Tunnel seismic wave advanced prediction method and system based on wave velocity model update iteration

Abstract

This invention provides a method and system for tunnel seismic wave advance prediction based on wave velocity model updating and iteration, belonging to the field of tunnel advance prediction technology. The method includes: acquiring seismic wave signals excited by a seismic wave excitation system; calculating the measured true wave velocity of the rock mass in the current tunnel outline area based on the seismic wave signals; using the measured true wave velocity as a constraint condition for inversion to construct a wave velocity model for the area to be excavated ahead of the tunnel; and iteratively updating the wave velocity model as excavation progresses, using the measured true wave velocity of newly exposed rock mass in each round of excavation as a constraint condition, until a final wave velocity model meeting the requirements is obtained. The method and apparatus provided by this invention, by calculating the measured true wave velocity of the rock mass in the current tunnel outline area and using the measured true wave velocity as a constraint condition for inversion to construct or update the wave velocity model until the accuracy requirements are met, overcomes the limitation of traditional static models that cannot correct deviations in a single inversion, thus improving the prediction accuracy of the wave velocity model.

Description

Technical Field

[0001] This invention relates to the field of tunnel advance prediction technology, and in particular to a method and system for advance prediction of tunnel seismic waves based on wave velocity model update iteration. Background Technology

[0002] To ensure construction safety and optimize construction plans, accurately predicting the spatial location and scale of adverse geological bodies ahead of the tunnel face, such as fault zones, cavities, and weak interlayers, is a core requirement in tunnel construction. Currently, tunnel seismic wave advance prediction typically employs methods such as TSP (Tunnel Seismic Prediction), TGP (Tunnel Geological Prediction), or TRT (Tunnel Reflection Tomography). The basic principle of these methods is to artificially generate seismic waves within the tunnel. These waves propagate within the rock mass and are reflected at wave impedance interfaces, where the reflected signals are received by detectors placed on the tunnel sidewalls. Subsequently, by analyzing the acquired signals, the apparent wave velocity distribution of the rock mass ahead of the tunnel face is obtained, and a geological structure model ahead of the tunnel face is constructed based on the travel-time-depth conversion process, thereby enabling the location of geological anomalies.

[0003] However, existing seismic wave prediction technologies still have limitations in practical engineering applications. On the one hand, existing prediction models typically rely on static inversion after a single acquisition, and their calculation accuracy is highly dependent on the initial wave velocity model settings. Because the prediction process is a unidirectional open-loop process, initial deviations generated in the inversion calculation will directly propagate to the final spatial positioning stage. On the other hand, the wave velocity distribution obtained by existing technologies is often a comprehensive feedback from the rock mass within a certain range ahead of the tunnel, and the results are mostly presented in the form of one-dimensional curves. This representation method is highly susceptible to misjudgment of the spatial location of geological anomalies, increasing the safety risks of engineering construction.

[0004] Therefore, improving the accuracy of wave velocity models in tunnel seismic wave advance prediction, so as to improve the spatial positioning and identification accuracy of adverse geological bodies ahead, has become a technical problem that the industry urgently needs to solve. Summary of the Invention

[0005] This invention provides a method and system for tunnel seismic wave advance prediction based on wave velocity model update and iteration, which solves the defects of existing technologies such as large single inversion wave velocity error and inability to correct deviation, and difficulty in identifying lateral differences in geological anomalies by comprehensive wave velocity. It realizes dynamic closed-loop iterative correction of wave velocity model, which greatly improves the spatial positioning accuracy and on-site detection efficiency of adverse geological bodies.

[0006] This invention provides a method for advance prediction of tunnel seismic waves based on wave velocity model update iteration, comprising: The seismic wave signal excited by the seismic wave excitation system is acquired, and the measured true wave velocity of the rock mass in the current outline area of ​​the tunnel is calculated based on the seismic wave signal; the seismic wave signal is acquired by a seismic wave acquisition system deployed inside the tunnel; the seismic wave acquisition system consists of multiple acquisition arrays; the seismic wave excitation system is set on both sides of the tunnel face; The measured real wave velocity is used as a constraint condition for inversion to construct a wave velocity model of the area to be excavated in front of the tunnel. As the tunnel is excavated, the seismic wave acquisition system is rolled and moved in the direction of excavation. The measured real wave velocity of the newly exposed rock mass in each round of excavation is used as a constraint condition to iteratively update the wave velocity model constructed or iteratively updated in the previous round until the final wave velocity model that meets the accuracy requirements is obtained.

[0007] In some embodiments, the seismic wave acquisition system includes alternating first-type acquisition arrays and second-type acquisition arrays; The first type of acquisition array includes three-component detectors respectively arranged at the center of the tunnel arch, the center of the bottom plate, the middle of the left wall, and the middle of the right wall; The second type of acquisition array includes three-component detectors respectively arranged on the upper part of the left wall, the lower part of the left wall, the upper part of the right wall, and the lower part of the right wall of the tunnel; The spacing between two adjacent sets of acquisition arrays is determined based on the average daily advance of the tunnel.

[0008] In some embodiments, the measured real wave velocity includes the rock mass wave velocity along the left contour line of the tunnel and the rock mass wave velocity along the right contour line of the tunnel; The rock mass wave velocity along the left contour line of the tunnel is determined by the average value of the direct wave velocity received by the three-component geophones located on the left side of the tunnel when the left seismic source is excited. The rock mass wave velocity along the right contour line of the tunnel is determined by the average value of the direct wave velocity received by the three-component geophones located on the right side of the tunnel when the right-side seismic source is excited.

[0009] In some embodiments, the wave velocity model is an iteratively updated wave velocity model based on the following steps: The spatial energy scan of the reflected wave signal is performed using a beam analysis method to identify the signal source direction of the reflected wave and obtain the direction information of the reflected wave. During the inversion calculation, the measured real wave velocity is used as the wave velocity numerical constraint, and the direction information is used as the spatial positioning constraint to dynamically correct the wave velocity model.

[0010] In some embodiments, as the tunnel is excavated, the seismic wave acquisition system is rolled and translated in the excavation direction, and the measured real wave velocity of the newly exposed rock mass in each round of excavation is used as a constraint condition to iteratively update the wave velocity model after the previous iteration, including: After each round of excavation, the last set of acquisition arrays in the seismic wave acquisition system is rolled and moved to the frontmost position. The seismic wave excitation system is rearranged at the new working face after each excavation cycle; Calculate the measured true wave velocity of the newly exposed rock mass within the current excavation cycle; The measured real wave velocity is fed back into the wave velocity model updated in the previous iteration to determine the current wave velocity model; The current wave velocity model is iteratively updated to determine the wave velocity model after this round of iterative updates.

[0011] In some embodiments, iteratively updating the wave velocity model after the previous iteration until a final wave velocity model meeting the accuracy requirements is obtained includes: When the error between the wave velocity at the corresponding position in the wave velocity model after a certain excavation cycle and the actual measured wave velocity of the newly excavated rock mass in multiple consecutive excavation cycles after that excavation cycle is less than a preset threshold, the iteration stops, and the wave velocity model after the latest iteration is taken as the final wave velocity model.

