A method for monitoring dynamic changes in underground structures in a target area using background noise

Abstract

A method for monitoring dynamic changes in the underground structure of a target area using background noise involves first deploying multiple seismic stations in the target area to form an array and collecting background noise signals over a period of time. Then, sub-arrays are selected cyclically, and spatial autocorrelation analysis is performed on the background noise signals at the seismic stations within the selected sub-arrays to calculate the underground velocity structure at each location. Finally, spatial interpolation is used to obtain the three-dimensional underground velocity structure of the target area over a given period. By comparing and observing the three-dimensional underground velocity structure of the target area over different time periods, it is possible to monitor the expansion of mining subsidence areas, changes in groundwater levels due to water injection operations, and changes in the underground structure in areas affected by reservoir impoundment, mining development, and shale gas extraction. This allows for the monitoring of dynamic changes in the underground structure of the target area. Therefore, this invention can monitor dynamic changes in underground structures and has a wide range of applications.

Description

Technical Field

[0001] This invention relates to a method for monitoring underground structures, belonging to the field of underground structure exploration, and particularly to a method for monitoring the dynamic changes of underground structures in a target area using background noise. Background Technology

[0002] The technology for exploring changes in underground structures is of great practical significance for understanding the Earth's internal structure, assessing resource distribution, preventing and mitigating natural disasters, and carrying out engineering construction. For example, by monitoring changes and dynamic responses in underground structures in real time, people can provide early warnings of earthquake risks and potential geological hazards, reducing casualties and property losses. However, due to limitations in observation methods, existing methods for monitoring underground structures mostly yield static results and cannot be used to monitor dynamic changes in underground structures.

[0003] Chinese invention patent application CN202011615586.4, filed on December 31, 2020, discloses a method for monitoring the health of underground structures based on the coefficient of thermal expansion and load strain. This 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 the load strain and coefficient of thermal expansion; solving for the normalized coefficient of thermal expansion and establishing four Euclidean distance matrices for the coefficient of thermal expansion; and judging the damage to the underground structure based on the changes in the Euclidean distance matrices between the load strain and the two evaluation periods. Although this invention can largely eliminate the influence of sensor errors, making structural damage identification more sensitive and accurate, and can fully utilize commonly used strain and temperature sensor data in existing monitoring without increasing data acquisition costs, it still has the following drawbacks:

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

[0005] The information disclosed in this background section is intended only to enhance understanding of the overall background of this application and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of existing methods for monitoring underground structures, which 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 using background noise.

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

[0008] A method for monitoring dynamic changes in 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 the m seismic stations during time period a and time period b, respectively;

[0010] Step 2: Select a seismic station as the central station, and select n neighboring 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. Then, the background noise signals collected by the n pairs of seismic stations in the time period a are analyzed using the spatial autocorrelation method to calculate the spatial autocorrelation function of the n pairs of seismic stations, and then the spatial autocorrelation coefficient of the sub-array is extracted.

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

[0012] Step 4: Repeat steps 2 and 3 above to ensure that each of the m seismic stations in the target area is analyzed once and only once as a central station, so as to obtain the underground velocity structure of the m sub-arrays. Then, treat the m sub-arrays as m measuring points and perform lateral spatial interpolation on the underground velocity structure of the m measuring points. The measuring points on the same lateral direction form a measuring line, thereby obtaining multiple two-dimensional profiles of the underground velocity structure 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 within time period a.

[0013] Step 5: Process the background noise signals collected by m seismic stations within the time period b using the methods described in Steps 2, 3, and 4 above to obtain the three-dimensional velocity structure below the target area within the time period b. Then, directly compare and observe whether there are any changes in the low-velocity or high-velocity areas in the three-dimensional velocity structure below the target area within the time periods a and b, so as to monitor the dynamic changes of the underground structure in the target area.

[0014] The second step, specifically the steps for extracting the spatial autocorrelation coefficients of the sub-arrays, includes:

[0015]

[0016] Equation (1) represents the polar coordinates of the central station's spectrum, and equation (2) represents the polar coordinates of the seismic stations in the sub-array paired with the central station's spectrum. ω represents the angular frequency, k represents the wave number, φ represents the incident angle of the micro-motion signal, and ζ(ω,φ) represents an orthogonal random process. The spatial autocorrelation function of this pair of seismic stations can then be calculated.

