VTI medium pre-stack anisotropy parameter step-by-step inversion method and device

Abstract

The application discloses a VTI medium pre-stack anisotropy parameter step-by-step inversion method and device, and the method comprises the following steps: acquiring pre-stack seismic data of a target work area; based on the Bayesian theory and the pre-stack seismic data, performing inversion on to-be-inverted parameters in a preset expression of a VTI medium P-wave reflection coefficient, wherein the to-be-inverted parameters comprise anisotropic P-wave velocity related to P-wave velocity and anisotropy parameter epsilon; based on the Bayesian theory and the seismic data, performing inversion on P-wave velocity, S-wave velocity and density parameters in an Aki-Richards approximate expression; and determining the anisotropy parameter epsilon according to the inversion result of the anisotropic P-wave velocity and the inversion result of the P-wave velocity.

Description

Technical Field

[0001] This invention belongs to the field of shale oil and gas geophysical exploration technology, specifically involving a stepwise inversion method and apparatus for pre-stack anisotropy parameters of VTI media. Background Technology

[0002] In recent years, unconventional natural gas resources such as coalbed methane, shale gas, tight gas, and natural gas hydrates have attracted increasing attention. Among them, shale gas, with its abundant reserves, wide distribution, long extraction cycle, and significant development potential, has gradually become a research hotspot in oil and gas exploration and development. Due to the complex structure and anisotropic characteristics of shale reservoirs, conventional pre-stack seismic inversion methods cannot meet the requirements for geophysical inversion and quantitative interpretation of shale reservoirs. Therefore, seismic anisotropic inversion methods applicable to shale reservoirs have gradually become a research focus.

[0003] In 1996, Rüger derived approximate equations for reflection and transmission of transverse isotropy (HTI) media with a horizontal axis of symmetry. Subsequently, between 1997 and 1998, he gave approximate equations for reflection and transmission of transverse isotropy (VTI) media with a vertical axis of symmetry.

[0004] The emergence of the reflection-transmission approximation equation has made pre-stack seismic inversion possible for anisotropic media, greatly promoting reservoir anisotropy research and leading to its widespread application.

[0005] In recent years, many geophysicists in China have carried out anisotropic inversion studies on HTI media and anisotropic studies on orthogonality anisotropy (OA) media. However, shale has strong horizontal bedding characteristics, namely VTI characteristics, and there are few related studies. Further research is needed on anisotropic inversion methods for VTI media.

[0006] Transverse isotropy (TI) is very common in sedimentary rocks, and geological bodies such as horizontal thin layers, shale, and strata with horizontal fractures often exhibit VTI properties. Among them, the geophysical response of shale has significant strong anisotropy (VTI) characteristics. However, conventional isotropic pre-stack seismic inversion methods are no longer sufficient for the geophysical inversion and quantitative interpretation of shale reservoirs. The classic anisotropic reflection coefficient expression contains many parameters to be determined, and the sensitivity differences between these parameters are large. Summary of the Invention

[0007] To address the aforementioned issues, this application proposes a stepwise inversion method and apparatus for pre-stack anisotropy parameters of VTI media. This method reduces the number of parameters to be inverted in conventional pre-stack anisotropy seismic inversion, improves the accuracy and stability of the inversion, and is better suited for predicting anisotropy parameters of actual shale reservoirs.

[0008] A first aspect of this application provides a stepwise inversion method for pre-stack anisotropy parameters of VTI media, comprising:

[0009] Acquire pre-stack seismic data for the target work area;

[0010] Based on Bayesian theory and the pre-stack seismic data, the parameters to be inverted in the preset expression of the P-wave reflection coefficient of the VTI medium are inverted. The parameters to be inverted include the anisotropic P-wave velocity related to the P-wave velocity and the anisotropy parameter ε.

[0011] Based on Bayesian theory and the earthquake data, the P-wave velocity, S-wave velocity and density parameters in the Aki-Richards approximation were inverted.

[0012] Based on the inversion results of the anisotropic P-wave velocity and the inversion results of the P-wave velocity, the anisotropic parameter ε is determined.

[0013] In some embodiments, the inversion of the parameters to be inverted in the preset expression for the P-wave reflection coefficient of the VTI medium based on Bayesian theory and the pre-stack seismic data includes:

[0014] Prestack angle gathers of the target work area are obtained from the prestack seismic data, and seismic wavelets are extracted from the prestack angle gathers to establish seismic wavelet matrices at different angles.

[0015] Based on the matrix form of the preset expression for the longitudinal wave reflection coefficient of the VTI medium and the seismic wavelet matrix, the first convolution model is constructed using convolution theory;

[0016] Based on the first convolution model, a first likelihood function representing the probability distribution of the observed data is constructed;

[0017] Based on the prior probability distribution function of the model parameters and the first likelihood function, the objective function of the first inversion is constructed using Bayesian theory and the maximum a posteriori probability principle.

[0018] The objective function of the first inversion is differentiated with respect to the model parameters to obtain the objective function of the first final inversion.

[0019] The first final inversion objective function is solved by iterative reweighted least squares method to obtain the inversion result of the parameters to be inverted.

[0020] In some embodiments, the preset expression for the longitudinal wave reflection coefficient of the VTI medium is:

[0021]

[0022] in:

[0023] The longitudinal wave reflection coefficient of the VTI medium;

[0024]

[0025] θ is the angle of incidence, V P0 V is the longitudinal wave velocity. s0 ρ is the transverse wave velocity, ρ is the density, and δ and ε are the anisotropy parameters of the VTI medium; ρV P0 , and V P0 e ε The parameters to be inverted are denoted as P-wave impedance, pseudo-isotropic S-wave modulus, and anisotropic P-wave velocity, respectively; the calculated Δ[.] represents the difference in physical quantities between the upper and lower strata above and below the adjacent interface. This represents the average physical quantities of the strata above and below the adjacent interface.

[0026] In some embodiments, the expression for determining the anisotropy parameter ε based on the inversion result of the anisotropic P-wave velocity and the inversion result of the P-wave velocity is as follows:

[0027]

[0028] Among them, V P0 e ε For the inversion results of anisotropic P-wave velocities, V P0 This is the result of the inversion of the longitudinal wave velocity.

[0029] In some embodiments, the linear expression of the first convolution model is:

[0030] d=Gm+n (9)

[0031] Where d represents the observed data, G represents the forward modeling operator, m represents the model parameters, and n represents the noise vector.

