A method and system suitable for shallow structure detection in urban areas

Abstract

The present invention provides a method and system suitable for shallow structure detection in urban areas, which relates to the field of exploration geophysics. The method comprises the following steps: extracting early wave information from artificial seismic source body wave data; forward modeling the early wave information using a numerical simulation method to obtain early wave simulation information; constructing a target function according to the early wave information and the early wave simulation information; inverting the target function to obtain an iterative search direction; updating the shear wave velocity and the longitudinal wave velocity using a mixed step length according to the iterative search direction to obtain an optimal shear wave velocity and an optimal longitudinal wave velocity; constructing a shallow structure detection image according to the optimal shear wave velocity and the optimal longitudinal wave velocity. The present invention can perform high-precision detection of shallow structures in urban areas.

Description

Technical Field

[0001] The invention relates to the field of geophysical exploration, and in particular to a method and system suitable for shallow structure detection in urban areas. Background Art

[0002] The detailed exploration of shallow urban structures is of great significance to earthquake disaster prevention and reduction, urban planning, and urban underground space utilization. The use of dense array observations and noise imaging methods allows us to conduct relatively low-cost detection of shallow structures in urban areas. However, due to the periodic frequency band of background noise and the characteristics of the surface waves themselves, the imaging results based on background noise are mostly relatively smooth S-wave velocities with low resolution.

[0003] Artificial seismic sources are important tools for detecting shallow structures, but traditional explosive seismic sources have a great impact on urban environmental protection and safety and cannot be used in urban areas. The green artificial sources developed in recent years with high-pressure gas shocks have provided a new tool for non-destructive detection in urban areas. The green methane detonation seismic source uses chemical reactions to produce high-pressure gas, and the excited seismic wave signals can be clearly received within a medium-scale range of more than ten kilometers horizontally. Therefore, the body wave signals generated by the green artificial seismic source, combined with the dense array observation mode, can be used to actively detect the crustal structure of several kilometers in the shallow depth. However, the detection of green artificial seismic sources is limited by cost and construction, the number of excitation points is small, and the accuracy of the P-wave velocity structure obtained by the imaging method based on travel time is limited.

[0004] Therefore, how to design a high-precision method for detecting shallow structures in urban areas has become a technical problem that needs to be urgently solved in this field. Summary of the invention

[0005] The purpose of the present invention is to provide a method and system suitable for detecting shallow structures in urban areas, wherein the method and system can detect shallow structures in urban areas with high precision.

[0006] To achieve the above object, the present invention provides the following solutions:

[0007] A method for detecting shallow structures in urban areas, the method comprising the following steps:

[0008] Extracting early arrival wave information from artificial seismic source body wave data, wherein the artificial seismic source body wave data is data obtained by geophone detection after methane explosion in urban areas;

[0009] forward modeling the early wave information using a numerical simulation method to obtain early wave simulation information;

[0010] Constructing an objective function according to the early wave information and the early wave simulation information, wherein the objective function is used to measure the mutual matching degree between the early wave information and the early wave simulation information;

[0011] Inverting the objective function to obtain an iterative search direction;

[0012] According to the iterative search direction, the shear wave velocity and the longitudinal wave velocity are updated using a mixed step length to obtain an optimal shear wave velocity and an optimal longitudinal wave velocity, wherein the mixed step length includes a weighted longitudinal wave velocity step length and a shear wave velocity weighted step length, and the optimal shear wave velocity and the optimal longitudinal wave velocity are the shear wave velocity and the longitudinal wave velocity when the early arrival wave information and the early arrival wave simulation information have the highest mutual matching degree;

[0013] A shallow structure detection image is constructed according to the optimal shear wave velocity and the optimal longitudinal wave velocity.

