Method for monitoring dynamic change of underground structure of target area by using background noise

Abstract

A method for monitoring the dynamic change of an underground structure of a target area by using background noise comprises the following steps: deploying a plurality of seismic stations in the target area to form an array to collect background noise signals within a period of time, then circularly selecting sub-arrays, performing spatial autocorrelation analysis on the seismic stations in the selected sub-arrays by using the background noise signals, and determining the dynamic change of the underground structure of the target area. And calculating the underground velocity structure of the position of each selected sub-array, and obtaining the underground three-dimensional velocity structure of the target area in a period of time by using a spatial interpolation method. The underground three-dimensional velocity structures of the target area in different time periods are compared and observed, so that the expansion of the mineral resource goaf in the area, the underground water level change of water pumping and injection operation and the like can be monitored, and the change of the underground structure in the areas of reservoir storage, mine development, shale gas exploitation and the like can be known; therefore, the dynamic change of the underground structure in the target area is monitored. Therefore, the dynamic change of the underground structure can be monitored, and the application range is wide.

Description

Technical Field

[0001] The invention relates to a method for monitoring underground structures, belongs to the field of underground structure exploration, and in particular to a method for monitoring dynamic changes of underground structures in a target area by using background noise. Background Art

[0002] The technology of exploring underground structural changes has important practical significance for understanding the internal structure of the earth, evaluating resource distribution, preventing and mitigating natural disasters, and carrying out engineering construction. For example, by real-time monitoring of underground structural changes and dynamic responses, people can warn of earthquake risks and geological disasters in advance, reducing casualties and property losses. However, due to the influence of observation methods, existing methods for monitoring underground structures mostly obtain static results and cannot be used to monitor dynamic changes in underground structures.

[0003] A Chinese invention patent with application number CN202011615586.4 and application date December 31, 2020 discloses a method for underground structure health monitoring based on thermal expansion coefficient and load strain. The method includes arranging temperature and strain sensors on the inner surface of the underground structure; collecting temperature and strain monitoring data of the underground structure; calculating load strain and thermal expansion coefficient; solving the normalized thermal expansion coefficient and establishing four thermal expansion coefficient Euclidean distance matrices; judging the damage to the underground structure based on the changes in the Euclidean distance matrix of the first and last two evaluation cycles according to the load strain. Although the method of the invention can eliminate the influence of sensor errors to a greater extent, it will be more sensitive and accurate in identifying structural damage, and can make full use of the strain and temperature sensor data commonly used in existing monitoring without adding extra data acquisition costs, it still has the following defects:

[0004] This design cannot be used to monitor dynamic changes in underground structures.

[0005] The information disclosed in this background technology section is only intended to increase the understanding of the overall background of the application, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art already known to ordinary technicians in this field. Summary of the invention

[0006] The purpose of the present invention is to overcome the defect that the existing method for monitoring underground structures cannot monitor the dynamic changes of underground structures, and to provide a method for monitoring the dynamic changes of underground structures in a target area by using background noise.

[0007] To achieve the above objectives, the technical solution of the present invention is:

[0008] A method for monitoring dynamic changes of underground structures in a target area using background noise, the method comprising the following steps:

[0009] Step 1: deploy m seismic stations in the target area, and collect background noise signals through m seismic stations in time periods a and b respectively;

[0010] Step 2: Select a seismic station as the central station, select n adjacent seismic stations around the central station to form a sub-array, n ≥ 2, all seismic stations in the sub-array are paired with the central station one by one to obtain n pairs of seismic stations, and then use the spatial autocorrelation method to analyze the background noise signals collected by the n pairs of seismic stations in the time period a, calculate the spatial autocorrelation function of the n pairs of seismic stations, and then extract the spatial autocorrelation coefficient of the sub-array;

[0011] Step 3: Generate a dispersion curve using the extracted spatial autocorrelation coefficient, and obtain the underground velocity structure at the location of the sub-array through the dispersion curve;

[0012] Step 4: Repeat the above steps 2 and 3 to ensure that the m seismic stations in the target area are analyzed once and only once as the central station to obtain the underground velocity structure of the m sub-arrays, and then regard the m sub-arrays as m measuring points, perform lateral spatial interpolation on the underground velocity structure of the m measuring points, and the measuring points in the same horizontal direction form a measuring line, thereby obtaining multiple two-dimensional profiles of the underground velocity structure of multiple measuring lines, and then perform longitudinal spatial interpolation on the multiple two-dimensional profiles to obtain the three-dimensional velocity structure below the target area in time period a;

[0013] Step 5: Process the background noise signals collected by the m seismic stations in the time period b according to the second, third and fourth steps above, so as to obtain the three-dimensional velocity structure below the target area in the time period b, and then directly compare and observe whether there are any changes in the low-speed area or high-speed area in the three-dimensional velocity structure below the target area in the time period a and the time period b, so as to monitor the dynamic changes of the underground structure of the target area.

