Gradient Regularization Method and System Based on Geological Structure Constraint

Abstract

The present invention discloses a gradient regularization method and system based on geological structure constraints, including: inputting a background velocity model for migration imaging to obtain a migration imaging profile; extracting reflection structure information on the migration imaging profile; calculating dip information at the reflection structure by means of dip scanning and similarity coefficient; between horizons, using a scattered data interpolation method to obtain dip information for the entire migration imaging profile; calculating the variance magnitude and an anisotropic Gaussian function; constructing a smoothing operator along the geological direction and imposing regularization; and outputting a regularization result. The present invention can flexibly select reflection structure information, impose geological structure smoothing between reflection horizons and horizons, adapt to different stages of inversion, and is robust and has strong noise adaptability. The present invention can also effectively improve the convergence and accuracy of velocity inversion, significantly enhance the medium wavenumber component of the inversion result, and obtain a velocity model with geological significance.

Description

Technical Field

[0001] This application relates to the field of geophysical exploration technologies, and particularly relates to a gradient regularization method and system based on geological structure constraints. Background Art

[0002] In exploration seismology, the imposition of model regularization is of great significance for seismic wave velocity inversion. By introducing prior information about the solution, regularization can significantly reduce the non-uniqueness of the tomographic inversion solution, improve the stability of the inverse problem, and make the inversion result more geologically meaningful. For different inversion targets, the imposition and utilization of prior information are also different. During the velocity inversion process, it is first necessary to impose the constraint of reflection horizon information. By constraining the velocity change with the reflection horizon, the medium and high wavenumber components of the background velocity can be effectively improved. Therefore, during the velocity inversion process, how to introduce reasonable structural / structural information is the most critical part. Currently, the methods for imposing velocity model regularization mainly include the following:

[0003] Mathematically, different forms of norm constraints are mainly imposed on model parameters to penalize a certain component in the model. Hu et al. (2009) used Tikhonov regularization to obtain the smooth component of the model; Loris et al. (2007) used the L1 norm to obtain a sparse solution; Qiu et al. (2016) used edge-guided TV regularization to impose geological structure feature constraints.

[0004] In the transform domain, relevant sparse constraints are imposed in a preconditioned manner to constrain the inversion solution. Li et al. (2012) updated the model in the Curvelet domain using a random data subset. Xue et al. (2017) imposed sparse constraints in the Seislet domain to improve the stability of the inversion. Li and Harris (2018) used dictionary learning to construct sparse basis functions to achieve adaptive sparse constraint regularization.

[0005] However, the above regularization methods only impose sparse constraints mathematically or impose sparse constraints along the structure, but the overall sparse constraints will harm weak signals and cannot constrain only the effective reflection signals.

[0006] From a geophysical perspective, regularization constraints are mainly achieved by applying prior information related to geology or geophysics. Clapp et al. (2004) utilized a spatially variable dip penalty filter as a model coarsening operator to construct an unsteady-phase steering filter that smooths along the structural dip. Zhou et al. (2009) adopted a three-dimensional anisotropic Gaussian smoothing filter as a smoothing operator. Guitton et al. (2012) constructed a directional Laplace operator along the dip direction of the subsurface structure and used an unsteady-phase spatial convolution operator to construct a smoothing operator, realizing full waveform inversion based on geological structure direction constraints. Zhou (2013) proposed constructing an anisotropic Gaussian function along the dip of the structure. Lewis et al. (2014) applied anisotropic diffusion equations to smooth the full waveform inversion gradient and constrain the inversion results. Wellington et al. (2015) achieved anisotropic unsteady-phase wavenumber filtering by controlling the lengths of operators in different directions of the Laplace operator.

[0007] The above methods construct anisotropic smoothing functions or anisotropic diffusion functions from a geophysical perspective to process the gradient. However, such methods can only maintain the structural direction at horizons, and are often isotropic or simply anisotropic processed at non-horizon locations, and their results depend on the accuracy of geological structure extraction.

