A method for monitoring the change of underground structure of a target area by using regional seismic activity

Abstract

A method for monitoring underground structure change of a target area by using regional seismic vibration comprises the following steps: setting a preset radius for a central station, identifying and selecting all stations within the preset radius, forming a sub-station array with all the stations, obtaining seismic surface wave data corresponding to each station in the sub-station array, and forming a data set; calculating the distance relationship between the data set and the stations in the sub-station array to obtain a spatial autocorrelation coefficient; then extracting a dispersion curve; obtaining the shear wave velocity structure of the underground medium below the sub-station array; repeating the above steps for each station until the sub-station array covers the monitoring area, obtaining the shear wave velocity structure of the underground medium of each sub-station array, then collecting the shear wave velocity structures of the underground medium of all sub-station arrays to obtain a velocity structure set, and constructing a three-dimensional velocity model of the underground medium of the region by spatial interpolation according to the velocity structure set. The method can dynamically obtain the three-dimensional structure change of the underground below the station network.

Description

TECHNICAL FIELD

[0001] The application relates to an improvement of a seismic dynamic monitoring underground structure dynamic change technology and belongs to the field of seismic dynamic monitoring. BACKGROUND

[0002] Earthquake, also known as ground motion or ground vibration, is a natural phenomenon that occurs in the process of rapid energy release of the earth's crust, causing the ground to vibrate. When an earthquake occurs, a special seismic wave, i.e. surface wave, is generated, which can reveal the properties of the underground medium and provide important information for understanding the internal structure of the earth. Obtaining the properties of the underground medium is crucial for various applications, including monitoring and positioning of seismic networks, monitoring of induced earthquakes, and detection of mined-out areas. By monitoring the dynamic changes of the properties of the underground medium, we can dynamically monitor the expansion of mined-out areas and changes in groundwater levels caused by water extraction and injection operations in the region. This monitoring is similar to the cloud map used in weather forecasting, which can provide intuitive information about changes in the earth's interior. However, the existing seismic monitoring method has a major limitation, which is unable to dynamically obtain the three-dimensional structure information of the underground beneath the network, i.e. unable to construct a three-dimensional velocity model.

[0003] The Chinese patent application with application number CN202410820820.9 and application date June 24, 2024 discloses a wharf structure stability analysis system under seismic waves, which comprises a stability analysis system. The application collects data of seismic waves of the target wharf through the acquisition unit and establishes a corresponding overall three-dimensional model of the target wharf. The collected data of seismic waves of the target wharf is adapted to the established overall three-dimensional model corresponding to the target wharf, so as to utilize the propagation and reflection characteristics of seismic waves in the ground. Through the arrival time and amplitude information of seismic wave data, the underground structure and geological information such as stratum distribution, lithology, fracture, fault, etc. are inferred through seismic inversion, so as to determine the stability of the wharf structure. The building information and geological information of the target wharf are provided through the Internet of Things unit and the artificial intelligence unit to improve the accuracy of stability analysis. However, the above-mentioned scheme does not solve the problem of being unable to dynamically obtain the three-dimensional structure of the underground beneath the network.

[0004] The information disclosed in this BACKGROUND section is only for the purpose of increasing the understanding of the general background of the patent application, and should not be considered as recognition or implicit admission in any form that this information constitutes prior art known to those skilled in the art. SUMMARY

[0005] The present application aims to overcome the problem that the prior art cannot dynamically obtain the underground three-dimensional structure under the seismic network, and provides a method for dynamically monitoring the underground structure change of a target area by using regional earthquakes.

[0006] To achieve the above object, the technical solution of the present application is: a method for dynamically monitoring the underground structure change of a target area by using regional earthquakes, the method comprising the following steps:

[0007] Firstly, one station in the seismic network in the monitoring area is taken as a center station, a preset radius is set for the center station, all stations within the preset radius are identified and selected, then all stations are combined to form a sub-array, seismic surface wave data corresponding to each station in the sub-array is obtained to form a data set;

[0008] Secondly, the spatial autocorrelation method is used to calculate the distance relationship between the data set and each station in the sub-array, and all spatial autocorrelation coefficients are obtained;

[0009] Thirdly, the Bessel function is used to fit all spatial autocorrelation coefficients to obtain a frequency dispersion curve;

[0010] Fourthly, the frequency dispersion curve is processed by an inversion algorithm to obtain the S-wave velocity structure of the underground medium under the sub-array;

[0011] Fifthly, the first to fourth steps are repeated for each station until the sub-array covers the monitoring area, the S-wave velocity structure of the underground medium of each sub-array is obtained, then the S-wave velocity structures of the underground medium of all sub-arrays are summarized to obtain a velocity structure set, and a three-dimensional velocity model of the underground medium of the monitoring area is constructed by spatial interpolation according to the velocity structure set.