[0032] In some embodiments, the step of constructing the first inversion objective function based on the prior probability distribution function of the model parameters and the first likelihood function, using Bayesian theory and the maximum a posteriori probability principle, includes:

[0033] Based on the likelihood function and the prior probability distribution function, the first posterior probability distribution function of the model parameters is constructed using Bayesian theory.

[0034] The first posterior probability distribution function is solved using the maximum a posteriori probability principle to obtain the first inversion objective function.

[0035] In some embodiments, the expression for the objective function of the first inversion is:

[0036]

[0037] Where d represents the observed data, m represents the model parameters, G represents the forward modeling operator, the superscript T denotes the transpose matrix, and C represents the model parameters. d Let be the covariance matrix of the noise, Q(m) be the regularization term that depends on the chosen prior distribution type, μ be the weight coefficient, and R(m) be the prior distribution function of the model parameters.

[0038] In some embodiments, the expression for the objective function of the first final inversion is:

[0039]

[0040] Where m represents the model parameters, d represents the observed data, and G is the forward modeling operator. T Let C be the transpose of G. d Let be the covariance matrix of the noise, Q(m) be the regularization term that depends on the chosen prior distribution type, and μ be the weighting coefficient.

[0041] A second aspect of this application discloses a three-parameter inversion device system for retrieving pre-stack anisotropic parameter distribution of VTI media, comprising:

[0042] The first acquisition module is used to acquire pre-stack seismic data of the target work area;

[0043] The first inversion module is used to invert the parameters to be inverted in the preset expression of the P-wave reflection coefficient of the VTI medium based on Bayesian theory and the pre-stack seismic data. The parameters to be inverted include anisotropic P-wave velocities related to P-wave velocity and anisotropy parameter ε.

[0044] The second inversion module is used to invert the P-wave velocity, S-wave velocity and density parameters in the Aki-Richards approximation based on Bayesian theory and the seismic data.

[0045] The first calculation module is used to determine the anisotropy parameter ε based on the inversion results of the anisotropic P-wave velocity and the inversion results of the P-wave velocity.

[0046] A third aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by one or more processors, implements the step-by-step inversion method for VTI medium pre-stack anisotropy parameters as described above.

[0047] A fourth aspect of this application provides an electronic device including a memory and a processor, wherein a computer program is stored on the memory and the processor is communicatively connected to each other, and when the computer program is executed by the processor, it implements the step-by-step inversion method for VTI medium pre-stack anisotropy parameters as described above.

[0048] Compared with the prior art, the technical solution of this application has the following advantages or beneficial effects: It adopts a step-by-step approach to estimate the Thomsen anisotropic parameter ε. First, it uses the parameters to be inverted in the preset expression of the longitudinal wave reflection coefficient of the VTI medium to obtain the anisotropic longitudinal wave velocity parameter V. P0 e ε Then, the P-wave velocity is obtained using a pre-stack isotropic three-parameter (P-wave velocity, S-wave velocity, and density) inversion method based on the Aki-Richards equations. Finally, the P-wave velocity is determined based on the anisotropic P-wave velocity parameter V. P0 e ε The Thomsen anisotropy parameter ε is extracted from the inversion results of the inversion of the P-wave velocity and the inversion results of the P-wave velocity, so as to realize the prediction of the anisotropy parameter of the VTI medium. This method can be better applied to the pre-stack seismic inversion of actual seismic data, and improves the accuracy and stability of anisotropy parameter inversion. Attached Figure Description

[0049] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0050] Figure 1 A flowchart of a step-by-step inversion method for pre-stack anisotropy parameters of VTI media provided in Embodiment 1 of this application;

[0051] Figure 2 A flowchart of a step-by-step inversion method for pre-stack anisotropy parameters of VTI media provided in Embodiment 2 of this application;

[0052] Figure 3 Example 2 of this application ρV P0 Inversion results of combined parameters;

[0053] Figure 4 This is Example 2 of this application. Inversion results of combined parameters;

[0054] Figure 5 Example 2V of this application P0 eε Inversion results of combined parameters;

[0055] Figure 6 This is the inversion result of the longitudinal wave velocity in Embodiment 2 of this application;

[0056] Figure 7 This is the prediction result of the Thomsen anisotropy parameter ε in Embodiment 2 of this application;

[0057] Figure 8 This is a structural block diagram of an anisotropic three-parameter inversion device provided in Embodiment 3 of this application;

[0058] Figure 9 This is a connection block diagram of an electronic device provided in Embodiment 5 of this application. Detailed Implementation

[0059] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0060] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0061] If the application documents contain similar descriptions such as "first, second, third", the following explanation shall be added: In the following description, the terms "first, second, third" are used only to distinguish similar objects and do not represent a specific order of objects. It is understood that "first, second, third" may be interchanged in a specific order or sequence where permitted, so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0062] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0063] Example 1

[0064] This embodiment provides a stepwise inversion method for pre-stack anisotropy parameters of VTI media. Figure 1 This embodiment provides a flowchart of a step-by-step inversion method for pre-stack anisotropy parameters of VTI media, as shown below. Figure 1 As shown, the method in this embodiment includes:

[0065] Step S100: Obtain pre-stack seismic data for the target work area. The pre-stack seismic data includes well logging data, pre-stack angle gathers, well curves, and rock physical information.

[0066] Step S200: Based on Bayesian theory and the pre-stack seismic data, the parameters to be inverted in the preset expression of the P-wave reflection coefficient of the VTI medium are inverted. The parameters to be inverted include the anisotropic P-wave velocity related to the P-wave velocity and the anisotropy parameter ε.

[0067] Step S300: Based on Bayesian theory and the seismic data, the P-wave velocity, S-wave velocity, and density parameters in the Aki-Richards approximation are inverted.

[0068] Step S400: Determine the anisotropic parameter ε based on the inversion results of the anisotropic P-wave velocity and the inversion results of the P-wave velocity.

[0069] The preset expression for the longitudinal wave reflection coefficient of the VTI medium is derived based on the Rüger approximation equation, that is, the approximate expression for the reflection coefficient of the P-wave under VTI conditions given by Rüger using the Thomsen parameters. The specific derivation process is as follows:

[0070] The Rüger, using the Thomsen parameters, provides an approximate expression for the reflection coefficient of the P-wave under VTI conditions:

[0071]

[0072] in, Z = ρVT, where θ is the longitudinal wave reflection coefficient of the VTI medium, θ is the incident angle, and Z = ρVT P0 For vertical longitudinal wave impedance, V is the vertical shear modulus of the transverse wave. P0 V is the longitudinal wave velocity. s0 Let ρ be the shear wave velocity, ρ be the density, δ and ε be the anisotropy parameters of the VTI medium, and Δ[.] be the difference in physical quantities between the upper and lower strata above and below the adjacent interface. This represents the average physical quantities of the strata above and below the adjacent interface.