[0008] The present invention introduces geological structure guidance and develops a gradient regularization method that integrates reflection horizon constraints. Different from conventional methods, the present invention not only requires smoothing along the structural direction at positions containing structures, but also requires smoothing along the structural direction between horizons, so that the entire gradient result can be constrained by the geological structure direction, obtaining a velocity model with more geological significance. Summary of the Invention

[0009] The purpose of this part is to outline some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Simplifications or omissions may be made in this part, as well as in the abstract and title of the present application, to avoid obscuring the purpose of this part, the abstract, and the title, but such simplifications or omissions cannot be used to limit the scope of the present invention.

[0010] In view of the problems existing in the above-mentioned existing gradient regularization methods and systems based on geological structure constraints, the present invention is proposed.

[0011] Therefore, the purpose of the present invention is to solve the problem of imposing gradient regularization in the velocity inversion process in exploration seismology.

[0012] To solve the above technical problems, the present invention provides the following technical solutions:

[0013] In a first aspect, an embodiment of the present invention provides a gradient regularization method based on geological structure constraints, including:

[0014] Input the background velocity model for migration imaging to obtain a migration imaging profile;

[0015] Extract the reflection structure information on the migration imaging profile;

[0016] Calculate the dip information at the reflection structure by means of dip scanning and similarity coefficient;

[0017] Taking the selected reflection structure as known scattered points, between horizons, use the scattered data interpolation method to obtain the dip information of the entire migration imaging profile;

[0018] Calculate the variance magnitude according to the structural points and non-structural points of the migration imaging profile;

[0019] Calculate the anisotropic Gaussian function according to the variance magnitude;

[0020] Construct a smoothing operator along the geological direction and apply regularization;

[0021] Output the regularization result.

[0022] As the gradient regularization method based on geological structure constraints of the present invention, wherein: extracting the reflection structure information on the migration imaging profile includes,

[0023] Preferentially extract the information of the main reflection structure horizons. In the initial stage of velocity inversion, only use the main reflection structure horizons to control the horizon changes of the entire migration imaging profile;

[0024] In the subsequent process of velocity inversion, gradually add more detailed reflection horizon information.

[0025] As the gradient regularization method based on geological structure constraints of the present invention, wherein: calculating the dip information at the reflection structure includes,

[0026] By means of dip scanning, calculate the similarity coefficient spectra of different dips, and extract the dip value corresponding to the maximum similarity coefficient as the dip at the reflection structure. The specific formula is as follows:

[0027]

[0028] Among them, ds is the integral of the function, x and z are the positions of the profile respectively, θ is the dip information, τ: z + θx = 0 is the integral path corresponding to the dip, a(z, x) is a template function with a value of 1, and st(z, x) is the calculated reflection structure profile.

[0029] As the gradient regularization method based on geological structure constraints of the present invention, wherein: taking the selected reflection structure as known scattered points, between horizons, the scattered data interpolation method is adopted to obtain the dip information of the entire migration imaging profile, including

[0030] Construct the following interpolation basis linear equations:

[0031]

[0032] wherein, x i =(z i , x i ) is the position at the known horizon structure, x is the point to be interpolated, is the interpolation basis function, θ(x) is the dip value, λ i is the interpolation coefficient;

[0033] Solve the linear equations to obtain the interpolation coefficients, and perform scattered data interpolation between two structures of the strata to obtain the dip information of the entire migration imaging profile.

[0034] As the gradient regularization method based on geological structure constraints of the present invention, wherein: calculating the variance magnitude according to the structural points and non-structural points of the migration imaging profile includes,

[0035] At the position of the structural point, use the following formula to determine the magnitudes of the variances σ u and σ v :

[0036]

[0037] wherein, is the minimum variance set along the strike direction of the geological interface at the structural point, is the minimum variance set in the direction perpendicular to the geological interface of the migration imaging profile at the structural point, and

[0038] At the position of the non-structural point, use the following formula to determine the magnitudes of the variances σ u and σ v :

[0039]

[0040]

[0041] wherein, is the maximum variance set along the strike direction of the geological interface at the structural point, is the maximum variance set in the direction perpendicular to the geological interface of the migration imaging profile at the structural point, d is the distance between the current point and the nearest reflection interface, d mis the maximum distance threshold coefficient, σ u is the variance magnitude of the offset imaging profile along the strike direction of the geological interface, σ v is the variance magnitude of the offset imaging profile in the direction perpendicular to the geological interface.

