CN116299708A - Visualization method and related equipment for tunnel surrounding rock loose ring evolution process - Google Patents

Abstract

The invention discloses a visualization method and related equipment for a loose ring evolution process of tunnel surrounding rock, relates to the field of geotechnical mechanics, and mainly aims to solve the problem that a method for accurately monitoring the loose ring evolution process of the tunnel surrounding rock for a long time is lacking at present. The method comprises the following steps: acquiring noise signals of at least two detectors in surrounding rock; determining a corresponding surrounding rock loosening ring transverse wave speed curve at a target detector based on a noise signal of the target detector, wherein the surrounding rock loosening ring transverse wave speed curve is related to depth change; determining a surrounding rock loose coil transverse wave speed model based on surrounding rock loose coil transverse wave speed curves corresponding to the at least two detectors; and acquiring the surrounding rock loose coil transverse wave speed model in a preset period to determine the evolution process of the surrounding rock loose coil. The method is used for the visualization process of the loose ring evolution process of the tunnel surrounding rock.

Description

Visualization method and related equipment for the evolution process of tunnel surrounding rock loose circle

technical field

The invention relates to the field of rock and soil mechanics, in particular to a visualization method and related equipment for the evolution process of the tunnel surrounding rock loose circle.

Background technique

After tunnel excavation, the stress of the surrounding rock usually decreases to zero in the radial direction, resulting in a decrease in the strength of the surrounding rock and stress concentration in the surrounding rock. When the stress exceeds the strength of the surrounding rock, the destruction of the surrounding rock of the tunnel gradually expands from the surface to the inside, until the three-dimensional stress balance is regained after a certain depth. It is defined as the loose circle of the surrounding rock, referred to as the loose circle. Relevant foreign scholars divide the tunnel surrounding rock loosening zone into failure zone, damage zone and disturbance zone, and the excavation disturbance zone can also be divided into excavation damage zone, unsaturated zone and stress redistribution zone. Domestic scholars usually divide the excavation disturbance area into the unloading damage area, the unloading influence area and the slight disturbance area. On the whole, the tunnel excavation disturbance area can be divided into three categories.

At present, the in-situ testing and observation techniques for the disturbance zone mainly focus on borehole optical observation and acoustic testing. However, the optical observation of the borehole completely relies on the observation hole on the surrounding rock wall of the tunnel to carry out image observation through equipment such as borehole TV. Due to the number and density of boreholes, and it is difficult to observe the long-term evolution process, the boreholes in the surrounding rock of the tunnel, especially the horizontal holes and downhill holes, are prone to collapse and water seepage. Long-term protection of boreholes Difficulty comes with a lot of extra cost. The traditional acoustic wave test needs to drill a hole and fill it with water to obtain the sound wave velocity between the acoustic wave transmitting transducer in the borehole and the receiving transducer on the tunnel surface. There is a lack of regional testing of the loose circle of the surrounding rock of the tunnel. If it is necessary to Observing the long-term evolution in the same observation area requires manual repeated observations, which is time-consuming and has low test repeatability. Therefore, there is currently a lack of a method for long-term evolution and visualization testing of the evolution process of the tunnel surrounding rock loose circle in engineering.

Contents of the invention

In view of the above problems, the present invention provides a visualization method and related equipment for the evolution process of the tunnel surrounding rock loosening circle.

In order to solve at least one of the above-mentioned technical problems, in the first aspect, the present invention provides a method for visualizing the evolution process of the tunnel surrounding rock loose circle, the method comprising:

obtaining noise signals of at least two geophones in the surrounding rock;

Determining the shear wave velocity curve of the surrounding rock loosening ring corresponding to the target geophone based on the noise signal of the target geophone, wherein the shear wave velocity curve of the surrounding rock loosening ring is related to the depth change;

Determining the shear wave velocity model of the surrounding rock loosening circle based on the shear wave velocity curves of the surrounding rock loosening circle corresponding to the at least two geophones;

The shear wave velocity model of the surrounding rock loosening circle is acquired at a preset period to determine the evolution process of the surrounding rock loosening circle.

Optionally, the above method also includes:

Determine the spatial autocorrelation coefficient based on the noise signal of the target detector and the noise signals of other detectors;

The dispersion curve is determined based on the above-mentioned spatial autocorrelation coefficient, wherein the above-mentioned dispersion curve is used to characterize the dispersion change process of the surface wave propagating along the side wall of the tunnel.

Optionally, the above-mentioned determination of the dispersion curve based on the above-mentioned spatial autocorrelation coefficient includes:

Determining the first kind of zero-order Bessel function based on the above-mentioned spatial autocorrelation coefficient;

The above-mentioned dispersion curve is determined based on the above-mentioned first-type zero-order Bessel function, wherein the above-mentioned dispersion curve is used to characterize the shear wave velocity of the surrounding rock of the above-mentioned tunnel.

Optionally, the above method also includes:

Based on the above-mentioned first kind of zero-order Bessel function, the propagation velocity of Rayleigh wave along the tunnel side wall at different frequencies is determined;

The dispersion curve is determined based on the above-mentioned Rayleigh wave propagation velocity along the tunnel side wall at different frequencies.

Optionally, the above-mentioned noise signal based on the target geophone determines the shear wave velocity curve of the surrounding rock loosening ring corresponding to the above-mentioned target geophone, including:

Based on the above dispersion curve, the shear wave velocity curve of the surrounding rock loose ring at the target geophone is determined by the least square method and constraint information.

Optionally, the above-mentioned determination of the shear-wave velocity curve of the surrounding rock loose ring at the target geophone through the least square method and constraint information based on the above-mentioned dispersion curve includes:

Based on the above dispersion curve and the number of layers of the shear wave velocity model of the surrounding rock, the shear wave velocity curve of the loose circle of the surrounding rock at the target geophone is determined by the least square method and constraint information. layer.

Optionally, the above method also includes:

Obtain optical images of the surrounding rock based on the borehole optical camera to determine the RMIBT value;

The above constraint conditions are determined based on the changes of the above RMIBT values at different times, wherein the above constraint conditions are the positive and negative directions of the update amount of the shear wave velocity model of the surrounding rock loose circle.

In the second aspect, the embodiment of the present invention also provides a visualization device for the evolution process of the tunnel surrounding rock loose circle, including:

an acquisition unit, configured to acquire noise signals of at least two geophones in the surrounding rock;

The first determination unit is configured to determine the shear wave velocity curve of the surrounding rock loosening ring corresponding to the target geophone based on the noise signal of the target geophone, wherein the shear wave velocity curve of the surrounding rock loosening ring is related to the depth change;

The second determination unit is used to determine the shear wave velocity model of the surrounding rock loosening circle based on the shear wave velocity curves of the surrounding rock loosening circle corresponding to the at least two geophones;

The third determining unit is configured to obtain the shear wave velocity model of the surrounding rock loosening circle at a preset period to determine the evolution process of the surrounding rock loosening circle.