[0017]

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

[0019]

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

[0021]

[0022] From k=ω / c(ω), ω=2πf, and c(ω) is the Rayleigh wave phase velocity, substituting into equation (5) yields the spatial autocorrelation coefficient of the subarray:

[0023]

[0024] In the third step, the obtained dispersion curve is the Rayleigh wave phase velocity dispersion curve. The specific steps to obtain the Rayleigh wave phase velocity dispersion curve include:

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

[0026]

[0027] In equation (7), Re(·) represents taking the real part, and * represents the complex conjugate. Substituting equation (7) into equation (6) yields the Rayleigh wave phase velocity c(ω). The above steps are performed on all 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 correlated with their corresponding frequencies to obtain the Rayleigh wave phase velocity dispersion curve of the sub-array.

[0028] In the third and fourth steps, the underground velocity structure is the apparent shear wave velocity structure or 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 by inverting the dispersion curve or calculating it using a half-wavelength empirical formula.

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

[0031] The durations of time periods a and b are between one hour and one month.

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

[0033] In the second step, the value of n ranges from 3 to 8.

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

[0035] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0036] 1. In this invention, a method for monitoring the dynamic changes of underground structures in a target area 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 period a and time period b, respectively. One seismic station is selected as the central station, and n neighboring seismic stations are selected around the central station to form a sub-array. The background noise signals collected by the sub-array are analyzed using the spatial autocorrelation method, and the spatial autocorrelation coefficient of the sub-array is extracted. The spatial autocorrelation coefficient is used to generate a dispersion curve, and the underground velocity structure at the location of the sub-array is obtained through the dispersion curve. Each of the m seismic stations in the target area is analyzed once and only once as the central station to obtain the underground velocity structure at the location of the m sub-arrays. The three-dimensional velocity structure below the target area in time period a is obtained by spatial interpolation. The background noise signals collected by the m seismic stations in time period b are processed in the above steps to obtain the three-dimensional velocity structure below the target area in time period b. Then, the low-velocity or high-velocity regions in the three-dimensional velocity structure below the target area in time period a and time period b are directly compared and observed to monitor the dynamic changes of the underground structure in the target area. In application, changes in the low-speed or high-speed zones within the three-dimensional velocity structure can reflect changes in the underground structure. For example, changes in the low-speed zone can reflect the movement of groundwater or oil and gas, or the progress of tunnel or mine excavation to a certain location. Therefore, by comparing and observing whether changes occur in the three-dimensional velocity structure beneath the target area during time period a and time period b, it is possible to determine whether the underground structure of the target area has changed. Thus, this invention can monitor the dynamic changes of underground structures.

[0037] 2. In this invention, a method for monitoring the dynamic changes of underground structures in a target area using background noise, each sub-array is used as a measuring point. Sub-arrays in the same horizontal direction are connected to form a measuring line. Multiple two-dimensional profiles of the underground velocity structure along multiple measuring lines are obtained through horizontal spatial interpolation. Then, vertical spatial interpolation is performed on these two-dimensional profiles to obtain the three-dimensional underground velocity structure of the target area. In application, by comparing and observing the three-dimensional underground velocity structure of the target area at different time periods, changes in low-velocity or high-velocity zones in the three-dimensional velocity structure can reflect the expansion of mining subsidence areas, changes in groundwater levels during water injection operations, etc. It can also be used to understand the dynamic changes of underground structures in areas such as reservoir impoundment, mine development, and shale gas extraction, and to detect water infiltration and the progress of mining, thereby serving safe production and disaster prevention and mitigation. Therefore, this invention not only dynamically monitors the dynamic changes of underground structures but also has a wide range of applications. Attached Figure Description

[0038] Figure 1 This is a flowchart of the process of obtaining the subarray underground velocity structure by processing the background noise signal using the spatial autocorrelation method in Embodiment 1 of the present invention.

[0039] Figure 2 yes Figure 1 An enlarged schematic diagram of step a.

[0040] Figure 3 yes Figure 1 A magnified diagram of step b.

[0041] Figure 4 yes Figure 1 A magnified diagram of step c.

[0042] Figure 5 yes Figure 1 An enlarged schematic diagram of step d in the middle section.

[0043] Figure 6 yes Figure 1 An enlarged schematic diagram of step e.

[0044] Figure 7 yes Figure 1 A magnified diagram of step f.

[0045] Figure 8 This is a schematic diagram of the sub-array in Embodiment 1 of the present invention.

