A tunnel blasting damage rapid measurement method and system based on a wall-climbing robot

Abstract

The application discloses a tunnel blasting damage rapid measurement method and system based on a wall-climbing robot, wherein the method comprises the following steps: S1, a three-dimensional optical fiber network is laid, the coordinates of each optical fiber are recorded, and a mapping relationship between optical fiber nodes and spatial coordinate positions is established; S2, an initial three-dimensional wave velocity model of a tunnel rock mass is constructed; S3, the velocity fields of reflected waves and diffracted waves are corrected respectively to obtain an optimized wave velocity model; S4, a wall-climbing robot with a charged electromagnetic exciter is used to excite artificial seismic sources at multiple positions in the tunnel, and a DAS system collects data through the optical fiber network; S5, the data collected in the step S4 are processed, the reflected waves and the diffracted waves are wavelength-separated, and after classification, the reflected waves and the diffracted waves are subjected to migration imaging respectively; and S6, the imaging results of the reflected waves and the diffracted waves are fused through a weight coefficient to generate a comprehensive geological model.

Description

TECHNICAL FIELD

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

[0002] At present, tunnel engineering is gradually extending to deep and complex geological conditions. In complex geological environments, engineering disaster prevention and control has become a key technical challenge for safe and efficient construction of deep engineering. Tunnel engineering needs to frequently detect geological structures during construction and operation, so there is an increasing demand for high-precision and high-efficiency detection technology.

[0003] Common tunnel surrounding rock damage detection technologies include geological radar, ultrasonic detection, and tomography using transmission waves. Geological radar is based on electromagnetic wave principles and is greatly affected by the internal reinforcement of the tunnel lining, making it difficult to detect defects in the surrounding rock structure inside the lining. Ultrasonic detection is based on the correlation between wave speed, amplitude, and spectral characteristics and internal structural damage when elastic waves propagate in rock media. It has become one of the core methods for quantifying damage evolution in indoor rock loading tests, but in construction sites with complex rock structures, the ultrasonic wave propagation path is easily affected by multiple scattering, resulting in low signal-to-noise ratio and complex wave speed-stress relationship. Tomography using transmission waves mainly inverts the rock velocity model through transmission wave travel time, and its resolution is limited by the wavelength and ray coverage density. Since transmission waves mainly reflect macroscopic velocity changes, when the defect structure characteristics are close to or smaller than the wavelength, transmission waves will cause signal distortion due to diffraction and scattering, resulting in low sensitivity to small-scale cracks, cavities, and other micro-defects 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] The above detection technologies based on the limitations of detection principles have different difficulties and challenges in detecting faults, folds, joints, cavities, fissures, and other geological structures commonly found in complex geological structure areas, as well as tunnel lining and surrounding rock structures.

[0005] There is a scheme in the prior art that uses reflection waves to perform three-dimensional tomographic migration imaging of tunnel surrounding rock structures: 10 geophones are arranged as receiving points at the intersection of the cross-plum type on both side walls and the vault behind the tunnel, and 12 seismic source points are symmetrically arranged near the side walls on both sides of the tunnel in front of the working face. A sledgehammer is used to excite seismic waves, and reflection points are migrated back to their original positions through three-dimensional tomographic imaging to detect the seismic signals and geological properties of the disaster-causing structures and disaster-causing water bodies in front of the working face. However, seismic migration based on reflection waves is mainly used for imaging large-scale, continuous structural interfaces, and has poor imaging effect on small-scale heterogeneous bodies such as cracks and cavities commonly found in complex geological areas.

[0006] In addition, there is a scheme in the prior art, which takes seismic diffraction scanning stack migration imaging as the basic principle, processes the diffraction wave in the seismic record based on the common reflection surface element stacking technology, and obtains a high-precision seismic imaging profile.

[0007] However, the above schemes are all based on traditional detection methods, the positions of the seismic source and the receiving point are fixed, the exciting position cannot be dynamically adjusted, the seismic wave propagation path is single, the data coverage angle is limited, and the redundancy is low. Therefore, it is necessary to design a new tunnel blasting damage rapid measurement method to solve the above problems. SUMMARY

[0008] To achieve the above object, the technical scheme adopted by the present application is:

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

[0010] S1, a three-dimensional optical fiber network is laid out, the coordinates of each optical fiber are recorded, and a mapping relationship between the optical fiber nodes and the spatial coordinate positions is established;

[0011] S2, an initial three-dimensional wave velocity model of the tunnel rock mass is constructed;

[0012] S3, velocity spectrum analysis is performed on the reflected wave and local correction is performed on the diffraction wave, and an optimized wave velocity model is obtained;

[0013] S4, the wall climbing robot with the electromagnetic exciter excites the artificial seismic source at multiple positions in the tunnel, and the DAS system collects data through the optical fiber network;

[0014] S5, the data collected in step S4 are processed, and the reflected wave and the diffraction wave are separated, and then the reflected wave and the diffraction wave are respectively migrated;

[0015] S6, the imaging results of the reflected wave and the diffraction wave are fused through a weight coefficient to generate a comprehensive geological model.