[0014] The present invention also provides a system suitable for detecting shallow structures in urban areas, the system comprising:

[0015] An extraction module, used to extract early arrival wave information from artificial seismic source body wave data, wherein the artificial seismic source body wave data is data obtained by detection after methane explosion;

[0016] A forward modeling module, used for forward modeling the early wave information using a numerical simulation method to obtain early wave simulation information;

[0017] An objective function construction module, used for constructing an objective function according to the early wave information and the early wave simulation information, wherein the objective function is used for measuring the mutual matching degree between the early wave information and the early wave simulation information;

[0018] An inversion module, used for inverting the objective function to obtain an iterative search direction;

[0019] a velocity updating module, for updating the shear wave velocity and the longitudinal wave velocity according to the iterative search direction using a mixed step length to obtain an optimal shear wave velocity and an optimal longitudinal wave velocity, wherein the mixed step length includes a weighted longitudinal wave velocity step length and a shear wave velocity weighted step length, and the optimal shear wave velocity and the optimal longitudinal wave velocity are the shear wave velocity and the longitudinal wave velocity when the early arrival wave information and the early arrival wave simulation information have the highest mutual matching degree;

[0020] The image construction module is used to construct a shallow structure detection image according to the optimal shear wave velocity and the optimal longitudinal wave velocity.

[0021] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0022] The present invention provides a method and system for shallow structure detection in urban areas, the method comprising the following steps: extracting early wave information from artificial seismic source body wave data; forward modeling the early wave information using a numerical simulation method to obtain early wave simulation information; constructing an objective function based on the early wave information and the early wave simulation information, the objective function being used to measure the mutual matching degree between the early wave information and the early wave simulation information; inverting the objective function to obtain an iterative search direction; updating the shear wave velocity and the longitudinal wave velocity using a mixed step size according to the iterative search direction to obtain an optimal shear wave velocity and an optimal longitudinal wave velocity; constructing a shallow structure detection image based on the optimal shear wave velocity and the optimal longitudinal wave velocity. The present invention simulates the early wave information to obtain the early wave simulation information, and then matches the early wave information with the early wave simulation information. When the matching degree between the early wave information and the early wave simulation information is the highest, the shear and longitudinal wave velocities obtained based on the early wave simulation information can best reflect the real shallow structure, thereby realizing high-precision detection of the shallow structure. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0024] Figure 1 A flow chart of a method for shallow structure detection in urban areas provided in Example 1 of the present invention;

[0025] Figure 2 This is a structural block diagram of a system suitable for shallow structure detection in urban areas provided in Example 2 of the present invention.

[0026] Explanation of symbols:

[0027] 1. Extraction module; 2. Forward modeling module; 3. Objective function construction module; 4. Inversion module; 5. Velocity update module; 6. Image construction module. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] The purpose of the present invention is to provide a method and system suitable for detecting shallow structures in urban areas, wherein the method and system can detect shallow structures in urban areas with high precision.

[0030] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0031] Embodiment 1:

[0032] See also Figure 1 The present invention provides a method for detecting shallow structures in urban areas, the method comprising the following steps:

[0033] S1: extracting early wave information from artificial source body wave data according to the geological background and the given early wave extraction range of the offset distance, wherein the artificial source body wave data is the data obtained by the geophone detection after the methane explosion in the urban area;

[0034] S2: forward modeling the early wave information using a numerical simulation method to obtain early wave simulation information;

[0035] S3: constructing an objective function according to the early wave information and the early wave simulation information, wherein the objective function is used to measure the mutual matching degree between the early wave information and the early wave simulation information;

[0036] S4: Invert the objective function to obtain an iterative search direction;

[0037] S5: according to the iterative search direction, the shear wave velocity and the longitudinal wave velocity are updated using a mixed step length to obtain an optimal shear wave velocity and an optimal longitudinal wave velocity, wherein the mixed step length includes a weighted longitudinal wave velocity step length and a shear wave velocity weighted step length, and the optimal shear wave velocity and the optimal longitudinal wave velocity are the shear wave velocity and the longitudinal wave velocity when the early arrival wave information and the early arrival wave simulation information have the highest mutual matching degree;

[0038] S6: constructing a shallow structure detection image according to the optimal shear wave velocity and the optimal longitudinal wave velocity.

