A method and system for seismic inversion of elastic parameters and fracture weakness in HTI media

Abstract

The application provides a HTI medium elastic parameter and fracture weakness seismic inversion method and system, and comprises the following steps: establishing a seismic forward model based on a longitudinal wave reflection coefficient equation of the HTI medium and a convolution model of a seismic wavelet; according to the fact that a prior distribution of model parameters of the seismic forward model conforms to a Gaussian distribution, analyzing a prior probability distribution function and the seismic forward model by using a Bayesian theory, and then obtaining a posterior probability distribution function of the model parameters; and inversely solving the posterior probability distribution function of the model parameters to obtain the HTI medium elastic parameter and the fracture weakness. In combination with prior information of the model parameters, the uncertainty of an inversion result can be analyzed through the posterior probability distribution, and useful reference information is provided for subsequent seismic interpretation work.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of seismic exploration, and particularly relates to a HTI medium elastic parameter and fracture weakness seismic inversion method and system. BACKGROUND

[0002] The statements in this section merely provide background information related to the present application and do not necessarily constitute the prior art.

[0003] Fracture property research is an important part of carbonate reservoir and unconventional oil and gas reservoir research. Some fracture systems can connect isolated pores, increase the effective porosity of the reservoir, and provide convenient conditions for oil and gas storage and migration. The underground fractures in the fracture reservoir make the reservoir have high permeability characteristics, and the storage space may store high-quality oil and gas resources. Therefore, the fracture reservoir with economic benefits becomes the target of seismic exploration.

[0004] Fractures are easy to induce anisotropy, so many anisotropy parameters have been proposed to characterize fracture properties. Normal and tangential fracture weakness are two dimensionless fracture weakness parameters, which are directly related to fracture-induced anisotropy and can be used to indicate fracture density and fluid information contained in the fractures. In addition to the anisotropy parameters for characterizing fractures, the importance of elastic parameters such as longitudinal and transverse wave velocities and density cannot be ignored. Elastic parameters are helpful for seismic reservoir description, and the combination of fracture weakness and elastic parameters can more effectively characterize the reservoir and be used to identify lithology and oil and gas reservoirs. Therefore, the elastic parameters and fracture weakness obtained by inversion from azimuthal seismic data are of great significance for the prediction and characterization of fracture reservoirs. Seismic data are affected by factors such as noise and frequency band limitation, so the inversion results have multiple solutions.

[0005] In order to study the anisotropy characteristics of fracture reservoirs, the traditional AVOAz deterministic inversion method is widely used. However, for deterministic inversion, the prior information of model parameters is not fully utilized, and the uncertainty analysis of the inversion results cannot be performed. SUMMARY

[0006] In order to overcome the shortcomings of the prior art, the present application provides a HTI medium elastic parameter and fracture weakness seismic inversion method and system, which combines the prior information of model parameters, analyzes the seismic forward model by using the Bayesian theory, obtains the posterior probability distribution function of the model parameters, and further obtains the HTI medium elastic parameter and fracture weakness. The posterior probability distribution can be used to analyze the uncertainty of the inversion results, and provide useful reference information for subsequent seismic interpretation work.

[0007] To achieve the above purpose, a first aspect of the present application provides a HTI medium elastic parameter and fracture weakness seismic inversion method, comprising:

[0008] establish a seismic forward model based on a P-wave reflection coefficient equation of the HTI medium and a convolution model of a seismic wavelet;

[0009] According to the prior distribution function of the model parameter of the seismic forward model and the noise term, the prior probability distribution function and the seismic forward model are analyzed by using the Bayesian theory, and then the posterior probability distribution function of the model parameter is obtained.

[0010] The posterior probability distribution function of the model parameter is inversely solved to obtain the elastic parameters of the HTI medium and the fracture weakness.

[0011] The second aspect of the present application provides a seismic inversion system for the elastic parameters of the HTI medium and the fracture weakness, comprising:

[0012] The model establishment module establishes a seismic forward model based on a P-wave reflection coefficient equation of the HTI medium and a convolution model of a seismic wavelet;

[0013] The inversion module analyzes the prior probability distribution function and the seismic forward model by using the Bayesian theory according to the prior distribution function of the model parameter of the seismic forward model and the noise term, and then obtains the posterior probability distribution function of the model parameter.

[0014] The solving module inversely solves the posterior probability distribution function of the model parameter to obtain the elastic parameters of the HTI medium and the fracture weakness.