[0016] Further, in step S2, the following steps are specifically included:

[0017] S21, the wave velocity of the tunnel lining material is measured in the laboratory as the initial model reference value;

[0018] S22, laboratory tests are performed on the drill core to obtain the density and wave velocity of different rock types, and the core data is associated with the position of the drill hole spiral optical fiber to establish an initial vertical velocity gradient model;

[0019] S23, divide the tunnel wall into multiple grids, and the wall-climbing robot moves to the center point of each grid along a preset path, and an electromagnetic exciter is started to generate an artificial seismic source, after each excitation, the DAS system collects data through the optical fiber network, records the time of the first arrival wave reaching each optical fiber node as the first arrival wave travel time data;

[0020] S24, using a simultaneous iterative reconstruction algorithm, inverting the large-scale velocity field of the rock mass to generate an initial three-dimensional wave velocity model.

[0021] Further, in step S24, when using the simultaneous 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 kth iteration, and the initial value wave velocity field v0 comes from the borehole core data and the calibrated value of the lining material;

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

[0027] Ω is a weight matrix, which is a diagonal matrix with a dimension of A×A, and A is the total number of rays, wherein any element is represented as γ = 0.2·drill hole fracture density;

[0028] G is a ray path matrix, which is a sparse matrix with a dimension of A×B, B is the total number of grids, and is used to describe the propagation path length of the rays in the grid, and the element G ij is the path length of the ith ray in the jth grid, and the ray path matrix G is generated based on the excitation point and the optical fiber node coordinates of the wall-climbing robot (10) through ray tracing;

[0029] Λ is a path normalization matrix, and the diagonal elements are the total path length Σ 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 measured first arrival wave travel time data of the DAS system, and ε is the convergence threshold;

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

[0032] In the initial state, the first arrival wave travel time data t is inputobs , the ray path matrix G and the initial wave velocity field v0, setting the values of λ0, γ, γ and ε, calculating the matrices Ω and Λ;

[0033] Calculate the residual Calculate the update 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 formula (2) is not met, continue iteration until the convergence condition of formula (2) is met, then output the current v k+1 as the initial three-dimensional wave velocity model, end the iteration.

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

[0035] Based on the symmetrical distribution of the excitation point and the receiving point, the reflected wave data is divided into a plurality of common point gathers, the common point position is the center of the excitation point position of the wall-climbing robot and the receiving point position in the optical fiber network, and each common point gather corresponds to a reflected signal set of a specific area in front of the tunnel;

[0036] The stacking energy corresponding to each candidate velocity is calculated by velocity scanning, and the velocity corresponding to the maximum energy is selected as the optimal interval velocity of the current common point. The reflected wave velocity spectrum analysis formula is:

[0037]

[0038] In the formula, V is the candidate velocity, that is, the interval 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 the velocity, M is the number of common point gathers, N is the number of seismic traces contained in each common point gather, A mn is the amplitude value of the mth common point gather, n seismic record, which is directly measured by the DAS system, t mn is the arrival time of the mth common point gather, n seismic wave, t0 is the two-way time of the wave vertically incident to the reflection interface from the common point and returned, x n is the offset, that is, the horizontal distance between the source and the receiving point of the n seismic record, δ is the impulse function, which is used for alignment of the corrected waveforms, δ is only 1 when the time is aligned, otherwise it is 0;

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

[0040] The velocity model of each layer is optimized in turn by using the layer stripping method, and the stacking energy is recalculated after each update until the velocity of each layer converges, thereby generating a reflection wave calibrated velocity field.

[0041] Further, in step S3, when the velocity field of the diffracted wave is corrected, the specific steps are as follows:

[0042] The diffracted wave is separated by a polarization angle threshold, and the scattering signal with an inclination angle greater than 30° is retained, and the reflected wave interference is suppressed;

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

[0044]

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

[0046] Based on the regularized least squares inversion, if the travel time residual of the diffracted wave is greater than a preset threshold, it indicates that the local velocity is abnormal, the local velocity anomaly area is updated, and reverse time migration imaging is performed using the corrected local velocity field;

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

[0048] Further, in step S4, based on the grid divided in step S23, the tunnel wall is divided into more fine grids, the wall-climbing robot moves to the center point of each grid according to a preset path, and an 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 the direct wave, the reflected wave and the diffracted wave, and the time of robot excitation and the acquisition time of the DAS system are synchronized through the optical fiber timing technology.