[0073] The Rüger approximation equation approximates the exact reflection coefficient well at small angles, which guarantees the use of this expression for inversion.

[0074] To make the Rüger approximation equation easier to express for linear inversion, equation (1) is rewritten based on the relationship between the elastic parameters:

[0075]

[0076] in:

[0077]

[0078]

[0079] The revised equation (2) is compared with the Aki-Richards approximation solution of the Zoeppritz equation used in pre-stack seismic inversion in isotropic media. The Aki-Richards approximation solution of the Zoeppritz equation is:

[0080]

[0081] in:

[0082]

[0083] V P V is the longitudinal wave velocity. S For transverse wave velocity, This represents the longitudinal wave reflection coefficient in an isotropic medium.

[0084] As can be seen from expressions (2) and (3), the expressions corresponding to A and A′, B and B′, and C and C′ are the same. Therefore, the VTI medium's response to AVO can be considered as consisting of two parts. Specifically, the expression is as follows:

[0085]

[0086] in:

[0087]

[0088]

[0089] For different model cases, the influence of the anisotropy term in the simplified Rüger approximation equation on the curve depends only on the anisotropy parameters δ and ε. Transforming and rearranging expression (4), we get:

[0090]

[0091] The above expression (5) becomes:

[0092]

[0093] in,

[0094] Because, sin 2 θ(1+tan 2 θ)=tan 2 θ, sin2 θtan 2 θ=tan 2 θ-sin 2 If θ, then expression (6) becomes:

[0095]

[0096] Assumption x refers to V P0 V s0 With parameters such as ρ, equation (7) becomes equation (8), which is the preset expression for the longitudinal wave reflection coefficient of the VTI medium.

[0097] The preset expression for the longitudinal wave reflection coefficient of the VTI medium is:

[0098]

[0099] in:

[0100] The longitudinal wave reflection coefficient of the VTI medium;

[0101] η′=ε-δ;

[0102] η′ is the non-elliptic anisotropy parameter, θ is the incident angle, and V P0 V is the longitudinal wave velocity. s0 ρ is the transverse wave velocity, ρ is the density, and δ and ε are the anisotropy parameters of the VTI medium; ρV P0 , and V P0 e ε The parameters to be inverted are denoted as P-wave impedance, pseudo-anisotropic S-wave modulus, and anisotropic P-wave velocity, respectively; the calculated Δ[.] represents the difference in physical quantities between the upper and lower strata above and below the adjacent interface. This represents the average physical quantities of the strata above and below the adjacent interface.

[0103] The preset expression for the longitudinal wave reflection coefficient of the VTI medium contains three parameters ρV to be inverted. P0 , V P0 e ε Term, ρV P0 Attribute A represents the longitudinal wave impedance. The B attribute represents the pseudo-isotropic transverse wave modulus, V. P0 e ε The C attribute represents anisotropic P-wave velocity. This means that the formation properties are directly inferred from the three-parameter approximation of the VTI medium's P-wave reflection coefficient, rather than the gradient variation between formations as inferred in existing technologies.

[0104] The Thomsen anisotropy parameter ε is estimated in a step-by-step manner. First, the parameters to be inverted are obtained by inverting the parameters in the preset expression based on the longitudinal wave reflection coefficient of the VTI medium. P0 e ε Then, the P-wave velocity is obtained using a pre-stack isotropic three-parameter (P-wave velocity, S-wave velocity, and density) inversion method based on the Aki-Richards equations. Finally, the P-wave velocity is determined based on the anisotropic P-wave velocity parameter V. P0 e ε The Thomsen anisotropy parameter ε is extracted from the inversion results of the inversion of the P-wave velocity and the inversion results of the P-wave velocity, so as to realize the prediction of the anisotropy parameter of the VTI medium. This method can be better applied to the pre-stack seismic inversion of actual seismic data, and improves the accuracy and stability of anisotropy parameter inversion.

[0105] Example 2

[0106] This embodiment takes a shale block as an example and uses a step-by-step inversion method for pre-stack anisotropy parameters of VTI media provided in this embodiment to predict the anisotropy parameter ε. Figure 2 A flowchart of a step-by-step inversion method for pre-stack anisotropy parameters of VTI media provided in this application embodiment is shown below. Figure 2 As shown, the method in this embodiment includes:

[0107] Step S100: Obtain pre-stack seismic data for the target work area. The pre-stack seismic data includes well logging data, pre-stack angle gathers, well curves, and rock physical information.

[0108] Step S200: Based on Bayesian theory and the pre-stack seismic data, the parameters to be inverted in the preset expression of the P-wave reflection coefficient of the VTI medium are inverted. The parameters to be inverted include anisotropic P-wave velocities that are only related to the P-wave velocity and the anisotropy parameter ε.

[0109] The preset expression for the longitudinal wave reflection coefficient of the VTI medium is derived based on the Rüger approximation equation, that is, the approximate expression for the reflection coefficient of the P-wave under VTI conditions given by Rüger using the Thomsen parameters. The specific derivation process is as follows:

[0110] The Rüger, using the Thomsen parameters, provides an approximate expression for the reflection coefficient of the P-wave under VTI conditions:

[0111]

[0112] in, Z = ρVT, where θ is the longitudinal wave reflection coefficient of the VTI medium, θ is the incident angle, and Z = ρVT P0 For vertical longitudinal wave impedance, V is the vertical shear modulus of the transverse wave. P0 V is the longitudinal wave velocity. s0 Let ρ be the shear wave velocity, ρ be the density, δ and ε be the anisotropy parameters of the VTI medium, and Δ[.] be the difference in physical quantities between the upper and lower strata above and below the adjacent interface. This represents the average physical quantities of the strata above and below the adjacent interface.

[0113] The Rüger approximation equation approximates the exact reflection coefficient well at small angles, which guarantees the use of this expression for inversion.

[0114] To make the expression of the Rüger approximation equation easier for linear inversion, expression (1) is rewritten based on the relationship between the elastic parameters:

[0115]

[0116] in:

[0117]

[0118]

[0119] The revised equation (2) is compared with the Aki-Richards approximation solution of the Zoeppritz equation used in pre-stack seismic inversion in isotropic media. The Aki-Richards approximation solution of the Zoeppritz equation is:

[0120]

[0121] in:

[0122]

[0123] V P V is the longitudinal wave velocity. S For transverse wave velocity, This represents the longitudinal wave reflection coefficient in an isotropic medium.