[0014] In the second step, the specific steps of extracting the sub-array spatial autocorrelation coefficient include:

[0015]

[0016] Formula (1) is the spectrum polar coordinates of the central station, and formula (2) is the spectrum polar coordinates of the seismic station paired with the central station in the sub-array. ω is the angular frequency, k is the wave number, φ is the incident angle of the micro-motion signal, and ζ(ω,φ) is an orthogonal random process. Then the spatial autocorrelation function of the pair of seismic stations can be obtained:

[0017]

[0018] (3) In the formula, g(ω,r,θ) is the spatial covariance function, h(ω,φ) is the frequency-azimuth spectral density, and taking the azimuth average of the spatial covariance function g(ω,r,θ) we can obtain:

[0019]

[0020] (4) where J0 is the first kind of zero-order Bessel function, and ρ(ω,r) is defined as the spatial autocorrelation coefficient of the angular frequency ω:

[0021]

[0022] Substituting k = ω / c(ω), ω = 2πf, c(ω) is the Rayleigh wave phase velocity, into equation (5), we can obtain the spatial autocorrelation coefficient of the sub-array:

[0023]

[0024] In the third step, the dispersion curve obtained is a Rayleigh wave phase velocity dispersion curve, and the specific steps of obtaining the Rayleigh wave phase velocity dispersion curve include:

[0025] After Fourier transformation, in the frequency domain, the spatial autocorrelation coefficient of the central station and the seismic station paired with the central station is expressed as:

[0026]

[0027] (7) In the formula, Re(·) represents the real part, and * represents the complex conjugate. Substituting formula (7) into formula (6) can obtain the Rayleigh wave phase velocity c(ω). The above steps are performed on the n pairs of seismic stations in the sub-array to obtain the Rayleigh wave phase velocities of the n pairs of seismic stations. The Rayleigh wave phase velocities of the n pairs of seismic stations are then associated with the corresponding frequencies to obtain the Rayleigh wave phase velocity dispersion curve of the sub-array.

[0028] In the third step and the fourth step, the underground velocity structure is the apparent shear wave velocity structure or the shear wave velocity structure of the underground medium.

[0029] In the third step, the underground velocity structure at the location of the sub-array is obtained through the dispersion curve by inverting the dispersion curve or calculating it using a half-wavelength empirical formula.

[0030] In the first step, the time period a and the time period b have the same duration, and the time period a and the time period b are independent of each other.

[0031] The duration of the time period a and the time period b is between 1 hour and one month.

[0032] In the first step, the value of m is greater than 40.

[0033] In the second step, the value range of n is 3-8.

[0034] In the first step, the m seismic stations form a square matrix. In the second step, the central station is selected by cyclic traversal, selecting in order of rows and columns, to ensure that each seismic station becomes a central station once and only once.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] 1. In a method for monitoring the dynamic changes of underground structures in a target area by using background noise, m seismic stations are deployed in the target area, and background noise signals are collected by the m seismic stations in time periods a and b respectively; one seismic station is selected as a central station, and n adjacent seismic stations are selected around the central station to form a sub-array, and then the background noise signals collected by the sub-array are analyzed by a spatial autocorrelation method, and the spatial autocorrelation coefficient of the sub-array is extracted, and a dispersion curve is generated by using the spatial autocorrelation coefficient, and the underground velocity structure at the location of the sub-array is obtained by the dispersion curve; the m seismic stations in the target area are analyzed once and only once as the central station to obtain the underground velocity structure at the location of the m sub-arrays, and then the three-dimensional velocity structure under the target area in time period a is obtained by a spatial interpolation method; the background noise signals collected by the m seismic stations in time period b are processed by the above steps to obtain the three-dimensional velocity structure under the target area in time period b, and then the low-speed area or high-speed area in the three-dimensional velocity structure under the target area in time periods a and b is directly compared and observed to monitor the dynamic changes of the underground structure in the target area. When applied, the changes in the low-speed area or high-speed area in the three-dimensional velocity structure can reflect the changes in the underground structure. For example, the changes in the low-speed area can reflect the movement of groundwater, oil and gas, or the excavation of tunnels and mines to a certain position. Therefore, by comparing and observing whether the three-dimensional velocity structure under the target area in time period a and time period b has changed, it can be known whether the underground structure of the target area has changed. Therefore, the present invention can monitor the dynamic changes of the underground structure.