[0049] Further, in step S5, the specific steps include:

[0050] Firstly, the data is preprocessed, the first arrival time is determined by detecting the mutation point of the waveform amplitude, the delayed signal and non-seismic source interference are removed, and the data is denoised and the signal gain is increased;

[0051] Then, the reflected wave and the diffracted wave are separated, the diffracted wave is separated based on the polarization angle threshold, the scattering signal with an inclination angle greater than 30° is retained, the near-vertical incidence component of the reflected wave is retained, the high-frequency diffracted wave component is filtered out in the frequency-wave number domain, and the low-frequency reflected wave signal is retained.

[0052] When performing reflection wave migration imaging, according to the optimized wave velocity model obtained in step S3, the reflection wave amplitude is stacked to the imaging point according to travel time by using Kirchhoff integral method, as follows:

[0053]

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

[0055] After that, the velocity field is dynamically optimized through residual curvature analysis until the reflection interface imaging is clear.

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

[0057]

[0058] In the formula, the forward wave field is the wave field propagated from the source point, and the backward wave field is the wave field propagated from the receiving point.

[0059] After that, local velocity correction is performed, the local velocity anomaly is inversed based on travel time residual for the diffraction imaging blurred area, and then the migration imaging is performed again after updating.

[0060] Further, in step S6, when the comprehensive geological model is generated by fusing the imaging results of the reflection wave and the diffraction wave through the weight coefficient, as follows:

[0061] I 最终 (x, z) = a I 反射 (x, z) + b I 绕射 (x, z) (7)

[0062] In the formula, a and b are weight coefficients, and a + b = 1.

[0063] Another aspect of the present application provides a tunnel blasting damage rapid measurement system based on a wall-climbing robot, which adopts the above-mentioned tunnel blasting damage rapid measurement method based on a wall-climbing robot and comprises a DAS system and a wall-climbing robot.

[0064] The DAS system comprises a fiber network for receiving the vibration signal and a demodulator for extracting relevant information by analyzing the waveform of the vibration signal;

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

[0066] The rotors and the vacuum pump are respectively used for providing lift and adsorption force, the plurality of driving wheels are used for walking on the tunnel wall, and the vibration exciter is used for generating an artificial seismic source.

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

[0068] The navigation module is used for planning a motion path, the camera module is used for identifying the lining quality of the tunnel surface, and the data transmission module is used for realizing remote control and transmitting navigation information and video information to a mobile terminal in real time;

[0069] The fiber network comprises circumferential fibers, longitudinal fibers and spiral winding fibers;

[0070] The circumferential fibers are arranged at equal intervals along the circumferential direction of the tunnel wall to form a closed ring and cover the full section of the tunnel;

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

[0072] When the spiral winding fiber is arranged, a hole is first drilled in the tunnel wall, and then the fiber is buried in the hole in a spiral winding manner.

[0073] Compared with the prior art, the tunnel blasting damage rapid measurement method and system based on the wall-climbing robot provided by the application use a three-dimensional fiber network to replace a traditional detector (i.e., a point sensor) to receive tunnel rock mass waveform data, the circumferential + longitudinal + hole spiral layout fiber network overcomes the defect of insufficient radial sensitivity of the fiber, the high-density characteristics of the DAS data can greatly improve the spatial resolution, and the problem of insufficient spatial coverage of the traditional detector is effectively solved; and the wall-climbing robot is used for dynamic vibration excitation on the tunnel wall, the introduction of the wall-climbing robot can realize dynamic vibration excitation and tunnel full-section coverage of the vibration excitation points, and in combination with the three-dimensional fiber network, a high-density data coverage network can be formed and multi-angle data fusion can be realized, so that the wave velocity field model accuracy and inversion data accuracy are greatly improved, and the economic efficiency and efficiency are also significantly better than those of the traditional method.

[0074] In addition, the conventional method relies on a single waveform to perform tunnel surrounding rock inversion migration imaging, and only supports detection of large-scale structures or small-scale defects; compared with the conventional method, the application separates reflected waves and diffracted waves after receiving full waveform data, and respectively performs migration imaging processing, then superimposes reflected wave imaging (large-scale structure) and diffracted wave imaging (small-scale defect) to form a comprehensive geological model, which can meet the imaging requirements of large-scale structure and small-scale heterogeneous body, namely macroscopic and microscopic imaging requirements, and significantly improves the detection comprehensiveness of a complex geological area. BRIEF DESCRIPTION OF DRAWINGS

[0075] Figure 1 A schematic diagram of a tunnel blasting damage rapid measurement system based on a wall climbing robot is provided in the application.

[0076] Figure 2 A schematic diagram of a fiber-optic network and a wall climbing robot in a tunnel is provided.

[0077] Figure 3 A schematic diagram of a spiral-wound fiber and a protective sleeve is provided.

[0078] Figure 4 A schematic diagram of the structure of a wall climbing robot is provided.

[0079] Figure 5 A flowchart of a tunnel blasting damage rapid measurement method based on a wall climbing robot is provided in the application.