[0012] In the second step, the spatial autocorrelation method is used to calculate the distance relationship between the data set and each station in the sub-array to obtain all spatial autocorrelation coefficients, specifically:

[0013] The polar coordinate form of the spectrum of the microseismic signal is:

[0014]

[0015] In the formula, ω and k constitute a function relationship, ζ=r(cosθ, sinθ), k=k(cosφ, sinφ), usually the spectrum of the microseismic signal is continuous and differentiable with respect to frequency and propagation direction, so the above formula can be rewritten as:

[0016] E[|dz(ω, φ| 2 ]=dH(ω, φ)=h(ω, φ)dωdφ;

[0017] In the formula, H(ω, φ) is the integration spectrum of X(t, r, θ) in azimuth, and h(ω, φ) is the frequency-azimuth density.

[0018] The spectrum of the microseismic record of the two points O(0, 0) and A(r, θ) on the circular array is respectively:

[0019]

[0020] In the formula, ω is the angular frequency, k is the wave number, φ is the incident angle of the microseismic signal, and ζ(ω, φ) is the orthogonal random process.

[0021] The spatial autocorrelation function S(r; θ) of the two points O and A is defined as:

[0022]

[0023] In the formula, g(ω, r; θ) is the spatial covariance function, h(ω, φ) is the frequency-azimuth spectral density, and the azimuth average of the spatial covariance function g(ω, r; θ) can be obtained:

[0024]

[0025] In the formula, J0 is the first-order zero Bessel function.

[0026] The spatial autocorrelation coefficient ρ(ω, r) of the angular frequency ω is defined as:

[0027]

[0028] By substituting k = ω / c(ω), ω = 2πf, and c(ω) into the above formula, we obtain:

[0029]

[0030] The Fourier transform of the two points O(0, 0) and A(r, θ) is respectively represented as S O (0, ω) and S A (r, ω), and the spatial autocorrelation coefficient of the two points in the frequency domain can be represented as:

[0031]

[0032] In the formula, Re(·) represents the real part, and * represents the complex conjugate.

[0033] The number of stations in the seismic network is greater than forty.

[0034] In the third step, the Bessel function is used to fit all the spatial autocorrelation coefficients, and the fitting methods include the least square method, the nonlinear least square method, and the piecewise fitting.

[0035] The first step is to set a preset radius for the central station, and the preset radius is set according to geological structure, detection depth and signal attenuation factors.

[0036] The detection depth is 4-10 times of the station interval, and the size of the sub-station array is set in proportion to the target detection depth.

[0037] In the fourth step, the dispersion curve is processed by inversion algorithm first, and then the shear wave velocity structure of the underground medium under the sub-station array is obtained, specifically: the dispersion curve is the relationship between the phase velocity of the underground medium and the frequency, the abscissa is the frequency, and the ordinate is the surface wave phase velocity. The fitting of the theoretical dispersion curve and the observed dispersion curve is compared with the preset velocity structure, and the best fitting shear wave velocity structure, i.e. the relationship between the shear wave velocity of the underground medium and the depth, is obtained by gradually adjusting.

[0038] In the fifth step, the shear wave velocity structure of the underground medium of all sub-station arrays is summarized to obtain the velocity structure set, and the shear wave velocity structure is also subjected to quality control to eliminate abnormal values and ensure data consistency.

[0039] Compared with the prior art, the beneficial effects of the present application are:

[0040] 1、In the method for monitoring the change of underground structure in a target area by using regional seismic dynamic monitoring of the application, the shear wave velocity structure of the underground medium of each sub-station array is obtained, and then the shear wave velocity structure of the underground medium of all sub-station arrays is summarized to obtain the velocity structure set. According to the velocity structure set, a three-dimensional velocity model of the underground medium of the region is constructed by spatial interpolation. The SPAC method can use existing seismic array data without additional seismic source excitation. By continuous or repeated observation, SPAC coefficients and dispersion curves at different time points are collected to monitor their changes, so that the change of the underground structure is dynamically obtained, and real-time or near real-time underground structure change information is obtained. It is very important for rapid response to geological disasters and other emergency situations. Therefore, the present application can dynamically obtain the change of the three-dimensional structure under the seismic network.