[0124] As can be seen from equations (2) and (3), the expressions corresponding to A and A′, B and B′, and C and C′ are the same. Therefore, the VTI medium's response to AVO can be considered as consisting of two parts. Specifically, the expression is as follows:

[0125]

[0126] in:

[0127]

[0128]

[0129] For different model cases, the influence of the anisotropy term in the simplified Rüger approximation equation on the curve depends only on the anisotropy parameters δ and ε. Transforming and rearranging expression (4), we get:

[0130]

[0131] Equation (5) above becomes:

[0132]

[0133] in,

[0134] Because, sin 2 θ(1+tan 2 θ)=tan 2 θ, sin 2 θtan 2 θ=tan 2 θ-sin 2 If θ, then expression (6) becomes:

[0135]

[0136] Assumption x refers to V P0 V s0 With parameters such as ρ, equation (7) becomes equation (8), which is the preset expression for the longitudinal wave reflection coefficient of the VTI medium.

[0137] The preset expression for the longitudinal wave reflection coefficient of the VTI medium is:

[0138]

[0139] in:

[0140] The longitudinal wave reflection coefficient of the VTI medium;

[0141] η′=ε-δ;

[0142] η′ is the non-elliptic anisotropy parameter, θ is the incident angle, and V P0 V is the longitudinal wave velocity. s0 ρ is the transverse wave velocity, ρ is the density, and δ and ε are the anisotropy parameters of the VTI medium; ρV P0 , and V P0 e εThe parameters to be inverted are denoted as P-wave impedance, pseudo-anisotropic S-wave modulus, and anisotropic P-wave velocity, respectively; the calculated Δ[.] represents the difference in physical quantities between the upper and lower strata above and below the adjacent interface. This represents the average physical quantities of the strata above and below the adjacent interface.

[0143] The preset expression for the longitudinal wave reflection coefficient of the VTI medium contains three parameters ρV to be inverted. P0 , V P0 e ε Term, ρV P0 Attribute A represents the longitudinal wave impedance. The B attribute represents the pseudo-isotropic transverse wave modulus, V. P0 e ε The C attribute represents the anisotropic P-wave velocity. This means that the formation properties are directly inferred from the preset expression for the P-wave reflection coefficient of the VTI medium, rather than the gradient variation between formations as inferred in existing technologies.

[0144] In some embodiments, step S200 includes the following steps:

[0145] Step S210: Obtain the pre-stack angle gathers of the study area, extract seismic wavelets from the pre-stack angle gathers, and establish seismic wavelet matrices W at different angles.

[0146] Step S220: Based on the matrix form of the preset expression of the longitudinal wave reflection coefficient of the VTI medium and the seismic wavelet matrix W at different angles, a convolution model is constructed using convolution theory.

[0147] Considering that real-world data is often affected by noise, the linear representation of the first convolution model is as follows:

[0148] d=Gm+n (9)

[0149] Where d = [d1, d2, ..., d M ] T Let M be the actual observation data detected by M detectors, representing the data observed in the actual seismic record, where m = [m1, m2, ..., m]. M ] T These are M model parameters derived from well logging data and other information. They directly represent the formation reflection coefficient, n is the noise vector, and G is the forward modeling operator.

[0150] The expression for the forward operator G is:

[0151]

[0152] in, For the seismic wavelet matrix, The reflection coefficient matrix is ​​obtained based on the preset expression for the longitudinal wave reflection coefficient of the VTI medium.

[0153] Step S230: Construct the first likelihood function of the observed data based on the first convolution model.

[0154] Assuming the noise in the seismic data follows a Gaussian distribution and is independent, this means the correlation coefficient between different noises is 0. The prior distribution function of noise n is:

[0155]

[0156] Where P0 is a constant, C d It is the covariance matrix of the noise.

[0157] Based on the first convolution model representing the general relationship between seismic data and subsurface media models, and since the Gaussian distribution conforms to the linear principle, the first likelihood function representing the probability distribution of the observed data is:

[0158]

[0159] Step S240: Based on the first likelihood function and the prior probability distribution function of the model parameters, the first inversion objective function is constructed using Bayesian theory and the maximum a posteriori probability principle.

[0160] In some embodiments, step S240 includes the following steps:

[0161] Step S241: Based on the first likelihood function and the prior probability distribution function, construct the posterior probability distribution function of the model parameters according to the Bayesian theory.

[0162] Bayesian theory is a nonprobabilistic theory that can be used to calculate conditional probability distributions. Mathematically, Bayesian theory can be expressed as:

[0163]

[0164] Wherein, P(m|d) is the posterior probability distribution of the model parameter m, P(d|m) is the likelihood function of the observed data d, P(d) is the non-zero marginal probability density of the observed data d, and P(m) is the prior probability distribution of the model parameter m, which is given before data collection.

[0165] Bayesian theory describes the distribution function of model parameters m given the observed data d, which is related to the probability density of the observed data d given the model parameters m.

[0166] Since the marginal probability density of the observed data d is a constant, then:

[0167] P(m|d)∝P(d|m)P(m) (13)

[0168] That is, based on Bayesian theory and the likelihood function (8) of the observed data, the posterior probability distribution of the model parameter m is obtained, and the expression of the posterior probability distribution of the model parameter m is:

[0169]

[0170] In some embodiments, probability distribution features are added to the prior probability distribution of the model parameter m to provide constraints on the prior information, thereby improving the stability of the inversion.

[0171] The probability distribution features include exponential distribution features, Gaussian distribution features, and Cauchy distribution features, etc.

[0172] In this embodiment, a Cauchy distribution feature is added to the prior probability distribution of the model parameter m, and the expression for the prior probability distribution of the model parameter m is:

[0173] P(m=e -μR(m) (15)

[0174] Where μ is the weight coefficient and R(m) is the prior distribution function of the model parameter m.

[0175] Then the expression for the posterior probability distribution of the model parameter m becomes:

[0176]

[0177] Step S242: Solve the posterior probability distribution function according to the maximum a posteriori probability principle to obtain the first inversion objective function.

[0178] The expression for the objective function of the first inversion is:

[0179]

[0180] Where J(m) is the objective function, d is the observed data, m is the model parameters, G is the forward operator, the superscript T denotes the transpose matrix, and C d Let be the covariance matrix of the noise, Q(m) be the regularization term that depends on the chosen prior distribution type, μ be the weight coefficient, and R(m) be the prior distribution function of the model parameters.

[0181] Step S250: Differentiate the first inversion objective function with respect to the model parameters to obtain the first final inversion objective function.