[0015] The third aspect of the present application provides a computer device, comprising a processor, a memory and a bus, the memory stores machine readable instructions executable by the processor, when the computer device is running, the processor and the memory communicate through the bus, and the machine readable instructions are executed by the processor to perform a seismic inversion method for the elastic parameters of the HTI medium and the fracture weakness.

[0016] The fourth aspect of the present application provides a computer readable storage medium, the computer readable storage medium stores a computer program, and the computer program is executed by the processor to perform a seismic inversion method for the elastic parameters of the HTI medium and the fracture weakness.

[0017] The above one or more technical solutions have the following beneficial effects:

[0018] In the present application, according to the established seismic forward model and the prior information of the model parameters, the prior probability distribution function and the seismic forward model are analyzed by using the Bayesian theory, and then the posterior probability distribution function of the model parameters is obtained, and the HTI medium elastic parameters and the fracture weakness are obtained by using the posterior probability distribution function of the model parameters; the prior information of the model parameters is fully utilized, the posterior probability can analyze the uncertainty of the inversion result, useful reference information is provided for subsequent seismic interpretation work, and the subsequent identification of the gas-bearing area in the fracture reservoir is facilitated.

[0019] Advantages of the additional aspects of the present application will become apparent in light of the following description. BRIEF DESCRIPTION OF DRAWINGS

[0020] The accompanying drawings, which form a part of the present application, are included to provide a further understanding of the application, and are incorporated in and constitute a part of this specification, illustrate embodiments of the present application and together with the description serve to explain the present application.

[0021] Figure 1 is the inversion method flow chart in the embodiment one of the present application;

[0022] Fig. 2 (a) is the synthetic azimuth angle gather under the horizontal plane without noise in the embodiment one of the present application;

[0023] Fig. 2 (b) is the synthetic azimuth angle gather under the horizontal plane with the signal-to-noise ratio of 2 in the embodiment one of the present application

[0024] Fig. 3 (a) is the posterior distribution of the P-wave velocity, the S-wave velocity and the density obtained by the Bayesian linear inversion without noise in the embodiment one of the present application;

[0025] Fig. 3 (b) is the posterior distribution of the fracture weakness obtained by the Bayesian linear inversion without noise in the embodiment one of the present application;

[0026] Fig. 4 (a) is the posterior distribution of the P-wave velocity, the S-wave velocity and the density obtained by the Bayesian linear inversion with the signal-to-noise ratio of 2 in the embodiment one of the present application;

[0027] Fig. 4 (b) is the posterior distribution of the fracture weakness obtained by the Bayesian linear inversion with the signal-to-noise ratio of 2 in the embodiment one of the present application;

[0028] Fig. 5 (a) is the scatter plot of the inversion result and the logging data under the horizontal plane without noise in the embodiment one of the present application;

[0029] Fig. 5 (b) is the scatter plot of the inversion result and the logging data under the horizontal plane with the signal-to-noise ratio of 2 in the embodiment one of the present application;

[0030] Figure 6(a) is a near-angle stack seismic profile in the first embodiment of the present application, with an average angle of 18° and azimuths of 40° and 130°, respectively;

[0031] Figure 6(b) is a medium-angle stack seismic profile in the first embodiment of the present application, with an average angle of 22° and azimuths of 40° and 130°, respectively;

[0032] Figure 6(c) is a far-angle stack seismic profile in the first embodiment of the present application, with an average angle of 26° and azimuths of 40° and 130°, respectively;

[0033] Figure 7(a) is a P-wave velocity inversion result in the first embodiment of the present application;

[0034] Figure 7(b) is a S-wave velocity inversion result in the first embodiment of the present application;

[0035] Figure 7(c) is a density inversion result in the first embodiment of the present application;

[0036] Figure 8(a) is a normal fracture weakness inversion result in the first embodiment of the present application;

[0037] Figure 8(b) is a shear fracture weakness inversion result in the first embodiment of the present application;

[0038] Figure 9(a) is a P-wave velocity posterior probability profile in the first embodiment of the present application;

[0039] Figure 9(b) is a S-wave velocity posterior probability profile in the first embodiment of the present application;

[0040] Figure 9(c) is a density posterior probability profile in the first embodiment of the present application;

[0041] Figure 10(a) is a normal fracture weakness posterior probability profile in the first embodiment of the present application;

[0042] Figure 10(b) is a shear fracture weakness posterior probability profile in the first embodiment of the present application;

[0043] Figure 11(a) is a comparison of P-wave velocity (Vp), S-wave velocity (Vs), density well-logging curves and inversion results in the first embodiment of the present application;

[0044] Figure 11(b) is a comparison of normal fracture weakness and shear fracture weakness well-logging curves and inversion results in the first embodiment of the present application. DETAILED DESCRIPTION

[0045] It should be noted that the following detailed description is merely exemplary and is intended to provide further description of the application. 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.