[0012] This invention provides a tunnel seismic wave advance prediction system based on wave velocity model update iteration, comprising: The data processing module is used to acquire seismic wave signals excited by the seismic wave excitation system, and calculate the measured true wave velocity of the rock mass in the current tunnel outline area based on the seismic wave signals; the seismic wave signals are acquired by a seismic wave acquisition system deployed inside the tunnel; the seismic wave acquisition system consists of multiple acquisition arrays; the seismic wave excitation system is located on both sides of the tunnel face; The inversion module is used to invert the measured real wave velocity as a constraint condition to construct a wave velocity model of the area to be excavated in front of the tunnel. The iteration module is used to roll and translate the seismic wave acquisition system in the direction of excavation as the tunnel is excavated. The measured real wave velocity of the newly exposed rock mass in each round of excavation is used as a constraint condition to iteratively update the wave velocity model constructed or iteratively updated in the previous round until the final wave velocity model that meets the accuracy requirements is obtained.

[0013] The present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the tunnel seismic wave advance prediction method based on wave velocity model update iteration.

[0014] The present invention provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the tunnel seismic wave advance prediction method based on wave velocity model update iteration.

[0015] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the tunnel seismic wave advance prediction method based on wave velocity model update iteration.

[0016] The tunnel seismic wave advance prediction method and system based on wave velocity model update and iteration provided by this invention overcomes the limitation of traditional methods that can only obtain one-dimensional comprehensive "apparent wave velocity" by setting up seismic wave excitation systems on both sides of the tunnel face and using a seismic wave acquisition system composed of multiple acquisition arrays to obtain seismic wave signals and calculate the measured real wave velocity of the rock mass in the current tunnel outline area. This provides a high-precision physical benchmark that fits the actual situation for subsequent inversion. By using the measured real wave velocity as a constraint for inversion, a wave velocity model of the area to be excavated in front of the tunnel is constructed, which significantly reduces the inversion ambiguity and initial model bias, and forces the inversion model to approximate the outline area. The system accurately identifies real geological conditions, effectively solving the problem of inaccurate spatial positioning of adverse geological bodies in traditional methods. By rolling and translating the seismic wave acquisition system in the direction of excavation as the tunnel is excavated, and using the measured real wave velocity of the newly exposed rock mass in each round of excavation as a constraint to iteratively update the wave velocity model until the accuracy requirements are met, a dynamic closed-loop correction result of "excavating a section and updating once" is achieved. This breaks through the limitation of traditional static models that cannot correct deviations in a single inversion, significantly reducing the error of wave velocity prediction. At the same time, the rolling and translating of the acquisition system also gives the observation system high reusability, greatly improving the efficiency of on-site detection and thus improving the prediction accuracy of the wave velocity model. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0018] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating the tunnel seismic wave advance prediction method based on wave velocity model update iteration provided by the present invention.

[0020] Figure 2 This is a diagram of the seismic wave excitation system layout for the tunnel seismic wave advance prediction method based on wave velocity model update iteration provided by the present invention.

[0021] Figure 3 This is one of the comparison charts showing the results of the tunnel seismic wave advance prediction method based on wave velocity model update iteration provided by this invention.

[0022] Figure 4 This is the second comparison chart of the results of the tunnel seismic wave advance prediction method based on wave velocity model update iteration provided by this invention.

[0023] Figure 5 This is a Class A seismic wave acquisition array diagram of the tunnel seismic wave advance prediction method based on wave velocity model update iteration provided by the present invention.

[0024] Figure 6 This is a Class B seismic wave acquisition array diagram of the tunnel seismic wave advance prediction method based on wave velocity model update iteration provided by the present invention.

[0025] Figure 7 This is one of the observation system layout diagrams for the tunnel seismic wave advance prediction method based on wave velocity model update iteration provided by this invention.

[0026] Figure 8 This is the second observation system layout diagram of the tunnel seismic wave advance prediction method based on wave velocity model update iteration provided by the present invention.

[0027] Figure 9 This is a diagram illustrating the continuous iterative working mode of the tunnel seismic wave advance prediction method based on wave velocity model update iteration provided by the present invention.

[0028] Figure 10 This is a flowchart of the rock mass wave velocity model iteration process for the tunnel seismic wave advance prediction method based on wave velocity model update and iteration provided by the present invention.

[0029] Figure 11 This is a schematic diagram of the tunnel seismic wave advance prediction system based on wave velocity model update and iteration provided by the present invention.

[0030] Figure 12 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0031] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0032] It should be noted that the terms "first," "second," etc., used in this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that comprises a series of steps, units, or modules is not necessarily limited to those explicitly listed, but may include other steps, units, or modules not explicitly listed or inherent to such processes, methods, products, or devices.

[0033] Figure 1 This is a flowchart illustrating the tunnel seismic wave advance prediction method based on wave velocity model update iteration provided by the present invention, as shown below. Figure 1 As shown, the method includes steps 110, 120 and 130.

[0034] Step 110: Obtain the seismic wave signal excited by the seismic wave excitation system, and calculate the measured true wave velocity of the rock mass in the current outline area of ​​the tunnel based on the seismic wave signal; the seismic wave signal is collected by a seismic wave acquisition system arranged in the tunnel; the seismic wave acquisition system consists of multiple acquisition arrays; the seismic wave excitation system is set on both sides of the tunnel face.

[0035] Specifically, the execution entity of the tunnel seismic wave advance prediction method based on wave velocity model update and iteration provided in this embodiment of the invention is a tunnel seismic wave advance prediction system based on wave velocity model update and iteration. This system can be implemented in software, such as a tunnel seismic wave advance prediction program based on wave velocity model update and iteration running on a computer; it can also be implemented in hardware, such as a computer or server that executes the tunnel seismic wave advance prediction method based on wave velocity model update and iteration.

[0036] In this embodiment of the invention, the observation system comprises two parts: a seismic wave acquisition system and a seismic wave excitation system. The seismic wave excitation system is located on both sides of the tunnel face.

[0037] The seismic wave acquisition system consists of multiple equally spaced acquisition arrays, positioned within the excavated tunnel section behind the tunnel face, for receiving seismic wave signals. In this embodiment of the invention, the seismic wave acquisition system comprises six equally spaced acquisition arrays (three of type A and three of type B): each acquisition array consists of four three-component geophones arranged at the same station number. This embodiment of the invention uses an alternating arrangement of type A and type B acquisition arrays.

[0038] In this embodiment of the invention, the seismic wave excitation system includes two seismic sources. Figure 2 This is a diagram of the seismic wave excitation system layout for the tunnel seismic wave advance prediction method based on wave velocity model update iteration provided by this invention, as shown in the diagram. Figure 2 As shown, the seismic wave excitation system consists of two seismic sources S L S R The seismic sources are arranged in boreholes on the left and right sides of the tunnel face, with a borehole depth of 'a' (generally 1.5-2 meters to reduce the influence of the surrounding rock relaxation zone and surface waves). The height of the borehole should be the same as that of the geophone A. Ⅱ A Ⅲ Maintaining consistency. Compared to the traditional single-source excitation mode, by setting up sources on both the left and right sides of the tunnel face, more targeted lateral wavefield information can be obtained.