[0182] The expression for the objective function of the first final inversion is:

[0183]

[0184] Where m represents the model parameters, d represents the observed data, and G is the forward modeling operator. T Let C be the transpose of G. d Let be the covariance matrix of the noise, Q(m) be the regularization term that depends on the chosen prior distribution type, and μ be the weighting coefficient.

[0185] Step S260: The iterative reweighted least squares algorithm is used to iteratively solve the objective function of the first final inversion, thereby obtaining the inversion result of the parameters to be inverted. The parameters to be inverted are ρV. P0 , V P0 e ε The inversion results are as follows: Figure 3 , Figure 4 , Figure 5 As shown.

[0186] Step S300: Based on Bayesian theory and the seismic data, the P-wave velocity, S-wave velocity, and density parameters in the Aki-Richards approximation are inverted.

[0187] In some embodiments, step S300 includes the following steps:

[0188] Step S310: Obtain the pre-stack angle gathers of the study area, extract seismic wavelets from the pre-stack angle gathers, and establish seismic wavelet matrices W at different angles.

[0189] Step S320: Based on the Aki-Richards approximation and the seismic wavelet matrix W at different angles, a second convolution model is constructed using convolution theory.

[0190] Step S330: Construct a second likelihood function for the observed data based on the second convolution model.

[0191] Step S340: Based on the second likelihood function and the prior probability distribution function of the model parameters, a second inversion objective function is constructed using Bayesian theory and the maximum a posteriori probability principle.

[0192] In some embodiments, step S340 includes the following steps:

[0193] Step S341: Based on the second likelihood function and the prior probability distribution function, construct the posterior probability distribution function of the model parameters according to the Bayesian theory.

[0194] Step S342: Solve the posterior probability distribution function according to the maximum a posteriori probability principle to obtain the second inversion objective function.

[0195] Step S350: Differentiate the second inversion objective function with respect to the model parameter m to obtain the second final inversion objective function.

[0196] Step S360: The iterative reweighted least squares algorithm is used to solve the objective function of the second final inversion, obtaining the inversion result of the P-wave velocity in the Aki-Richards approximation. The inversion result of the P-wave velocity is as follows: Figure 6 As shown.

[0197] Step S400: Based on the inversion results of the anisotropic P-wave velocity and the P-wave velocity inversion results, determine the anisotropy parameter ε. The predicted results of the anisotropy parameter ε are as follows: Figure 7 As shown.

[0198] The expression for determining the anisotropy parameter ε based on the inversion results of the anisotropic P-wave velocity and the inversion results of the P-wave velocity is as follows:

[0199]

[0200] Among them, V P0 e ε For the inversion results of anisotropic P-wave velocities, V P0 This is the result of the inversion of the longitudinal wave velocity.

[0201] The Thomsen anisotropy parameter ε is estimated in a step-by-step manner. First, the parameters to be inverted are obtained by inverting the parameters in the preset expression based on the longitudinal wave reflection coefficient of the VTI medium. P0 e ε Then, the P-wave velocity is obtained using a pre-stack isotropic three-parameter (P-wave velocity, S-wave velocity, and density) inversion method based on the Aki-Richards equations. Finally, the P-wave velocity is determined based on the anisotropic P-wave velocity parameter V. P0 e ε The Thomsen anisotropy parameter ε is extracted from the inversion results of the inversion of the P-wave velocity and the inversion results of the P-wave velocity, so as to realize the prediction of the anisotropy parameter of the VTI medium. This method can be better applied to the pre-stack seismic inversion of actual seismic data, and improves the accuracy and stability of anisotropy parameter inversion.

[0202] Example 3

[0203] This embodiment provides a VTI medium pre-stack anisotropic parameter distribution inversion device 400. This system embodiment can be used to execute the method embodiment of this application. For details not disclosed in this device 400 embodiment, please refer to the method embodiment of this application. Figure 8 A schematic diagram of an anisotropic three-parameter inversion device 400 provided in this application embodiment is shown below. Figure 8As shown, the device 400 provided in this embodiment includes:

[0204] The first acquisition module 410 is used to acquire pre-stack seismic data of the target work area.

[0205] The first inversion module 420 is used to invert the parameters to be inverted in the preset expression of the P-wave reflection coefficient of the VTI medium based on Bayesian theory and the pre-stack seismic data. The parameters to be inverted include anisotropic P-wave velocities related to P-wave velocity and anisotropy parameter ε.

[0206] The preset expression for the longitudinal wave reflection coefficient of the VTI medium is derived based on the Rüger approximation equation, that is, the approximate expression for the reflection coefficient of the P-wave under VTI conditions given by Rüger using the Thomsen parameters. The specific derivation process is as follows:

[0207] The Rüger, using the Thomsen parameters, provides an approximate expression for the reflection coefficient of the P-wave under VTI conditions:

[0208]

[0209] in, Z = ρVT, where θ is the longitudinal wave reflection coefficient of the VTI medium, θ is the incident angle, and Z = ρVT P0 For vertical longitudinal wave impedance, V is the vertical shear modulus of the transverse wave. P0 V is the longitudinal wave velocity. s0 Let ρ be the shear wave velocity, ρ be the density, δ and ε be the anisotropy parameters of the VTI medium, and Δ[.] be the difference in physical quantities between the upper and lower strata above and below the adjacent interface. This represents the average physical quantities of the strata above and below the adjacent interface.

[0210] The Rüger approximation equation approximates the exact reflection coefficient well at small angles, which guarantees the use of this expression for inversion.

[0211] To make the expression of the Rüger approximation equation easier for linear inversion, expression (1) is rewritten based on the relationship between the elastic parameters:

[0212]

[0213] in:

[0214]

[0215]

[0216] The rewritten expression (2) is compared with the Aki-Richards approximation solution of the Zoeppritz equation used in pre-stack seismic inversion in isotropic media. The Aki-Richards approximation solution of the Zoeppritz equation is:

[0217]

[0218] in:

[0219]

[0220] V P V is the longitudinal wave velocity. S For transverse wave velocity, This represents the longitudinal wave reflection coefficient in an isotropic medium.