[0037] 2. In a method of using background noise to monitor the dynamic changes of underground structures in a target area, each sub-array is used as a measuring point, and the sub-arrays in the same horizontal direction are connected into a measuring line. Multiple two-dimensional profiles of underground velocity structures of multiple measuring lines are obtained by horizontal spatial interpolation, and then these two-dimensional profiles are subjected to longitudinal spatial interpolation to obtain the three-dimensional velocity structure of the underground of the target area. When applied, by comparing and observing the three-dimensional velocity structure of the underground of the target area in different time periods, the changes in the low-speed area or high-speed area in the three-dimensional velocity structure can reflect the expansion of the mining area in the target area, the changes in the groundwater level of the pumping and injection operations, etc., and can also be used to understand the dynamic changes of underground structures in areas such as reservoir water storage, mine development, and shale gas mining, and detect the progress of water infiltration and mine mining, thereby serving safety production and disaster prevention and mitigation. Therefore, the present invention can not only dynamically monitor the dynamic changes of underground structures, but also has a wide range of applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 This is a flow chart of using the spatial autocorrelation method in Example 1 of the present invention to process the background noise signal to obtain the underground velocity structure of the sub-array.

[0039] Figure 2 yes Figure 1 Schematic diagram of step a in FIG.

[0040] Figure 3 yes Figure 1 Schematic diagram of step b in FIG.

[0041] Figure 4 yes Figure 1 Schematic diagram of step c in FIG.

[0042] Figure 5 yes Figure 1 Schematic diagram of step d in FIG.

[0043] Figure 6 yes Figure 1 Schematic diagram of step e in FIG.

[0044] Figure 7 yes Figure 1 An enlarged schematic diagram of step f in FIG.

[0045] Figure 8 It is a schematic diagram of the neutron array according to Example 1 of the present invention.

[0046] Fig. 9 It is a schematic diagram of the spatial autocorrelation coefficient of the subarray in Example 1 of the present invention.

[0047] Fig.10 It is a two-dimensional cross-sectional diagram of the underground velocity structure of the survey line obtained after performing lateral spatial interpolation on the survey points in Example 1 of the present invention.

[0048] Fig.11 It is a two-dimensional cross-sectional diagram of the underground velocity structure of multiple measuring lines in Example 1 of the present invention.

[0049] Fig.12 It is a three-dimensional velocity structure diagram obtained by performing longitudinal spatial interpolation on the two-dimensional profiles of the underground velocity structures of multiple survey lines in Example 1 of the present invention.

[0050] Fig.13 It is a schematic diagram of the sub-array selected in Example 2 of the present invention.

[0051] Fig.14 It is a schematic diagram of the sub-array selected in Example 2 of the present invention.

[0052] Fig.15 It is a schematic diagram of the sub-array selected in Example 2 of the present invention. DETAILED DESCRIPTION

[0053] The present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0054] See also Figure 1 — Fig.15 , a method for monitoring the dynamic changes of underground structures in a target area using background noise, the method comprising the following steps:

[0055] Step 1: deploy m seismic stations in the target area, and collect background noise signals through m seismic stations in time periods a and b respectively;

[0056] Step 2: Select a seismic station as the central station, select n adjacent seismic stations around the central station to form a sub-array, n ≥ 2, all seismic stations in the sub-array are paired with the central station one by one to obtain n pairs of seismic stations, and then use the spatial autocorrelation method to analyze the background noise signals collected by the n pairs of seismic stations in the time period a, calculate the spatial autocorrelation function of the n pairs of seismic stations, and then extract the spatial autocorrelation coefficient of the sub-array;

[0057] Step 3: Generate a dispersion curve using the extracted spatial autocorrelation coefficient, and obtain the underground velocity structure at the location of the sub-array through the dispersion curve;