[0041] 2、In the method for monitoring the change of underground structure in a target area by using regional seismic dynamic monitoring of the application, since no artificial seismic source is needed, the SPAC method is suitable for monitoring in complex environments such as cities, and can use existing seismic array data without additional seismic source excitation, so it has flexibility in data acquisition. In addition, high-resolution underground structure images can be obtained, which helps to more accurately understand the properties of the underground medium. Therefore, the present application has higher flexibility in data acquisition and can more intuitively display the underground structure.

[0042] 3、The method for monitoring underground structure changes in a target area by using regional seismic dynamics, wherein the deviation of the SPAC coefficient is subjected to cluster analysis, changes caused by human activities, environmental changes and other factors are distinguished from changes caused by underground structure changes, the monitored underground structure changes are associated with ground manifestations, and the influence of underground structure changes on the stability of the ground is better understood. BRIEF DESCRIPTION OF DRAWINGS

[0043] Figure 1 is a schematic diagram of a station array of the present application.

[0044] Figure 2 is a schematic diagram of a spatial autocorrelation coefficient of the present application.

[0045] Figure 3 is an observation data graph of the present application.

[0046] Figure 4 is a calculated power graph of the present application.

[0047] Figure 5 is a calculated coefficient schematic diagram of the present application.

[0048] Figure 6 is a phase velocity graph of the present application.

[0049] Figure 7 is a dispersion curve graph of the present application.

[0050] Figure 8 is a shear wave velocity structure graph below an underground station array of the present application. DETAILED DESCRIPTION

[0051] The present application is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0052] Reference Figures 1 to 8 A method for monitoring underground structure changes in a target area by using regional seismic dynamics, the method for monitoring underground structure changes in a target area by using regional seismic dynamics comprising the following steps:

[0053] Firstly, one station in a seismic station network in a monitoring area is taken as a center station, a preset radius is set for the center station, all stations within the preset radius are identified and selected, all stations are then combined to form a sub-station array, seismic surface wave data corresponding to each station in the sub-station array is obtained, and a data set is formed;

[0054] Secondly, a spatial autocorrelation method is used to calculate the distance relationship between the data set and each station in the sub-station array, and all spatial autocorrelation coefficients are obtained;

[0055] Third step, all the spatial autocorrelation coefficients are fitted by Bessel function to obtain the dispersion curve;

[0056] Fourth step, the dispersion curve is processed by inversion algorithm, and then the S-wave velocity structure of the underground medium under the sub-array is obtained;

[0057] Fifth step, repeat the first step to the fourth step for each station until the sub-array covers the monitoring area, obtain the S-wave velocity structure of the underground medium of each sub-array, then collect the S-wave velocity structure of the underground medium of all sub-arrays to obtain the velocity structure set, and construct the three-dimensional velocity model of the underground medium of the monitoring area by spatial interpolation according to the velocity structure set.

[0058] In the second step, the spatial autocorrelation method is used to calculate the distance relationship between the above data set and each station in the sub-array, and all the spatial autocorrelation coefficients are obtained, which are as follows:

[0059] The polar form of the spectrum of the microseismic signal is:

[0060]

[0061] In the formula, ω and k constitute a function relationship, ζ=r (cosθ, sinθ), k=k (cosφ, sinφ), usually the spectrum of the microseismic is continuous and differentiable for frequency and propagation direction, so the above formula can be rewritten as:

[0062] E[|dz(ω,φ)| 2 ]=dH(ω,φ)=h(ω,φ)dωdφ;

[0063] In the formula, H(ω, φ) is the integral spectrum of X(t, r, θ) for azimuth, and h(ω, φ) is the frequency-azimuth density.

[0064] The spectrum of the microseismic record of the two points O(0, 0) and A(r, θ) on the circular array is respectively:

[0065]

[0066] In the formula, ω is the angular frequency, k is the wave number, φ is the incidence angle of the microseismic signal, and ζ(ω, φ) is the orthogonal random process.