[0046] Figure 9 This is a schematic diagram of the spatial autocorrelation coefficients of the sub-array in Embodiment 1 of the present invention.

[0047] Figure 10 This is a two-dimensional cross-sectional view of the underground velocity structure of the measuring line obtained by performing lateral spatial interpolation on the measuring points in Embodiment 1 of the present invention.

[0048] Figure 11 This is a two-dimensional cross-sectional view of the underground velocity structure of multiple survey lines in Embodiment 1 of the present invention.

[0049] Figure 12 This is a three-dimensional velocity structure diagram obtained by longitudinal spatial interpolation of the two-dimensional cross-sections of the underground velocity structure of multiple survey lines in Embodiment 1 of the present invention.

[0050] Figure 13 This is a schematic diagram of the sub-array selected in Embodiment 2 of the present invention.

[0051] Figure 14 This is a schematic diagram of the sub-array selected in Embodiment 2 of the present invention.

[0052] Figure 15 This is a schematic diagram of the sub-array selected in Embodiment 2 of the present invention. Detailed Implementation

[0053] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0054] See Figure 1 — Figure 15 A method for monitoring dynamic changes in 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 the m seismic stations during time period a and time period b, respectively;

[0056] Step 2: Select a seismic station as the central station, and select n neighboring 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. Then, the background noise signals collected by the n pairs of seismic stations in the time period a are analyzed using the spatial autocorrelation method to calculate the spatial autocorrelation function of the n pairs of seismic stations, and then the spatial autocorrelation coefficient of the sub-array is extracted.

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

[0058] Step 4: Repeat steps 2 and 3 above to ensure that each of the m seismic stations in the target area is analyzed once and only once as a central station, so as to obtain the underground velocity structure of the m sub-arrays. Then, treat the m sub-arrays as m measuring points and perform lateral spatial interpolation on the underground velocity structure of the m measuring points. The measuring points on the same lateral direction form a measuring line, thereby obtaining multiple two-dimensional profiles of the underground velocity structure 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 within time period a.

[0059] Step 5: Process the background noise signals collected by m seismic stations within the time period b using the methods described in Steps 2, 3, and 4 above to obtain the three-dimensional velocity structure below the target area within the time period b. Then, directly compare and observe whether there are any changes in the low-velocity or high-velocity areas in the three-dimensional velocity structure below the target area within the time periods a and b, so as to monitor the dynamic changes of the underground structure in the target area.

[0060] The second step, specifically the steps for extracting the spatial autocorrelation coefficients of the sub-arrays, includes:

[0061]

[0062] Equation (1) represents the polar coordinates of the central station's spectrum, and equation (2) represents the polar coordinates of the seismic stations in the sub-array paired with the central station's spectrum. ω represents the angular frequency, k represents the wave number, φ represents the incident angle of the micro-motion signal, and ζ(ω,φ) represents an orthogonal random process. The spatial autocorrelation function of this pair of seismic stations can then be calculated.

[0063]

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

[0065]

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

[0067]

[0068] From k=ω / c(ω), ω=2πf, and c(ω) is the Rayleigh wave phase velocity, substituting into equation (5) yields the spatial autocorrelation coefficient of the subarray:

[0069]

[0070] In the third step, the obtained dispersion curve is the Rayleigh wave phase velocity dispersion curve. The specific steps to obtain the Rayleigh wave phase velocity dispersion curve include:

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

[0072]

[0073] In equation (7), Re(·) represents taking the real part, and * represents the complex conjugate. Substituting equation (7) into equation (6) yields the Rayleigh wave phase velocity c(ω). The above steps are performed on all 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 correlated with their corresponding frequencies to obtain the Rayleigh wave phase velocity dispersion curve of the sub-array.

[0074] In the third and fourth steps, the underground velocity structure is the apparent shear wave velocity structure or 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 by inverting the dispersion curve or calculating it using a half-wavelength empirical formula.

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

[0077] The durations of time periods a and b are between one hour and one month.

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

[0079] In the second step, the value of n ranges from 3 to 8.

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

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

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

[0083] The reason why the value of n is limited to 3-8 in this invention is that the depth of underground structure detection using the spatial autocorrelation method is related to the spacing between seismic stations. Generally, the detection depth is 4-10 times the spacing between stations, so the size of the subarray depends on the target detection depth. Data from one subarray is processed to obtain an underground structure profile, similar to a borehole profile. This result is a comprehensive average of the underground velocity structure within the subarray's range. Therefore, the subarray should not be too large or too small. If it is too large, it cannot reflect lateral details; if it is too small, the detection depth may be insufficient.