[0046] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations of the present invention.

[0047] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0048] Example 1

[0049] like Figure 1 As shown in the figure, this embodiment discloses a seismic inversion method for elastic parameters and crack weakness of HTI medium, including:

[0050] A forward seismic model was established based on the formula for the longitudinal wave reflection coefficient of HTI medium and the phase convolution of seismic wavelet.

[0051] Based on the fact that the prior distribution function of the model parameters in the earthquake forward model follows a Gaussian distribution, and the noise term also follows a Gaussian distribution, Bayesian theory is used to analyze the prior probability distribution function and the earthquake forward model, thereby obtaining the posterior probability distribution function of the model parameters.

[0052] The posterior probability distribution function of the model parameters is inverted and solved to obtain the analytical expression of the posterior mean of the model parameters. Finally, the elastic parameters and crack weakness of the HTI medium are obtained.

[0053] This embodiment utilizes the convolution model and Bayesian theory to obtain the posterior distributions of elastic parameters and crack weakness. The posterior distributions consist of explicit expressions for the posterior mean and covariance. The linear approximation of the P-wave reflection coefficient in HTI media and the convolution model of the wavelet are used as the mechanism for seismic forward modeling. The approximation of the P-wave reflection coefficient in HTI media can be expressed as the sum of an isotropic background term and an anisotropic disturbance term. The linear approximation of the P-wave reflection coefficient is shown below:

[0054]

[0055]

[0056]

[0057] Where α represents the longitudinal wave velocity, β represents the transverse wave velocity, and ρ represents the density; and Δα, Δβ, and Δρ represent the average values ​​at the reflecting interface; Δα, Δβ, and Δρ represent the differences between the reflecting interfaces; θ represents the longitudinal wave incident angle, and φ represents the azimuth angle; δ N Indicates the weakness of the normal fracture, which is highly sensitive to the fluid contained within the fracture; δ T This represents the tangential crack weakness, which is highly sensitive to crack density; Δδ N and Δδ Trepresents the difference between the fracture weakness at the reflection interface.

[0058]

[0059] where α α , α β , and α ρ , and are the weight coefficients in equations (2) and (3); g is the square of the ratio of the transverse wave velocity to the longitudinal wave velocity, which is a process parameter.

[0060] When and , the parameters in equation (2) can be properly simplified, i.e. and Therefore, equation (1) can be rewritten as:

[0061]

[0062] In a set of time intervals and a set of incident angles and azimuth angles, the time-continuous reflectivity function as equation (5) can be written in the form of a matrix as equation (6).

[0063] R pp = ADm (6)

[0064]

[0065] m = [ln α, ln β, ln ρ, δ N , δ T ] T (8)

[0066] where θ1to θ n represent different incident angles; φ1to φ m represent different azimuth angles; t represents time, which can be converted into depth by time-depth conversion; A is the coefficient matrix; m is the model parameter; and D is the differential matrix, which is used to obtain the derivative of the model parameter m.

[0067] Assume that the prior distribution of the model parameter m obeys a Gaussian distribution with a prior mean μ m and a prior covariance matrix C m . Based on the convolution model, the azimuthal seismic amplitude d can be represented as the product of the wavelet matrix W, the coefficient matrix A, the differential matrix D, and the model parameter m, plus a noise term e.

[0068] d = WADm + e (9)

[0069]

[0070]

[0071] where d(θ i ,φ i ) represents the seismic amplitude corresponding to a certain incidence angle and a certain azimuth; W(θ i ,φ i ) represents the seismic wavelet corresponding to a certain incidence angle and a certain azimuth; and e is assumed to be a noise term with mean 0 and covariance matrix C d , which is Gaussian distributed and independent of m.

[0072] The linear forward operator G is expressed as:

[0073] G = WA D (13)

[0074] The posterior distribution function of the model parameters based on the Bayesian framework is as follows:

[0075]

[0076] where p(m|d) represents the posterior distribution function of the model parameters.