[0067] The spatial autocorrelation function S(r, θ) of the two points O and A is defined as:

[0068]

[0069] In the formula, g(ω, r, θ) is the spatial covariance function, h(ω, φ) is the frequency-azimuth spectral density, and the azimuth average of the spatial covariance function g(ω, r, θ) can be obtained:

[0070]

[0071] where J0 is the first kind zero order Bessel function;

[0072] Let ρ(ω, r) be the spatial autocorrelation coefficient at angular frequency ω:

[0073]

[0074] Substitute k = ω / c(ω), ω = 2πf, c(ω) is the phase velocity of Rayleigh wave into the above formula:

[0075]

[0076] The Fourier transform of O(0, 0) and A(r, θ) are S O (0, ω) and S A (r, ω) respectively, then the spatial autocorrelation coefficient of the two points in the frequency domain can be expressed as:

[0077]

[0078] where Re(·) represents the real part, and * represents the complex conjugate.

[0079] The stations in the seismic network are greater than forty.

[0080] In the third step, the above all spatial autocorrelation coefficients are fitted by Bessel function, and the fitting method includes least square method, nonlinear least square method, and piecewise fitting.

[0081] In the first step, a preset radius is set for the central station, and the preset radius is set according to geological structure, detection depth and signal attenuation factor.

[0082] The detection depth is 4-10 times of the station spacing, and the size of the subarray is set in proportion to the target detection depth.

[0083] In the fourth step, the frequency dispersion curve is processed by inversion algorithm first, and then the shear wave velocity structure of the underground medium under the subarray is obtained, specifically: the frequency dispersion curve is the relationship between the phase velocity of the underground medium and the frequency, the abscissa is the frequency, and the ordinate is the surface wave phase velocity. Compare the fitting of the preset velocity structure calculation theoretical frequency dispersion curve and the observed frequency dispersion curve, and gradually adjust to obtain the best fitting shear wave velocity structure, that is, the relationship between the shear wave velocity of the underground medium and the depth.

[0084] In the fifth step, the shear wave velocity structures of the underground medium of all subarrays are summarized to obtain the velocity structure set, and further including, the shear wave velocity structure is quality controlled, and the abnormal value is removed to ensure the consistency of the data.

[0085] The supplementary explanation of the present application is as follows:

[0086] The data recorded by each seismograph in the array for each earthquake is recorded, and the array layout is determined, that is, the observation between each station is calculated, and the distance information is used in the extraction of the dispersion curve from the spatial autocorrelation coefficient. The data recorded by all stations for each earthquake can obtain a three-dimensional velocity structure. The earthquakes affecting the array around the array occur frequently but not simultaneously, so by comparing the three-dimensional velocity structures obtained from each earthquake data, some changes in the underground structure can be found, such as where the mine excavation is, where the water storage of the reservoir penetrates, and so on. The earthquakes above 6.0 within 1000 km, above 5.0 within 500 km, above 4.0 within 200 km, and small earthquakes near the array occur intermittently, and the surface wave data set of these earthquakes is processed.

[0087] Embodiment 1:

[0088] A method for monitoring changes in underground structure in a target area using regional earthquakes, the method for monitoring changes in underground structure in a target area using regional earthquakes comprising the following steps:

[0089] First, a station in the seismic network in the monitoring area is selected as the center station, and a preset radius is set for the center station. Then all stations within the preset radius are identified and selected, and all stations are combined to form a sub-array. The seismic surface wave data of each station in the corresponding sub-array is obtained to form a data set.

[0090] Second, the spatial autocorrelation method is used to calculate the distance relationship between the data set and each station in the sub-array, and all spatial autocorrelation coefficients are obtained.

[0091] Third, all spatial autocorrelation coefficients are fitted by Bessel function to obtain the dispersion curve.

[0092] Fourth, the dispersion curve is processed by inversion algorithm, and then the shear wave velocity structure of the underground medium under the sub-array is obtained.

[0093] Fifth, repeat the first to fourth steps for each station until the sub-array covers the monitoring area. The shear wave velocity structure of the underground medium of each sub-array is obtained, and then the shear wave velocity structure of the underground medium of all sub-arrays is summarized to obtain a velocity structure set. According to the velocity structure set, a three-dimensional velocity model of the underground medium of the monitoring area is constructed by spatial interpolation.