[0039] Before step S1, the method further includes:

[0040] S7: Acquire dense array continuous data, where the dense array continuous data is data obtained through dense array detection;

[0041] S8: extracting the surface wave signal from the continuous data of the dense array to obtain surface wave dispersion information;

[0042] S9: Inverting the surface wave dispersion information to obtain the shear wave velocity of the dense array continuous data;

[0043] S10: Acquire artificial source body wave data;

[0044] S11: extracting body wave signals from the artificial source body wave data to obtain body wave travel time information;

[0045] S12: Perform travel time analysis on the body wave travel time information to obtain the longitudinal wave velocity of the artificial source body wave data.

[0046] In step S2, the early wave information is forward modeled using a numerical simulation method to obtain early wave simulation information, specifically including: using damping-based elastic wave equation forward modeling to achieve early wave simulation. The numerical simulation method uses a second-order time and tenth-order space finite difference method.

[0047] In step S3, the relational expression of the objective function is as follows:

[0048]

[0049] Wherein, E is the objective function, u is the early arrival wave simulated seismic record, the early arrival wave simulated seismic record is obtained from the early arrival wave simulation information, and w is the intercepted early arrival wave record of artificial seismic source body wave data, the intercepted early arrival wave record of artificial seismic source body wave data is obtained from the early arrival wave information.

[0050] Subsequently, the adjoint state method is used to obtain the gradient in preparation for the subsequent calculation of the search direction.

[0051] In step S4, the objective function is inverted to obtain an iterative search direction, which specifically includes:

[0052] The block diagonal pseudo-Hessian inverse matrix is ​​selected as the preconditioning factor of the preconditioned conjugate gradient method;

[0053] According to the preconditioned conjugate gradient method, the iterative search direction is obtained; the calculation method of the preconditioned conjugate gradient method is as follows:

[0054]

[0055] Among them, d k is the kth iteration search direction, (H b ) -1 is the block diagonal pseudo-Hessian inverse matrix, J k is the kth iteration gradient, τ k-1 is the conjugate gradient method coefficient for the k-1th iteration, d k-1 Search direction for the k-1th iteration.

[0056] Introducing the Hessian matrix in the inversion process can effectively suppress the crosstalk between the parameters in the update direction, but the calculation and storage cost of the Hessian matrix is ​​large. Therefore, the present invention estimates the Hessian inverse matrix using a block diagonal pseudo-Hessian inverse matrix, and the block diagonal pseudo-Hessian inverse matrix is:

[0057]

[0058] Among them, (H b ) -1 is the block diagonal pseudo-Hessian inverse matrix, H pp and H ss are the main diagonal elements, representing the geometric diffusion effect, H ps With H sp are non-main diagonal elements, representing the coupling effect between parameters; the four elements in the block diagonal pseudo-Hessian inverse matrix, namely H pp , H ss , H ps and H sp It is calculated by the pseudo Hessian matrix, and the pseudo Hessian matrix is:

[0059]

[0060] Where k is the shear wave velocity s or the longitudinal wave velocity p, l is the shear wave velocity s or the longitudinal wave velocity p, T is the transpose, * is the conjugate, m is the physical parameter of the longitudinal and shear wave velocity, S is the coefficient matrix, and A is (S -1 ) T (S -1 ) * The present invention uses the amplitude field value g of the impulse response extracted from the wave field forward propagation inherent information to approximate the matrix. j,n For the above (S -1 ) T (S -1 ) * The calculation formula of the matrix is ​​as follows:

[0061]

[0062] Where, ns is the number of sources and receivers, g j,n is the amplitude field value of the impulse response, j and n are the grid positions.

[0063] In step S5, according to the iterative search direction, the shear wave velocity and the longitudinal wave velocity are updated using a mixed step size to obtain an optimal shear wave velocity and an optimal longitudinal wave velocity, specifically including:

[0064] The shear wave velocity and the longitudinal wave velocity are updated by the following formula:

[0065]

[0066] in, is the longitudinal wave velocity at the kth iteration, is the shear wave velocity at the kth iteration, are the physical parameters such as the longitudinal and shear wave velocities of the k+1th iteration, is the kth iteration longitudinal and transverse wave velocity and other physical parameters, α k is the overall step length, is the weighted step size of the longitudinal wave velocity, is the weighted step length of the shear wave velocity. The weighted step length can protect the characteristics of each parameter and improve the accuracy of the inversion. is the search direction of the longitudinal wave velocity in the kth iteration, is the search direction of the shear wave velocity in the kth iteration.

