Optimized inversion method and device based on VTI

Abstract

The invention provides a VTI-based optimization inversion method and device. The method comprises the following steps: step 1, establishing an inversion equation and an inversion objective function based on a VTI medium longitudinal wave anisotropic reflection coefficient formula; 2, calculating the gradient of the inversion objective function; and step 3, carrying out iterative updating on the model variable of the inversion equation based on the gradient to obtain a final inversion result. According to the optimization inversion method based on the VTI, the problems that a conventional VTI medium reflection coefficient equation is low in nonlinear inversion calculation efficiency, low in linear inversion precision, high in model dependence and high in multiplicity of solutions can be solved.

Description

Technical Field

[0001] The present invention relates to the technical field of seismic exploration, and particularly to an optimized inversion method based on VTI and an optimized inversion device based on VTI. Background Art

[0002] Shale is a sedimentary rock mainly composed of clay minerals (such as kaolinite, hydromica, etc.) and has obvious thin bedding structures. Due to its organic matter content, clay, and the oriented arrangement of microcracks, shale exhibits VTI characteristics, that is, it exhibits transverse isotropy with a vertical axis of symmetry (i.e., VTI).

[0003] VTI reflection coefficient equation inversion is a seismic exploration inversion method for VTI media. In VTI media, the propagation characteristics of seismic waves are different from those of conventional isotropic media. Therefore, the reflection coefficient equation inversion method for VTI media can more accurately deduce the structure and physical property characteristics of underground formations. Specifically, the VTI reflection coefficient equation inversion method uses seismic exploration data and relevant geophysical theories to deduce the reflection coefficient sequence in VTI media. This sequence contains characteristic information such as the morphology, velocity, and density of underground formations. Then, by processing this reflection coefficient sequence, an image or model of the underground formation can be obtained. When performing VTI reflection coefficient equation inversion, the special properties of VTI media, such as anisotropy and lateral inhomogeneity, need to be considered. At the same time, the problems of multi-solution and uncertainty that may exist in the inversion process also need to be solved.

[0004] Conventional VTI reflection coefficient equation inversion is a linear inversion based on the approximation equation of the reflection coefficient. Although the inversion efficiency is high, there is a problem of low accuracy; the non-linear inversion based on the exact VTI inversion formula has high accuracy but low efficiency and is not suitable for practical application. Summary of the Invention

[0005] Aiming at the problems existing in the prior art, the present invention proposes an optimized inversion method based on VTI, which can solve the problems of low computational efficiency of non-linear inversion of the reflection coefficient equation of conventional VTI media, low accuracy of linear inversion, high model dependence, and strong multi-solution.

[0006] According to one aspect of the present invention, an optimized inversion method based on VTI is proposed, and the method includes:

[0007] Step 1: Establish an inversion equation and an inversion objective function based on the VTI medium longitudinal wave anisotropic reflection coefficient formula;

[0008] Step 2: Calculate the gradient of the inversion objective function;

[0009] Step 3: Iteratively update the model variables of the inversion equation based on the gradient to obtain the final inversion result.

[0010] Preferably, the formula for the P-wave anisotropic reflection coefficient of the VTI medium is:

[0011]

[0012]

[0013] where represents the P-wave reflection coefficient of the VTI, θ represents the P-wave incident angle, Δ represents the difference between the physical quantity below the interface and the physical quantity above the interface, α represents the P-wave impedance, β represents the anisotropic shear modulus term of the S-wave, ξ represents the anisotropic P-wave velocity term, K represents the square of the ratio of the average S-wave velocity to the average P-wave velocity, which is a constant, Zp represents the P-wave impedance, σ represents the equivalent anisotropic parameter, μ represents the shear modulus of the S-wave, Vp represents the P-wave velocity, ε and δ represent the Thomsen anisotropic parameters, represents the average S-wave velocity, represents the average P-wave velocity.

[0014] Preferably, the inversion equation is:

[0015] d = A(m)

[0016] where d represents the data space, A represents the mapping function, m represents the elastic model variables, m = [α(t), β(t), ξ(t)], and t represents the model parameters of the t-th layer;

[0017] The inversion objective function is:

[0018]

[0019] where n is the random noise distribution, * represents the convolution operation, ω represents the seismic wavelet matrix, which is extracted from the actual seismic data.