[0094] Embodiment 2:

[0095] Embodiment 2 is basically the same as Embodiment 1, the difference is:

[0096] The spatial autocorrelation method is used to calculate the distance relationship between the above data set and the stations in the sub-array to obtain the spatial autocorrelation coefficient, specifically as follows:

[0097] The polar coordinate form of the microseismic signal spectrum is:

[0098]

[0099] In the formula, ω and k constitute a function relationship, ζ = r (cos θ, sin θ), k = k (cos φ, sin φ), and generally the spectrum of microseismic is continuous and differentiable with respect to frequency and propagation direction, so the above formula can be rewritten as:

[0100] E [|dz (ω, φ)| 2 ] = dH (ω, φ) = h (ω, φ) dωdφ;

[0101] In the formula, H (ω, φ) is the integral spectrum of X (t, r, θ) with respect to the azimuth, and h (ω, φ) is the frequency-azimuth density.

[0102] As shown in Figure 1 , the spectrum of the microseismic record of the two points O (0, 0) and A (r, θ) on the circular array is respectively:

[0103]

[0104] In the formula, ω is the angular frequency, k is the wave number, φ is the incidence angle of the microseismic signal, and ζ (ω, φ) is an orthogonal random process.

[0105] The spatial autocorrelation function S (r, θ) of the two points O and A is defined as:

[0106]

[0107] In the formula, g (ω, r, θ) is the spatial covariance function, h (ω, φ) is the frequency-azimuth spectral density, and the azimuth average of the spatial covariance function g (ω, r, θ) can be obtained:

[0108]

[0109] In the formula, J0 is the first-order zero Bessel function;

[0110] Define ρ (ω, r) as the spatial autocorrelation coefficient of the angular frequency ω:

[0111]

[0112] By substituting k = ω / c (ω), ω = 2πf, and c (ω) into the above formula, we obtain:

[0113]

[0114] The formula shows that the Rayleigh wave phase velocity can be solved by the first type zero-order Bessel function and the spatial autocorrelation coefficient, and the corresponding spatial autocorrelation coefficient is shown as Figure 2

[0115] The Fourier transform of the points O(0, 0) and A(r, θ) is respectively represented as S O (0, ω) and S A (r, ω), and the spatial autocorrelation coefficient of the two points in the frequency domain can be represented as:

[0116]

[0117] In the formula, Re(·) represents the real part, and * represents the complex conjugate.

[0118] Example 3:

[0119] Example 3 is basically the same as example 1, and the difference is that:

[0120] The preset radius is set according to the geological structure, the detection depth and the signal attenuation factor. The detection depth is 4-10 times the station interval, and the size of the sub-array is set in proportion to the target detection depth. After processing the data of a sub-array, a subsurface structure profile is obtained, which is similar to a borehole profile. This result is the comprehensive average result of the structure within the sub-array range. Therefore, the sub-array should not be too large or too small. If it is too large, it cannot reflect the details of the horizontal direction. If it is too small, the detection depth may not be enough. During data processing, the optimal sub-array size can be selected to determine the number of seismographs in the sub-array.

[0121] Example 4:

[0122] Example 4 is basically the same as example 1, and the difference is that:

[0123] The lowest frequency is determined according to the detection depth, which also limits the depth that can be detected. After the lowest frequency is determined, the reciprocal is known as the maximum period. According to the Nyquist theorem, the data length is at least twice the period, and the frequency will not be distorted. In general, a length of more than 10 times is taken as the segmentation length for data processing. For example, if the lowest frequency is 0.2 Hz, the corresponding period is 5 s, and the segmentation data length can be taken as 40.96 s, 81.92 s or 163.84 s, etc. This is conducive to fast Fourier transform (FFT). The length of the observed data is usually several minutes or even several hours, which is usually divided into several periods, and each period is calculated by cross-correlation and then stacked and averaged to improve the signal-to-noise ratio. The averaged result is used to fit the Bessel function and then extract the dispersion curve.​

[0124] Example 5:

[0125] Example 5 is substantially the same as Example 1, except that:

[0126] Seismic surface wave is a kind of plane wave propagating on the earth's surface excited by earthquake, generally has dispersion characteristics, that is, the propagation speed of waves of different frequencies is different, and the relationship curve between the propagation speed and the frequency is the dispersion curve, so that the underground velocity structure can be inverted or inferred according to the empirical formula by observing and extracting the dispersion curve, and the size of the velocity of each part of the underground structure reflects the situation, for example, if the velocity of a certain place is obviously low, it may be a mine tunnel, or a cave caused by karst collapse, or a man-made air-raid shelter, etc., and the possible reasons can be inferred by the height and low of the velocity reflected by the three-dimensional velocity structure of the underground and the shape formed by the high and low values.