[0080] BRIEF DESCRIPTION OF DRAWINGS

[0081] 10, wall climbing robot; 11, main body; 12, rotor; 13, drive wheel; 14, vacuum pump; 15, exciter; 16, camera module; 20, fiber-optic network; 21, circumferential fiber; 22, longitudinal fiber; 23, spiral-wound fiber; 24, protective sleeve; 30, demodulator; 40, receiving point; 50, abnormal geological body boundary; 60, ellipsoid. DETAILED DESCRIPTION

[0082] To make the technical means, creative features, purposes and effects achieved by the application easy to understand, the following further describes how the application is implemented in combination with specific embodiments.

[0083] The application provides a tunnel blasting damage rapid measurement method and system based on a wall climbing robot. In one specific embodiment, referring to Figs. Figure 1 and Figure 2 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 comprises an optical fiber network 20 for receiving the vibration signals and a demodulator 30. Each section of the optical fiber network 20 can be regarded as an independent sensor for receiving the vibration signals. The demodulator 30 can extract the relevant information such as amplitude, frequency, velocity and acceleration by analyzing the waveform of the vibration signals.

[0085] The optical fiber network 20 comprises circumferential optical fibers 21, longitudinal optical fibers 22 and spiral optical fibers 23. The circumferential optical fibers 21 are arranged along the tunnel wall in a circumferential direction at equal intervals to form a closed loop and cover the whole cross section of the tunnel. The longitudinal optical fibers 22 are arranged in parallel along the axial direction of the tunnel and form a grid with the circumferential optical fibers 21. The spiral optical fibers 23 are arranged by first drilling holes in the tunnel wall and then spirally embedding the optical fibers in the holes at a certain angle. In addition, a protective sleeve 24 can be covered on the outer layer of the optical fibers to prevent damage during construction. The structure composed of the spiral optical fibers 23 and the protective sleeve 24 is shown in FIG. 2. Figure 3

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

[0087] Figure 1 In this embodiment, the basic principle of tunnel seismic wave three-dimensional tomography is shown in FIG. 1. The wall-climbing robot 10 is excited at the position shown in the figure, 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 point and receiving point 40, the position of the reflector or diffraction point can define an ellipsoid 60.

[0088] In this embodiment, as shown in FIG. 2, the wall-climbing robot 10 comprises a main body 11, a plurality of rotors 12 and a plurality of drive wheels 13 mounted on the main body 11, a vacuum pump 14 and an exciter 15. The rotors 12 and the vacuum pump 14 are respectively used to provide lift and adsorption force. The plurality of drive wheels 13 are used to walk on the tunnel wall for the wall-climbing robot 10. The exciter 15 is used to generate an artificial seismic source. Figure 4

[0089] Further, the wall-climbing robot 10 is also provided with a navigation module, a camera module 16 and a data transmission module. The navigation module is used to plan the motion path and accurately detect the tunnel excitation blind area. The camera module 16 is used to identify the quality of the tunnel surface lining and improve the accuracy of defect identification. The camera module 16 can also be used to identify the quality of the surrounding rock surface. 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. ​​

[0090] Referring to Figure 5 The application also provides a tunnel blasting damage rapid measurement method based on a wall-climbing robot, which comprises the following steps:

[0091] S1, a three-dimensional optical fiber network 20 is laid out, the coordinates of each optical fiber are recorded, and a mapping relationship between the optical fiber nodes and the spatial coordinate positions is established;

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

[0093] S3, the velocity fields of the reflected waves and the encircling waves are corrected respectively to obtain an optimized wave velocity model;

[0094] S4, the wall-climbing robot 10 with the electromagnetic exciter 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;

[0095] S5, the data collected in step S4 are processed, the reflected waves and the diffracted waves are separated by wavelength, and then the reflected waves and the diffracted waves are classified and subjected to migration imaging respectively;

[0096] S6, the imaging results of the reflected waves and the diffracted waves are fused through a weight coefficient to generate a comprehensive geological model.

[0097] In one specific embodiment:

[0098] In step S1, specifically, when the three-dimensional optical fiber network 20 is laid out, the optical fibers are laid out at equal intervals along the circumferential direction of the tunnel wall to form a closed loop covering the full cross section of the tunnel; a plurality of optical fibers are laid out in parallel along the axial direction of the tunnel to form a grid that is orthogonal to the circumferential optical fibers 21; the optical fibers are buried in the boreholes in a spiral winding manner at a certain angle to form the three-dimensional optical fiber network 20, effectively solving the problem that the optical fibers are not sensitive to radial vibration. Then, a special coupling agent such as epoxy resin is used to fix the optical fibers to avoid loosening, and an outer protective sleeve 24 is covered to prevent construction damage. Further, the TGD-OFDR reflectometer can be used to detect the connectivity of the optical fibers to ensure that the optical fiber network 20 has no breakpoints. Finally, the total station is used to measure and record the coordinates of each optical fiber, and a mapping relationship between the optical fiber nodes and the spatial coordinate positions is established.