[0077] Finally, the analytical expressions of the posterior mean and covariance of the model parameters are obtained using the linear Bayesian inversion method. The posterior distribution expression is as follows:

[0078]

[0079]

[0080] where m and m are the posterior mean and posterior covariance matrix, respectively; d obs is the observed seismic data. According to the Bayesian theory, the posterior distribution also follows a Gaussian distribution. m is the model parameter, which is expressed in the logarithmic form of the elastic parameters and the fracture weakness. The target parameters are a, b, p, d N , and d T . The medium elastic parameters and fracture weakness can be obtained after a certain transformation of the model parameters.

[0081] The feasibility and stability of the proposed method are verified using synthetic seismic data. Figures 3(a)-3(b)The synthetic seismic angle gathers with different signal-to-noise ratios are generated by the anisotropic reflection coefficients calculated from the logging curves of P-and S-wave velocities, density, normal fracture weakness and shear fracture weakness, and the Ricker wavelet with dominant frequency of 35 Hz. According to the incident angles of 10°, 20° and 30°, the synthetic seismic angle gathers with different noise levels and different azimuths are extracted for inversion test. Then the mean and covariance of the model parameters are obtained by the Bayesian linear AVOAz seismic inversion method. Figures 3(a)-3(b) are the elastic parameters and fracture weakness obtained from the inversion of the noise-free synthetic seismic gathers. The black curve is the true value, the blue curve represents the initial model, the red curve is the inversion result obtained by the proposed method, the red dashed line represents the 95% posterior confidence interval, and the background color represents the posterior distribution. Similarly, Figures 4(a)-4(b) is the inversion result based on the synthetic seismic gathers with signal-to-noise ratio of 2. Figures 3(a)-3(b) and Figures 4(a)-4(b) show that the inversion results of P-and S-wave velocities and density are in good agreement with the logging curves, while the inversion result of fracture weakness is slightly worse. This is because the elastic parameters are more sensitive to the anisotropic reflection coefficients than the fracture weakness. From Figures 4(a)-4(b) it can be seen that the proposed method has good robustness to noise. The 95% posterior confidence interval of the elastic parameters is relatively small, and the reliability of the inversion result of the elastic parameters is relatively high, while the 95% posterior confidence interval of the fracture weakness is relatively wide, and the reliability of the inversion result of the fracture weakness is relatively low, which shows that the uncertainty of the inversion result of the elastic parameters is smaller. The uncertainty evaluation of the inversion result can provide useful reference information for seismic interpretation. The scatter plots of the inversion results with different noise levels are shown in Figures 5(a)-5(b) , Figures 5(a)-5(b) show that the inversion results of the elastic parameters and fracture weakness are in good agreement with the logging data. In addition, the correlation coefficients between the inversion values and the true values are calculated by the Pearson correlation coefficient formula. The correlation coefficients of the elastic parameters and fracture weakness are shown in Table 1, and the results show that when the noise changes from no noise to signal-to-noise ratio of 2, the correlation coefficient decreases slightly. In addition, even if some random noise is added, the inversion result is still in good agreement with the logging data, which verifies the feasibility and stability of the proposed method.

[0082] Table 1 Correlation coefficients between the inversion values and the true values under different noise levels

[0083]