In order to achieve the above object, according to the third aspect of the present invention, a computer-readable storage medium is provided, the above-mentioned computer-readable storage medium includes a stored program, wherein, when the above-mentioned program is executed by a processor, the above-mentioned tunnel surrounding rock Steps of the visualization method for the evolution process of loose circles.

In order to achieve the above object, according to the fourth aspect of the present invention, an electronic device is provided, including at least one processor and at least one memory connected to the processor; wherein, the processor is used to call the program instructions in the memory , performing the above-mentioned steps of the method for visualizing the evolution process of the tunnel surrounding rock loosening circle.

With the help of the above-mentioned technical scheme, the visualization method and related equipment for the evolution process of the tunnel surrounding rock loosening circle provided by the present invention, for the lack of a method that can accurately monitor the evolution process of the tunnel surrounding rock loosening circle for a long time, the present invention acquires the surrounding rock loosening circle evolution process. Noise signals of at least two geophones in the rock; determining the shear wave velocity curve of the surrounding rock loosening ring corresponding to the target geophone based on the noise signal of the target geophone, wherein the shear wave velocity curve of the surrounding rock loosening ring is related to the depth change; The shear wave velocity model of the surrounding rock loosening zone is determined based on the shear wave velocity curves of the surrounding rock loosening zone corresponding to the at least two geophones; the above-mentioned shear wave velocity model of the surrounding rock loosening zone is obtained at a preset period to determine the evolution process of the surrounding rock loosening zone. In the above scheme, the typical noise of tunnels in different periods is effectively used, such as blasting noise in the construction period and traffic noise in the operation period. The results of the velocity profile can obtain the three-dimensional shear wave velocity model of the surrounding rock of the tunnel. Through the inversion of the environmental noise signal, the shear wave velocity of multiple survey lines can be obtained. The range of the loose circle can be determined by the magnitude of the shear wave velocity, and regional large-scale tunneling can be realized. Test and evaluation, and due to the periodic acquisition model, through long-term monitoring of background noise, the signal within the time period can be selectively intercepted for shear wave velocity inversion, and the evolution process of the loose circle at different times can be obtained nearly continuously.

Correspondingly, the visualization device, device and computer-readable storage medium for the evolution process of the tunnel surrounding rock loosening circle provided by the embodiments of the present invention also have the above-mentioned technical effects.

The above description is only an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention, it can be implemented according to the contents of the description, and in order to make the above and other purposes, features and advantages of the present invention more obvious and understandable , the specific embodiments of the present invention are enumerated below.

Description of drawings

Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiment. The drawings are only for the purpose of illustrating a preferred embodiment and are not to be considered as limiting the invention. Also throughout the drawings, the same reference numerals are used to designate the same parts. In the attached picture:

Fig. 1 shows a schematic flow diagram of a method for visualizing the evolution process of the tunnel surrounding rock loose circle provided by an embodiment of the present invention;

Fig. 2 shows a schematic top view of the layout of a long-term evolution visualization test device for the loosening circle of the tunnel surrounding rock provided by the embodiment of the present invention;

Fig. 3 shows a schematic view of the layout of a visual test device for the long-term evolution of the loosening circle of the surrounding rock of the tunnel provided by the embodiment of the present invention;

Fig. 4 shows a front view schematic diagram of a long-term evolution visualization test device for the tunnel surrounding rock loosening circle provided by the embodiment of the present invention;

Fig. 5 shows a schematic structural diagram of a long-term evolution visualization test device for the loosening circle of the surrounding rock of the tunnel provided by the embodiment of the present invention;

Fig. 6 shows a schematic diagram of the host structure of a long-term evolution visualization test and analysis host of the tunnel surrounding rock loose circle provided by the embodiment of the present invention;

Fig. 7 shows a schematic diagram of monitoring environmental background noise of a geophone provided by an embodiment of the present invention;

Fig. 8 shows a typical three-layer schematic diagram of a tunnel surrounding rock loosening circle provided by an embodiment of the present invention;

Fig. 9 shows a schematic diagram of shear wave velocity inversion at 11 geophones on a survey line in a loose surrounding rock circle of a tunnel provided by an embodiment of the present invention;

Fig. 10 shows a schematic diagram of shear wave velocity inversion composed of multiple geophones in a tunnel surrounding rock loose circle provided by an embodiment of the present invention;

Fig. 11 shows a schematic diagram of RMIBT constrained inversion of a small number of borehole optical observation calculations provided by an embodiment of the present invention;

Fig. 12 shows a schematic block diagram of the composition of a visualization device for the evolution process of the tunnel surrounding rock loose circle provided by the embodiment of the present invention;

Fig. 13 shows a schematic block diagram of an electronic device for visualizing the evolution process of the tunnel surrounding rock loosening circle provided by an embodiment of the present invention.

Detailed ways

Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the drawings, it should be understood that the invention may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided for more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

In order to solve the current problem of lacking a method for long-term and accurate monitoring of the evolution process of the tunnel surrounding rock loose circle, the embodiment of the present invention provides a visualization method for the evolution process of the tunnel surrounding rock loose circle evolution process, as shown in Figure 1, the method includes :

S101. Obtain noise signals of at least two geophones in the surrounding rock;

Exemplarily, as shown in Fig. 2, Fig. 3 and Fig. 4, the arrangement plan view, side view and front view of the long-term evolution visualization test device of the tunnel surrounding rock loose circle provided by this method are provided. In the method, the test device mainly includes the test device 1 and on-site analysis host 2. Wherein, the test device 1 mainly includes a high-frequency seismic wave detector 1.1 and a borehole optical camera 1.5. The high-frequency geophone is connected with the on-site analysis host through wireless communication, and the drilling optical camera is connected with the on-site analysis host through wired communication cables. The high-frequency geophones are arranged horizontally and equidistantly on the side wall of the tunnel along the axis of the tunnel, and the borehole optical cameras are arranged in a small number of observation holes on the side wall of the tunnel. Due to the main frequency characteristics (about 10Hz to 300Hz) of the background noise of the tunnel environment (construction blasting, conventional construction noise, vehicle noise, etc.) The distance between detectors is set to 10m, and the frequency bandwidth and sampling rate of the detectors are consistent with the background noise frequency band. According to the actual needs of the site, the spacing of a small number of observation boreholes can be 100m, and the depth of observation boreholes should be more than 10m, so as to achieve the purpose of constrained noise inversion. Observation boreholes are mostly up-dip holes to prevent boreholes from collapsing. If necessary, a geophone survey line can be added at the top of the tunnel to realize the inversion of the shear wave velocity profile of the high-density loose ring of surrounding rock in the tunnel.

