measured by the DAS system, and L ray is the diffraction wave propagation path length, obtained by ray tracing calculation, and V current is the local velocity of the current velocity field; Based on the regularized least squares inversion, update the local velocity anomaly area to solve the problem of blurred diffracted wave imaging, and use the corrected local velocity field for reverse time migration imaging. In step S3, after completing the correction of the velocity fields of the reflected wave and the diffracted wave, the locally corrected velocity field of the diffracted wave is fused with the calibrated velocity field of the reflected wave to generate an optimized wave velocity model.

6. The rapid measurement method for tunnel blasting damage based on a wall-climbing robot according to claim 5, wherein In step S4, specifically, based on the grids divided in step S23, the tunnel wall is divided into a plurality of finer grids. The wall-climbing robot (10) moves to the center point of each grid along a preset path, and the electromagnetic vibrator (15) is activated to generate an artificial seismic source. After each excitation, the DAS system records the full waveform data of the optical fiber network (20), including the direct wave, the reflected wave, and the diffracted wave, and synchronizes the excitation time of the robot and the acquisition time of the DAS system through the optical fiber time synchronization technology.

7. The rapid tunnel blasting damage measurement method based on a wall-climbing robot according to claim 6, wherein, In step S5, it specifically includes the following steps: First, preprocess the data. Determine the arrival time of the first arrival wave by detecting the mutation points of the waveform amplitude, eliminate the delayed signals and non-seismic source interferences, and perform data denoising and signal gain. Then, separate the reflected wave and the diffracted wave. Based on the polarization angle threshold, separate the diffracted wave, retain the scattering signals with an inclination angle greater than 30°, retain the nearly vertically incident components of the reflected wave, and filter out the high-frequency diffracted wave components in the frequency-wavenumber domain, retaining the low-frequency reflected wave signals. When performing the reflected wave migration imaging, according to the optimized wave velocity model obtained in step S3, use the Kirchhoff integral method to weight and superimpose the reflected wave amplitude to the imaging point according to the travel time, as shown in the following formula: Where A i is the reflection wave amplitude from the source point S i to the receiving point R i ; r si is the distance from the source point S i to the imaging point (x, z); r ri is the distance from the receiving point R i to the imaging point (x, z); V(x, z) is the calibrated velocity field; Then, dynamically optimize the velocity field through the residual curvature analysis until the imaging of the reflection interface is clear. When performing the diffracted wave migration imaging, according to the optimized wave velocity model obtained in step S3, use the reverse time migration algorithm to accurately capture the asymmetric path of the diffracted wave through the interference imaging of the forward and backward propagation of the full wave field, as shown in the following formula: In the formula, the forward propagation wave field is the wave field propagating forward from the seismic source point, and the backward propagation wave field is the wave field propagating backward from the receiving point. After that, perform local velocity correction. For the blurred area of the diffracted wave imaging, invert the local velocity anomaly based on the travel time residual, and re-perform the migration imaging after updating.

8. The rapid tunnel blasting damage measurement method based on a wall-climbing robot according to claim 7, characterized in that, In step S6, when generating the comprehensive geological model by fusing the imaging results of the reflected wave and the diffracted wave through the weight coefficient, as shown in the following formula: I 最终 (x, z) = α·I 反射 (x, z) + β·I 绕射 (x, z) (7) In the formula, α and β are weight coefficients, and α + β = 1.

9. A rapid measurement system for tunnel blasting damage based on a wall-climbing robot, characterized in that, Adopt the tunnel blasting damage rapid measurement method based on a wall-climbing robot according to any one of claims 1-8, and it includes a DAS system and a wall-climbing robot (10); The DAS system includes an optical fiber network (20) and a demodulator (30). The optical fiber network (20) is used to receive vibration signals, and the demodulator (30) is used to extract relevant information by analyzing the waveform of the vibration signals. The wall-climbing robot (10) includes a main body (11) and a plurality of rotors (12), a plurality of driving wheels (13), a vacuum pump (14), and a vibrator (15) installed on the main body (11); The rotors (12) and the vacuum pump (14) are respectively used to provide lift force and adsorption force. The plurality of driving wheels (13) are used for the wall-climbing robot (10) to walk on the tunnel wall, and the vibrator (15) is used to generate an artificial seismic source.

10. The rapid tunnel blasting damage measurement system based on a wall-climbing robot according to claim 9, wherein, The wall-climbing robot (10) is further configured with a navigation module, a camera module (16), and a data transmission module; The navigation module is used for planning the movement path, the camera module (16) is used for identifying the lining quality of the tunnel surface, and the data transmission module is used for realizing remote control and transmitting the navigation information and video information to the mobile terminal in real time; The optical fiber network (20) includes circumferential optical fibers (21), longitudinal optical fibers (22) and helically wound optical fibers (23); The circumferential optical fibers (21) are arranged at equal intervals along the circumferential direction of the tunnel wall to form a closed loop covering the entire cross-section of the tunnel; The longitudinal optical fibers (22) are arranged in parallel along the tunnel axis direction and are orthogonal to the circumferential optical fibers (21) to form a grid; When the helically wound optical fibers (23) are arranged, holes are first drilled in the tunnel wall, and then the optical fibers are buried in the holes in a helically wound manner.

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