[0042] As the gradient regularization method based on geological structure constraints of the present invention, wherein: calculating the anisotropic Gaussian function according to the variance magnitude includes,

[0043] Calculating the anisotropic Gaussian function g(x0,z0) according to the variance of the offset imaging profile, and the specific formula is as follows:

[0044]

[0045] wherein, x0 and z0 are the center point positions calculated currently, σ u is the variance magnitude of the offset imaging profile along the strike direction of the geological interface, σ v is the variance magnitude of the offset imaging profile in the direction perpendicular to the geological interface.

[0046] As the gradient regularization method based on geological structure constraints of the present invention, wherein: constructing a smoothing operator along the geological direction and imposing regularization includes,

[0047] Constructing a smoothing operator by rotating the Cartesian coordinate system to the local geological structure coordinate system, and the relationship between the Cartesian coordinate system (x,y) and the geological structure coordinate system (u,v) is described by the rotation operator T, and the specific formula is as follows:

[0048] g s (x0,z0) = T(x0,z0)g(x0,z0)

[0049]

[0050] wherein, u is the direction along the strike of the geological interface, and v is the direction perpendicular to the geological interface.

[0051] Solving the regularization in two steps includes calculating the gradient Grad k of the objective functional, and performing post-processing on the functional gradient to realize the model regularization constraint of the objective functional, and the iteration formula is as follows:

[0052]

[0053]

[0054] wherein, k is the number of iterations, Grad k is the calculated conventional gradient result, is the smoothing operator along the geological direction, is the processed gradient result, α k is the step operator, m k is the previous velocity inversion result, m k+1 is the updated velocity inversion result.

[0055] In a second aspect, an embodiment of the present invention provides a gradient regularization system based on geological structure constraints, including:

[0056] A model building module for performing migration in a background velocity model to obtain a migrated imaging profile and extract reflection structure information on the migrated imaging profile;

[0057] A calculation module for calculating dip information at the reflection structure, constructing an interpolation basis function through the dip information at the reflection structure to obtain dip information of the entire migrated imaging profile, calculating the variance of the migrated imaging profile through structural points and non-structural points, then calculating an anisotropic Gaussian function, and finally calculating a smoothing operator along the geological direction and applying regularization;

[0058] An output module for outputting a regularization result.

[0059] In a third aspect, an embodiment of the present invention provides a computing device, including:

[0060] A memory and a processor;

[0061] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the gradient regularization method based on geological structure constraints according to any one of claims 1 to 7 are implemented.

[0062] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium storing computer-executable instructions, and when the computer-executable instructions are executed by a processor, the steps of the gradient regularization method based on geological structure constraints according to any one of claims 1 to 7 are implemented.

[0063] Advantages of the present invention: The present invention introduces geological structure guidance and develops a gradient regularization method that integrates reflection horizon constraints. Different from conventional methods, the present invention not only requires smoothing along the geological direction at positions containing structures, but also requires smoothing along the geological direction between horizons, so that the entire gradient result can be subjected to constraints in the direction of the geological structure, and a more geologically meaningful velocity model can be obtained. Specifically, it is manifested as:

[0064] 1. By picking up the corresponding reflection imaging structure information on the migrated imaging section at different stages of inversion, effective structural information can be selected to participate in model constraints, which can not only effectively reduce the influence of noise, but also obtain robust formation dip information.

[0065] 2. By using the method of scattered data interpolation between horizons, dip information between layers can be obtained, making the anisotropic function within the layer calculated therefrom consistent with the direction at the structural location, which is more in line with the geological evolution law.

[0066] 3. By distinguishing structural points from non-structural points and setting different variance coefficients, different constraints can be imposed according to the reliability of information. At structural points, the constraint strength is large, and away from structural points, the constraint on the velocity model becomes smaller and smaller.

[0067] 4. This method can flexibly select reflection structure information, is more convenient to adapt to different stages of inversion, and this method is relatively robust and has strong adaptability to noise.

[0068] 5. This method can effectively improve the convergence and accuracy of velocity inversion, and significantly enhance the medium wave number component in the velocity estimation result, obtaining a velocity model with geological significance. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for description in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts. Among them:

[0070] Figure 1 is the flow chart of the gradient regularization method and system based on geological structure constraint of the present invention.