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

[0100] S21, the wave velocity of the tunnel lining material is measured in the laboratory as a reference value of the initial model;

[0101] S22, the laboratory test is performed on the drill core to obtain the density and wave velocity of different rock types, and the core data is associated with the positions of the spiral optical fibers in the boreholes to establish an initial vertical velocity gradient model;

[0102] S23, the tunnel wall is divided into a plurality of grids, which can be divided by a coarse grid, such as a 5m*5m grid, to improve the calculation efficiency; the wall climbing robot 10 moves to the center point of each grid according to the preset path, and the electromagnetic exciter 15 generates an artificial seismic source; after each excitation, the DAS system collects data through the optical fiber network 20, records the time of the first arrival wave reaching each optical fiber node as the first arrival wave travel time data;

[0103] S24, a synchronous iterative reconstruction algorithm is used to invert the large-scale velocity field of the rock mass to generate an initial three-dimensional wave velocity model.

[0104] Further, in step S24, when the synchronous iterative reconstruction algorithm is used, 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 kth iteration, and the initial value wave velocity field v0 comes from the borehole core data and the calibrated value of the lining material;

[0109] λ k is an adaptive step factor, i.e. a dynamic attenuation coefficient, used to balance the convergence speed and stability, λ0 is the initial step, and β is the attenuation rate;

[0110] Ω is a weight matrix, which is a diagonal matrix with a dimension of A*A, and A is the total number of rays, i.e. the number of ray paths between all excitation points and receiving points, wherein any element is represented as γ=0.2·drill fracture density;

[0111] G is a ray path matrix, which is a sparse matrix with a dimension of A*B, B is the total number of grids, and is used to describe the propagation path length of the ray in the grid, and the element G ij is the path length of the ith ray in the jth grid, and the ray path matrix G is generated based on the coordinates of the excitation points and the optical fiber nodes of the wall climbing robot (10) through ray tracing;

[0112] Λ is a path normalization matrix, and the diagonal elements are the total path length Σ 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 measured first arrival wave travel time data of 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, the first arrival wave travel time data t obs , the ray path matrix G and the initial wave velocity field v0, the values of λ0, β, γ and ε are set, and the matrices Ω and Λ are calculated; in this embodiment, the initial step length λ0=1.0, the attenuation rate β=0.05, γ=0.1, and ε=1% are taken;

[0116] The residual error is calculated The update amount Δv is calculated k =λ k ·Ω·G T ·Λ -1 ·r k , the wave velocity field v k+1 is updated by v k +Δv k , if the convergence condition of formula (2) is not met, the iteration is continued until the convergence condition of formula (2) is met, and the current v k+1 is output as the initial three-dimensional wave velocity model, and the iteration is ended.

[0117] In step S3, the velocity field of the reflected wave and the diffraction wave is corrected respectively to obtain the optimized wave velocity model. The reflected wave imaging depends on the velocity model of the large-scale layered structure, and if the error of the initial velocity field is large, it will cause the reflected interface position to deviate or the imaging to be blurred, so the interlayer velocity optimization is needed. The diffraction wave is generated by small-scale defects such as cracks and cavities, and its propagation path is affected by local velocity anomalies. The traditional velocity field (based on reflected wave or first arrival wave) may ignore the local velocity mutation, resulting in blurred diffraction wave imaging or positioning deviation, so the targeted correction is needed.

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

[0119] Based on the symmetrical distribution of the excitation point and the receiving point, the reflected wave data is divided into a plurality of 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, and each common midpoint gather corresponds to a reflected signal set of a specific area in front of the tunnel;

[0120] The stacking energy corresponding to each candidate velocity is calculated by velocity scanning, and the velocity corresponding to the maximum energy is selected as the optimal layer velocity of the current common midpoint. The formula for reflected wave spectral analysis is:

[0121]

[0122] where V is the candidate velocity, i.e. the layer velocity value to be evaluated, the scanning range is usually ±20% of the initial velocity, E(V) is the stacking energy corresponding to the candidate velocity V, used to evaluate the rationality of the velocity, M is the number of common midpoint gathers, N is the number of seismic traces (i.e. received signals of different offsets) included in each common midpoint gather, A mn is the amplitude value of the nth seismic trace in the mth common midpoint gather, obtained by direct measurement of 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 time of the wave vertically incident from the common midpoint to the reflection interface and returned, x n is the offset, i.e. the horizontal distance between the source and the receiving point of the nth seismic trace, δ is a pulse function used for alignment of the corrected waveforms, δ is only 1 when the time is aligned, otherwise it is 0.

[0123] The convergence condition is that after single-layer velocity correction, the rate of change of the reflection wave stacking energy is less than the convergence threshold k, i.e.