[0058] Step 4: Repeat the above steps 2 and 3 to ensure that the m seismic stations in the target area are analyzed once and only once as the central station to obtain the underground velocity structure of the m sub-arrays, and then regard the m sub-arrays as m measuring points, perform lateral spatial interpolation on the underground velocity structure of the m measuring points, and the measuring points in the same horizontal direction form a measuring line, thereby obtaining multiple two-dimensional profiles of the underground velocity structure of multiple measuring lines, and then perform longitudinal spatial interpolation on the multiple two-dimensional profiles to obtain the three-dimensional velocity structure below the target area in time period a;

[0059] Step 5: Process the background noise signals collected by the m seismic stations in the time period b according to the second, third and fourth steps above, so as to obtain the three-dimensional velocity structure below the target area in the time period b, and then directly compare and observe whether there are any changes in the low-speed area or high-speed area in the three-dimensional velocity structure below the target area in the time period a and the time period b, so as to monitor the dynamic changes of the underground structure of the target area.

[0060] In the second step, the specific steps of extracting the sub-array spatial autocorrelation coefficient include:

[0061]

[0062] Formula (1) is the spectrum polar coordinates of the central station, and formula (2) is the spectrum polar coordinates of the seismic station paired with the central station in the sub-array. ω is the angular frequency, k is the wave number, φ is the incident angle of the micro-motion signal, and ζ(ω,φ) is an orthogonal random process. Then the spatial autocorrelation function of the pair of seismic stations can be obtained:

[0063]

[0064] (3) In the formula, g(ω,r,θ) is the spatial covariance function, h(ω,φ) is the frequency-azimuth spectral density, and taking the azimuth average of the spatial covariance function g(ω,r,θ) we can obtain:

[0065]

[0066] (4) where J0 is the first kind of zero-order Bessel function, and ρ(ω,r) is defined as the spatial autocorrelation coefficient of the angular frequency ω:

[0067]

[0068] Substituting k = ω / c(ω), ω = 2πf, c(ω) is the Rayleigh wave phase velocity, into equation (5), we can obtain the spatial autocorrelation coefficient of the sub-array:

[0069]

[0070] In the third step, the dispersion curve obtained is a Rayleigh wave phase velocity dispersion curve, and the specific steps of obtaining the Rayleigh wave phase velocity dispersion curve include:

[0071] After Fourier transformation, in the frequency domain, the spatial autocorrelation coefficient of the central station and the seismic station paired with the central station is expressed as:

[0072]

[0073] (7) In the formula, Re(·) represents the real part, and * represents the complex conjugate. Substituting formula (7) into formula (6) can obtain the Rayleigh wave phase velocity c(ω). The above steps are performed on the n pairs of seismic stations in the sub-array to obtain the Rayleigh wave phase velocities of the n pairs of seismic stations. The Rayleigh wave phase velocities of the n pairs of seismic stations are then associated with the corresponding frequencies to obtain the Rayleigh wave phase velocity dispersion curve of the sub-array.

[0074] In the third step and the fourth step, the underground velocity structure is the apparent shear wave velocity structure or the shear wave velocity structure of the underground medium.

[0075] In the third step, the underground velocity structure at the location of the sub-array is obtained through the dispersion curve by inverting the dispersion curve or calculating it using a half-wavelength empirical formula.

[0076] In the first step, the time period a and the time period b have the same duration, and the time period a and the time period b are independent of each other.

[0077] The duration of the time period a and the time period b is between 1 hour and one month.

[0078] In the first step, the value of m is greater than 40.

[0079] In the second step, the value range of n is 3-8.

[0080] In the first step, the m seismic stations form a square matrix. In the second step, the central station is selected by cyclic traversal, selecting in order of rows and columns, to ensure that each seismic station becomes a central station once and only once.

[0081] The supplementary technical features of the present invention are as follows:

[0082] The reason why the value of m in the present invention is greater than 40 is that if there are too few seismic stations, the underground velocity structure obtained will be relatively rough, and the more seismic stations there are, the closer the result will be to the real underground structure.