[0071] Figure 2 is the migrated imaging result diagram under the background velocity of the gradient regularization method and system based on geological structure constraint of the present invention.

[0072] Figure 3 is the obtained gradient result diagram under the background velocity of the gradient regularization method and system based on geological structure constraint of the present invention.

[0073] Figure 4 is the extracted imaging structure result diagram of the gradient regularization method and system based on geological structure constraint of the present invention.

[0074] Figure 5 is the dip information diagram at the reflection structure position of the gradient regularization method and system based on geological structure constraint of the present invention.

[0075] Figure 6 This is the dip information map of the entire profile obtained by the scattered data interpolation method for the gradient regularization method and system based on geological structure constraints of the present invention.

[0076] Figure 7 This is the variance magnitude map in the direction parallel to the geological interface for the gradient regularization method and system based on geological structure constraints of the present invention.

[0077] Figure 8 This is the variance magnitude map in the direction perpendicular to the geological interface for the gradient regularization method and system based on geological structure constraints of the present invention.

[0078] Figure 9 This is the impulse test model map for the gradient regularization method and system based on geological structure constraints of the present invention.

[0079] Figure 10 This is the result display map of the isotropic basis function for the gradient regularization method and system based on geological structure constraints of the present invention.

[0080] Figure 11 This is the result display map of the anisotropic Gaussian basis function along the structure for the gradient regularization method and system based on geological structure constraints of the present invention.

[0081] Figure 12 This is the isotropic smoothing result map for the gradient regularization method and system based on geological structure constraints of the present invention.

[0082] Figure 13 This is the gradient regularization result map based on geological structure constraints for the gradient regularization method and system based on geological structure constraints of the present invention. Detailed implementation manners

[0083] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following detailed description of the specific implementation manners of the present invention will be given in conjunction with the accompanying drawings of the specification.

[0084] In the following description, many specific details are set forth to facilitate a thorough understanding of the present invention. However, the present invention may be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0085] Secondly, the so-called "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation manner of the present invention. The appearances of "in one embodiment" in different places in this specification do not all refer to the same embodiment, nor are they separate or alternative embodiments that exclude each other with other embodiments.

[0086] Next, the present invention will be described in detail with reference to the schematic diagrams. When describing the embodiments of the present invention in detail, for the convenience of explanation, the cross-sectional views showing the device structure will be enlarged locally out of the general scale, and the schematic diagrams are only examples and should not limit the scope of protection of the present invention herein. In addition, in actual production, three-dimensional spatial dimensions including length, width, and depth should be included.

[0087] Embodiment 1

[0088] Refer to Figure 1 , an embodiment of the present invention provides a gradient regularization method based on geological structure constraints, including:

[0089] As Figure 1 shown, the specific process of the present invention is as follows:

[0090] S1: Input the background velocity model for migration imaging to obtain a migration imaging profile.

[0091] S2: Extract the reflection structure information on the migration imaging profile. It should be noted that:

[0092] Preferably, extract the information of the main reflection structure horizons. In the initial stage of velocity inversion, only use the main reflection structure horizons to control the horizon changes of the entire migration imaging profile;

[0093] In the subsequent process of velocity inversion, gradually add more detailed reflection horizon information.

[0094] S3: Calculate the dip information at the reflection structure by using the method of dip scanning and similarity coefficient. It should be noted that:

[0095] By using the method of dip scanning, calculate the similarity coefficient spectra of different dips, and extract the dip value corresponding to the maximum similarity coefficient as the dip at the reflection structure. The specific formula is as follows:

[0096]

[0097] where ds is the integral of the function, x and z are the positions of the profile respectively, θ is the dip information, τ: z + θx = 0 is the integral path corresponding to the dip, a(z, x) is a template function with a value of 1, and st(z, x) is the calculated reflection structure profile.

[0098] S4: Take the selected reflection structure as the known scattered points, and use the scattered data interpolation method between horizons to obtain the dip information of the entire migration imaging profile. It should be noted that:

[0099] Construct the interpolation basis linear equations as follows:

[0100]

[0101] where x i =(z i , x i ) is the position at a known horizon structure, x is the interpolation point to be interpolated, is the interpolation basis function, θ(x) is the dip value, and λ i is the interpolation coefficient;

[0102] Solve the linear equations to obtain the interpolation coefficients, and perform scattered data interpolation between two structures of the formation and the formation to obtain the dip information of the entire migration imaging profile.