[0039] The seismic source and detectors were properly arranged according to the above observation system layout. The three-component detectors needed to be tightly coupled to the cave wall rock mass, and the seismic source (such as emulsion explosives) needed to ensure sufficient excitation energy. The left seismic source S was excited sequentially. L With right hypocenter S R All detectors synchronously acquire seismic wave data, i.e., seismic wave signals.

[0040] The actual measured wave velocity of the rock mass in the current tunnel outline area is calculated based on the seismic wave signal. The basic logic of this calculation is as follows: using the known spatial geometric distance between each seismic source and the geophones in each acquisition array, combined with the first arrival travel time of the direct wave in the acquired seismic wave signal, the actual propagation velocity of the seismic wave in the rock mass in the current excavated section outline area is calculated.

[0041] Understandably, in order to improve the robustness of the calculation results, this embodiment of the invention performs comprehensive processing on multiple sets of signals received by multiple acquisition arrays (such as taking the average value of groups) to eliminate the random errors caused by the influence of local surrounding rock relaxation or coupling quality on a single measuring point, thereby obtaining the true wave velocity that can represent the rock mass characteristics of the current left and right contour lines of the tunnel.

[0042] Step 120: Use the measured real wave velocity as a constraint condition to perform inversion and construct a wave velocity model of the area to be excavated in front of the tunnel.

[0043] Specifically, in this embodiment of the invention, constructing an initial tunnel rock mass wave velocity model is the foundation for subsequent dynamic iterative updates. Traditional seismic wave advance prediction methods often rely on single inversion calculations of "apparent wave velocity," which leads to inaccurate spatial positioning due to the lack of accurate initial models and directional information constraints. By introducing the measured actual wave velocities of the left and right contour lines obtained in the above steps as numerical constraints, and combining them with the reflected wave direction information obtained from cluster analysis as spatial positioning constraints, a high-precision two-dimensional inversion model of the rock mass in front of the tunnel face is achieved.

[0044] In this embodiment of the invention, since seismic reflected waves may come from different directions in front of the tunnel (including the side, rear, and even the top), in order to effectively distinguish the source of the signal and avoid misjudging the location of geological anomalies (such as unfavorable geological bodies), this embodiment of the invention uses data collected by a three-component geophone to identify the direction through a beamforming analysis method.

[0045] After obtaining the measured actual wave velocity and reflected wave direction information, the geometric space for inversion needs to be defined. Inversion methods such as least squares are used to determine the frontal length of the tunnel face. (100~150m), width A two-dimensional model is used to model the distribution of tunnel rock mass wave velocity within a range of 40~80m. The initial background wave velocity of the model is determined by the rock mass wave velocity of the current left and right contour lines of the tunnel.

[0046] Based on the initial background wave velocity and seismic reflection wave signal obtained from the model, this embodiment of the invention uses the least squares method for inversion. When the objective function converges or reaches the preset number of iterations, the output m is the initial wave velocity model of the area to be excavated in front of the tunnel.

[0047] Step 130: As the tunnel is excavated, the seismic wave acquisition system is rolled and moved in the direction of excavation. The measured real wave velocity of the newly exposed rock mass in each round of excavation is used as a constraint condition to iteratively update the wave velocity model constructed or iteratively updated in the previous round until the final wave velocity model that meets the accuracy requirements is obtained.

[0048] Specifically, traditional methods, due to their "one-time acquisition, one-time inversion" model, directly transmit errors to the travel time-depth conversion stage of the reflected waves, and cannot correct for deviations. This invention fully utilizes the characteristics of continuous tunnel construction, designing a closed-loop dynamic iterative inversion model of "excavating a section, updating once."

[0049] This invention presents an observation system capable of multiple iterative rolling and forward movement. After each round of excavation, when preparing for the next round of deployment: the seismic wave excitation system (left and right sources) is moved forward along the tunnel excavation direction and repositioned in the boreholes on both sides of the new tunnel face. Furthermore, it is not necessary to redeploy all the geophones; only the set of acquisition arrays located at the very end of the seismic wave acquisition system (i.e., furthest from the tunnel face) needs to be disassembled and rolled to the very front of the entire acquisition array, maintaining the original standard spacing with the current second acquisition array. The spatial positions of the remaining geophone sets remain unchanged.

[0050] After the observation system completes its rolling translation, data acquisition is performed again. Since the tunnel has been excavated to a certain distance, the rock mass that was originally in the "predicted area to be excavated" has now been "excavated and exposed". At this time, the system calculates and obtains the measured real wave velocity of the rock mass along the left and right contour lines of the tunnel within the current excavation cycle.

[0051] Subsequently, the latest measured wave velocity extracted is used as a hard constraint and fed back into the wave velocity model updated in the previous iteration. The wave velocities corresponding to the station numbers in the left and right contour areas of the tunnel in the model are dynamically corrected and replaced. Under this new constraint, the inversion calculation is rerun through least squares inversion, and then the wave velocity model of the entire prediction range (e.g., the model width Q remains unchanged, and the length moves forward with the tunnel face) is iteratively updated, so that it gradually approximates the true wave velocity distribution of the rock mass in front of the tunnel face.

[0052] In this embodiment of the invention, the process of "excavation-rolling-collection-feedback-update" will be continuously repeated. When the error between the predicted wave velocity and the measured true value at the corresponding position in multiple consecutive excavation cycles is less than a preset threshold (e.g., 2%~5%), the system determines that the model has converged, stops iterating, and outputs the final wave velocity model. Because this model integrates the true wave velocity constraints of the tunnel's left and right asymmetry and the spatial direction constraints of the reflected waves, its characterization of the location, scale, and wave velocity characteristics of the adverse geological bodies ahead is much closer to the actual geological distribution than the traditional "one-dimensional apparent wave velocity curve".

[0053] For example, in the advance prediction work of a water conveyance tunnel project, the tunnel cross-section is a city gate type, with a clear height of 6.8m and a clear width of 6.7m. Based on the method provided in this embodiment of the invention, the tunnel advance prediction work is carried out, and the specific steps are as follows: Step 1: Establish the observation system. Set up a seismic wave excitation system at station 3817: drill one hole each in the left and right walls of the tunnel, 1.6m from the tunnel floor and 2m deep; place 0.2kg of emulsion explosive at the bottom of each hole.

[0054] A seismic wave acquisition system was deployed at chainage 3797: A Class A acquisition array was deployed at chainage 3797, and one three-component geophone was deployed at each of the following locations: the center point of the tunnel arch, the center point of the tunnel floor, the left wall 1.6m from the tunnel floor, and the right wall 1.6m from the tunnel floor.

[0055] A Class B acquisition array is deployed at station 3795. At this station, one three-component geophone is deployed on the left side wall of the tunnel at 1.2m and 2m from the tunnel floor, and one three-component geophone is deployed on the right side wall of the tunnel at 1.2m and 2m from the tunnel floor.

[0056] A Class A acquisition array is deployed at station 3793, a Class B acquisition array at station 3791, a Class A acquisition array at station 3789, and a Class B acquisition array at station 3787; the detectors are deployed in the same manner.