Exemplarily, as shown in Figure 5, the on-site host 2 includes a host communication module 2.1, a human-computer interaction device 2.2, a central processing unit 2.3, a storage module 2.4, an independent power supply module 2.5, an environmental noise signal processing module 2.6, and a surrounding rock loosening ring Inversion module 2.7, borehole optical image analysis module 2.8, surrounding rock loose circle 3D visualization module 2.9, mobile analysis terminal 2.10. Among them, the host communication module 2.1 is used as an interface to connect the on-site analysis host 2 and the test device 1, the human-computer interaction device 2.2 can be a touch screen or an optical rotary button and other devices, and the central processing unit 2.3 handles the control of signal transmission, storage and calculation instructions, etc. The storage module 2.4 is responsible for storing and processing information, the independent power supply module 2.5 is responsible for independent power supply, the environmental noise signal processing module 2.6 is responsible for signal processing and dispersion curve extraction of the noise signal tested for a long time, and the surrounding rock loose circle inversion module 2.7 will invert the dispersion curve to obtain the shear wave velocity of the surrounding rock loose circle at 1.1 of the high-frequency geophone, and the drilling optical image analysis module 2.8 will be responsible for image processing, calculation of RMIBT and other parameters, and guide the shear wave velocity inversion The model updates the gradient direction of . The 3D visualization module 2.9 of the surrounding rock loose circle converts the 1D shear wave velocity curve of each geophone point line into a 2D shear wave velocity profile of each survey line, and generates a 3D shear wave velocity model using the 2D shear wave velocity profiles of multiple survey lines.

Exemplarily, as shown in Figure 6, the test device 1 specifically includes a high-frequency geophone 1.1, a pre-signal amplification filter 1.2, an A/D conversion module 1.3, a data transmission wireless communication module 1.4, and a drilling optical camera probe 1.5 , front-end image data processor 1.6, image data digitization module 1.7 and data transmission line communication module 1.8. The high-frequency geophone 1.1 has a self-powered module, which can be installed on the side wall of the surrounding rock of the tunnel to be tested for continuous sampling for a long time, and the collected environmental noise signal of the tunnel is further selected through the pre-signal amplification filter 1.2. Effective signal frequency band, and Through the A/D conversion module 1.3, the electrical signal is converted into a digital signal, and through the data transmission wireless communication module 1.4, the real-time wireless signal is transmitted to the on-site analysis host 2 through the host communication module 2.1, and the drilling optical camera probe 1.5 communicates with the host through a wired cable. On-site analysis host 2 is connected to realize power supply. The borehole optical camera probe 1.5 is only placed in the observation borehole when testing is required, the borehole optical camera probe will observe the rock mass image of the borehole wall through the pre-image data processor 1.6 for preliminary noise filtering, and use the image data digitization module 1.7 Digitize the rock mass image, and finally connect with the on-site analysis host through the host communication module 2.1 through the wired communication module 1.8 for data transmission.

Exemplarily, as shown in Figure 7, this method collects the noise signals during the tunnel construction and operation period through the high-frequency geophone 1.1 (referred to as the geophone in this scheme), and the noise source signal can be the blasting and construction noise of the further construction of the tunnel , can also be the vehicle noise inside the tunnel during the operation period, which is not specifically limited here. It solves the problem of testing and analyzing the long-term evolution process in the existing technology. It must be detected at regular intervals. The collected data is discrete and time-consuming. However, this solution can selectively monitor the background noise for a long time. By intercepting the signals in the time period and inverting the shear wave velocity, the nearly continuous evolution process of the loose circle at different times can be obtained.

S102. Based on the noise signal of the target geophone, determine the shear wave velocity curve of the surrounding rock loosening ring corresponding to the target geophone, wherein the shear wave velocity curve of the surrounding rock loosening ring is related to the depth change;

Exemplarily, this method evenly segments the collected data according to time-frequency, and distinguishes the part with larger noise as much as possible, because the larger the signal-to-noise ratio, the smaller the noise mixed in the signal, and the sound quality of the sound playback The higher the value, the lower the signal-to-noise ratio part of the collected data segment will be eliminated, and each data segment will be processed with a narrowband band-pass filter with different center frequencies, and the higher signal-to-noise ratio part will be substituted into the later signal processing to determine the above-mentioned target detection The shear wave velocity curve of the surrounding rock loose ring corresponding to the device.

S103. Determine the shear wave velocity model of the surrounding rock loosening circle based on the shear wave velocity curves of the surrounding rock loosening circle corresponding to the at least two geophones;

Exemplarily, the same shear wave velocity inversion method is used for different geophone center points, and the inversion steps are repeated to obtain shear wave velocity curves of surrounding rock loose circles at multiple geophone points. By performing shear wave velocity curves at different positions Linear interpolation can obtain the two-dimensional shear wave velocity profile of the geophone survey line. Therefore, this scheme realizes three-dimensional imaging through the observation of different survey lines of environmental noise, and effectively utilizes the typical noise of the tunnel in different periods, such as blasting noise during the construction period and operation period. The traffic noise, the inversion shear wave velocity profile is extracted from the dispersion curve of these noises, and the three-dimensional shear wave velocity model of the tunnel surrounding rock is obtained through the results of the shear wave velocity profile of multiple survey lines.

S104. Obtain the shear wave velocity model of the surrounding rock loosening circle at a preset period to determine the evolution process of the surrounding rock loosening circle.

Exemplarily, at different time points, the repeated inversion method can obtain the shear wave velocity model of the loose circle of the surrounding rock of the tunnel at different times, that is, the long-term evolution process of the loose circle of the surrounding rock of the tunnel can be obtained. The time interval of the analysis here can be selected according to the needs of specific projects. For example, the analysis can be performed in units of days during tunnel excavation, and the analysis can be performed in units of months during the tunnel operation period. Through the inversion of the environmental noise signal, the shear wave velocity of multiple survey lines is obtained, and the range of the loose circle is determined by the magnitude of the shear wave velocity, thereby realizing the detection of the long-term evolution process of the loose circle of the surrounding rock of the tunnel, and realizing the regional large-scale Testing and evaluation of loose rings of tunnel surrounding rock.