[0103] S5: Calculate the variance magnitude based on the structural points and non-structural points of the migration imaging profile. It should be noted that:

[0104] At the position of the structural point, the variances σ u and σ v are determined using the following formula:

[0105]

[0106] where is the minimum variance set along the strike direction of the geological interface at the structural point, is the minimum variance set in the direction perpendicular to the geological interface of the migration imaging profile at the structural point. Generally, it is required that

[0107] At the position of the non-structural point, the variances σ u and σ v are determined using the following formula:

[0108]

[0109]

[0110] where is the maximum variance set along the strike direction of the geological interface at the structural point, is the maximum variance set in the direction perpendicular to the geological interface of the migration imaging profile at the structural point; d is the distance between the current point and the nearest reflection interface, and d m is the maximum distance threshold coefficient, σ u is the variance magnitude of the migration imaging profile along the strike direction of the geological interface, and σ v is the variance magnitude of the migration imaging profile in the direction perpendicular to the geological interface.

[0111] S6: Calculate the anisotropic Gaussian function based on the variance magnitude. It should be noted that:

[0112]

[0113] Calculate the anisotropic Gaussian function g(x0, z0) according to the variance of the migration imaging profile. The specific formula is as follows:

[0114]

[0115] where x0 and z0 are the positions of the center point currently being calculated, and σ u is the variance of the migration imaging profile along the strike direction of the geological interface, and σ v is the variance of the migration imaging profile in the direction perpendicular to the geological interface.

[0116] S7: Construct a smoothing operator along the geological direction and apply regularization. It should be noted that:

[0117] Construct a smoothing operator by rotating the Cartesian coordinate system to the local geological structure coordinate system. The relationship between the Cartesian coordinate system (x, y) and the geological structure coordinate system (u, v) is described by the rotation operator T. The specific formula is as follows:

[0118] g s (x0, z0) = T(x0, z0)g(x0, z0)

[0119]

[0120] where u is the direction along the strike of the geological interface and v is the direction perpendicular to the geological interface.

[0121] Solve the regularization in two steps, including calculating the gradient Grad k of the objective functional, and performing post-processing on the functional gradient to achieve the model regularization constraint of the objective functional. The iterative formula is as follows:

[0122]

[0123]

[0124] where k is the number of iterations, Grad k is the result of the calculated conventional gradient, is the smoothing operator along the geological direction, is the processed gradient result, α k is the step operator, m k is the previous velocity inversion result, and m k+1 is the updated velocity inversion result.

[0125] S8: Output the regularization result.

[0126] The embodiment of the present invention also provides a gradient regularization system based on geological structure constraints, including

[0127] The model building module is used to perform migration in the background velocity model to obtain a migrated imaging profile and extract the reflection structure information on the migrated imaging profile;

[0128] The calculation module is used to calculate the dip information at the reflection structure, construct an interpolation basis function through the dip information at the reflection structure to obtain the dip information of the entire migrated imaging profile, calculate the variance of the migrated imaging profile through structural points and non-structural points, then calculate the anisotropic Gaussian function, and finally calculate the smoothing operator along the geological direction and apply regularization;

[0129] The output module is used to output the regularization result.

[0130] The embodiment of the present invention also provides a computing device, including:

[0131] A memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the gradient regularization method based on geological structure constraints as proposed in the above embodiment.

[0132] This computing device can be a terminal, and this computing device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of this computing device is used to provide computing and control capabilities. The memory of this computing device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of this computing device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a carrier network, NFC (Near Field Communication), or other technologies. The display screen of this computing device can be a liquid crystal display screen or an electronic ink display screen, and the input device of this computing device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the housing of the computing device, or an external keyboard, touchpad, or mouse, etc.

[0133] This embodiment also provides a storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the gradient regularization method based on geological structure constraints as proposed in the above embodiment.

[0134] The storage medium proposed in this embodiment and the data storage method proposed in the above embodiment belong to the same inventive concept. The technical details not described in detail in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0135] Embodiment 2

[0136] Refer to Figures 2 to 13, for another embodiment of the present invention, a verification test of the gradient regularization method and system based on geological structure constraints is provided to verify and illustrate the technical effects adopted in this method.