[0083] The reason why the value range of n in the present invention is limited to 3-8 is that the depth of underground structure detected by spatial autocorrelation method is related to the distance between seismic stations. Generally, the detection depth is 4-10 times of the distance between stations, so the size of the sub-array depends on the target detection depth. After processing the data of a sub-array, an underground structure profile is obtained, which is similar to a borehole profile. This result is a comprehensive average result of the underground velocity structure within the sub-array. Therefore, the sub-array should not be too large or too small. If it is too large, it cannot reflect the lateral details. If it is too small, the detection depth may not be enough.

[0084] Embodiment 1:

[0085] See Figure 1 — Fig.12 , a method for monitoring the dynamic changes of the underground structure in the target area by using background noise, comprising the following steps: The first step: Deploy m seismic stations in the target area, and collect background noise signals through the m seismic stations respectively in time period a and time period b; The second step: Select one seismic station as the central station, and select n adjacent seismic stations around the central station to form a sub-array, n≥2. All the seismic stations in the sub-array are paired with the central station one by one to obtain n pairs of seismic stations. Then, analyze the background noise signals collected by the n pairs of seismic stations in time period a by using the spatial autocorrelation method, calculate the spatial autocorrelation functions of the n pairs of seismic stations, and then extract the spatial autocorrelation coefficient of the sub-array; The third step: Generate a dispersion curve by using the extracted spatial autocorrelation coefficient, and obtain the underground velocity structure at the position where the sub-array is located through the dispersion curve; The fourth step: Repeat the above second step and third step to ensure that each of the m seismic stations in the target area is used as the central station and analyzed once and only once, so as to obtain the underground velocity structures at the positions of m sub-arrays. Then, regard the m sub-arrays as m measuring points, perform lateral spatial interpolation on the underground velocity structures of the m measuring points, and the measuring points in the same horizontal direction form a measuring line, so as to obtain multiple two-dimensional profiles of the underground velocity structures of multiple measuring lines. Then, perform longitudinal spatial interpolation on the multiple two-dimensional profiles to obtain the three-dimensional velocity structure below the target area in time period a; The fifth step: Perform the above second step, third step and fourth step on the background noise signals collected by the m seismic stations in time period b, so as to obtain the three-dimensional velocity structure below the target area in time period b. Then, directly compare and observe whether there are changes in the low-velocity area or high-velocity area in the three-dimensional velocity structures below the target area in time periods a and b to monitor the dynamic changes of the underground structure in the target area.

[0086] When applied, the spatial autocorrelation method is based on two assumptions: ① The microseismic signal (background noise signal) conforms to a stationary random process in space and time; ② Among the different component waves contained in the microseismic signal, the fundamental surface wave is the main one.

[0087] The spectral polar coordinate form of the microseismic signal is:

[0088]

[0089] In formula (1), ω and k form a functional relationship, ζ = r(cosθ, sinθ), k = k(cosφ, sinφ). Usually, the wave spectrum of microseisms can be considered continuously differentiable with respect to frequency and propagation direction. Therefore, the above formula can be rewritten as:

[0090] E[丨dz(ω, φ)丨 2 = dH(ω, φ) = h(ω, φ)dωdφ (2)

[0091] (2) In the formula, H(ω,φ) is the integrated spectrum of X(t,r,θ) with respect to the azimuth, and h(ω,φ) is called the frequency-azimuth density.

[0092] like Figure 8 As shown in the figure, let O(0,0) be the central station of the sub-array, and one of the seismic stations paired with the sub-array be A(r,θ), then the frequency spectra of the micromotion records at the two points are:

[0093]

[0094] ω is the angular frequency, k is the wave number, φ is the incident angle of the micro-motion signal, ζ(ω,φ) is an orthogonal random process, then the spatial autocorrelation function S(r,θ) of points O and A is defined as:

[0095]

[0096] (5) In the formula, g(ω,r,θ) is the spatial covariance function, h(ω,φ) is the frequency-azimuth spectral density, and taking the azimuth average of the spatial covariance function g(ω,r,θ) can be obtained:

[0097]

[0098] (6) where J0 is the first kind of zero-order Bessel function, and ρ(ω,r) is defined as the spatial autocorrelation coefficient of the angular frequency ω:

[0099]

[0100] Substituting k = ω / c(ω), ω = 2πf, c(ω) as the Rayleigh wave phase velocity into equation (7), we can obtain the spatial autocorrelation coefficient of the sub-array:

[0101]

[0102] The spatial autocorrelation coefficient of the sub-array is Fig. 9 As shown, at the same time, it can be seen from formula (8) that the Rayleigh wave phase velocity can be calculated through the first-kind zero-order Bessel function and the spatial autocorrelation coefficient.