Exemplarily, this solution fixes the number of layers in the process of inverting the shear wave velocity according to the characteristics of the loose circle of the surrounding rock of the tunnel, which can effectively speed up the inversion speed and improve the inversion accuracy. Although the definition and classification of the loose circle of surrounding rock in the tunnel are different, they are generally divided into three areas. There are obvious differences in the shear wave velocity in the three areas. This scheme can effectively save calculation by fixing the number of layers in the process of shear wave velocity inversion. resources to speed up imaging.

By virtue of the above-mentioned technical solution, the visualization method for the evolution process of the tunnel surrounding rock loosening circle provided by the present invention, for the current lack of a method that can accurately monitor the evolution process of the tunnel surrounding rock loosening circle for a long time, the present invention acquires the The noise signals of at least two geophones; the shear wave velocity curve of the surrounding rock loosening ring corresponding to the target geophone is determined based on the noise signal of the target geophone, wherein the shear wave velocity curve of the surrounding rock loosening ring is related to the depth change; based on the above at least The shear-wave velocity curves of the surrounding rock loosening circle corresponding to the two geophones determine the shear-wave velocity model of the surrounding rock loosening circle; the above-mentioned shear-wave velocity model of the surrounding rock loosening circle is obtained at a preset period to determine the evolution process of the surrounding rock loosening circle. In the above scheme, the typical noise of tunnels in different periods is effectively used, such as blasting noise in the construction period and traffic noise in the operation period. The results of the velocity profile can obtain the three-dimensional shear wave velocity model of the surrounding rock of the tunnel. Through the inversion of the environmental noise signal, the shear wave velocity of multiple survey lines can be obtained. The range of the loose circle can be determined by the magnitude of the shear wave velocity, and regional large-scale tunneling can be realized. Test and evaluation, and due to the periodic acquisition model, through long-term monitoring of background noise, the signal within the time period can be selectively intercepted for shear wave velocity inversion, and the evolution process of the loose circle at different times can be obtained nearly continuously.

In one embodiment, the above method also includes:

Determine the spatial autocorrelation coefficient based on the noise signal of the target detector and the noise signals of other detectors;

The dispersion curve is determined based on the above-mentioned spatial autocorrelation coefficient, wherein the above-mentioned dispersion curve is used to characterize the dispersion change process of the surface wave propagating along the side wall of the tunnel.

Exemplarily, the spatial autocorrelation coefficient is often used to quantitatively describe the spatial dependence of things, and can be used to measure the spatial distribution characteristics of physical or ecological variables and their impact on the field. The dispersion curve is a curve representing the relationship between the period (or wavelength, frequency) of the dispersed wave and the wave velocity.

In an embodiment, the above-mentioned determination of the dispersion curve based on the above-mentioned spatial autocorrelation coefficient includes:

Determining the first kind of zero-order Bessel function based on the above-mentioned spatial autocorrelation coefficient;

The above-mentioned dispersion curve is determined based on the above-mentioned first-type zero-order Bessel function, wherein the above-mentioned dispersion curve is used to characterize the shear wave velocity of the surrounding rock of the above-mentioned tunnel.

Exemplarily, this method first calculates the spatial autocorrelation coefficient between the target detector and other detector points to fit the first kind of zero-order Bessel function, as follows:

In the above formula, W _a (ω) is the power spectrum of the center point of the geophone observation array, and W _b (ω, r, θ) is the mutual power spectrum between adjacent geophones. ω is the signal frequency, r is the equal spacing between the detectors, and θ is the azimuth. Re is to take the real part of the power spectrum.

Exemplarily, the propagation velocity C(ω) is calculated by the first kind of zero-order Bessel function, so as to pick up the dispersion curve:

In one embodiment, the above method also includes:

Based on the above-mentioned first kind of zero-order Bessel function, the propagation velocity of Rayleigh wave along the tunnel side wall at different frequencies is determined;

The dispersion curve is determined based on the above-mentioned Rayleigh wave propagation velocity along the tunnel side wall at different frequencies.

Exemplarily, the propagation speed C(ω) of the Rayleigh wave along the tunnel side wall at different frequencies is calculated by the first kind of zero-order Bessel function, so as to pick up the propagation speed of the Rayleigh wave along the tunnel side wall at different frequencies Determine the dispersion curve.

In one embodiment, the above-mentioned noise signal based on the target geophone is used to determine the shear wave velocity curve of the surrounding rock loose ring corresponding to the above-mentioned target geophone, including:

Based on the above dispersion curve, the shear wave velocity curve of the surrounding rock loose ring at the target geophone is determined by the least square method and constraint information.

Exemplarily, the least squares method defines the objective function as the dispersion curve J(r,w), which is the observed value G. This method uses the least squares method and constraint information to invert the acoustic velocity structure layer of the loose ring of the surrounding rock of the tunnel to Determine the shear wave velocity curve of the surrounding rock loose ring at the target geophone.

In one embodiment, the above-mentioned determination of the shear-wave velocity curve of the surrounding rock loose ring at the target geophone through the least square method and constraint information based on the above-mentioned dispersion curve includes:

Based on the above dispersion curve and the number of layers of the shear wave velocity model of the surrounding rock, the shear wave velocity curve of the loose circle of the surrounding rock at the target geophone is determined by the least square method and constraint information. layer.

Exemplarily, as shown in Figure 8, this scheme divides the velocity model into three layers (disturbance area, damage area, and damage area), and uses the least square method to invert the acoustic velocity structure layer of the loose ring of surrounding rock in the tunnel. The multiplication defines the objective function as the dispersion curve J(r,w), that is, the observed value G=[G1,G2,G3...Gm], the model parameter X=[X1,X2,X3...Xm], and X takes a 3-layer tunnel For the shear wave velocity model of surrounding rock, the least squares method defines the objective function as the least squares of G and the forward modeling result M of X:

For the above formula, in this method, m is taken as 3, and Taylor expansion is performed on the objective function, and the minimum value is taken after omitting the quadratic and higher order items:

In the above formula, A is the Jacobian matrix of the first-order partial derivatives of the model parameters. Let g= ^AT |G－X ⁽⁰⁾ |, the update amount of the model at each time can be obtained:

ΔX＝(A ^T A) ^-1 g

As shown in Figure 9, where the abscissa is the shear wave velocity in m/s, and the ordinate is the depth in m, the figure shows the curves corresponding to the initial model, real model and inversion results respectively, where, The initial model is the initial model of the inversion, and the real model is the real velocity model. After several iterations, when the model is updated until the objective function is less than the preset threshold, the inversion ends, and the inversion result is obtained, and the shear wave changes with depth The curve, as shown in Figure 10, in the schematic diagram of the shear wave velocity inversion composed of multiple geophones in the loose surrounding rock circle of the tunnel, the abscissa is the horizontal axis, the unit is m, and the ordinate is the depth. The higher the value, the closer to the tunnel. The unit is m, and the color legend on the right represents the shear wave velocity of the surrounding rock loosening circle.