[0020] Preferably, the gradient is expressed as:

[0021]

[0022] where represents the gradient;

[0023]

[0024]

[0025]

[0026] Among them,

[0027]

[0028]

[0029]

[0030] Preferably, the gradient is solved by the following formula:

[0031]

[0032] The calculation result of the gradient is:

[0033]

[0034]

[0035]

[0036] Among them, the Lagrangian multiplier formula is:

[0037]

[0038] Among them, k 1 、k 2 、k 3 represent the coefficients of the reflection coefficient formula; λ represents the adjoint state variable; ω represents the wavelet function, ι represents the Lagrangian multiplier formula;

[0039] ι = f(t, θ) + C·λ[t], C represents the constraint condition.

[0040] Preferably, the model variables are iteratively updated using the limited-memory quasi-Newton iterative optimization algorithm, and the iterative equation is:

[0041]

[0042] Among them, n represents the nth iteration, σ n represents the iteration step size, H n represents the approximation of the Hessian matrix, where,

[0043] Among them, I represents the initial matrix, y n represents the iteration difference matrix, S n represents the gradient difference matrix.

[0044] Preferably, the method further includes:

[0045] Compare the final inversion result with the actual logging data to determine the inversion accuracy.

[0046] Another aspect of the present invention provides an optimized inversion device based on VTI, including:

[0047] A modeling module for establishing an inversion equation and an inversion objective function based on the VTI medium longitudinal wave anisotropic reflection coefficient formula;

[0048] A calculation module for calculating the gradient of the inversion objective function;

[0049] An inversion module for iteratively updating the model variables of the inversion equation based on the gradient to obtain the final inversion result.

[0050] Another aspect of the present invention provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the thin reservoir seismic prediction method described above is implemented.

[0051] Another aspect of the present invention provides an electronic device, including:

[0052] A memory storing executable instructions;

[0053] A processor that runs the executable instructions in the memory to implement the thin reservoir seismic prediction method.

[0054] The beneficial effects of the VTI-based optimized inversion method and device of the present invention are as follows: Based on the VTI medium reflection coefficient approximation formula, a convolution model is used to construct the inversion objective function, the adjoint state function is used to calculate the gradient of the objective function, and the limited memory quasi-Newton iterative optimization algorithm is used for solution, which can effectively improve the inversion accuracy, and at the same time improve the efficiency and stability of the inversion; thus solving the problems of low computational efficiency of the non-linear inversion of the conventional VTI medium reflection coefficient equation, low accuracy of the linear inversion, high model dependence, and strong multi-solution property.

[0055] The method and device of the present invention have other characteristics and advantages, which will be obvious in the accompanying drawings incorporated herein and the subsequent specific embodiments, or will be described in detail in the accompanying drawings incorporated herein and the subsequent specific embodiments, and these drawings and specific embodiments are jointly used to explain the specific principles of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] By describing the exemplary embodiments of the present invention in more detail in conjunction with the accompanying drawings, the above and other objects, features, and advantages of the present invention will become more obvious. Among them, in the exemplary embodiments of the present invention, the same reference numerals generally represent the same components.

[0057] Figure 1 The flowchart of the VTI-based optimization inversion method according to an embodiment of the present invention is shown.

[0058] Figure 2 The parameters to be inverted used in the VTI-based optimization inversion method according to an exemplary embodiment of the present invention are shown.

[0059] Figure 3 The synthetic seismic record used in the VTI-based optimization inversion method according to an exemplary embodiment of the present invention is shown.

[0060] Figure 4 The inversion result obtained by the VTI-based optimization inversion method according to an exemplary embodiment of the present invention is shown.

[0061] Figure 5 The comparison error of the VTI-based optimization inversion method according to an exemplary embodiment of the present invention is shown. Detailed implementation manners

[0062] The preferred embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the preferred embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present invention will be more thorough and complete, and the scope of the present invention can be fully conveyed to those skilled in the art.

[0063] The present invention provides a VTI-based optimization inversion method, including the following steps:

[0064] Step 1: Establish an inversion equation and an inversion objective function based on the VTI medium P-wave anisotropic reflection coefficient formula;

[0065] Step 2: Calculate the gradient of the inversion objective function;

[0066] Step 3: Iteratively update the model variables of the inversion equation based on the gradient to obtain the final inversion result.