[0103] If the spatial autocorrelation coefficient is calculated in the frequency domain by Fourier transform, the Fourier transforms of points O(0,0) and A(r,θ) are expressed as SO(0,ω) and SA(r,ω) respectively, and the spatial autocorrelation coefficient of the two points in the frequency domain is expressed as:

[0104]

[0105] (9) In formula, Re(·) represents the real part, * represents the complex conjugate, and formula (9) is substituted into formula (8) to obtain the Rayleigh wave phase velocity c(ω). The n pairs of seismic stations in the sub-array are processed in the same way to obtain the Rayleigh wave phase velocities of the n pairs of seismic stations. The Rayleigh wave phase velocities of the n pairs of seismic stations are then associated with the corresponding frequencies to obtain the Rayleigh wave phase velocity dispersion curve of the sub-array. The one-dimensional apparent shear wave or shear wave velocity structure of the stratum below the sub-array is obtained by interpolation or inversion based on the Rayleigh wave phase velocity dispersion curve. When the m seismic stations are analyzed once and only once as the central station, the underground velocity structure of the m sub-arrays is obtained. The m sub-arrays are then regarded as m measuring points, and the underground velocity structure of the m measuring points is interpolated horizontally in space. The measuring points in the same horizontal direction form a survey line, thereby obtaining multiple two-dimensional profiles of the underground velocity structure of multiple survey lines, such as Fig.10 , 11 As shown; by performing longitudinal spatial interpolation on multiple two-dimensional profiles, the three-dimensional velocity structure below the target area in time period a can be obtained, as shown in Fig.12 As shown; the same processing method is used to obtain the three-dimensional velocity structure below the target area in time period b, and then the low-speed area or high-speed area in the three-dimensional velocity structure below the target area in time period a and time period b is directly compared to observe whether there is any change, because the change of the low-speed area or high-speed area can reflect the change of the underground structure of the target area, such as the change of the low-speed area can reflect the movement of underground fluids, the progress of mining, etc., thereby monitoring the dynamic changes of the underground structure of the target area.

[0106] Embodiment 2:

[0107] The basic content is the same as that of Example 1, except that:

[0108] See also Fig.13 — Fig.15 , m seismic stations form a square matrix. In the second step, the central station is selected by cyclic traversal, selecting in the order of rows and columns to ensure that each seismic station becomes a central station once and only once.

[0109] When used, the subarrays are named with row and column numbers, such as Fig.13 , 14 , as shown in 15; looping through rows and columns, using the background noise signal data recorded by each seismic station of all sub-arrays within a period of time (such as 1 hour, 1 day, etc.), analyzing with the spatial autocorrelation method, the velocity structure under each sub-array is obtained, and then the three-dimensional velocity structure under the target area is obtained by the spatial interpolation method; the background noise signal data recorded by each seismic station of all sub-arrays in different consecutive time periods are processed in the same way, and the time-varying dynamic velocity structure under the target area can be obtained.

[0110] The above description is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiment. Any equivalent modifications or changes made by ordinary technicians in this field based on the contents disclosed by the present invention should be included in the protection scope recorded in the claims.

Claims

1. A method for monitoring the dynamic changes of underground structures in a target area using background noise, characterized in that: The method comprises the following steps: Step 1: deploy m seismic stations in the target area, and collect background noise signals through m seismic stations in time periods a and b respectively; Step 2: Select a seismic station as the central station, select n adjacent seismic stations around the central station to form a sub-array, n ≥ 2, all seismic stations in the sub-array are paired with the central station one by one to obtain n pairs of seismic stations, and then use the spatial autocorrelation method to analyze the background noise signals collected by the n pairs of seismic stations in the time period a, calculate the spatial autocorrelation function of the n pairs of seismic stations, and then extract the spatial autocorrelation coefficient of the sub-array; Step 3: Generate a dispersion curve using the extracted spatial autocorrelation coefficient, and obtain the underground velocity structure at the location of the sub-array through the dispersion curve; Step 4: Repeat the above steps 2 and 3 to ensure that the m seismic stations in the target area are analyzed once and only once as the central station to obtain the underground velocity structure of the m sub-arrays, and then regard the m sub-arrays as m measuring points, perform lateral spatial interpolation on the underground velocity structure of the m measuring points, and the measuring points in the same horizontal direction form a measuring line, thereby obtaining multiple two-dimensional profiles of the underground velocity structure of multiple measuring lines, and then perform longitudinal spatial interpolation on the multiple two-dimensional profiles to obtain the three-dimensional velocity structure below the target area in time period a; Step 5: Process the background noise signals collected by the m seismic stations in the time period b according to the second, third and fourth steps above, so as to obtain the three-dimensional velocity structure below the target area in the time period b, and then directly compare and observe whether there are any changes in the low-speed area or high-speed area in the three-dimensional velocity structure below the target area in the time period a and the time period b, so as to monitor the dynamic changes of the underground structure of the target area.