Exemplarily, when using the inversion of the least squares method in this scheme, the velocity model can be divided into three layers (disturbance area, damage area, damage area) according to the characteristics of the tunnel surrounding rock loosening circle. In the same layer, the Approximately the same velocity is used to represent the change of layer thickness and velocity through long-term monitoring and inversion, and the visualization process of the loose circle of the surrounding rock of the tunnel in the entire monitoring site changes over time can be analyzed.

In one embodiment, the above method also includes:

Obtain optical images of the surrounding rock based on the borehole optical camera to determine the RMIBT value;

The above constraint conditions are determined based on the changes of the above RMIBT values at different times, wherein the above constraint conditions are the positive and negative directions of the update amount of the shear wave velocity model of the surrounding rock loose circle.

Exemplarily, the inversion of the least squares method requires a lot of prior information, and is very sensitive to the initial model and parameter settings of the inversion. limit, so that the inversion results can be solved in the correct direction.

Exemplarily, a new deep rock mass integrity evaluation method—RMIBT method (Rock Mass Integrity Index Based on High-definition Digital Borehole Televiewer) is proposed based on the test results of high-definition digital borehole cameras. The RMIBT value is obtained by the weight ratio of the length of the fractured rock mass, which is suitable for deep rock mass integrity evaluation under the condition of deep core cake, structural plane and different directions of the drill hole, and high stress core fracture conditions. , and applied to many deep projects to prove its effectiveness. RMIBT can dynamically evaluate the integrity of macroscopic rock mass and the evolution of cracks in the loose range, providing a basis for engineering rock mass evaluation and support.

Exemplarily, this method observes the optical image of the borehole through a small amount of optical observation in the tunnel, and determines the weight proportion of the length of the complete rock mass in the wall of the borehole through the RMIBT value. The calculation formula is:

The above formula: L is the total length of the evaluation section, and the unit is m; l1, l2, l3, l4, and l5 are the lengths of 0.1-0.3m, 0.3-0.5m, 0.5-0.75m, 0.75m, and 0.75m respectively. For interval lengths from m to 1m and greater than 1m, the division of li is based on the joint spacing classification standard recommended by the International Society of Rock Mechanics and the division of domestic and foreign engineering practices and related codes; ai is the coefficient of the i-th interval, where a1 = 0.19, a2=0.41, a3=0.63, a4=0.77, a5=1; lij is the length of the rock mass in the i-th interval; ni is the number of rock mass segments in the i-th interval.

Exemplarily, for the convenience of understanding, FIG. 11 shows a schematic diagram of RMIBT constrained inversion of a small number of borehole optical observation calculations provided by the embodiment of the present invention. The RMIBT of this section can be calculated as follows:

RMIBT=(0.19×l1+0.41×l2+0.63×l3+0.77×l4+l5)/L=(0.19×0.7+0.41×1.07+0.77×0.91)/3=0.45

Exemplarily, this solution observes the change of the RMIBT value of the borehole optical image at different times, and constrains the update amount ΔX direction of the shear wave velocity model at the position at this moment. Constraint is a negative value, on the contrary, when the RMIBT value becomes larger, the mandatory constraint of ΔX is a positive value. In this way, a small amount of borehole information can be used to effectively limit the shear wave velocity inversion in a large field area, and improve the stability and accuracy of the inversion.

Exemplarily, this solution constrains the shear-wave velocity inversion process through the method of light-acoustic combination, which can greatly improve the multiple solutions of the shear-wave velocity inversion and improve the stability of the inversion. This program introduces a very small number of boreholes, carries out borehole optical observation, and constrains the direction of the shear wave velocity model update gradient through the optical information of the borehole wall and rock wall, which can avoid the wrong update of the shear wave velocity parameters during the inversion process, and avoid the inverse The inversion falls into the local optimal solution, making the inversion as close as possible to the global optimal solution, which enhances the stability of the inversion.

Furthermore, the key step of this scheme is to extract the dispersion curve from the collected noise surface wave data. According to the layout of the inner geophone of the tunnel, this scheme can use the spatial autocorrelation method to extract the data of the noise signal, and in a small number of drilling Under the constraints of borehole optical observation borehole, the inversion of the shear wave velocity of the loose ring of the surrounding rock of the tunnel is carried out. Specifically, it can be achieved through the following steps:

Step 1: Collect the noise signal during the tunnel construction and operation period through the geophone. The noise source signal can be the blasting and construction noise of the further construction of the tunnel, or the vehicle noise inside the tunnel during the operation period.

Step 2: Segment the collected data evenly according to time-frequency, distinguish the parts with high noise as much as possible, remove the parts with low signal-to-noise ratio in the collected data segments, and process them with narrowband bandpass filters with different center frequencies Each data is short, and the part with high signal-to-noise ratio is substituted into the later signal processing.

Step 3: Calculate the spatial autocorrelation coefficient between the center point of the geophone and other geophone points to fit the first kind of zero-order Bessel function as follows:

In the above formula, W _a (ω) is the power spectrum of the center point of the detector observation array and W _b (ω,r,θ) is the cross power spectrum between adjacent detectors. ω is the signal frequency, r is the equal spacing between the detectors, and θ is the azimuth. Re is to take the real part of the power spectrum.

Step 4: Calculate the Rayleigh wave propagation velocity C(ω) along the tunnel side wall at different frequencies by using the first kind of zero-order Bessel function, so as to pick up the dispersion curve:

Step 5: Invert the acoustic velocity structure layer of the loose circle of surrounding rock in the tunnel by the least square method. The least square method defines the objective function as the dispersion curve J(r,w), which is the observed value G, G=[G1,G2, G3...Gm] and the least squares of the model parameters (the shear wave velocity model of the surrounding rock of the 3-layer tunnel) X=[X1, X2, X3...Xm] forward modeling result M is:

Perform Taylor expansion on the objective function, taking the minimum value after omitting quadratic and higher order terms:

In the above formula, A is the Jacobian matrix of the first-order partial derivatives of the model parameters. Let g= ^AT |G－X ⁽⁰⁾ |, the update amount of the model at each time can be obtained:

ΔX＝(A ^T A) ^-1 g

After several iterations, the inversion ends when the model is updated until the objective function is smaller than the preset threshold, and the curve of shear wave changing with depth is obtained.