[0067] Specifically, the calculation formula of the longitudinal wave velocity weighted step length is as follows:

[0068]

[0069] in, is the weighted step size of the longitudinal wave velocity, is the longitudinal wave velocity test step length, is the longitudinal wave velocity at the kth iteration, The longitudinal wave velocity at the kth iteration is The early arrival wave simulates the earthquake record at 1000 Hz. The longitudinal wave velocity at the kth iteration is The early arrival wave simulates the earthquake record at 1000 Hz. The longitudinal wave velocity at the kth iteration is Early arrival wave simulation earthquake record Actual earthquake records with early arrival waves The difference between , T is the transpose.

[0070] The calculation formula of the shear wave velocity weighted step length is as follows:

[0071]

[0072] in, is the weighted step size of the longitudinal wave velocity, is the longitudinal wave velocity test step length, is the shear wave velocity at the kth iteration, The shear wave velocity at the kth iteration is The early arrival wave simulates the earthquake record at 1000 Hz. The shear wave velocity at the kth iteration is The early arrival wave simulates the earthquake record at 1000 Hz. The shear wave velocity at the kth iteration is Early arrival wave simulation earthquake record Actual earthquake records with early arrival waves The difference between , T is the transpose.

[0073] In addition, the overall step size can ensure the overall convergence of the inversion and improve the computational efficiency. The overall step size is estimated using a non-monotonic search technique, and the calculation formula for the overall step size is as follows:

[0074]

[0075] Among them, E k+1 is the objective function after the k+1th iteration, C k is the average value of the objective function after the kth iteration, C k =((η k Q k C k-1 +E k-1 )) / Q k , Q k =η k Q k-1 +1 is the number of objective functions stored for k iterations, Q k-1 is the number of objective functions stored for k-1 iterations, η k ∈(0,1) is the degree of nonlinearity, C k-1 is the average value of the objective function after the k-1th iteration, E k-1 is the objective function after the k-1th iteration, d k is the search direction for the kth iteration, is the first-order derivative of the objective function after the kth iteration.

[0076] This hybrid inversion strategy comprehensively considers the sensitivity differences of various parameters and the overall convergence of the inversion, which can reduce the crosstalk between inversion parameters and improve the inversion accuracy and convergence speed.

[0077] The present invention conducts dense array observations in urban areas and combines the detection mode of sparse excitation of green artificial sources, uses background noise imaging to obtain shallow shear wave velocity; uses artificial sources to carry out first arrival travel time tomography inversion to obtain shallow longitudinal wave velocity. The shear wave velocity and longitudinal wave velocity obtained by background noise imaging and artificial source first arrival travel time tomography are used as initial models, and on this basis, early wave waveform information is introduced to carry out elastic wave multi-parameter early wave waveform inversion, and finally obtain high-precision shallow longitudinal and shear wave velocity structures. The present invention can fully tap the waveform information in dense observations, and form high-precision imaging of shallow structures in urban areas within a horizontal observation range of more than ten kilometers that can be carried out at a relatively low cost.

[0078] Embodiment 2:

[0079] See also Figure 2 The present invention provides a system suitable for detecting shallow structures in urban areas, the system comprising:

[0080] Extraction module 1, used to extract early arrival wave information from artificial seismic source body wave data, wherein the artificial seismic source body wave data is data obtained by detection after methane explosion;

[0081] A forward modeling module 2 is used to perform forward modeling on the early wave information using a numerical simulation method to obtain early wave simulation information;

[0082] An objective function construction module 3 is used to construct an objective function according to the early wave information and the early wave simulation information, wherein the objective function is used to measure the mutual matching degree between the early wave information and the early wave simulation information;

[0083] An inversion module 4 is used to invert the objective function to obtain an iterative search direction;

[0084] A velocity updating module 5 is used for updating the shear wave velocity and the longitudinal wave velocity according to the iterative search direction using a mixed step length to obtain an optimal shear wave velocity and an optimal longitudinal wave velocity, wherein the mixed step length includes a weighted longitudinal wave velocity step length and a shear wave velocity weighted step length, and the optimal shear wave velocity and the optimal longitudinal wave velocity are the shear wave velocity and the longitudinal wave velocity when the early arrival wave information and the early arrival wave simulation information have the highest mutual matching degree;

[0085] The image construction module 6 is used to construct a shallow structure detection image according to the optimal shear wave velocity and the optimal longitudinal wave velocity.