2. A method for monitoring dynamic changes of underground structures in a target area using background noise according to claim 1, characterized in that: In the second step, the specific steps of extracting the sub-array spatial autocorrelation coefficient include: Formula (1) is the spectrum polar coordinates of the central station, and formula (2) is the spectrum polar coordinates of the seismic station paired with the central station in the sub-array. ω is the angular frequency, k is the wave number, φ is the incident angle of the micro-motion signal, and ζ(ω,φ) is an orthogonal random process. Then the spatial autocorrelation function of the pair of seismic stations can be obtained: (3) In the formula, g(ω,r,θ) is the spatial covariance function, h(ω,φ) is the frequency-azimuth spectral density, and taking the azimuth average of the spatial covariance function g(ω,r,θ) we can obtain: (4) where J0 is the first kind of zero-order Bessel function, and ρ(ω,r) is defined as the spatial autocorrelation coefficient of the angular frequency ω: Substituting k = ω / c(ω), ω = 2πf, c(ω) is the Rayleigh wave phase velocity, into equation (5), we can obtain the spatial autocorrelation coefficient of the sub-array:

3. The method of using background noise to monitor the dynamic changes of underground structures in a target area according to claim 2, characterized in that: In the third step, the dispersion curve obtained is a Rayleigh wave phase velocity dispersion curve, and the specific steps of obtaining the Rayleigh wave phase velocity dispersion curve include: After Fourier transformation, in the frequency domain, the spatial autocorrelation coefficient of the central station and the seismic station paired with the central station is expressed as: (7) In the formula, Re(·) represents the real part, and * represents the complex conjugate. Substituting formula (7) into formula (6) can obtain the Rayleigh wave phase velocity c(ω). The above steps are performed on the n pairs of seismic stations in the sub-array to obtain the Rayleigh wave phase velocities of the n pairs of seismic stations. The Rayleigh wave phase velocities of the n pairs of seismic stations are then associated with the corresponding frequencies to obtain the Rayleigh wave phase velocity dispersion curve of the sub-array.

4. A method for monitoring dynamic changes of underground structures in a target area using background noise according to claim 1, 2 or 3, characterized in that: In the third step and the fourth step, the underground velocity structure is the apparent shear wave velocity structure or the shear wave velocity structure of the underground medium.

5. A method for monitoring dynamic changes of underground structures in a target area using background noise according to claim 1, 2 or 3, characterized in that: In the third step, the underground velocity structure at the location of the sub-array is obtained through the dispersion curve by inverting the dispersion curve or calculating it using a half-wavelength empirical formula.

6. A method for monitoring dynamic changes of underground structures in a target area using background noise according to claim 1, 2 or 3, characterized in that: In the first step, the time period a and the time period b have the same duration, and the time period a and the time period b are independent of each other.

7. The method of using background noise to monitor dynamic changes of underground structures in a target area according to claim 6, characterized in that: The duration of the time period a and the time period b is between 1 hour and one month.

8. A method for monitoring dynamic changes of underground structures in a target area using background noise according to claim 1, 2 or 3, characterized in that: In the first step, the value of m is greater than 40.

9. A method for monitoring dynamic changes of underground structures in a target area using background noise according to claim 1, 2 or 3, characterized in that: In the second step, the value range of n is 3-8.

10. A method for monitoring dynamic changes of underground structures in a target area using background noise according to claim 1, 2 or 3, characterized in that: In the first step, the m seismic stations form a square matrix. In the second step, the central station is selected by cyclic traversal, selecting in order of rows and columns, to ensure that each seismic station becomes a central station once and only once.

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