[0057] Step two: Initial data acquisition and construction of the initial tunnel rock mass wave velocity model. After the observation system was set up, the emulsion explosives in the borehole at chainage 3817 were detonated to collect data and perform inversion calculations; the tunnel rock mass wave velocity model was then constructed.

[0058] Step 3: After one cycle of tunnel excavation, a second data acquisition and rock mass wave velocity model update iteration is performed. In the next cycle, when the tunnel face is excavated to chainage 3819, a seismic wave excitation system is deployed at that chainage. Type B acquisition arrays are deployed at chainages 3799, 3795, and 3791, and Type A acquisition arrays are deployed at chainages 3797, 3793, and 3789 (compared to the previous step, only the Type B acquisition array previously deployed at chainage 3787 is moved to chainage 3799; the other acquisition arrays remain unchanged). Data acquisition is performed again, and the true rock mass wave velocities along the left and right contour lines of the tunnel from chainage 3817 to 3819 are calculated. These velocities are then used as constraints for another inversion calculation to update the tunnel rock mass wave velocity model. This work... The value is set to 5. The value is set to 3%.

[0059] Step four involves data acquisition and iterative updates to the wave velocity model as the tunnel excavation progresses. After nine iterations, the iteration stopped at chainage 3835, meeting the required conditions, and the wave velocity model for the tunnel segment from chainage 3835 to 3955 was output. Simultaneously, traditional seismic wave methods were performed at chainage 3835, with a prediction range of 3835–3955. Subsequently, the wave velocity of the rock mass along the left and right contour lines of the tunnel segment from chainage 3835 to 3955 was continuously measured. Figure 3 This is one of the comparison charts showing the results of the tunnel seismic wave advance prediction method based on wave velocity model update iteration provided by this invention. Figure 4This is the second comparison chart of the results of the tunnel seismic wave advance prediction method based on wave velocity model update iteration provided by this invention, as shown in Figure 2. Figure 3 and Figure 4 As shown in the figure, this figure compares the actual wave velocities of the rock mass along the left and right contour lines of the tunnel with the wave velocities of the rock mass along the left and right contour lines of the tunnel in the tunnel rock mass wave velocity model constructed using the method provided in this embodiment of the invention, as well as the comprehensive apparent wave velocity using the traditional seismic wave method. It can be seen that the average error values ​​of the left and right contour line rock mass wave velocities in the tunnel rock mass wave velocity model constructed using the method provided in this embodiment of the invention are much lower than the average error value of the comprehensive apparent wave velocity using the traditional seismic wave method.

[0060] The tunnel seismic wave advance prediction method based on wave velocity model update and iteration provided in this invention overcomes the limitation of traditional methods that can only obtain one-dimensional comprehensive "apparent wave velocity" by setting up seismic wave excitation systems on both sides of the tunnel face and using a seismic wave acquisition system composed of multiple acquisition arrays to obtain seismic wave signals and calculate the measured real wave velocity of the rock mass in the current tunnel outline area. This provides a high-precision physical benchmark that fits reality for subsequent inversion. By using the measured real wave velocity as a constraint for inversion, a wave velocity model of the area to be excavated in front of the tunnel is constructed, which significantly reduces the inversion ambiguity and initial model bias, and forces the inversion model to approximate the outline area. The system accurately identifies real geological conditions, effectively solving the problem of inaccurate spatial positioning of adverse geological bodies in traditional methods. By rolling and translating the seismic wave acquisition system in the direction of excavation as the tunnel is excavated, and using the measured real wave velocity of the newly exposed rock mass in each round of excavation as a constraint to iteratively update the wave velocity model until the accuracy requirements are met, a dynamic closed-loop correction result of "excavating a section and updating once" is achieved. This breaks through the limitation of traditional static models that cannot correct deviations in a single inversion, significantly reducing the error of wave velocity prediction. At the same time, the rolling and translating of the acquisition system also gives the observation system high reusability, greatly improving the efficiency of on-site detection and thus improving the prediction accuracy of the wave velocity model.

[0061] In some embodiments, the seismic wave acquisition system includes alternating first-type acquisition arrays and second-type acquisition arrays; The first type of acquisition array includes three-component detectors respectively arranged at the center of the tunnel arch, the center of the bottom plate, the middle of the left wall, and the middle of the right wall; The second type of acquisition array includes three-component detectors respectively arranged on the upper part of the left wall, the lower part of the left wall, the upper part of the right wall, and the lower part of the right wall of the tunnel; The spacing between two adjacent sets of acquisition arrays is determined based on the average daily advance of the tunnel.

[0062] Specifically, in this embodiment of the invention, the observation system comprises two parts: a seismic wave acquisition system and a seismic wave excitation system. The seismic wave acquisition system includes alternating first-type and second-type acquisition arrays (in practical applications, these can be referred to as type A arrays and type B arrays, respectively). Initially, the observation system may consist of multiple sets of acquisition arrays, for example, three sets of first-type acquisition arrays and three sets of second-type acquisition arrays (i.e., three sets of type A arrays and three sets of type B arrays) arranged at equal intervals. The spacing between adjacent sets of acquisition arrays is determined based on the average daily tunnel advance; typically, the spacing between adjacent acquisition arrays is set to be equal to the average daily tunnel advance.

[0063] In this embodiment of the invention, two types of acquisition arrays, A and B, are arranged alternately. Each acquisition array consists of four three-component detectors arranged at the same station number. Figure 5 This is a Class A seismic wave acquisition array diagram of the tunnel seismic wave advance prediction method based on wave velocity model update iteration provided by the present invention, as shown in the figure. Figure 5 As shown, the four detectors of the Class A acquisition array are respectively located in the middle of the tunnel arch, the middle of the tunnel floor, the middle of the left wall, and the middle of the right wall.

[0064] Figure 6 This is a Class B seismic wave acquisition array diagram of the tunnel seismic wave advance prediction method based on wave velocity model update iteration provided by the present invention, as shown in the figure. Figure 6 As shown, the four detectors of the Class B acquisition array are respectively located at the upper part of the left wall of the tunnel, the lower part of the left wall of the tunnel, the upper part of the right wall of the tunnel, and the lower part of the right wall of the tunnel. Among them, detector B... Ⅲ B Ⅳ Distance from the tunnel floor is (A distance of about 0.5 meters is generally acceptable), and detector B Ⅰ B Ⅱ The distance is l. Detector A Ⅱ A Ⅲ The horizontal height is located at B Ⅰ B Ⅲ With B Ⅱ B Ⅳ The center point; Detector A Ⅰ Detector A should be located at the center point of the tunnel arch. Ⅳ It should be located at the center point of the tunnel floor; the spacing between two adjacent acquisition arrays is... ,in, This represents the average daily progress of the tunnel.