[0086] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0087] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for detecting shallow structures in urban areas, characterized in that: The following steps are involved: Extracting early arrival wave information from artificial seismic source body wave data, wherein the artificial seismic source body wave data is data obtained by geophone detection after methane explosion in urban areas; forward modeling the early wave information using a numerical simulation method to obtain early wave simulation information; Constructing an objective function according to the early wave information and the early wave simulation information, wherein the objective function is used to measure the mutual matching degree between the early wave information and the early wave simulation information; Inverting the objective function to obtain an iterative search direction; According to the iterative search direction, the shear wave velocity and the longitudinal wave velocity are updated using a mixed step length to obtain an optimal shear wave velocity and an optimal longitudinal wave velocity, wherein the mixed step length includes a weighted longitudinal wave velocity step length and a shear wave velocity weighted step length, and the optimal shear wave velocity and the optimal longitudinal wave velocity are the shear wave velocity and the longitudinal wave velocity when the early arrival wave information and the early arrival wave simulation information have the highest mutual matching degree; Constructing a shallow structure detection image according to the optimal shear wave velocity and the optimal longitudinal wave velocity; The objective function relationship is as follows: Wherein, E is the objective function, u is the early wave simulated seismic record, the early wave simulated seismic record is obtained from the early wave simulation information, w is the intercepted early wave record of artificial source body wave data, the intercepted early wave record of artificial source body wave data is obtained from the early wave information; The objective function is inverted to obtain an iterative search direction, which specifically includes: The block diagonal pseudo-Hessian inverse matrix is ​​selected as the preconditioning factor of the preconditioned conjugate gradient method; According to the preconditioned conjugate gradient method, the iterative search direction is obtained; the calculation method of the preconditioned conjugate gradient method is as follows: Among them, d k is the kth iteration search direction, (H b ) -1 is the block diagonal pseudo-Hessian inverse matrix, J k is the kth iteration gradient, τ k-1 is the conjugate gradient method coefficient for the k-1th iteration, d k-1 Search direction for the k-1th iteration.

2. The method for detecting shallow structures in urban areas according to claim 1, characterized in that: Before the step of extracting early arrival wave information from artificial seismic source body wave data, it also includes: Acquiring dense array continuous data, wherein the dense array continuous data is data obtained through dense array detection; Extracting surface wave signals from the continuous data of the dense array to obtain surface wave dispersion information; Inverting the surface wave dispersion information to obtain the shear wave velocity of the dense array continuous data; Acquire artificial seismic source body wave data; Extracting body wave signals from the artificial seismic source body wave data to obtain body wave travel time information; The travel time information of the body wave is analyzed to obtain the longitudinal wave velocity of the artificial source body wave data.

3. The method for detecting shallow structures in urban areas according to claim 1, characterized in that: The block diagonal pseudo-Hessian inverse matrix is: Among them, (H b ) -1 is the block diagonal pseudo-Hessian inverse matrix, H pp and H ss are the main diagonal elements, representing the geometric diffusion effect, H ps and H sp are non-main diagonal elements, representing the coupling effect between parameters; the elements in the block diagonal pseudo-Hessian inverse matrix are calculated by the pseudo-Hessian matrix, and the pseudo-Hessian matrix is: Where k is the shear wave velocity s or the longitudinal wave velocity p, l is the shear wave velocity s or the longitudinal wave velocity p, T is the transpose, * is the conjugate, m is the physical parameter of the longitudinal and shear wave velocity, S is the coefficient matrix, and A is (S -1 ) T (S -1 ) * The matrix approximation, the (S -1 ) T (S -1 ) * The calculation formula of the matrix is ​​as follows: Where, ns is the number of sources and receivers, g j,n is the amplitude field value of the impulse response, j and n are the grid positions.