[0124] The layer stripping method is used to optimize the velocity model of the shallow layer to the deep layer in turn, and the stacking energy is recalculated after each update until the layer velocity converges to generate the reflection wave calibration velocity field.

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

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

[0127] The diffracted wave is separated by the polarization angle threshold, the scattering signal with an inclination angle greater than 30° is retained, and the reflection wave interference is suppressed;

[0128] The diffracted wave travel time residual is calculated by the following formula to compare the measured travel time of the diffracted wave with the predicted travel time of the current velocity field:

[0129]

[0130] where t obs is the measured travel time of the diffracted wave measured by the DAS system, L rag is the length of the propagation path of the diffracted wave, calculated by ray tracing, V current is the local velocity of the current velocity field;

[0131] Based on the regularized least squares inversion, if the diffracted wave travel time residual is greater than the preset threshold, it indicates that the local velocity is abnormal, the local velocity anomaly area is updated, and the inverse time migration imaging is performed using the corrected local velocity field.

[0132] In step S3, after the correction of the velocity field of the reflected wave and the diffracted wave is completed, the local velocity field of the diffracted wave after correction is fused with the calibration 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, on the basis of the grid divided in step S23, the tunnel wall is divided into more fine grids, such as 1 m x 1 m grids, and the wall-climbing robot 10 moves to the center point of each grid according to a preset path, and the electromagnetic vibrator 15 is started 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 robot excitation time and the DAS system collection time through the optical fiber timing technology.

[0135] In step S5, the data collected in step S4 is processed, and the reflected wave and the diffracted wave are separated by wavelength, and then offset imaging is performed for the reflected wave and the diffracted wave respectively. Specifically, the following steps are included:

[0136] First, the data is preprocessed, the first arrival time is determined by detecting the mutation point of the waveform amplitude, the delayed signal and the non-seismic source interference are removed, and the data is denoised and the signal gain is increased;

[0137] Then, the reflected wave and the diffracted wave are separated, the diffracted wave is separated based on the polarization angle threshold, the scattering signal with an inclination angle greater than 30° is retained, the near-vertical incidence component of the reflected wave is retained, 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 offset 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 stack the reflected wave amplitude to the imaging point according to the travel time, as follows:

[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); and V(x, z) is the calibrated velocity field.

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

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

[0143]

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

[0145] Thereafter, local velocity correction is performed, local velocity anomalies are inverted based on travel time residuals for diffraction imaging blurred areas, and after updating, migration imaging is performed again.

[0146] In step S6, the imaging results of the reflection wave and the diffraction wave are fused through the weight coefficient to generate a comprehensive geological model. As follows:

[0147] I 最终 (x, z) = a I 反射 (x, z) + b I 绕射 (x, z) (7)

[0148] In the formula, a and b are weight coefficients, a+b=1, and a=0.7 and b=0.3 can be taken, and the values of a and b can be dynamically adjusted according to the signal-to-noise ratio.

[0149] In summary, the tunnel blasting damage rapid measurement method and system based on the wall-climbing robot provided by the application adopts a three-dimensional optical fiber network 20 to replace the traditional detector (i.e., a point sensor) to receive tunnel rock mass waveform data, and overcomes the defect of insufficient radial sensitivity of the optical fiber through the circumferential + longitudinal + borehole spiral layout of the optical fiber network 20. The high-density characteristics of the DAS data can greatly improve the spatial resolution, effectively solving the problem of insufficient spatial coverage of the traditional detector. Furthermore, the wall-climbing robot 10 is used to perform full-range dynamic excitation on the tunnel wall. Compared with the traditional method of arranging a small number of fixed source points on the tunnel side wall, the introduction of the wall-climbing robot 10 can realize dynamic excitation and full-face coverage of the excitation point tunnel. In combination with the three-dimensional optical fiber network 20, a high-density data coverage network can be formed, and multi-angle data fusion can be realized, thereby greatly improving the wave velocity field model precision and inversion data accuracy, and being significantly superior to the traditional method in terms of economy and efficiency.

[0150] In addition, the conventional method relies on a single waveform to perform tunnel surrounding rock inversion migration imaging, and only supports detection of large-scale structures or small-scale defects; compared with the conventional method, after receiving full waveform data, the reflection wave and the diffraction wave are separated, and migration imaging processing is respectively performed, then the reflection wave imaging (large-scale structure) and the diffraction wave imaging (small-scale defect) are superposed to form a comprehensive geological model, the imaging requirements of large-scale structure and small-scale heterogeneous body, i.e. macro and micro, can be considered, and the detection comprehensiveness of a complex geological area is significantly improved.

[0151] Finally, it should be pointed out that the above embodiments are only used to illustrate the technical solutions of the present application but not limit the present application, although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced equivalently without departing from the purpose and scope of the technical solutions of the present application, and all of them should be covered in the scope of claims of the present application.