[0065] Figure 7 This is one of the observation system layout diagrams for the tunnel seismic wave advance prediction method based on wave velocity model update iteration provided by this invention, such as... Figure 7As shown in the figure, the observation system layout is illustrated. The tunnel height is L, the tunnel width is L, and the distance between the seismic wave excitation system and the seismic wave receiving system is W (W is typically taken as 20-30m). A three-dimensional Cartesian coordinate system is established, and the tunnel excavation direction is defined as... Forward, the horizontal leftward direction of the tunnel excavation section is Positive direction, vertically upward. Positive. Let the coordinates of the center point of the tunnel face floor be... The coordinates of the seismic source and all detectors are: The epicenter is S L The epicenter S R Detector A1 Ⅰ Detector A1 Ⅱ Detector A1 Ⅲ Detector A1 Ⅳ Detector B1 Ⅰ Detector B1 Ⅱ Detector B1 Ⅲ Detector B1 Ⅳ Detector A2 Ⅰ Detector A2 Ⅱ Detector A2 Ⅲ Detector A2 Ⅳ Detector B2 Ⅰ Detector B2 Ⅱ Detector B2 Ⅲ Detector B2 Ⅳ Detector A3 Ⅰ Detector A3 Ⅱ Detector A3 Ⅲ Detector A3 Ⅳ Detector B3 Ⅰ Detector B3 Ⅱ Detector B3 Ⅲ Detector B3 Ⅳ .

[0066] The tunnel seismic wave advance prediction method based on wave velocity model update and iteration provided in this invention uses alternating first and second type acquisition arrays to acquire seismic waves. This not only achieves uniform circumferential coverage of the detectors on the tunnel cross section, effectively expanding the search range of seismic wave signals and significantly reducing the accidental interference of local adverse geological bodies on the overall data quality, but also provides sufficient spatial constraint nodes for independently extracting the true wave velocity of the left and right contour lines and accurately identifying the three-dimensional direction of reflected waves. Furthermore, this cross-arrangement mode is adapted to the depth of the tunnel excavation process, constructing a "rolling forward" observation mechanism that can be iterated multiple times. After each excavation, only the last set of arrays needs to be moved to the front to achieve rapid reuse of the observation system. While ensuring high-precision prediction, this method completely avoids the tedious rewiring required by traditional methods each time, greatly improving the efficiency of on-site construction operations.

[0067] In some embodiments, the measured real wave velocity includes the rock mass wave velocity along the left contour line of the tunnel and the rock mass wave velocity along the right contour line of the tunnel; The rock mass wave velocity along the left contour line of the tunnel is determined by the average value of the direct wave velocity received by the three-component geophones located on the left side of the tunnel when the left seismic source is excited. The rock mass wave velocity along the right contour line of the tunnel is determined by the average value of the direct wave velocity received by the three-component geophones located on the right side of the tunnel when the right-side seismic source is excited.

[0068] Specifically, in this embodiment of the invention, the measured real wave velocity includes the rock mass wave velocity along the left contour line of the tunnel and the rock mass wave velocity along the right contour line of the tunnel. By calculating the real wave velocities on the left and right sides respectively, it is possible to effectively identify the significant differences in geological conditions on the left and right sides when the tunnel passes through fault zones, weak interlayers, or the edge of karst cavities, providing accurate asymmetric data constraints for the subsequent construction of two-dimensional or three-dimensional wave velocity models with lateral identification capabilities.

[0069] The wave velocity of the rock mass along the left contour line of the tunnel is determined by the average value of the direct wave velocities received by the three-component geophones located on the left side of the tunnel when the left-side seismic source is excited. In this embodiment of the invention, the wave velocity of the rock mass along the left contour line of the tunnel is determined by the left-side seismic source S. L During excitation, all detectors located on the left side of the tunnel (detector A1 in the middle of the left wall of the Class A array) Ⅱ A2 Ⅱ A3 Ⅱ And detector B1 located on the upper and lower left wall of the Class B array. Ⅰ B1 Ⅲ B2 Ⅰ B2 Ⅲ B3Ⅰ B3 Ⅲ The direct wave velocity was calculated from a total of 9 waves.

[0070] The single-point direct wave velocity of each of the above detectors is calculated based on spatial geometric relationships. The calculation principle for the single-point direct wave velocity is: the straight-line spatial distance between the detector and the left-side seismic source divided by the first arrival travel time of the direct wave measured by that detector. The calculation method is as follows: The wave velocity of the rock mass along the right contour line of the tunnel is determined by the average value of the direct wave velocities received by three-component geophones located on the right side of the tunnel when the right-side seismic source is activated. R During excitation, all detectors (A1) located on the right side of the tunnel are activated. Ⅲ A2 Ⅲ A3 Ⅲ B1 Ⅱ B1 Ⅳ B2 Ⅱ B2 Ⅳ B3 Ⅱ B3 Ⅳ The direct wave velocity is obtained by calculating the direct wave velocity of the source on the right. Similarly, the single-point direct wave velocity of each geophone on the right is calculated by dividing the straight-line distance from the source on the right to each geophone on the right by the corresponding first arrival travel time of the direct wave. The calculation method is as follows: In the above formula, For detector The measured direct wave velocity, For detector When the direct wave first arrives, The distance between the seismic wave acquisition system and the seismic wave excitation system. The distance between the left hypocenter and the left wall of the tunnel. The distance between the right seismic source and the right side wall of the tunnel. The distance between the receiving arrays. For the height difference of the Class B detector array detectors, The wave velocity of the rock mass along the left contour line of the tunnel. The wave velocity of the rock mass along the right contour line of the tunnel.

[0071] The tunnel seismic wave advance prediction method based on wave velocity model update iteration provided in this invention eliminates refraction and scattering interference caused by the opposite side wave signal passing through unknown and complex geological bodies ahead of the tunnel face by utilizing the spatial correspondence between the same-side source and the same-side geophone. Simultaneously, by averaging the wave velocities of geophones at multiple different spatial locations (upper, middle, lower, and multiple arrays at different depths) on the same side, the method largely offsets the testing errors caused by localized surrounding rock relaxation or poor coupling of a single geophone. This yields highly representative and reliable true wave velocities of the left and right contour lines, laying a solid data foundation for subsequent high-precision wave velocity model inversion iteration.

[0072] In some embodiments, the wave velocity model is an iteratively updated wave velocity model based on the following steps: The spatial energy scan of the reflected wave signal is performed using a beam analysis method to identify the signal source direction of the reflected wave and obtain the direction information of the reflected wave. During the inversion calculation, the measured real wave velocity is used as the wave velocity numerical constraint, and the direction information is used as the spatial positioning constraint to dynamically correct the wave velocity model.

[0073] Specifically, in traditional one-dimensional wave velocity models, all reflected signals are assumed to originate directly in front of the tunnel face, which easily leads to prediction errors. In this embodiment of the invention, a beamforming analysis method is used to calculate the direction of the reflected wave. The process involves: pre-setting the range and interval of the azimuth and elevation angles to be scanned; and calculating the output power for each (azimuth, elevation) direction pair. . All scanning directions This forms a power spectrum. The peak positions in the power spectrum, i.e. The azimuth and elevation angles corresponding to the maximum values ​​correspond to the direction from which the signal is most likely to originate.

[0074] In the inversion calculation process, the measured actual wave velocity is used as the numerical constraint, and the direction information is used as the spatial positioning constraint to dynamically correct the wave velocity model. First, simulated seismic wave data is obtained through forward modeling. Then, the simulated reflected seismic wave data is compared with the observed reflected seismic wave data to obtain the difference between the two. The goal of the inversion is to obtain the correction amount for the wave velocity model, which can reduce this difference, and finally obtain the tunnel rock mass wave velocity model.