It is necessary to limit the initial model of the inversion through a small number of optical observation boreholes in the tunnel, so that the inversion results can be solved in the correct direction:

A small amount of optical observation of the optical image of the borehole in the tunnel, and the weight proportion of the length of the complete rock mass in the borehole wall is determined by the RMIBT value. The calculation formula is:

In the formula: L is the total length of the evaluation section, in m; l1, l2, l3, l4, and l5 are the lengths of 0.1-0.3m, 0.3-0.5m, 0.5-0.75m, 0.75 For interval lengths from m to 1m and greater than 1m, the division of li is based on the joint spacing classification standard recommended by the International Society of Rock Mechanics and the division of domestic and foreign engineering practices and related codes; ai is the coefficient of the i-th interval, where a1 = 0.19, a2=0.41, a3=0.63, a4=0.77, a5=1; lij is the length of the rock mass in the i-th interval; ni is the number of rock mass segments in the i-th interval.

By observing the changes in the initial RMIBT value of the borehole optical image statistics at different times, the direction of the update amount ΔX of the shear wave velocity model at this time is constrained. If the RMIBT becomes smaller, the mandatory constraint of ΔX is a negative value. Otherwise, the RMIBT value becomes When is large, the mandatory constraint of ΔX is a positive value.

Step 6: Use the same shear wave velocity inversion method for the center points of different geophones, and repeat steps 1 to 5 to obtain shear wave velocity curves of loose surrounding rock circles at multiple geophone points. By analyzing the shear wave velocity curves at different positions The two-dimensional shear wave velocity profile of the geophone survey line can be obtained by performing linear interpolation. By analyzing different survey lines and repeating steps 1 to 6, a three-dimensional shear wave velocity model of the loose circle of the tunnel surrounding rock at the current moment can be obtained.

Step 7: At different time points, repeat steps 1 to 6 to obtain the shear wave velocity model of the tunnel surrounding rock loosening circle at different times, that is, the long-term evolution process of the tunnel surrounding rock loosening circle can be obtained. The time interval of the analysis here can be selected according to the needs of specific projects. For example, the analysis can be performed in units of days during tunnel excavation, and the analysis can be performed in units of months during the tunnel operation period. Therefore, according to the characteristics of the tunnel surrounding rock loose circle, the velocity model is divided into three layers as a whole, and the same velocity can be approximately used to represent the same layer, and the layer thickness and velocity changes can be obtained through long-term monitoring and inversion. The visualization process of the loosening circle of the surrounding rock of the tunnel in the entire monitoring site changes over time.

With the above technical solution, the visualization method for the evolution process of the tunnel surrounding rock loose circle provided by the present invention has the following advantages compared with the prior art:

(1) The regional large-scale test and evaluation of the loosening circle of the surrounding rock of the tunnel is realized. The existing technology is to use a single borehole to conduct optical imaging to observe the verification of the hole wall rock, or to test the longitudinal wave velocity of a single borehole to realize the surrounding rock. For the evaluation of rock loose circles, such a test method has the problem of one-hole view, and it is impossible to judge the range of surrounding rock loose circles for the area outside the test borehole. This method obtains the shear wave velocity of multiple measuring lines through the inversion of the environmental noise signal, and determines the range of the loose circle through the magnitude of the shear wave velocity, which can realize regional large-scale testing and evaluation.

(2) The detection of the long-term evolution process of the loose circle of the surrounding rock of the tunnel is realized. In order to realize the test and analysis of the long-term evolution process in the existing technology, it must be detected at intervals, and the collected data is discrete and time-consuming. This method monitors the background noise for a long time, and selectively intercepts the signal within the time period to invert the shear wave velocity, and can obtain the nearly continuous evolution process of the loose circle at different times.

(3) Three-dimensional imaging can be realized by observing different survey lines of environmental noise. This method effectively utilizes the typical noise in different periods of the tunnel, such as blasting noise in the construction period and traffic noise in the operation period, and can be extracted through the dispersion curve of these noises The shear wave velocity profile is inverted, and the three-dimensional shear wave velocity model of the tunnel surrounding rock can be obtained through the results of the shear wave velocity profile of multiple survey lines.

(4) Constraining the shear-wave velocity inversion process by combining light-acoustic method can greatly improve the multiple solutions of shear-wave velocity inversion and improve the stability of the inversion. This method introduces a very small number of boreholes, carries out borehole optical observation, and constrains the direction of the shear wave velocity model update gradient through the optical information of the borehole wall and rock wall, which can avoid the wrong update of the shear wave velocity parameters during the inversion process, and avoid the inversion Falling into a local optimal solution makes the inversion as close as possible to the global optimal solution and enhances the stability of the inversion.

(5) According to the characteristics of the loose circle of the surrounding rock of the tunnel, the number of layers in the process of inversion of the shear wave velocity is fixed, which can effectively speed up the inversion speed and improve the inversion accuracy. Although the definitions and classifications of loose circles of surrounding rock in tunnels are different, they are generally divided into three areas. There are obvious differences in shear wave velocity in the three areas. This method can effectively save calculation by fixing the number of layers in the process of shear wave velocity inversion. resources to speed up imaging.

Further, as an implementation of the above method shown in FIG. 1 , an embodiment of the present invention also provides a visualization device for the evolution process of the tunnel surrounding rock loose circle, which is used to implement the above method shown in FIG. 1 . This device embodiment corresponds to the foregoing method embodiment. For the convenience of reading, this device embodiment does not repeat the details in the foregoing method embodiment one by one, but it should be clear that the device in this embodiment can correspond to the foregoing method implementation. Everything in the example. As shown in Figure 12, the device includes: an acquisition unit 21, a first determination unit 22, a second determination unit 23, and a third determination unit 24, wherein

An acquisition unit 21, configured to acquire noise signals of at least two geophones in the surrounding rock;

The first determination unit 22 is configured to determine the shear wave velocity curve of the surrounding rock loosening ring corresponding to the target geophone based on the noise signal of the target geophone, wherein the shear wave velocity curve of the surrounding rock loosening ring is related to the depth change;

The second determination unit 23 is used to determine the shear wave velocity model of the surrounding rock loosening circle based on the shear wave velocity curves of the surrounding rock loosening circle corresponding to the at least two geophones;

The third determining unit 24 is configured to acquire the shear wave velocity model of the surrounding rock loosening circle at a preset period to determine the evolution process of the surrounding rock loosening circle.

Exemplarily, the above units are also used for:

Determine the spatial autocorrelation coefficient based on the noise signal of the target detector and the noise signals of other detectors;

The dispersion curve is determined based on the above-mentioned spatial autocorrelation coefficient, wherein the above-mentioned dispersion curve is used to characterize the dispersion change process of the surface wave propagating along the side wall of the tunnel.