[0137] Taking the whale model as an example, the application effects of the gradient regularization method and system based on geological structure constraints proposed in this patent are tested.

[0138] The migration imaging result obtained by using the background velocity for prestack depth migration is as Figure 2 shown.

[0139] Correspondingly, the gradient result calculated by using the adjoint state method is as Figure 3 shown.

[0140] The reflection imaging structure information on the migration imaging profile is extracted by using the Markov decision process, as Figure 4 shown.

[0141] The dip angle information of the imaging structure is calculated by using the dip angle scan and similarity coefficient method, and the calculation result is as Figure 5 shown. Using the dip angle information at the imaging structure in Figure 5 , an interpolation basis function is constructed, and then the dip angle value between layers is calculated by using the scattered data interpolation method, and the calculation result is as Figure 6 shown; then the variance size of the profile is calculated according to the imaging structure and dip angle result, which are respectively as Figures 7 to 8 shown.

[0142] The pulse test model is as Figure 9 shown, where the black position is a pulse with a unit of 1.

[0143] The result of using the isotropic smoothing operator is as Figure 10 shown.

[0144] The anisotropic smoothing operator along the structure constructed by using the method proposed in the present invention is as Figure 11 shown; it can be seen that the smoothing operator of the present invention can effectively capture the underground structure information and realize smoothing along the structure. Then the constructed smoothing operator is applied to the gradient result of velocity inversion. Among them, as Figure 12 is the gradient result smoothed by the isotropic operator, and as Figure 13 is the smoothing result of the anisotropic Gaussian function along the structure proposed in the present invention.

[0145] By comparison, it can be found that the present invention can effectively smooth along the underground structure information to obtain the desired velocity model result, thus verifying the effectiveness of the invention.

[0146] In addition, this embodiment also verifies that the accuracy and applicability of the present invention are superior to traditional methods, and the specific results are shown in the following table:

[0147] Table 1: Comparison between the present invention and traditional methods.

[0148]

[0149]

[0150] It should be noted that in exploration seismology, velocity inversion is a strongly non-linear inverse problem, and the inversion solution has multi-solution and instability. By applying regularization, the non-uniqueness of the solution can be effectively reduced, and a stable inversion solution can be obtained. However, conventional regularization methods only impose sparse constraints mathematically or impose sparse constraints along the structure, but the overall sparse constraints will damage weak signals and cannot be constrained only for effective reflection signals. Or by constructing anisotropic smoothing functions or anisotropic diffusion functions to process the gradient. However, such methods can only maintain the structural direction at horizons, and non-horizon areas are often isotropic processing or simple anisotropic processing. In order to constrain the inversion solution during the velocity inversion process and obtain a velocity model with geological significance, the present invention uses the reflection horizon information in the migration imaging during the inversion process as a constraint, extracts the reflection structure, calculates the structural dip angle, obtains the horizon information of the entire profile by using the scattered data interpolation method, designs an anisotropic smoothing function along the horizon, constrains the change of velocity, and effectively improves the medium and high wavenumber components in velocity modeling.

[0151] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

Claims

1. A gradient regularization method based on geological structure constraints, characterized in that: including Performing migration imaging on the input background velocity model to obtain a migration imaging profile; Extracting the reflection structure information on the migration imaging profile; Calculating the dip information at the reflection structure by means of dip scanning and similarity coefficient; Taking the selected reflection structure as known scattered points, and between horizons, using the scattered data interpolation method to obtain the dip information of the entire migration imaging profile, specifically including: Constructing the following interpolation basis linear equations: Among them, x i =(z i , x i ) is the position at a known horizon structure, x is the point to be interpolated, is the interpolation basis function, θ(x) is the dip value, λ i is the interpolation coefficient; Solving the linear equations to obtain interpolation coefficients, and performing scattered data interpolation between two structures of strata to obtain the dip information of the entire migration imaging profile; Calculating the variance magnitude according to the structural points and non-structural points of the migration imaging profile; Calculating the anisotropic Gaussian function according to the variance magnitude; Constructing a smoothing operator along the geological direction and applying regularization; Outputting the regularization result.