Claims

1. A method for measuring tunnel blasting damage quickly based on a wall-climbing robot, characterized in that, The method comprises the following steps: S1, laying a three-dimensional optical fiber network (20), recording the coordinates of each optical fiber, and establishing a mapping relationship between the optical fiber nodes and the spatial coordinate positions; S2, constructing an initial three-dimensional wave velocity model of the tunnel rock mass; S3, respectively performing velocity spectrum analysis on the reflected wave and local correction on the diffracted wave to obtain an optimized wave velocity model; S4, exciting an artificial seismic source at multiple positions in the tunnel through a wall-climbing robot (10) with an electromagnetic exciter (15), and collecting data through the optical fiber network (20) by the DAS system; S5, processing the data collected in step S4, and performing wave field separation on the reflected wave and the diffracted wave, and then respectively performing migration imaging on the reflected wave and the diffracted wave; S6, fusing the imaging results of the reflected wave and the diffracted wave through a weight coefficient to generate a comprehensive geological model.

2. The method according to claim 1, wherein, In step S2, the following steps are specifically included: S21, measuring the wave velocity of the tunnel lining material in the laboratory as the initial model reference value; S22, performing laboratory testing on the drill core to obtain the density and wave velocity of different rock types, and correlating the core data with the spiral optical fiber positions in the drill hole to establish an initial vertical velocity gradient model; S23, dividing the tunnel wall into multiple grids, and moving the wall-climbing robot (10) to the center point of each grid according to a preset path, starting the electromagnetic exciter (15) to generate an artificial seismic source, and after each excitation, collecting data through the optical fiber network (20) by the DAS system to record the arrival time of the first arrival wave at each optical fiber node as the first arrival wave travel time data; S24, using a simultaneous iterative reconstruction algorithm to invert the large-scale velocity field of the rock mass to generate an initial three-dimensional wave velocity model.

3. The method according to claim 2, wherein, In step S24, when the simultaneous iterative reconstruction algorithm is used, the iterative formula is as follows: The convergence condition is: where v k is the three-dimensional wave velocity field model for the kth iteration, and the initial wave velocity field v0is derived from the borehole core data and the calibrated value of the lining material. λ k λ is an adaptive step factor, i.e. a dynamic damping factor, for balancing convergence speed and stability, λ0is an initial step size, and β is a damping rate. Ω is a weight matrix, Ω = diag(ωi), i = 1, 2,..., A, where any element ωi is represented as γ = 0.2 · borehole fracture density; G is a ray path matrix, which is a sparse matrix with dimension A x B, B is the total number of grids, used to describe the 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 based on the coordinates of the wall-climbing robot (10) excitation point and the optical fiber node by ray tracing; A is a path normalization matrix, diagonal elements are the total path length of each ray j G ij , to eliminate the influence of ray path on the residual t obs is the measured first arrival travel time data for the DAS system, and ε is a convergence threshold. When generating the initial three-dimensional wave velocity model, the following steps are specifically included: In the initial state, input the first arrival 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; Compute residual Compute update Δv k = λ k · Ω · G T · Λ -1 · r k , iteratively update wave velocity field v k+1 = v k + Δv k , if the convergence condition of equation (2) is not satisfied, continue 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 iteration.

4. The method according to claim 3, wherein, In step S3, when correcting the velocity field of the reflected wave, the following steps are specifically included: Based on the symmetrical distribution of the excitation point and the receiving point, the reflected wave data is divided into multiple common point gathers, the common point position is the center of the excitation point position of the wall-climbing robot (10) and the receiving point position in the optical fiber network (20), and each common point gather corresponds to a reflected signal set of a specific area in front of the tunnel; The stacking energy corresponding to each candidate velocity is calculated by velocity scanning, and the velocity corresponding to the maximum energy is selected as the optimal interval velocity of the current common point, and the reflected wave velocity spectrum analysis formula is: where 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, used to evaluate the rationality of the velocity, M is the number of common- midpoint gathers, N is the number of seismic traces contained in each common- midpoint gather, A mn is the amplitude value of the nth seismic record in the mth common- midpoint gather, obtained by direct measurement of 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 time of the wave vertically incident from the common- midpoint to the reflecting interface and returned, x n is the offset, i.e., the horizontal distance between the source and the receiving point of the nth seismic record, δ is the impulse function, used for the aligned corrected waveform, δ is only 1 when the time is aligned, otherwise 0; The convergence condition is: after velocity correction of a single layer, the energy change rate of the reflection wave stack is less than the convergence threshold κ, that is The layer stripping method is used to optimize the velocity model of the shallow layer to the deep layer in turn, and the stacking energy is recalculated after each update until the layer velocity converges to generate the reflected wave calibration velocity field.