[0084] Example 1:

[0085] See Figure 1 — Figure 12 , A method for monitoring the dynamic changes of the underground structure in the target area using background noise, including 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 during the time period a and the 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 during the time period a 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 using the extracted spatial autocorrelation coefficient, and obtain the underground velocity structure at the location of the sub-array through the dispersion curve; The fourth step: Repeat the above second and third steps 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 to obtain the underground velocity structures at the locations of the m sub-arrays. Then, regard the m sub-arrays as m measurement points, perform lateral spatial interpolation on the underground velocity structures of the m measurement points. The measurement points in the same horizontal direction form a survey line, so as to obtain multiple two-dimensional profiles of the underground velocity structures of multiple survey lines. Then, perform longitudinal spatial interpolation on the multiple two-dimensional profiles to obtain the three-dimensional velocity structure under the target area during the time period a; The fifth step: Perform the above second, third, and fourth steps on the background noise signals collected by the m seismic stations during the time period b to obtain the three-dimensional velocity structure under the target area during the 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 under the target area during the time period a and the time period 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 mode surface wave is the main one. <000​​​​​​​​​​​​​​(2) In the formula, H(ω,φ) is the integral spectrum of X(t,r,θ) in the orientation, and h(ω,φ) is called the frequency-azimuth density.

[0092] like Figure 8 As shown, let the central station of the subarray be O(0,0), and one of the seismic stations paired with the subarray be A(r,θ). Then the spectra of the micromotion records at the two points are as follows:

[0093]

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

[0095]

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

[0097]

[0098] (6) In the formula, J0 is the zeroth order Bessel function of the first kind, 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, and c(ω) being the Rayleigh wave phase velocity into equation (7), we obtain the spatial autocorrelation coefficient of the subarray:

[0101]

[0102] The spatial autocorrelation coefficient of the sub-array is as follows Figure 9 As shown, at the same time, it can be seen from equation (8) that the Rayleigh wave phase velocity can be obtained by using the first-order zero-order Bessel function and the spatial autocorrelation coefficient.

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

[0104]

[0105] In equation (9), Re(·) represents the real part, and * represents the complex conjugate. Substituting equation (9) into equation (8) yields the Rayleigh wave phase velocity c(ω). The same process is applied to all n pairs of seismic stations in the subarray 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 correlated with their corresponding frequencies to obtain the Rayleigh wave phase velocity dispersion curve of the subarray. Based on the Rayleigh wave phase velocity dispersion curve, the one-dimensional apparent shear wave or shear wave velocity structure of the strata below the subarray is obtained by interpolation or inversion. When all m seismic stations are analyzed once as the central station and only once, the underground velocity structure of the m subarray locations is obtained. The m subarrays are then considered as m measuring points. Lateral spatial interpolation is performed on the underground velocity structure of the m measuring points. Measuring points on the same lateral direction form measuring lines, thus obtaining multiple two-dimensional profiles of the underground velocity structure of multiple measuring lines, such as... Figure 10 , 11 As shown; by performing longitudinal spatial interpolation on multiple two-dimensional profiles, the three-dimensional velocity structure below the target area within time period a can be obtained, as shown. Figure 12 As shown; then, the three-dimensional velocity structure below the target area within time period b is obtained using the same processing method. Then, the low-speed or high-speed regions in the three-dimensional velocity structure below the target area within time period a and time period b are directly compared and observed to see if there are any changes. This is because changes in the low-speed or high-speed regions can reflect changes in the underground structure of the target area. For example, changes in the low-speed regions can reflect the movement of underground fluids, the progress of mining, etc., thereby monitoring the dynamic changes in the underground structure of the target area.

[0106] Example 2:

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

[0108] See Figure 13 — Figure 15 The matrix consists of m seismic stations. In the second step, the central station is selected by iterative traversal, selecting stations in the order of rows and columns to ensure that each seismic station becomes the central station once and only once.

[0109] When applying this method, the sub-arrays are named using their row and column numbers, such as... Figure 13 , 14 As shown in Figure 15, by cyclically processing the rows and columns, using the background noise signal data recorded by each seismic station of all subarrays within a certain period (such as 1 hour, 1 day, etc.), the spatial autocorrelation method is used to analyze and obtain the velocity structure below each subarray. Then, the spatial interpolation method is used to obtain the three-dimensional velocity structure below the target area. By sequentially performing the same processing on the background noise signal data recorded by each seismic station of all subarrays within consecutive different time periods, the time-varying dynamic velocity structure below the target area can be obtained.