[0221] As can be seen from expressions (2) and (3), the expressions corresponding to A and A′, B and B′, and C and C′ are the same. Therefore, the VTI medium's response to AVO can be considered as consisting of two parts. Specifically, the expression is as follows:

[0222]

[0223] in:

[0224]

[0225]

[0226] For different model cases, the influence of the anisotropy term in the simplified Rüger approximation equation on the curve depends only on the anisotropy parameters δ and ε. Transforming and rearranging expression (4), we get:

[0227]

[0228] The above expression (5) becomes:

[0229]

[0230] in,

[0231] Because, sin 2 θ(1+tan 2 θ)=tan 2 θ, sin 2 θtan 2 θ=tan 2 θ-sin 2 If θ, then expression (6) becomes:

[0232]

[0233] Assumption x refers to V P0 V s0 With parameters such as ρ, equation (7) becomes equation (8), which is the preset expression for the longitudinal wave reflection coefficient of the VTI medium.

[0234] The preset expression for the longitudinal wave reflection coefficient of the VTI medium is:

[0235]

[0236] in:

[0237] The longitudinal wave reflection coefficient of the VTI medium;

[0238] η′=ε-δ;

[0239] η′ is the non-elliptic anisotropy parameter, θ is the incident angle, and V P0 V is the longitudinal wave velocity. s0 ρ is the transverse wave velocity, ρ is the density, and δ and ε are the anisotropy parameters of the VTI medium; ρV P0 , and V P0 e ε The parameters to be inverted are denoted as P-wave impedance, pseudo-anisotropic S-wave modulus, and anisotropic P-wave velocity, respectively; the calculated Δ[.] represents the difference in physical quantities between the upper and lower strata above and below the adjacent interface. This represents the average physical quantities of the strata above and below the adjacent interface.

[0240] The preset expression for the longitudinal wave reflection coefficient of the VTI medium contains three parameters ρV to be inverted. P0 , V P0 e ε Term, ρV P0 Attribute A represents the longitudinal wave impedance. The B attribute represents the pseudo-isotropic transverse wave modulus, V. P0 e ε The C attribute represents anisotropic P-wave velocity. This means that the formation properties are directly inferred from the three-parameter approximation of the VTI medium's P-wave reflection coefficient, rather than the gradient variation between formations as inferred in existing technologies.

[0241] The first inversion module 430 includes a first construction module, a second construction module, and

[0242] The first construction module is used to obtain the pre-stack angle gathers of the study area from the pre-stack seismic data of the target work area, extract seismic wavelets from the pre-stack angle gathers, and establish seismic wavelet matrices W at different angles.

[0243] The second construction module is used to construct a convolution model based on the matrix form of the preset expression of the longitudinal wave reflection coefficient of the VTI medium and the seismic wavelet matrix W at different angles, using convolution theory.

[0244] Considering that real-world data is often affected by noise, the linear representation of the first convolution model is as follows:

[0245] d=Gm+n (9)

[0246] Where d = [d1, d2, ..., d M ] T Let M be the actual observation data detected by M detectors, representing the data observed in the actual seismic record, where m = [m1, m2, ..., m]. M ] T These are M model parameters derived from well logging data and other information. They directly represent the formation reflection coefficient, n is the noise vector, and G is the forward modeling operator.

[0247] The expression for the forward operator G is:

[0248]

[0249] in, For the seismic wavelet matrix, The reflection coefficient matrix is ​​obtained based on the preset expression for the longitudinal wave reflection coefficient of the VTI medium.

[0250] The third building module is used to construct a first likelihood function of the observed data based on the first convolution model.

[0251] Assuming the noise in the seismic data follows a Gaussian distribution and is independent, this means the correlation coefficient between different noises is 0. The prior distribution function of noise n is:

[0252]

[0253] Where P0 is a constant, C d It is the covariance matrix of the noise.

[0254] Based on the first convolution model representing the general relationship between seismic data and subsurface media models, and since the Gaussian distribution conforms to the linear principle, the first likelihood function representing the probability distribution of the observed data is:

[0255]

[0256] The fourth construction module is used to construct the first inversion objective function based on the prior probability distribution function of the first likelihood function and model parameters, using Bayesian theory and the maximum a posteriori probability principle. Then, according to the maximum a posteriori probability principle, the posterior probability distribution function is solved to obtain the first inversion objective function.

[0257] Bayesian theory is a nonprobabilistic theory that can be used to calculate conditional probability distributions. Mathematically, Bayesian theory can be expressed as:

[0258]

[0259] Wherein, P(m|d) is the posterior probability distribution of the model parameter m, P(d|m) is the likelihood function of the observed data d, P(d) is the non-zero marginal probability density of the observed data d, and P(m) is the prior probability distribution of the model parameter m, which is given before data collection.

[0260] Bayesian theory describes the distribution function of model parameters m given the observed data d, which is related to the probability density of the observed data d given the model parameters m.

[0261] Since the marginal probability density of the observed data d is a constant, then:

[0262] P(m|d)∝P(d|m)P(m) (13)

[0263] That is, based on Bayesian theory and the likelihood function (8) of the observed data, the posterior probability distribution of the model parameter m is obtained, and the expression of the posterior probability distribution of the model parameter m is:

[0264]

[0265] In some embodiments, probability distribution features are added to the prior probability distribution of the model parameter m to provide constraints on the prior information, thereby improving the stability of the inversion.

[0266] The probability distribution features include exponential distribution features, Gaussian distribution features, and Cauchy distribution features, etc.

[0267] In this embodiment, a Cauchy distribution feature is added to the prior probability distribution of the model parameter m, and the expression for the prior probability distribution of the model parameter m is:

[0268] P(m=e -μR(m) (15)

[0269] Where μ is the weight coefficient and R(m) is the prior distribution function of the model parameter m.

[0270] Then the expression for the posterior probability distribution of the model parameter m becomes:

[0271]

[0272] Based on the maximum a posteriori probability principle, the posterior probability distribution function is solved to obtain the first inversion objective function.

[0273] The expression for the objective function of the first inversion is:

[0274]

[0275] Where J(m) is the objective function, d is the observed data, m is the model parameters, G is the forward operator, the superscript T denotes the transpose matrix, and C d Let be the covariance matrix of the noise, Q(m) be the regularization term that depends on the chosen prior distribution type, μ be the weight coefficient, and R(m) be the prior distribution function of the model parameters.

[0276] The fifth building module is used to differentiate the first inverted objective function with respect to the model parameters to obtain the first final inverted objective function.

[0277] The expression for the objective function of the first final inversion is:

[0278]

[0279] Where m represents the model parameters, d represents the observed data, and G is the forward modeling operator. T Let C be the transpose of G. d Let be the covariance matrix of the noise, Q(m) be the regularization term that depends on the chosen prior distribution type, and μ be the weighting coefficient.

[0280] The second calculation module is used to iteratively solve the objective function of the first final inversion using an iterative reweighted least squares algorithm to obtain the inversion results of the parameters to be inverted. The second inversion module 430 is used to invert the P-wave velocity, S-wave velocity, and density parameters in the Aki-Richards approximation based on Bayesian theory and the seismic data.