Exemplarily, the above-mentioned determination of the dispersion curve based on the above-mentioned spatial autocorrelation coefficient includes:

Determining the first kind of zero-order Bessel function based on the above-mentioned spatial autocorrelation coefficient;

The above-mentioned dispersion curve is determined based on the above-mentioned first-type zero-order Bessel function, wherein the above-mentioned dispersion curve is used to characterize the shear wave velocity of the surrounding rock of the above-mentioned tunnel.

Exemplarily, the above units are also used for:

Based on the above-mentioned first kind of zero-order Bessel function, the propagation velocity of Rayleigh wave along the tunnel side wall at different frequencies is determined;

The dispersion curve is determined based on the above-mentioned Rayleigh wave propagation velocity along the tunnel side wall at different frequencies.

Exemplarily, the determination of the shear wave velocity curve of the surrounding rock loose ring corresponding to the target geophone based on the noise signal of the above target geophone includes:

Based on the above dispersion curve, the shear wave velocity curve of the surrounding rock loose ring at the target geophone is determined by the least square method and constraint information.

Exemplarily, the above-mentioned determination of the shear-wave velocity curve of the surrounding rock loose ring at the target geophone through the least square method and constraint information based on the above-mentioned dispersion curve includes:

Based on the above dispersion curve and the number of layers of the shear wave velocity model of the surrounding rock, the shear wave velocity curve of the loose circle of the surrounding rock at the target geophone is determined by the least square method and constraint information. layer.

Exemplarily, the above units are also used for:

Obtain optical images of the surrounding rock based on the borehole optical camera to determine the RMIBT value;

The above constraint conditions are determined based on the changes of the above RMIBT values at different times, wherein the above constraint conditions are the positive and negative directions of the update amount of the shear wave velocity model of the surrounding rock loose circle.

By virtue of the above technical solution, the visualization device for the evolution process of the tunnel surrounding rock loosening circle provided by the present invention solves the problem that there is currently a lack of a method that can accurately monitor the evolution process of the tunnel surrounding rock loosening circle for a long time. The noise signals of at least two geophones; the shear wave velocity curve of the surrounding rock loosening ring corresponding to the target geophone is determined based on the noise signal of the target geophone, wherein the shear wave velocity curve of the surrounding rock loosening ring is related to the depth change; based on the above at least The shear wave velocity curves of the surrounding rock loosening circle corresponding to the two geophones determine the shear wave velocity model of the surrounding rock loosening circle; the above shear wave velocity model of the surrounding rock loosening circle is obtained at a preset period to determine the evolution process of the surrounding rock loosening circle. In the above scheme, the typical noise of tunnels in different periods is effectively used, such as blasting noise in the construction period and traffic noise in the operation period. The results of the velocity profile can obtain the three-dimensional shear wave velocity model of the surrounding rock of the tunnel. Through the inversion of the environmental noise signal, the shear wave velocity of multiple survey lines can be obtained. The range of the loose circle can be determined by the magnitude of the shear wave velocity, and regional large-scale tunneling can be realized. Test and evaluation, and due to the periodic acquisition model, through long-term monitoring of background noise, the signal within the time period can be selectively intercepted for shear wave velocity inversion, and the evolution process of the loose circle at different times can be obtained nearly continuously.

The processor includes a kernel, and the kernel fetches corresponding program units from the memory. One or more kernels can be set, and a visualization method for the evolution process of the tunnel surrounding rock loosening circle can be realized by adjusting the kernel parameters, which can solve the current problem of lacking a long-term and accurate method for monitoring the evolution process of the tunnel surrounding rock loosening circle evolution process.

An embodiment of the present invention provides a computer-readable storage medium. The above-mentioned computer-readable storage medium includes a stored program. When the program is executed by a processor, the above-mentioned method for visualizing the evolution process of the tunnel surrounding rock loose circle is realized.

An embodiment of the present invention provides a processor, and the processor is used to run a program, wherein, when the program is running, the above method for visualizing the evolution process of the tunnel surrounding rock loose circle is executed.

An embodiment of the present invention provides an electronic device, the electronic device includes at least one processor, and at least one memory connected to the processor; wherein, the processor is used to call the program instructions in the memory to execute the above-mentioned tunnel Visualization method for the evolution process of surrounding rock loose circle

An embodiment of the present invention provides an electronic device 30. As shown in FIG. 13, the electronic device includes at least one processor 301, at least one memory 302 connected to the processor, and a bus 303; The bus 303 completes mutual communication; the processor 301 is used to call the program instructions in the memory to execute the above-mentioned method for visualizing the evolution process of the tunnel surrounding rock loose circle.

The intelligent electronic device in this article may be a PC, a PAD, a mobile phone, and the like.

The present application also provides a computer program product adapted to execute a program initialized with the following method steps when executed on a process management electronic device:

obtaining noise signals of at least two geophones in the surrounding rock;

Determining the shear wave velocity curve of the surrounding rock loosening ring corresponding to the target geophone based on the noise signal of the target geophone, wherein the shear wave velocity curve of the surrounding rock loosening ring is related to the depth change;

Determining the shear wave velocity model of the surrounding rock loosening circle based on the shear wave velocity curves of the surrounding rock loosening circle corresponding to the at least two geophones;

The shear wave velocity model of the surrounding rock loosening circle is acquired at a preset period to determine the evolution process of the surrounding rock loosening circle.

Further, the above method also includes:

Determine the spatial autocorrelation coefficient based on the noise signal of the target detector and the noise signals of other detectors;

The dispersion curve is determined based on the above-mentioned spatial autocorrelation coefficient, wherein the above-mentioned dispersion curve is used to characterize the dispersion change process of the surface wave propagating along the side wall of the tunnel.

Further, the above-mentioned determination of the dispersion curve based on the above-mentioned spatial autocorrelation coefficient includes:

Determining the first kind of zero-order Bessel function based on the above-mentioned spatial autocorrelation coefficient;

The above-mentioned dispersion curve is determined based on the above-mentioned first-type zero-order Bessel function, wherein the above-mentioned dispersion curve is used to characterize the shear wave velocity of the surrounding rock of the above-mentioned tunnel.

Further, the above method also includes:

Based on the above-mentioned first kind of zero-order Bessel function, the propagation velocity of Rayleigh wave along the tunnel side wall at different frequencies is determined;

The dispersion curve is determined based on the above-mentioned Rayleigh wave propagation velocity along the tunnel side wall at different frequencies.

Further, the above-mentioned noise signal based on the target geophone determines the shear-wave velocity curve of the surrounding rock loosening ring corresponding to the above-mentioned target geophone, including:

Based on the above dispersion curve, the shear wave velocity curve of the surrounding rock loose ring at the target geophone is determined by the least square method and constraint information.