[0067] The VTI-based optimization inversion method of the present invention can solve the problems of low computational efficiency in the non-linear inversion of the conventional VTI medium reflection coefficient equation, low accuracy in the linear inversion, high model dependence, and strong multi-solution property.

[0068] Example 1

[0069] Figure 1 The flowchart of the VTI-based optimization inversion method according to an embodiment of the present invention is shown. As Figure 1 shown, the method includes steps 1 to 3.

[0070] Step 1: Establish an inversion equation and an inversion objective function based on the P-wave anisotropic reflection coefficient formula for VTI media.

[0071] The P-wave anisotropic reflection coefficient formula for VTI media is:

[0072]

[0073]

[0074] Where, represents the VTI P-wave reflection coefficient, θ represents the P-wave incident angle, Δ represents the difference between the physical quantity below the interface and the physical quantity above the interface, α represents the P-wave impedance, β represents the anisotropic shear modulus term of the S-wave, ξ represents the anisotropic P-wave velocity term, K represents the square of the ratio of the average S-wave velocity to the average P-wave velocity, which is a constant, Zp represents the P-wave impedance, σ represents the equivalent anisotropic parameter, μ represents the shear modulus of the S-wave, Vp represents the P-wave velocity, ε and δ represent the Thomsen anisotropic parameters, represents the average S-wave velocity, represents the average P-wave velocity.

[0075] The forward equation can be expressed as A(m)=d, where m represents the model variable, d represents the data space, and A represents the mapping function that maps the model variable m to the data space d. Then, the inversion equation can be expressed as d = A(m). The inversion process is mainly to estimate the model variable from the seismic data. According to the given anisotropic parameter equation, m can represent the elastic model variable:

[0076] m = [α(t), β(t), ξ(t)]

[0077] To solve the inversion problem, first define an objective function to simulate the error functional between the real data and the predicted data. In this embodiment, the following inversion objective function is defined:

[0078]

[0079] Where, n is the random noise distribution, * represents the convolution operation, ω represents the seismic wavelet matrix, which is extracted from the actual seismic data.

[0080] As long as the error functional, that is, the objective function reaches the minimum value, the optimal solution of the inversion can be obtained.

[0081] Step 2: Calculate the gradient of the inversion objective function.

[0082] The gradient of the objective function f(m) is solved by using the adjoint state calculation, where the gradient is expressed as:

[0083]

[0084] Among them, represents the gradient, and each gradient can be expressed as follows:

[0085]

[0086]

[0087]

[0088] Among them,

[0089]

[0090]

[0091]

[0092] The Lagrangian multiplier formula is:

[0093]

[0094] Among them, k 1 , k 2 , k 3 represent the coefficients of the reflection coefficient formula; λ represents the adjoint state variable; ω represents the wavelet function, ι represents the Lagrangian multiplier formula, and the objective function is optimized by introducing constraint conditions.

[0095] ι = f(t, θ) + C·λ[t], C represents the constraint condition.

[0096] The gradient of the objective function can be solved by the following formula. Considering that each term model of the objective function is decoupled, each term of the gradient can be calculated independently.

[0097]

[0098] The calculation result of the gradient is:

[0099]

[0100]

[0101]

[0102] Step 3: Iteratively update the model variables of the inversion equation based on the gradient to obtain the final inversion result.

[0103] Use the limited-memory quasi-Newton iterative optimization algorithm to iteratively update the model variables, and the iterative equation is:

[0104]

[0105] where n represents the nth iteration, and σ n represents the iteration step size, and H n represents the approximation of the Hessian matrix, where

[0106] where I represents the initial matrix, and y n represents the iteration difference matrix, and S n represents the gradient difference matrix.

[0107] The quasi - Newton iteration method is an iterative method for solving unconstrained optimization problems. It does not directly depend on the Newton method, but its idea is based on the Newton method. The core idea of this method is to use the first - order derivative of the function to approximate the second - order derivative, thereby obtaining an iterative formula.

[0108] In the limited - memory quasi - Newton iteration optimization algorithm, past gradient information (or called "limited memory") is used to approximate the current Jacobian matrix. The main advantage of this method is that it does not require recalculating the Jacobian matrix at each step, which is very useful when dealing with large - scale problems.