[0281] In some embodiments, the second inversion module step S300 includes a sixth construction module.

[0282] The sixth construction module is used to obtain pre-stack angle gathers of the study area, extract seismic wavelets from the pre-stack angle gathers, and establish seismic wavelet matrices W at different angles.

[0283] The seventh building module is used to construct a second convolution model based on the Aki-Richards approximation expression and the seismic wavelet matrix W at different angles, using convolution theory.

[0284] The eighth building module is used to construct a second likelihood function of the observed data based on the second convolution model.

[0285] The ninth building module is used to construct the second inversion objective function based on the prior probability distribution function of the second likelihood function and the model parameters, using Bayesian theory and the maximum a posteriori probability principle.

[0286] In some embodiments, the ninth construction module is specifically used to construct the posterior probability distribution function of the model parameters based on the second likelihood function and the prior probability distribution function, according to the Bayesian theory; and then solve the posterior probability distribution function according to the maximum a posteriori probability principle to obtain the second inversion objective function.

[0287] The tenth building module is used to differentiate the second inversion objective function with respect to the model parameter m to obtain the second final inversion objective function.

[0288] The third calculation module is used to solve the objective function of the second final inversion using the iterative reweighted least squares algorithm to obtain the inversion result of the P-wave velocity in the Aki-Richards approximation expression.

[0289] The first calculation module 440 is used to determine the anisotropy parameter ε based on the inversion results of the anisotropic P-wave velocity and the inversion results of the P-wave velocity.

[0290] The expression for determining the anisotropy parameter ε based on the inversion results of the anisotropic P-wave velocity and the inversion results of the P-wave velocity is as follows:

[0291]

[0292] Among them, V P0 e ε For the inversion results of anisotropic P-wave velocities, V P0 This is the result of the inversion of the longitudinal wave velocity.

[0293] Example 4

[0294] This embodiment also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it can implement the method steps as described in the above embodiments. This embodiment will not repeat the details here.

[0295] Computer-readable storage media may individually include computer programs, data files, data structures, etc., or combinations thereof. The computer-readable storage media or computer program may be specifically designed and understood by those skilled in the art of computer software, or the computer-readable storage media may be known and available to those skilled in the art of computer software. Examples of computer-readable storage media include: magnetic media, such as hard disks, floppy disks, and magnetic tapes; optical media, such as CD-ROMs and DVDs; magneto-optical media, such as optical discs; and hardware devices specifically configured to store and execute computer programs, such as read-only memory (ROM), random access memory (RAM), flash memory; or servers, application stores, etc. Examples of computer programs include machine code (e.g., code generated by a compiler) and files containing high-level code that can be executed by a computer using an interpreter. The described hardware devices may be configured to function as one or more software modules to perform the operations and methods described above, and vice versa. Furthermore, computer-readable storage media may be distributed across networked computer systems, allowing for the decentralized storage and execution of program code or computer programs.

[0296] Example 5

[0297] Figure 9 A connection block diagram of an electronic device provided in an embodiment of this application, such as... Figure 9 As shown, the electronic device 500 may include: one or more processors 510, memory 512, multimedia components 513, input / output (I / O) interface 514, and communication components 515.

[0298] The processor 510 is used to execute all or part of the steps in the method as described in Embodiment 1 or Embodiment 2. The memory 512 is used to store various types of data, which may include, for example, instructions for any application or method in the electronic device, as well as application-related data.

[0299] The processor 512 may be implemented as an application-specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field-programmable gate array (FPGA), a controller, a microcontroller, a microprocessor, or other electronic components, and is used to execute the methods in the above embodiments.

[0300] The memory 512 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0301] Multimedia component 513 may include a screen, which may be a touchscreen, and an audio component for outputting and / or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in memory or transmitted via a communication component. The audio component also includes at least one speaker for outputting audio signals.

[0302] I / O interface 514 provides an interface between processor 510 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons can be virtual buttons or physical buttons.

[0303] The communication component 515 is used for wired or wireless communication between the electronic device 500 and other devices.

[0304] Wired communication includes communication via network ports, serial ports, etc.; wireless communication includes Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, 5G, or one or more combinations thereof. Therefore, the corresponding communication component 515 may include: a Wi-Fi module, a Bluetooth module, and an NFC module.

[0305] It should also be understood that the methods or systems disclosed in the embodiments provided in this application can also be implemented in other ways. The method or system embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings illustrate the possible architecture, functions, and operations of methods and apparatuses according to various embodiments of this application. In this regard, each block in the flowchart or block diagram may represent a module, computer program segment, or part of a computer program, which includes one or more computer programs for implementing the specified logical functions.

[0306] It should also be noted that in some alternative implementations, the functions marked in the boxes may occur in a different order than those shown in the accompanying drawings, and may even be executed substantially in parallel. Sometimes they may also be executed in reverse order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and combinations of boxes in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified functions or actions, or using a combination of dedicated hardware and computer programs.

[0307] In this application, the terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "including one..." does not exclude the presence of other identical elements in the process, method, apparatus, or device that includes the element; the use of terms such as "first" and "second" is for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly indicating the number or sequence of the indicated technical features; in the description of this application, unless otherwise stated, the terms "multiple" or "many" mean at least two; if a server is described, it should be noted that a server can be an independent physical server or terminal, or a server cluster consisting of multiple physical servers, or a cloud server capable of providing basic cloud computing services such as cloud servers, cloud databases, cloud storage, and CDN; if a smart terminal or mobile device is described in this application, it should be noted that a smart terminal or mobile device can be a mobile phone, tablet computer, smartwatch, netbook, wearable electronic device, personal digital assistant (PDA), augmented reality (AR) device, virtual reality (VR) device, smart TV, smart speaker, personal computer (PC). The application may include, but is not limited to, computers (PCs), etc., and does not impose any special restrictions on the specific form of smart terminals or mobile devices.

[0308] Finally, it should be noted that in the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "a single example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0309] Although embodiments of this application have been shown and described above, it is to be understood that the above embodiments are exemplary and the content is only for the purpose of facilitating understanding of this application, and is not intended to limit this application. Any person skilled in the art to which this application pertains may make any modifications and changes in form and detail of the implementation without departing from the spirit and scope disclosed in this application, but the scope of protection of this application shall still be determined by the scope defined in the appended claims.