2. The gradient regularization method based on geological structure constraints according to claim 1, wherein: Extracting the reflection structure information on the migration imaging profile includes Preferentially extracting the information of the main reflection structure horizons, and in the initial stage of velocity inversion, only using the main reflection structure horizons to control the horizon changes of the entire migration imaging profile; In the subsequent process of velocity inversion, gradually adding more detailed reflection horizon information.

3. The gradient regularization method based on geological structure constraint according to claim 1, wherein: Calculating the dip information at the reflection structure includes By means of dip scanning, calculating the similarity coefficient spectra of different dips, and extracting the dip value corresponding to the maximum similarity coefficient as the dip at the reflection structure, the specific formula is as follows: where ds is the integral of the function, x and z are the positions of the profile respectively, θ is the dip information, τ: z + θx = 0 is the integral path corresponding to the dip, a(z, x) is a template function with a value of 1, and st(z, x) is the calculated reflection structure profile.

4. The gradient regularization method based on geological structure constraints according to claim 1, wherein: Calculating the variance magnitude according to the structural points and non-structural points of the migration imaging profile includes At the construction point location, the variances σ u and σ v are determined in magnitude by the following formula: wherein, is the minimum variance set along the strike direction of the geological interface at the structural point, is the minimum variance set in the direction perpendicular to the geological interface of the migration imaging profile at the structural point, and At non-structural point positions, the variances σ u and σ v are determined in magnitude by the following formula: Among them, is the maximum variance set in the direction of the strike of the geological interface at the structural point, is the maximum variance set in the direction perpendicular to the geological interface of the migration imaging profile at the structural point, d is the distance between the current point and the nearest reflection interface, d m is the maximum distance threshold coefficient, σ u is the variance magnitude of the migration imaging profile in the direction of the strike of the geological interface, σ v is the variance magnitude of the migration imaging profile in the direction perpendicular to the geological interface.

5. The gradient regularization method based on geological structure constraints according to claim 1, wherein: Calculating the anisotropic Gaussian function according to the variance magnitude includes Calculating the anisotropic Gaussian function g(x0, z0) according to the variance of the migration imaging profile, the specific formula is as follows: where x0 and z0 are the positions of the currently calculated center point, and σ u is the variance magnitude of the migration imaging profile along the strike direction of the geological interface, and σ v is the variance magnitude of the migration imaging profile in the direction perpendicular to the geological interface.

6. The gradient regularization method based on geological structure constraints according to claim 1, wherein: Constructing a smoothing operator along the geological direction and applying regularization includes Constructing a smoothing operator by rotating the Cartesian coordinate system to the local geological structure coordinate system, and the relationship between the Cartesian coordinate system (x, y) and the geological structure coordinate system (u, v) is described by the rotation operator T, the specific formula is as follows: g s (x0,z0) = T(x0,z0)g(x0,z0) where u is the direction along the strike of the geological interface, and v is the direction perpendicular to the geological interface; Solve the regularization in two steps, including calculating the gradient Grad of the objective functional k , and performing post-processing on the functional gradient to achieve the model regularization constraint of the objective functional. The iterative formula is as follows: where k is the number of iterations, Grad k is the calculated conventional gradient result, is the smoothing operator along the geological direction, is the processed gradient result, α k is the step operator, m k is the previous velocity inversion result, m k+1 is the updated velocity inversion result.

7. A gradient regularization system based on geological structure constraints, applying the method according to claim 1, characterized in that, including: A model building module for performing migration in the background velocity model to obtain a migration imaging profile and extracting the reflection structure information on the migration imaging profile; A calculation module for calculating the dip information at the reflection structure, constructing an interpolation basis function through the dip information at the reflection structure to obtain the dip information of the entire migration imaging profile, calculating the variance of the migration imaging profile through structural points and non-structural points, then calculating the anisotropic Gaussian function, and finally calculating the smoothing operator along the geological direction and applying regularization; An output module for outputting the regularization result.

8. A computing device, including: A memory and a processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the gradient regularization method based on geological structure constraints described in any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are executed by a processor, the steps of the gradient regularization method based on geological structure constraints described in any one of claims 1 to 6 are implemented.

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