[0109] The present invention uses the limited - memory quasi - Newton iteration optimization algorithm to iteratively update the model variables, while ensuring the inversion accuracy, and solves the problem of low efficiency of the exact VTI inversion formula.

[0110] Example 2

[0111] This embodiment proposes an optimization inversion method based on VTI, including the following steps:

[0112] Step 1: Establish an inversion equation and an inversion objective function based on the VTI medium P - wave anisotropic reflection coefficient formula;

[0113] Step 2: Calculate the gradient of the inversion objective function;

[0114] Step 3: Iteratively update the model variables of the inversion equation based on the gradient to obtain the final inversion result;

[0115] Step 4: Compare the final inversion result with the actual logging data to determine the inversion accuracy.

[0116] Among them, the VTI medium P - wave anisotropic reflection coefficient formula is:

[0117]

[0118]

[0119] Among them, represents the VTI P-wave reflection coefficient, θ represents the P-wave incident angle, Δ represents the difference between the physical quantities below and above the interface, α represents the P-wave impedance, β represents the anisotropic shear modulus term of the S-wave, ξ represents the anisotropic P-wave velocity term, K represents the square of the ratio of the average S-wave velocity to the average P-wave velocity, which is a constant, Zp represents the P-wave impedance, σ represents the equivalent anisotropic parameter, μ represents the shear modulus of the S-wave, Vp represents the P-wave velocity, ε and δ represent the Thomsen anisotropic parameters, represents the average S-wave velocity, represents the average P-wave velocity.

[0120] The inversion equation is:

[0121] d = A(m)

[0122] Among them, d represents the data space, A represents the mapping function, m represents the elastic model variable, m = [αθ(t), β(t), ξ(t)], and t represents the model parameters of the t-th layer;

[0123] The inversion objective function is:

[0124]

[0125] Among them, n is the random noise distribution, * represents the convolution operation, ω represents the seismic wavelet matrix, which is extracted from the actual seismic data.

[0126] The gradient is expressed as:

[0127]

[0128] Among them, represents the gradient;

[0129]

[0130]

[0131]

[0132] Among them,

[0133]

[0134]

[0135]

[0136] The gradient is solved by the following formula:

[0137]

[0138] The calculation result of the gradient is:

[0139]

[0140]

[0141]

[0142] Among them, the Lagrangian multiplier formula is:

[0143]

[0144] Among them, k 1 , k 2 , k 3 represent the coefficients of the reflection coefficient formula; λ represents the adjoint state variable; ω represents the wavelet function, ι represents the Lagrangian multiplier formula, and the objective function is optimized by introducing constraints;

[0145] C represents the constraint condition.

[0146] The model variables are iteratively updated using the limited-memory quasi-Newton iteration optimization algorithm, and the iteration equation is:

[0147]

[0148] Among them, n represents the nth iteration, σ n represents the iteration step size, H n represents the approximation of the Hessian matrix, where,

[0149] Among them, I represents the initial matrix, y n represents the iteration difference matrix, S n represents the gradient difference matrix.

[0150] Figure 2 and 3 show the initial data used in the VTI-based optimization inversion method according to this embodiment, where Figure 2 shows the parameter to be inverted, Figure 3 shows the synthetic seismic record. Figure 4 shows the inversion result obtained by the VTI-based optimization inversion method according to this embodiment. Figure 5 shows the comparison error of the VTI-based optimization inversion method according to this embodiment, that is, the error obtained by comparing the synthetic seismic record with the inversion result. It can be seen from Figure 5 that the error of this method is small and a relatively reliable inversion result can be obtained.

[0151] For other detailed descriptions of this exemplary embodiment, reference may be made to the corresponding descriptions in the foregoing embodiments, which will not be elaborated herein.

[0152] Example 3

[0153] This embodiment provides an optimized inversion device based on VTI, including:

[0154] A modeling module, configured to establish an inversion equation and an inversion objective function based on the VTI medium longitudinal wave anisotropic reflection coefficient formula;

[0155] A calculation module, configured to calculate the gradient of the inversion objective function;

[0156] An inversion module, configured to iteratively update the model variables of the inversion equation based on the gradient to obtain a final inversion result.