Claims

1. A stepwise inversion method for pre-stack anisotropy parameters of VTI media, characterized in that, include: Acquire pre-stack seismic data for the target work area; Based on Bayesian theory and the pre-stack seismic data, the parameters to be inverted in the preset expression for the P-wave reflection coefficient of the VTI medium are inverted. These parameters include parameters related to P-wave velocity and anisotropy parameters. The relevant anisotropic longitudinal wave velocity; Based on Bayesian theory and the pre-stack seismic data, the P-wave velocity, S-wave velocity and density parameters in the Aki-Richards approximation were inverted. Based on the anisotropic P-wave velocity and the P-wave velocity, determine the anisotropic parameters. ; The process of inverting the parameters to be inverted in the preset expression for the P-wave reflection coefficient of the VTI medium based on Bayesian theory and the pre-stack seismic data includes: Prestack angle gathers of the target work area are obtained from the prestack seismic data, and seismic wavelets are extracted from the prestack angle gathers to establish seismic wavelet matrices at different angles. Based on the matrix form of the preset expression for the longitudinal wave reflection coefficient of the VTI medium and the seismic wavelet matrix, the first convolution model is constructed using convolution theory; Based on the first convolution model, a first likelihood function representing the probability distribution of the observed data is constructed; Based on the prior probability distribution function of the model parameters and the first likelihood function, the objective function of the first inversion is constructed using Bayesian theory and the maximum a posteriori probability principle. The objective function of the first inversion is differentiated with respect to the model parameters to obtain the objective function of the first final inversion. The first final inversion objective function is solved by iterative reweighted least squares method to obtain the inversion result of the parameters to be inverted; The preset expression for the longitudinal wave reflection coefficient of the VTI medium is: （8） in: The longitudinal wave reflection coefficient of the VTI medium; ， ； Angle of incidence For the longitudinal wave velocity, For transverse wave velocity, For density, and For the anisotropy parameters of the VTI medium; , and The parameters to be inverted are denoted as P-wave impedance, pseudo-isotropic S-wave modulus, and anisotropic P-wave velocity, respectively; the calculation... The calculation is based on the difference in physical quantities between the upper and lower strata at adjacent interfaces. This represents the average physical properties of the strata above and below the adjacent interface. The anisotropic parameters are determined based on the anisotropic P-wave velocity and the P-wave velocity. The expression is: in, For anisotropic longitudinal wave velocities, This represents the longitudinal wave velocity.

2. The method according to claim 1, characterized in that, The linear expression of the first convolution model is as follows: in, For observation data, For the orthogonal operator, For model parameters, This is the noise vector.

3. The method according to claim 1, characterized in that, The steps of constructing the first inversion objective function based on the prior probability distribution function of the model parameters and the first likelihood function, using Bayesian theory and the maximum a posteriori probability principle, include: Based on the first likelihood function and the prior probability distribution function, the first posterior probability distribution function of the model parameters is constructed using Bayesian theory. The first posterior probability distribution function is solved using the maximum a posteriori probability principle to obtain the first inversion objective function.

4. The method according to claim 1, characterized in that, The expression for the objective function of the first inversion is: in, For observation data, For model parameters, For forward calculus operators, superscript Represents the transpose matrix. It is the covariance matrix of the noise. These are the weighting coefficients. It is the prior distribution function of the model parameters.

5. The method according to claim 1, characterized in that, The expression for the objective function of the first final inversion is: in, For model parameters, For observation data, For the orthogonal operator, for The transpose of the matrix, It is the covariance matrix of the noise. This is a regularization term that depends on the type of prior distribution chosen. These are the weighting coefficients.

6. A device for inverting the pre-stack anisotropic parameter distribution of VTI media, characterized in that, include: The first acquisition module is used to acquire pre-stack seismic data of the target work area; The first inversion module is used to invert the parameters to be inverted in the preset expression of the P-wave reflection coefficient of the VTI medium based on Bayesian theory and the pre-stack seismic data. The parameters to be inverted include P-wave velocity and anisotropy parameters. The relevant anisotropic longitudinal wave velocity; The second inversion module is used to invert the P-wave velocity, S-wave velocity and density parameters in the Aki-Richards approximation based on Bayesian theory and the pre-stack seismic data. The first calculation module is used to determine the anisotropic parameter ε based on the anisotropic P-wave velocity and the P-wave velocity; The first inversion module includes a first construction module, a second construction module, a third construction module, a fourth construction module, a fifth construction module, and a second calculation module; The first construction module is used to obtain the pre-stack angle gathers of the target work area from the pre-stack seismic data, extract seismic wavelets from the pre-stack angle gathers, and establish seismic wavelet matrices at different angles. The second construction module is used to construct the first convolution model based on the matrix form of the preset expression of the longitudinal wave reflection coefficient of the VTI medium and the seismic wavelet matrix, using convolution theory. The third construction module is used to construct a first likelihood function representing the probability distribution of the observed data based on the first convolution model; The fourth construction module is used to construct the first inversion objective function based on the prior probability distribution function of the model parameters and the first likelihood function, using Bayesian theory and the maximum a posteriori probability principle. The fifth building module is used to differentiate the first inverted objective function with respect to the model parameters to obtain the first final inverted objective function; The second calculation module is used to solve the first final inversion objective function using the iterative reweighted least squares method to obtain the inversion result of the parameters to be inverted; The preset expression for the longitudinal wave reflection coefficient of the VTI medium is: （8） in: The longitudinal wave reflection coefficient of the VTI medium; ， ； Angle of incidence For the longitudinal wave velocity, For transverse wave velocity, For density, and For the anisotropy parameters of the VTI medium; , and The parameters to be inverted are denoted as P-wave impedance, pseudo-isotropic S-wave modulus, and anisotropic P-wave velocity, respectively; the calculation... The calculation is based on the difference in physical quantities between the upper and lower strata at adjacent interfaces. This represents the average physical properties of the strata above and below the adjacent interface. The anisotropic parameters are determined based on the anisotropic P-wave velocity and the P-wave velocity. The expression is: in, For anisotropic longitudinal wave velocities, This represents the longitudinal wave velocity.

7. A computer-readable storage medium, characterized in that, The computer program stored in the computer-readable storage medium, when executed by one or more processors, implements the step-by-step inversion method for VTI medium pre-stack anisotropy parameters as described in any one of claims 1 to 5.

8. An electronic device, characterized in that, It includes a memory and one or more processors, wherein a computer program is stored in the memory, and the memory and the processor are communicatively connected to each other. When the computer program is executed by the processor, it performs the step-by-step inversion method for VTI media pre-stack anisotropy parameters as described in any one of claims 1 to 5.

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