[0110] The above description is only a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. Any equivalent modifications or changes made by those skilled in the art based on the content disclosed in the present invention should be included within the scope of protection set forth in the claims.

Claims

1. A method for monitoring dynamic changes in underground structures in a target area using background noise, characterized in that: The method includes the following steps: Step 1: Deploy m seismic stations in the target area and collect background noise signals through the m seismic stations during time period a and time period b, respectively; Step 2: Select a seismic station as the central station, and select n neighboring 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. Then, the background noise signals collected by the n pairs of seismic stations in the time period a are analyzed using the spatial autocorrelation method to calculate the spatial autocorrelation function of the n pairs of seismic stations, and then the spatial autocorrelation coefficient of the sub-array is extracted. Step 3: Generate dispersion curves using the extracted spatial autocorrelation coefficients, and obtain the underground velocity structure at the location of the sub-array through the dispersion curves; Step 4: Repeat steps 2 and 3 above to ensure that each of the m seismic stations in the target area is analyzed once and only once as a central station, so as to obtain the underground velocity structure of the m sub-arrays. Then, treat the m sub-arrays as m measuring points and perform lateral spatial interpolation on the underground velocity structure of the m measuring points. The measuring points on the same lateral direction form a measuring line, thereby obtaining multiple two-dimensional profiles of the underground velocity structure 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 within time period a. Step 5: Process the background noise signals collected by m seismic stations within the time period b using the methods described in Steps 2, 3, and 4 above to obtain the three-dimensional velocity structure below the target area within the time period b. Then, directly compare and observe whether there are any changes in the low-velocity or high-velocity areas in the three-dimensional velocity structure below the target area within the time periods a and b, so as to monitor the dynamic changes of the underground structure in the target area.

2. The method for monitoring dynamic changes in underground structures in a target area using background noise according to claim 1, characterized in that: The second step, specifically the steps for extracting the spatial autocorrelation coefficients of the sub-arrays, includes: Equation (1) represents the polar coordinates of the central station's spectrum, and equation (2) represents the polar coordinates of the seismic stations in the sub-array paired with the central station's spectrum. ω represents the angular frequency, k represents the wave number, φ represents the incident angle of the micro-motion signal, and ζ(ω,φ) represents an orthogonal random process. The spatial autocorrelation function of this pair of seismic stations can then be calculated. (3) In the formula, g(ω,r,θ) is the spatial covariance function, and h(ω,φ) is the frequency-azimuth spectral density. Taking the azimuth average of the spatial covariance function g(ω,r,θ), we can obtain: (4) In the formula, J0 is the zeroth order Bessel function of the first kind, and ρ(ω,r) is defined as the spatial autocorrelation coefficient of the angular frequency ω: From k=ω / c(ω), ω=2πf, and c(ω) is the Rayleigh wave phase velocity, substituting into equation (5) yields the spatial autocorrelation coefficient of the subarray:

3. The method for monitoring dynamic changes in underground structures in a target area using background noise according to claim 2, characterized in that: In the third step, the obtained dispersion curve is the Rayleigh wave phase velocity dispersion curve. The specific steps to obtain the Rayleigh wave phase velocity dispersion curve include: After Fourier transform, the spatial autocorrelation coefficient between the central station and the paired seismic station in the frequency domain is expressed as: In equation (7), Re(·) represents taking the real part, and * represents the complex conjugate. Substituting equation (7) into equation (6) yields the Rayleigh wave phase velocity c(ω). The above steps are performed on all 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 correlated with their corresponding frequencies to obtain the Rayleigh wave phase velocity dispersion curve of the sub-array.

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

5. A method for monitoring dynamic changes in underground structures in a target area using background noise, as described in 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 by inverting the dispersion curve or calculating it using a half-wavelength empirical formula.

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

7. A method for monitoring dynamic changes in underground structures in a target area using background noise according to claim 6, characterized in that: The durations of time periods a and b are between one hour and one month.

8. A method for monitoring dynamic changes in underground structures in a target area using background noise, as described in 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 in underground structures in a target area using background noise, as described in claim 1, 2, or 3, characterized in that: In the second step, the value of n ranges from 3 to 8.

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

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