[0084] In order to verify the feasibility of the method, a set of actual data from a certain area in southwest China is used for example test. The data set used includes the seismic gathers and logging data of the gas-bearing fractured reservoir. There are a large number of vertical or near-vertical fractures in the target reservoir, so the target reservoir can be equivalent to HTI medium. Figures 6(a)-6(c)are the seismic stack sections for near, middle and far angles, respectively, with azimuths of 40° and 130°. Figures 7(a)-7(c) are the inverted elastic parameters, Figures 8(a)-8(b) is the inverted fracture weakness. It can be seen that the inversion results have high resolution and good lateral continuity, and some rich details are well displayed in the inversion section. The well location is at the 133th trace, the black curve represents the well log, and the well log has good consistency with the inversion section. Figures 7(a)-7(c) and Figures 8(a)-8(b) The red dashed ellipse in indicates the fractured gas-bearing reservoir. At the fractured gas-bearing reservoir, the inverted P-wave velocity and S-wave velocity show relatively low values, but are not significant, so it is difficult to distinguish the target reservoir. At the fractured gas-bearing reservoir, the inverted density shows a low value, which is consistent with the existing theory that density is sensitive to gas-bearing reservoirs. However, the inverted normal and tangential fracture weaknesses show obvious high values in this area, which can be used to indicate the fractured gas-bearing reservoir. In addition, the sensitivity of fracture weakness to the fractured gas-bearing reservoir is higher than that of the elastic parameters. In summary, the five inverted parameters are helpful to identify the fractured gas-bearing reservoir. The posterior probability section can be used to evaluate the uncertainty of the inverted elastic parameters and fracture weaknesses. Figures 9(a)-9(c) and Figures 10(a)-10(b) show the posterior probability sections of P-wave velocity, S-wave velocity, density, normal fracture weakness and tangential fracture weakness. From Figures 9(a)-9(c) and Figures 10(a)-10(b), it can be seen that the posterior probability sections show the reliability of the inversion results and indicate the uncertainty. For the areas with relatively low posterior probability in the inversion results, it is necessary to improve the reliability of the inversion results in this area, which provides useful information for improving the inversion accuracy in the next step. The proposed method can evaluate the uncertainty of the inversion results, which is an advantage that traditional deterministic inversion methods do not have. In order to further verify the feasibility of the proposed inversion method and the reliability of the inverted parameters, Figures 11(a)-11(b) shows the comparison of the well trace log and the inversion results. As Figures 11(a)-11(b) shown, the inverted elastic parameters and fracture weaknesses have good consistency with the well log, indicating the accuracy of the inversion results and verifying the feasibility of the proposed method in fractured reservoirs.

[0085] The embodiment derives explicit analytic expressions of the posterior distribution of the elastic parameters and the fracture weakness, and applies the method to the uncertainty analysis of the inversion results. The method is tested by using synthetic data and actual data. The synthetic data test results show that the inversion results are in good agreement with the logging data, the inversion effect of the longitudinal wave velocity, the transverse wave velocity and the density is better than that of the fracture weakness, and the uncertainty of the inversion obtained elastic parameters is lower than that of the fracture weakness. In addition, the method still has good performance under noise conditions. The application of actual data also verifies the feasibility of the method, and the inversion obtained elastic parameters and fracture weakness can effectively indicate the fracture gas reservoir area. In addition, the method provides another tool for evaluating the inversion results, and the uncertainty of the inversion results can be analyzed by the posterior probability, which provides useful reference information for subsequent seismic interpretation work. The feasibility of the invention is verified by the test of synthetic data and actual data, and the method is helpful to identify the gas-bearing area in the fractured reservoir.

[0086] Embodiment two

[0087] The embodiment aims to provide a HTI medium elastic parameter and fracture weakness seismic inversion system, comprising:

[0088] The model establishment module establishes a seismic forward model based on the longitudinal wave reflection coefficient equation of the HTI medium and the convolution model of the seismic wavelet;

[0089] The inversion module: according to the prior distribution of the model parameters of the seismic forward model, the prior probability distribution function and the seismic forward model are analyzed by using the Bayesian theory, and then the posterior probability distribution function of the model parameters is obtained;

[0090] The solving module: the posterior probability distribution function of the model parameters is inverted and solved, and the HTI medium elastic parameter and fracture weakness are obtained.

[0091] Embodiment three

[0092] The embodiment aims to provide a computing device, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to realize the steps of the above method.

[0093] Embodiment four

[0094] The embodiment aims to provide a computer readable storage medium.

[0095] A computer readable storage medium, having a computer program stored thereon, the program being executed by a processor to perform the steps of the above method.

[0096] The steps involved in the apparatuses of the above embodiments two, three and four correspond to the method of embodiment one, and the specific implementation can refer to the relevant description of embodiment one. The term "computer readable storage medium" should be understood as including a single medium or multiple media of one or more instruction sets; it should also be understood as including any medium capable of storing, encoding or carrying the instruction set for execution by the processor and causing the processor to perform any of the methods in the present application.

[0097] Those skilled in the art should understand that each module or step of the present application described above can be realized by a general computer device, alternatively, they can be realized by program codes executable by a computing device, so that they can be stored in a storage device for execution by a computing device, or they can be respectively made into each integrated circuit module, or a plurality of modules or steps among them can be made into a single integrated circuit module to realize. The present application is not limited to any specific combination of hardware and software.

[0098] Although the specific embodiments of the present application are described above in combination with the drawings, it is not a limitation on the scope of protection of the present application, and those skilled in the art should understand that various modifications or changes made by those skilled in the art on the basis of the technical solutions of the present application without creative labor are still within the scope of protection of the present application.