Further, the above-mentioned determination of the shear-wave velocity curve of the surrounding rock loose ring at the target geophone by the least square method and constraint information based on the above-mentioned dispersion curve includes:

Based on the above dispersion curve and the number of layers of the shear wave velocity model of the surrounding rock, the shear wave velocity curve of the loose circle of the surrounding rock at the target geophone is determined by the least square method and constraint information. layer.

Further, the above method also includes:

Obtain optical images of the surrounding rock based on the borehole optical camera to determine the RMIBT value;

The above constraint conditions are determined based on the changes of the above RMIBT values at different times, wherein the above constraint conditions are the positive and negative directions of the update amount of the shear wave velocity model of the surrounding rock loose circle.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, electronic devices (systems), and computer program products according to embodiments of the present application. It should be understood that each procedure and/or block in the flowchart and/or block diagram, and a combination of procedures and/or blocks in the flowchart and/or block diagram can be realized by computer program instructions. These computer program instructions may be provided to a general purpose computer, special purpose computer, embedded processor, or processor of other programmable process management electronic equipment to produce a machine such that instructions executed by the processor of the computer or other programmable process management electronic equipment Produce means for realizing the functions specified in one or more procedures of the flowchart and/or one or more blocks of the block diagram.

In a typical configuration, an electronic device includes one or more processors (CPUs), memory and a bus. Electronic devices may also include input/output interfaces, network interfaces, and the like.

Memory may include non-permanent memory in computer-readable media, random access memory (RAM) and/or non-volatile memory, such as read-only memory (ROM) or flash memory (flash RAM), memory includes at least one memory chip. The memory is an example of a computer readable medium.

Computer-readable media, including both permanent and non-permanent, removable and non-removable media, can be implemented by any method or technology for storage of information. Information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer-readable storage media for a computer include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), Read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other Optical storage, magnetic cassette tape, magnetic tape magnetic disk storage or other magnetic storage electronic device or any other non-transmission medium that can be used to store information that can be accessed by computing electronic devices. As defined herein, computer-readable media excludes transitory computer-readable media, such as modulated data signals and carrier waves.

It should also be noted that the term "comprises", "comprises" or any other variation thereof is intended to cover a non-exclusive inclusion such that a process, method, article, or electronic device comprising a set of elements includes not only those elements, but also Including other elements not expressly listed, or also including elements inherent in such process, method, article or electronic equipment. Without further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article or electronic device comprising the element.

Those skilled in the art should understand that the embodiments of the present application may be provided as methods, systems or computer program products. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein. .

The above are only examples of the present application, and are not intended to limit the present application. For those skilled in the art, various modifications and changes may occur in this application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims (10)

1. A visualization method for the evolution process of the tunnel surrounding rock loose circle, characterized in that it comprises: obtaining noise signals of at least two geophones in the surrounding rock; Determining the shear wave velocity curve of the surrounding rock loose circle corresponding to the target geophone based on the noise signal of the target geophone, wherein the shear wave velocity curve of the surrounding rock loose circle is related to the depth change; determining the shear wave velocity model of the surrounding rock loosening circle based on the shear wave velocity curves of the surrounding rock loosening circle corresponding to the at least two geophones; The shear wave velocity model of the surrounding rock loosening circle is acquired at a preset period to determine the evolution process of the surrounding rock loosening circle. 2. The method according to claim 1, further comprising: Determine the spatial autocorrelation coefficient based on the noise signal of the target detector and the noise signals of other detectors; A dispersion curve is determined based on the spatial autocorrelation coefficient, wherein the dispersion curve is used to characterize a dispersion variation process of surface waves propagating along a tunnel side wall. 3. The method according to claim 2, wherein the determining the dispersion curve based on the spatial autocorrelation coefficient comprises: determining a first-type zero-order Bessel function based on the spatial autocorrelation coefficient; The dispersion curve is determined based on the first zero-order Bessel function, wherein the dispersion curve is used to characterize the shear wave velocity of the surrounding rock of the tunnel. 4. The method according to claim 3, further comprising: Determining the propagation velocity of the Rayleigh wave along the side wall of the tunnel at different frequencies based on the first type of zero-order Bessel function; A dispersion curve is determined based on the propagation velocity of the Rayleigh wave along the side wall of the tunnel at different frequencies. 5. The method according to claim 4, wherein the determination of the corresponding shear wave velocity curve of the surrounding rock loose circle at the target geophone based on the noise signal of the target geophone comprises: Based on the dispersion curve, the shear wave velocity curve of the surrounding rock loose ring at the target geophone is determined through the least square method and constraint information. 6. The method according to claim 5, wherein said determination of the surrounding rock loose ring shear wave velocity curve at the target geophone place by least squares and constraint information based on said dispersion curve comprises: Based on the dispersion curve and the number of layers of the shear wave velocity model of the surrounding rock, the shear wave velocity curve of the loose circle of the surrounding rock at the target geophone is determined by the least square method and constraint information, wherein the number of layers of the shear wave velocity model is based on the layered characteristics of the loose circle Take three layers. 7. The method according to claim 6, further comprising: Obtain optical images of the surrounding rock based on the borehole optical camera to determine the RMIBT value; The constraint condition is determined based on the change of the RMIBT value at different times, wherein the constraint condition is the positive and negative directions of the update amount of the shear wave velocity model of the surrounding rock loose circle. 8. A visualization device for the evolution process of the tunnel surrounding rock loose circle, characterized in that, an acquisition unit, configured to acquire noise signals of at least two geophones in the surrounding rock; The first determining unit is configured to determine the shear wave velocity curve of the surrounding rock loose circle corresponding to the target geophone based on the noise signal of the target geophone, wherein the shear wave velocity curve of the surrounding rock loose circle is related to the depth change; The second determination unit is configured to determine the shear wave velocity model of the surrounding rock loosening circle based on the shear wave velocity curves of the surrounding rock loosening circle corresponding to the at least two geophones; The third determining unit is configured to acquire the shear wave velocity model of the surrounding rock loosening circle at a preset period to determine the evolution process of the surrounding rock loosening circle. 9. A computer-readable storage medium, characterized in that the computer-readable storage medium includes a stored program, wherein, when the program is executed by a processor, any one of claims 1 to 7 is implemented. The steps of visualizing the evolution process of the tunnel surrounding rock loose circle. 10. An electronic device, characterized in that the electronic device comprises at least one processor and at least one memory connected to the processor; wherein the processor is used to call program instructions in the memory to execute The step of visualizing the evolution process of the tunnel surrounding rock loose circle as described in any one of claims 1 to 7.

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