[0075] In this embodiment of the invention, the least squares inversion method is used, and its objective function is: In the formula This is the earthquake travel time response. This is earthquake travel time observation data. For the data covariance matrix, As the initial model, This is the initial model covariance matrix.

[0076] In this embodiment of the invention, when applying the least squares objective function for inversion iteration, the system not only uses the measured actual wave velocities of the left and right contour lines obtained above as "hard" numerical constraints, forcing the boundary wave velocities of the model to approximate the true values; but also uses the reflected wave direction information obtained through cluster analysis as the ray path constraint in the spatial dimension. By incorporating the reflected wave direction information into the inversion equation, the spatial attitude of the reflection interface can be precisely locked, making the wave velocity correction obtained from the inversion not only more accurate numerically but also more consistent with the actual geological structure in terms of spatial distribution.

[0077] The tunnel seismic wave advance prediction method based on wave velocity model update iteration provided in this invention effectively corrects the deviation of the initial wave velocity model by using dual constraints based on wave velocity value and signal direction, significantly improving the signal-to-noise ratio of data processing and the accuracy of spatial positioning of adverse geological bodies. By performing inversion calculation using the least squares method, the optimal wave velocity correction amount is stably and efficiently solved, thereby driving the tunnel rock mass wave velocity model to gradually and smoothly converge to the true wave velocity distribution of the rock mass in front of the tunnel face in closed-loop iteration of multiple excavation cycles. From the mathematical calculation level, the convergence of the dynamic iterative inversion process and the high accuracy of the final prediction results are guaranteed.

[0078] In some embodiments, as the tunnel is excavated, the seismic wave acquisition system is rolled and translated in the excavation direction, and the measured real wave velocity of the newly exposed rock mass in each round of excavation is used as a constraint condition to iteratively update the wave velocity model after the previous iteration, including: After each round of excavation, the last set of acquisition arrays in the seismic wave acquisition system is rolled and moved to the frontmost position. The seismic wave excitation system is rearranged at the new working face after each excavation cycle; Calculate the measured true wave velocity of the newly exposed rock mass within the current excavation cycle; The measured real wave velocity is fed back into the wave velocity model updated in the previous iteration to determine the current wave velocity model; The current wave velocity model is iteratively updated to determine the wave velocity model after this round of iterative updates.

[0079] Specifically, in this embodiment of the invention, the tunnel seismic wave advance prediction is not a one-time static prediction, but a dynamic process that continuously reveals the geological truth and self-corrects as the excavation process progresses. By combining the "rolling forward movement" of the seismic wave observation system with the "feedback iteration" of the wave velocity model, the accuracy of the wave velocity model ahead of the tunnel is gradually approximated.

[0080] After each round of excavation, that is, after one cycle of tunnel excavation (the tunnel excavation progress is denoted as...), The next round of observation system deployment and data acquisition will be carried out. The true wave velocity of the rock mass along the left and right contour lines of the tunnel within the most recent excavation cycle will be calculated and used as a constraint to dynamically correct the wave velocity of the rock mass along the left and right contour lines of the tunnel in the tunnel rock mass wave velocity model. The tunnel rock mass wave velocity model will then be iteratively updated to gradually approximate the true wave velocity distribution of the rock mass. After iterative updates, the range (prediction range) of the tunnel rock mass wave velocity model is ( , + ); Model width is (40~80m) remains unchanged.

[0081] The setup method for this observation system is as follows: the seismic wave excitation system moves along the tunnel excavation direction (...). (Direction) movement The last acquisition array of the seismic wave acquisition system rolled towards the front of the acquisition array in the direction of tunnel excavation, maintaining a distance from the second acquisition array. The positions of the remaining detectors remain unchanged, and the seismic wave excitation mode remains unchanged. Figure 8 This is the second observation system layout diagram of the tunnel seismic wave advance prediction method based on wave velocity model update iteration provided by this invention, as shown in the diagram. Figure 8 As shown in the figure, this diagram illustrates the layout of the observation system after one cycle of tunnel excavation.

[0082] The method for calculating the actual wave velocity of rock mass within the recent excavation cycle is as follows: The actual wave velocity of the rock mass along the left contour line of the recently excavated inner tunnel is determined by the left-side seismic source S. L During excitation, all detectors (A1) located on the left side of the tunnel are activated. Ⅱ A2 Ⅱ A3 Ⅱ B1 Ⅰ B1 Ⅲ B2 Ⅰ B2 Ⅲ B3 Ⅰ B3 Ⅲ The direct wave velocity is obtained by calculation, and the calculation method is as follows: The actual wave velocity of the rock mass along the right contour line of the recently excavated inner tunnel is determined by the right-side seismic source S. R During excitation, all detectors (A1) located on the right side of the tunnel are activated. Ⅲ A2 Ⅲ A3 Ⅲ B1 Ⅱ B1 Ⅳ B2 Ⅱ B2 Ⅳ B3 Ⅱ B3 Ⅳ The direct wave velocity is obtained by calculation, and the calculation method is as follows: in, For detector The measured direct wave velocity of the rock mass during the most recent excavation cycle; For detector When the direct wave first arrives and travels; The rock mass wave velocity of the left contour line of the tunnel in the tunnel rock mass wave velocity model constructed in step 120; The rock mass wave velocity of the right contour line of the tunnel in the tunnel rock mass wave velocity model constructed in step 120; This refers to the distance between the seismic wave acquisition system and the seismic wave excitation system. This refers to the distance between the left-side epicenter and the left-side wall of the tunnel. This refers to the distance between the right-side epicenter and the right-side wall of the tunnel. This refers to the distance between the receiving arrays. The height difference of the Class B detector array detectors; For excavation progress; The actual wave velocity of the rock mass along the left contour line of the recently excavated inner tunnel; This represents the actual wave velocity of the rock mass along the right contour line of the recently excavated inner tunnel.

[0083] After calculating the measured true wave velocity of the newly exposed rock mass in the current excavation cycle, this measured true wave velocity is fed back into the wave velocity model updated in the previous iteration to determine the current wave velocity model. Specifically, the predicted wave velocity value at the corresponding station position in the wave velocity model is replaced with the measured true wave velocity value. At this time, the wave velocity of the excavated section in the wave velocity model is updated with the measured true value. Using the updated model as the new initial model, the above iterative update steps are executed again for constraint inversion. In this process, the updated measured true wave velocity serves as a strong constraint condition, and through gradient transfer in the inversion algorithm, the wave velocity distribution of the unexcavated area ahead is automatically corrected.

[0084] The tunnel seismic wave advance prediction method based on wave velocity model update and iteration provided by this invention can complete the re-deployment of the observation system in the next round by moving one of the acquisition arrays after each cycle of excavation, which greatly improves the efficiency of on-site operations and breaks through the limitation of single inversion of traditional static models.