Claims

1. A seismic inversion method for elastic parameters and crack weakness of HTI medium, characterized in that, include: A forward modeling model for earthquakes is established based on the equation of longitudinal wave reflection coefficient in HTI medium and the convolution model of seismic wavelet. Based on the seismic P-wave velocity, S-wave velocity, density, P-wave incident angle, azimuth angle, normal fracture weakness, tangential fracture weakness, and the average values ​​of seismic P-wave velocity, S-wave velocity, and density at the reflecting interface and the differences between the reflecting interfaces, the equation for the P-wave reflection coefficient of HTI medium is established. The equation for the longitudinal wave reflection coefficient of the HTI medium is simplified based on the ratio of the difference between longitudinal wave velocities at the reflecting interfaces to the average value of longitudinal wave velocities at the reflecting interfaces, the ratio of the difference between transverse wave velocities at the reflecting interfaces to the average value of transverse wave velocities at the reflecting interfaces, and the numerical relationship between the difference between densities at the reflecting interfaces and the average value of densities at the reflecting interfaces. The product of the wavelet matrix constructed from the incident angle and azimuth angle of the longitudinal wave, the coefficient matrix formed by different times and corresponding different incident angles and azimuth angles, and the differential matrix represents the linear forward modeling operator; Since the prior distribution of the model parameters and the noise term of the earthquake forward model both conform to Gaussian distribution, the model parameters of the earthquake forward model are analyzed using Bayesian theory to obtain the posterior probability distribution function of the model parameters. The posterior probability distribution function of the model parameters is inverted and solved to obtain the elastic parameters and crack weakness of the HTI medium.

2. The seismic inversion method for elastic parameters and crack weakness of HTI medium as described in claim 1, characterized in that, The model parameters include seismic P-wave velocity, S-wave velocity, density, normal fracture weakness, and tangential fracture weakness.

3. The seismic inversion method for elastic parameters and crack weakness of HTI medium as described in claim 1, characterized in that, According to the convolution model, the azimuth earthquake amplitude is the sum of the product of the wavelet matrix constructed by the incident angle and azimuth angle of the P-wave, the coefficient matrix constructed by different times and corresponding different incident angles and azimuth angles, the differential matrix, and the model parameters, plus the earthquake noise. Using the azimuth earthquake amplitude combined with Bayesian theory, the posterior probability distribution function of the model parameters is obtained.

4. The seismic inversion method for elastic parameters and crack weakness of HTI medium as described in claim 1, characterized in that, By using linear forward modeling operators and combining the prior mean and prior covariance of the Gaussian distribution that the prior distribution of the model parameters follows, the posterior probability distribution function of the model parameters is inverted and solved to obtain the posterior mean and posterior covariance of the model parameters.

5. A seismic inversion system for elastic parameters and crack weakness of HTI medium, characterized in that, include: Model building module: Establishes a forward seismic model based on the P-wave reflection coefficient equation of HTI medium and the convolution model of seismic wavelet; Inversion Module: Based on the Gaussian distribution of the prior distribution function of the model parameters and the noise term of the seismic forward model, Bayesian theory is used to analyze the model parameters of the seismic forward model to obtain the posterior probability distribution function of the model parameters. The P-wave reflection coefficient equation of the HTI medium is simplified based on the numerical relationships of the ratio of the difference in P-wave velocity between the reflecting interfaces to the average value of the P-wave velocity at the reflecting interfaces, the ratio of the difference in S-wave velocity between the reflecting interfaces to the average value of the S-wave velocity at the reflecting interfaces, and the ratio of the difference in density between the reflecting interfaces to the average value of the density at the reflecting interfaces. The product of the wavelet matrix constructed from the P-wave incident angle and azimuth angle, the coefficient matrix constructed from different times and corresponding different incident angles and azimuth angles, and the differential matrix represents the linear forward modeling operator. Solution module: Inversely solves the posterior probability distribution function of the model parameters to obtain the elastic parameters and crack weakness of the HTI medium.

6. A computer device, characterized in that, include: The computer device includes a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the computer device is running, the processor communicates with the memory via the bus. When the machine-readable instructions are executed by the processor, they perform a seismic inversion method for HTI medium elastic parameters and fracture weakness as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs a seismic inversion method for HTI medium elastic parameters and crack weakness as described in any one of claims 1 to 4.

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