[0127] The above merely describes the preferred embodiments of the present application, and the protection scope of the present application is not limited to the above-described embodiments, but any equivalent modifications or changes made by those skilled in the art according to the disclosed content of the present application shall be included in the protection scope recorded in the claims.

Claims

1. A method for monitoring changes in subsurface structures in a target area using regional seismic activity, the method comprising: The method for monitoring the underground structure change of a target area by using regional seismic data comprises the following steps: ​ In the first step, a station in a seismic network in the monitoring area is selected as a center station, a preset radius is set for the center station, all stations within the preset radius are identified and selected, and then all the stations are combined to form a sub-array, seismic surface wave data of each station in the sub-array are obtained, and a data set is formed; In the second step, a spatial autocorrelation method is used to calculate the distance relationship between the data set and each station in the sub-array, and all spatial autocorrelation coefficients are obtained; The frequency spectrum of the microseismic signal is in polar coordinate form: ； where With constitute a functional relationship, , the spectrum of the usual microseisms is continuously differentiable with respect to frequency and propagation direction, so the above equation can be rewritten as: ； wherein is the integrated spectrum of the azimuth, is the frequency-azimuth density; The circular array is set up on , The spectrum of the microseismic record of two points is respectively: ； ； wherein is the angular frequency, is the wave number, is the micro-motion signal incident angle, is the orthogonal random process; The Two-point spatial autocorrelation function is defined as: ； where is the spatial covariance function, is the frequency-azimuth spectral density, taken as the azimuth average of the spatial covariance function is the spatial covariance function, ； wherein is the first kind zeroth order Bessel function; Definitions the spatial autocorrelation coefficient for angular frequency of ； From , , is the phase velocity of the Rayleigh wave, substituting the above formula gives: ； , The Fourier transform of the points is denoted as , The spatial autocorrelation coefficient of the two points in the frequency domain is denoted as ； wherein represents the real part, represents the complex conjugate; In the third step, all spatial autocorrelation coefficients are fitted by using a Bessel function to obtain a dispersion curve; In the fourth step, an inversion algorithm is used to process the dispersion curve, and then the shear wave velocity structure of the underground medium under the sub-array is obtained; In the fifth step, the first to fourth steps are repeated for each station until the sub-array covers the monitoring area, the shear wave velocity structure of the underground medium of each sub-array is obtained, the shear wave velocity structures of all sub-arrays are collected to obtain a velocity structure set, and a three-dimensional velocity model of the underground medium of the monitoring area is constructed by spatial interpolation based on the velocity structure set.

2. The method for monitoring the underground structure change of the target area by using regional earthquake according to claim 1, characterized in that: The number of stations in the seismic network is greater than 40.

3. The method of dynamically monitoring underground structural changes in a target area using regional earthquakes according to claim 1, characterized in that: In the third step, the fitting method includes least squares method, nonlinear least squares method, and piecewise fitting.

4. The method of dynamically monitoring underground structural changes in a target area using regional earthquakes according to claim 1, characterized in that: In the first step, a preset radius is set for the center station, and the preset radius is set according to geological structure, detection depth, and signal attenuation factors.

5. The method of using regional earthquakes to dynamically monitor underground structural changes in a target area according to claim 4, characterized in that: The detection depth is 4-10 times the distance between stations, and the size of the sub-array is set in proportion to the target detection depth.

6. The method of using regional earthquakes to dynamically monitor underground structural changes in a target area according to claim 1, characterized in that: In the fourth step, the dispersion curve is processed by using an inversion algorithm, and then the shear wave velocity structure of the underground medium under the sub-array is obtained. Specifically, the dispersion curve is the relationship between the phase velocity of the underground medium and the frequency, the abscissa is the frequency, and the ordinate is the surface wave phase velocity. The fitting of the theoretical dispersion curve and the observed dispersion curve is compared according to the preset velocity structure, and the best fitting shear wave velocity structure, i.e., the relationship between the shear wave velocity of the underground medium and the depth, is obtained by gradual adjustment.

7. The method of claim 1, wherein: In the fifth step, the velocity structure set is obtained by collecting the shear wave velocity structures of all sub-arrays, and quality control is performed on the shear wave velocity structure to remove abnormal values and ensure data consistency. ​

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