[0085] In some embodiments, iteratively updating the wave velocity model after the previous iteration until a final wave velocity model meeting the accuracy requirements is obtained includes: When the error between the wave velocity at the corresponding position in the wave velocity model after a certain excavation cycle and the actual measured wave velocity of the newly excavated rock mass in multiple consecutive excavation cycles after that excavation cycle is less than a preset threshold, the iteration stops, and the wave velocity model after the latest iteration is taken as the final wave velocity model.

[0086] Specifically, in this embodiment of the invention, as the tunnel is continuously excavated, the observation system continuously moves forward. This embodiment proposes a closed-loop iterative correction mechanism of "excavating a section, updating once, and verifying once". In order to ensure that the final output wave velocity model can truly reflect the characteristics of the rock mass in front of the tunnel face, a scientific and objective model iteration stopping condition, i.e., convergence criterion, must be set.

[0087] Figure 9 This is a diagram illustrating the continuous iterative working mode of the tunnel seismic wave advance prediction method based on wave velocity model update iteration provided by this invention. Figure 10 This is a flowchart of the rock mass wave velocity model iteration process for the tunnel seismic wave advance prediction method based on wave velocity model update and iteration provided by the present invention, as shown in the figure. Figure 9 and Figure 10As shown, as the tunnel is continuously excavated, the measured real wave velocity of the rock mass exposed by subsequent excavation is calculated, and the wave velocity model is continuously updated accordingly. When the model accuracy meets the preset requirements, the iteration stops, and the wave velocity model is finally output.

[0088] As the tunnel is excavated, the wave velocity model is iteratively updated repeatedly. After that, the first... Second-rate The tunnel rock mass wave velocity model constructed through iterative updates shows the rock mass wave velocities along the left and right contour lines of the tunnel and the near-surface rock mass wave velocities. The error in the actual wave velocity of the rock mass along the left and right contour lines of the tunnel at the corresponding chainage within the second excavation cycle is less than [missing information]. When the iteration stops, it is considered that the tunnel rock mass wave velocity model has reached the required accuracy and is close to the actual wave velocity of the rock mass. The current iteration is then set to... The latest iteration of the wave velocity model is output as the final wave velocity model. This final wave velocity model will provide an extremely reliable wave velocity basis for the spatial location of adverse geological bodies ahead.

[0089] Conduct the first In the next iteration, the method for calculating the true wave velocity of the rock mass along the left contour line of the tunnel within the most recently excavated cycle is as follows: gather Detector A1 should be selected. Ⅱ A2 Ⅱ A3 Ⅱ B1 Ⅰ B1 Ⅲ B2 Ⅰ B2 Ⅲ B3 Ⅰ B3 Ⅲ Seismic wave data collected.

[0090] Proceed to the first In the next iteration, the method for calculating the true wave velocity of the rock mass along the right contour line of the tunnel within the most recently excavated cycle is as follows: gather Detector A1 should be selected. Ⅲ A2 Ⅲ A3 Ⅲ B1 Ⅱ B1 Ⅳ B2 Ⅱ B2 Ⅳ B3 Ⅱ B3 Ⅳ Seismic wave data collected.

[0091] in, This represents the actual wave velocity of the rock mass along the left contour line of the tunnel in the most recently excavated cycle. This represents the actual wave velocity of the rock mass along the right contour line of the recently excavated inner tunnel. For the first The rock mass wave velocity of the tunnel left contour line in the tunnel rock mass wave velocity model constructed after the next iteration update; For the first The rock mass wave velocity of the right contour line of the tunnel in the tunnel rock mass wave velocity model constructed after the next iteration update; This refers to the excavation progress. The direct wave arrival time of the detector; The angle between the line connecting the detector and the seismic source and the horizontal plane; The angle between the line connecting the detector and the seismic source and the central plane of the tunnel; This is the horizontal distance between the detector and the seismic source.

[0092] The tunnel seismic wave advance prediction method based on wave velocity model update iteration provided in this invention overcomes the inherent defect of traditional seismic wave prediction methods, which cannot correct one-time wave velocity errors caused by static and open-loop inversion modes, by setting a scientific and objective model iteration stopping condition and designing a closed-loop posterior mechanism of "verifying the previous inversion prediction value with the measured value of subsequent excavation". This mechanism ensures that the wave velocity model can be continuously dynamically corrected and converged during the rolling translation process of tunnel excavation, so that the predicted wave velocity can infinitely approach the actual wave velocity distribution of the rock mass in front of the tunnel. This solves the problem of inaccurate spatial positioning of adverse geological bodies in the reflected wave travel time-depth conversion link, significantly improves the accuracy and reliability of tunnel advance prediction, and provides solid data support for reducing tunnel construction risks.

[0093] The apparatus provided in the embodiments of the present invention will be described below. The apparatus described below can be referred to in correspondence with the method described above.

[0094] Figure 11 This is a schematic diagram of the tunnel seismic wave advance prediction system based on wave velocity model update iteration provided by the present invention, as shown below. Figure 11 As shown, the device includes a data processing module 111, an inversion module 112, and an iteration module 113 connected in sequence.

[0095] The data processing module 111 is used to acquire seismic wave signals excited by the seismic wave excitation system, and calculate the measured true wave velocity of the rock mass in the current outline area of ​​the tunnel based on the seismic wave signals; the seismic wave signals are acquired by a seismic wave acquisition system arranged in the tunnel; the seismic wave acquisition system consists of multiple acquisition arrays; the seismic wave excitation system is set on both sides of the tunnel face; Inversion module 112 is used to invert the measured real wave velocity as a constraint condition to construct a wave velocity model of the area to be excavated in front of the tunnel. The iteration module 113 is used to roll and translate the seismic wave acquisition system in the excavation direction as the tunnel is excavated, and use the measured real wave velocity of the newly exposed rock mass in each round of excavation as a constraint condition to iteratively update the wave velocity model after the previous round of construction or iteration until the final wave velocity model that meets the accuracy requirements is obtained.

[0096] The tunnel seismic wave advance prediction system based on wave velocity model update and iteration provided in this invention overcomes the limitation of traditional methods that can only obtain one-dimensional comprehensive "apparent wave velocity" by setting up seismic wave excitation systems on both sides of the tunnel face and using a seismic wave acquisition system composed of multiple acquisition arrays to obtain seismic wave signals and calculate the measured real wave velocity of the rock mass in the current tunnel outline area. This provides a high-precision physical benchmark that fits reality for subsequent inversion. By using the measured real wave velocity as a constraint for inversion, a wave velocity model of the area to be excavated in front of the tunnel is constructed, which significantly reduces the inversion ambiguity and initial model bias, and forces the inversion model to approximate the outline area. The system accurately identifies real geological conditions, effectively solving the problem of inaccurate spatial positioning of adverse geological bodies in traditional methods. By rolling and translating the seismic wave acquisition system in the direction of excavation as the tunnel is excavated, and using the measured real wave velocity of the newly exposed rock mass in each round of excavation as a constraint to iteratively update the wave velocity model until the accuracy requirements are met, a dynamic closed-loop correction result of "excavating a section and updating once" is achieved. This breaks through the limitation of traditional static models that cannot correct deviations in a single inversion, significantly reducing the error of wave velocity prediction. At the same time, the rolling and translating of the acquisition system also gives the observation system high reusability, greatly improving the efficiency of on-site detection and thus improving the prediction accuracy of the wave velocity model.