4. The method for detecting shallow structures in urban areas according to claim 1, characterized in that: According to the iterative search direction, the shear wave velocity and the longitudinal wave velocity are updated using a mixed step size to obtain an optimal shear wave velocity and an optimal longitudinal wave velocity, specifically including: The shear wave velocity and the longitudinal wave velocity are updated by the following formula: in, is the longitudinal wave velocity at the kth iteration, is the shear wave velocity at the kth iteration, are the physical parameters such as longitudinal and shear wave velocities of the k+1th iteration, is the kth iteration longitudinal and transverse wave velocity and other physical parameters, α k is the overall step length, is the weighted step size of the longitudinal wave velocity, is the shear wave velocity weighting step size, is the search direction of the longitudinal wave velocity in the kth iteration, is the search direction of the shear wave velocity in the kth iteration.

5. The method for detecting shallow structures in urban areas according to claim 4, characterized in that: The calculation formula of the P-wave velocity weighted step length is as follows: in, is the weighted step size of the longitudinal wave velocity, is the longitudinal wave velocity test step length, is the longitudinal wave velocity at the kth iteration, The longitudinal wave velocity at the kth iteration is The early arrival wave simulates the earthquake record at 1000 Hz. The longitudinal wave velocity at the kth iteration is The early arrival wave simulates the earthquake record at 1000 Hz. The longitudinal wave velocity at the kth iteration is Early arrival wave simulation earthquake record Actual earthquake records with early arrival waves The difference between , T is the transpose.

6. The method for detecting shallow structures in urban areas according to claim 4, characterized in that: The calculation formula of the shear wave velocity weighted step length is as follows: in, is the weighted step size of the longitudinal wave velocity, is the longitudinal wave velocity test step length, is the shear wave velocity at the kth iteration, The shear wave velocity at the kth iteration is The early arrival wave simulates the earthquake record at 1000 Hz. The shear wave velocity at the kth iteration is The early arrival wave simulates the earthquake record at 1000 Hz. The shear wave velocity at the kth iteration is Early arrival wave simulation earthquake record Actual earthquake records with early arrival waves The difference between , T is the transpose.

7. The method for detecting shallow structures in urban areas according to claim 4, characterized in that: The overall step length is estimated using a non-monotonic search technique, and the calculation formula for the overall step length is as follows: Among them, E k+1 is the objective function after the k+1th iteration, C k is the average value of the objective function after the kth iteration, C k =((η k Q k C k-1 +E k-1 )) / Q k , Q k =η k Q k-1 +1 is the number of objective functions stored for iteration k, Qk-1 is the number of objective functions stored for iteration k-1, η k ∈(0,1) is the degree of nonlinearity, C k-1 is the average value of the objective function after the k-1th iteration, E k-1 is the objective function after the k-1th iteration, d k is the search direction for the kth iteration, is the first-order derivative of the objective function after the kth iteration.

8. A system suitable for detecting shallow structures in urban areas, used to implement the method suitable for detecting shallow structures in urban areas as claimed in any one of claims 1 to 7, characterized in that: include: An extraction module, used to extract early arrival wave information from artificial seismic source body wave data, wherein the artificial seismic source body wave data is data obtained by detection after methane explosion; A forward modeling module, used for forward modeling the early wave information using a numerical simulation method to obtain early wave simulation information; An objective function construction module, used for constructing an objective function according to the early wave information and the early wave simulation information, wherein the objective function is used for measuring the mutual matching degree between the early wave information and the early wave simulation information; An inversion module, used for inverting the objective function to obtain an iterative search direction; a velocity updating module, for updating the shear wave velocity and the longitudinal wave velocity according to the iterative search direction using a mixed step length to obtain an optimal shear wave velocity and an optimal longitudinal wave velocity, wherein the mixed step length includes a weighted longitudinal wave velocity step length and a shear wave velocity weighted step length, and the optimal shear wave velocity and the optimal longitudinal wave velocity are the shear wave velocity and the longitudinal wave velocity when the early arrival wave information and the early arrival wave simulation information have the highest mutual matching degree; The image construction module is used to construct a shallow structure detection image according to the optimal shear wave velocity and the optimal longitudinal wave velocity.

Images