5. The method according to claim 4, wherein, In step S3, when correcting the velocity field of the diffracted wave, the following steps are specifically included: The diffracted wave is separated by a polarization angle threshold to retain the scattering signals with an inclination angle greater than 30° and suppress the reflected wave interference; The diffracted wave travel time residual is calculated by the following formula to compare the measured travel time of the diffracted wave with the predicted travel time of the current velocity field: In the formula, t obs is the measured travel time of the diffracted wave measured by the DAS system, L ray is the path length of the diffracted wave, which is calculated by ray tracing, V current is the local velocity of the current velocity field; Based on the regularized least squares inversion, the local velocity anomaly area is updated to solve the imaging ambiguity problem of the diffracted wave, and inverse time migration imaging is performed using the corrected local velocity field; In step S3, after the correction of the velocity field of the reflected wave and the diffracted wave is completed, the local velocity field of the diffracted wave after correction is fused with the calibration velocity field of the reflected wave to generate an optimized wave velocity model.

6. The method according to claim 5, wherein, In step S4, specifically, on the basis of the grid divided in step S23, the tunnel wall is divided into a plurality of grids more finely, the wall-climbing robot (10) moves to the center point of each grid according to a preset path, and the electromagnetic exciter (15) is started to generate an artificial seismic source. After each excitation, the DAS system records full waveform data of the optical fiber network (20), including direct waves, reflected waves and diffracted waves, and the robot excitation time and the DAS system acquisition time are synchronized through the optical fiber timing technology.

7. The method according to claim 6, wherein, In step S5, specifically, the following steps are included: First, the data is preprocessed, the first arrival time is determined by detecting the mutation point of the waveform amplitude, the delayed signals and non-seismic source interference are removed, the data is denoised and the signal gain is processed; Then, the reflected wave and the diffracted wave are separated, the diffracted wave is separated based on the polarization angle threshold, the scattering signal with an inclination angle greater than 30° is reserved, the near-vertical incidence component of the reflected wave is reserved, the high-frequency diffracted wave component is filtered out in the frequency-wave number domain, and the low-frequency reflected wave signal is reserved; When performing reflected wave migration imaging, according to the optimized wave velocity model obtained in step S3, the Kirchhoff integral method is adopted, and the reflected wave amplitude is weighted and stacked to the imaging point according to the travel time, as follows: where A i is the source point S i to the receiver 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 receiver point R i to the imaging point (x, z); and V(x, z) is the velocity field after calibration. Then the velocity field is dynamically optimized through residual curvature analysis until the reflected interface imaging is clear; 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 the full wave field forward and backward propagation interference imaging is performed to accurately capture the asymmetric path of the diffracted wave, as follows: In the formula, the forward wave field is the wave field propagated from the source point, and the backward wave field is the wave field propagated from the receiving point; Thereafter, local velocity correction is performed, local velocity anomalies are inverted based on the travel time residual in the diffracted imaging blurred area, and the migration imaging is performed again after updating.

8. The method according to claim 7, wherein, In step S6, when generating a comprehensive geological model by fusing the imaging results of the reflected wave and the diffracted wave through the weight coefficient, the following formula is used: I 最终 (x, z) = a - 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 tunnel blasting damage rapid measurement system based on a wall-climbing robot, characterized in that, The wall-climbing robot-based tunnel blasting damage rapid measurement method according to any one of claims 1-8 is adopted, and the DAS system and the wall-climbing robot (10) are included; 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 signal; The wall-climbing robot (10) includes a main body (11) and a plurality of rotors (12), a plurality of drive wheels (13), a vacuum pump (14) and an exciter (15) mounted on the main body (11); The rotors (12) and the vacuum pump (14) are respectively used to provide lift and adsorption force, the plurality of drive wheels (13) are used to walk on the tunnel wall for the wall-climbing robot (10), and the exciter (15) is used to generate an artificial seismic source.

10. The tunnel blasting damage fast measurement system based on the wall- climbing robot according to claim 9, characterized in that, The wall-climbing robot (10) is further provided with a navigation module, a camera module (16) and a data transmission module; The navigation module is used for planning a motion path, the camera module (16) is used for identifying tunnel surface lining quality, and the data transmission module is used for realizing remote control and transmitting navigation information and video information to a mobile terminal in real time. The optical fiber network (20) comprises a circumferential optical fiber (21), a longitudinal optical fiber (22) and a spiral winding optical fiber (23). The circumferential optical fiber (21) is arranged along the circumferential direction of the tunnel wall at equal intervals to form a closed ring and cover the full section of the tunnel. The longitudinal optical fiber (22) is arranged in parallel along the axial direction of the tunnel and forms a grid with the circumferential optical fiber (21). When the spiral winding optical fiber (23) is arranged, a hole is first drilled in the tunnel wall, and then the optical fiber is buried in the hole in a spiral winding manner.

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