[0097] Figure 12 This is a schematic diagram of the structure of the electronic device provided by the present invention, such as... Figure 12 As shown, the electronic device may include: a processor 1210, a communications interface 1220, a memory 1230, and a communications bus 1240, wherein the processor 1210, the communications interface 1220, and the memory 1230 communicate with each other via the communications bus 1240. The processor 1210 can call logical commands in the memory 1230 to execute the methods described in the above embodiments, for example: Seismic wave signals excited by a seismic wave excitation system are acquired, and the measured true wave velocity of the rock mass in the current tunnel outline area is calculated based on the seismic wave signals. The seismic wave signals are acquired by a seismic wave acquisition system deployed inside the tunnel. The seismic wave acquisition system consists of multiple acquisition arrays. The seismic wave excitation system is set on both sides of the tunnel face. The measured true wave velocity is used as a constraint condition for inversion to construct a wave velocity model of the area to be excavated in front of the tunnel. As the tunnel is excavated, the seismic wave acquisition system is rolled and translated in the excavation direction. The measured true wave velocity of the newly exposed rock mass in each round of excavation is used as a constraint condition to iteratively update the wave velocity model constructed or iteratively updated in the previous round until a final wave velocity model that meets the accuracy requirements is obtained.

[0098] Furthermore, the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, and can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0099] The processor in the electronic device provided in this embodiment of the invention can call logical instructions in the memory to implement the above method. Its specific implementation method is the same as the aforementioned method implementation method and can achieve the same beneficial effects, which will not be repeated here.

[0100] This invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the methods provided in the above embodiments.

[0101] The specific implementation method is the same as the aforementioned method implementation method and can achieve the same beneficial effects, so it will not be repeated here.

[0102] This invention provides a computer program product, including a computer program that, when executed by a processor, implements the method described above.

[0103] The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0104] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0105] 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 foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for advance prediction of tunnel seismic waves based on wave velocity model update iteration, characterized in that, include: Obtain the seismic wave signal excited by the seismic wave excitation system, and calculate the measured real wave velocity of the rock mass in the current outline area of ​​the tunnel based on the seismic wave signal; The seismic wave signal is acquired by a seismic wave acquisition system deployed inside the tunnel; the seismic wave acquisition system consists of multiple acquisition arrays; the seismic wave excitation system is located on both sides of the tunnel face; The measured real wave velocity is used as a constraint condition for inversion to construct a wave velocity model of the area to be excavated in front of the tunnel. As the tunnel is excavated, the seismic wave acquisition system is rolled and moved in the direction of excavation. The measured real wave velocity of the newly exposed rock mass in each round of excavation is used as a constraint condition to iteratively update the wave velocity model constructed or iteratively updated in the previous round until the final wave velocity model that meets the accuracy requirements is obtained.

2. The tunnel seismic wave advance prediction method based on wave velocity model update iteration according to claim 1, characterized in that, The seismic wave acquisition system includes alternating first-type and second-type acquisition arrays; The first type of acquisition array includes three-component detectors respectively arranged at the center of the tunnel arch, the center of the bottom plate, the middle of the left wall, and the middle of the right wall; The second type of acquisition array includes three-component detectors respectively arranged on the upper part of the left wall, the lower part of the left wall, the upper part of the right wall, and the lower part of the right wall of the tunnel; The spacing between two adjacent sets of acquisition arrays is determined based on the average daily advance of the tunnel.

3. The tunnel seismic wave advance prediction method based on wave velocity model update iteration according to claim 2, characterized in that, The measured actual wave velocity includes the rock mass wave velocity along the left contour line of the tunnel and the rock mass wave velocity along the right contour line of the tunnel. The rock mass wave velocity along the left contour line of the tunnel is determined by the average value of the direct wave velocity received by the three-component geophones located on the left side of the tunnel when the left seismic source is excited. The rock mass wave velocity along the right contour line of the tunnel is determined by the average value of the direct wave velocity received by the three-component geophones located on the right side of the tunnel when the right-side seismic source is excited.

4. The tunnel seismic wave advance prediction method based on wave velocity model update iteration according to claim 2, characterized in that, The wave speed model is an iteratively updated wave speed model based on the following steps: The spatial energy scan of the reflected wave signal is performed using the beam analysis method to identify the signal source direction of the reflected wave and obtain the direction information of the reflected wave. During the inversion calculation, the measured real wave velocity is used as the wave velocity numerical constraint, and the direction information is used as the spatial positioning constraint to dynamically correct the wave velocity model.

5. The tunnel seismic wave advance prediction method based on wave velocity model update iteration according to claim 1, characterized in that, As the tunnel is excavated, the seismic wave acquisition system is rolled and translated in the excavation direction. The measured actual wave velocity of the newly exposed rock mass in each round of excavation is used as a constraint condition to iteratively update the wave velocity model after the previous iteration, including: After each round of excavation, the last set of acquisition arrays in the seismic wave acquisition system is rolled and moved to the frontmost position. The seismic wave excitation system is rearranged at the new working face after each excavation cycle; Calculate the measured true wave velocity of the newly exposed rock mass within the current excavation cycle; The measured real wave velocity is fed back into the wave velocity model updated in the previous iteration to determine the current wave velocity model; The current wave velocity model is iteratively updated to determine the wave velocity model after this round of iterative updates.

6. The tunnel seismic wave advance prediction method based on wave velocity model update iteration according to claim 1, characterized in that, The iterative update of the wave velocity model after the previous iteration until a final wave velocity model meeting the accuracy requirements is obtained includes: When the error between the wave velocity at the corresponding position in the wave velocity model after a certain excavation cycle and the actual measured wave velocity of the newly excavated rock mass in multiple consecutive excavation cycles after that excavation cycle is less than a preset threshold, the iteration stops, and the wave velocity model after the latest iteration is taken as the final wave velocity model.

7. A tunnel seismic wave advance prediction system based on wave velocity model update iteration, characterized in that, include: The data processing module is used to acquire seismic wave signals excited by the seismic wave excitation system and calculate the measured real wave velocity of the rock mass in the current outline area of ​​the tunnel based on the seismic wave signals. The seismic wave signal is acquired by a seismic wave acquisition system deployed inside the tunnel; the seismic wave acquisition system consists of multiple acquisition arrays; the seismic wave excitation system is located on both sides of the tunnel face; The inversion module is used to invert the measured real wave velocity as a constraint condition to construct a wave velocity model of the area to be excavated in front of the tunnel. The iteration module is used to roll and translate the seismic wave acquisition system in the direction of excavation as the tunnel is excavated. The measured real wave velocity of the newly exposed rock mass in each round of excavation is used as a constraint condition to iteratively update the wave velocity model constructed or iteratively updated in the previous round until the final wave velocity model that meets the accuracy requirements is obtained.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the tunnel seismic wave advance prediction method based on wave velocity model update iteration as described in any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the tunnel seismic wave advance prediction method based on wave velocity model update iteration as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the tunnel seismic wave advance prediction method based on wave velocity model update iteration as described in any one of claims 1 to 6.

Images