[0157] Wherein, the VTI medium longitudinal wave anisotropic reflection coefficient formula is:

[0158]

[0159]

[0160] Wherein, represents the VTI longitudinal wave reflection coefficient, θ represents the longitudinal wave incident angle, Δ represents the difference between the physical quantity below the interface and the physical quantity above, α represents the longitudinal wave impedance, β represents the anisotropic shear modulus term of the transverse wave, ξ represents the anisotropic longitudinal wave velocity term, K represents the square of the ratio of the average transverse wave velocity to the average longitudinal wave velocity, which is a constant, Zp represents the longitudinal wave impedance, σ represents the equivalent anisotropic parameter, μ represents the shear modulus of the transverse wave, Vp represents the longitudinal wave velocity, ε and δ represent the Thomsen anisotropic parameters, represents the average transverse wave velocity, represents the average longitudinal wave velocity.

[0161] The inversion equation is:

[0162] d = A(m)

[0163] Wherein, d represents the data space, A represents the mapping function, m represents the elastic model variable, m = [α(t), β(t), ξ(t)], and t represents the model parameter of the t-th layer;

[0164] The inversion objective function is:

[0165]

[0166] Wherein, n is the random noise distribution, * represents the convolution operation, ω represents the seismic wavelet matrix, which is extracted from actual seismic data.

[0167] The gradient is expressed as:

[0168]

[0169] where represents the gradient;

[0170]

[0171]

[0172]

[0173] where

[0174]

[0175]

[0176]

[0177] The gradient is solved by the following formula:

[0178]

[0179] The calculation result of the gradient is:

[0180]

[0181]

[0182]

[0183] where the Lagrangian multiplier formula is:

[0184]

[0185] where k 1 、k 2 、k 3 represent the coefficients of the reflection coefficient formula; λ represents the adjoint state variable; ω represents the wavelet function, ι represents the Lagrangian multiplier formula, and the objective function is optimized by introducing constraint conditions;

[0186] ι = f(t, θ) + C·λ[t], C represents the constraint condition.

[0187] The model variables are iteratively updated using the limited-memory quasi-Newton iterative optimization algorithm, and the iterative equation is:

[0188]

[0189] where n represents the nth iteration, σ n represents the iteration step size, and Hn represents the approximation of the Hessian matrix, where,

[0190] where, I represents the initial matrix, y n represents the iteration difference matrix, and S n represents the gradient difference matrix.

[0191] For other detailed descriptions of this exemplary embodiment, reference may be made to the corresponding descriptions in the foregoing embodiments, which will not be elaborated herein.

[0192] Example 4

[0193] This embodiment provides a computer-readable storage medium storing a computer program, and when the computer program is executed by a processor, the foregoing optimization inversion method based on VTI is implemented.

[0194] A computer-readable storage medium can be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the above. More specific examples (non-exhaustive list) of the computer-readable storage medium include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punched card or raised structures in a groove storing instructions thereon, and any suitable combination of the above. The computer-readable storage medium used herein is not construed as an instantaneous signal itself, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through an optical fiber cable), or electrical signals transmitted through wires.

[0195] For other detailed descriptions of this exemplary embodiment, reference may be made to the corresponding descriptions in the foregoing embodiments, which will not be elaborated herein.

[0196] Example 5

[0197] This embodiment provides an electronic device, including:

[0198] a memory storing executable instructions;

[0199] A processor runs executable instructions in a memory to implement the foregoing VTI-based optimization inversion method.

[0200] The computer-readable program instructions described herein can be downloaded to various computing / processing devices from a computer-readable storage medium or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network may include copper transmission cables, optical fiber transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in each computing / processing device.

[0201] The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state-setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages such as Smalltalk, C++, etc., and conventional procedural programming languages such as the "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, executed as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider). In some embodiments, by using the state information of the computer-readable program instructions to customize an electronic circuit, such as a programmable logic circuit, a field-programmable gate array (FPGA), or a programmable logic array (PLA), the electronic circuit can execute the computer-readable program instructions to implement various aspects of the present disclosure.

[0202] Aspects of the present disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0203] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when the instructions are executed by the processor of the computer or other programmable data processing apparatus, an apparatus is created that implements the functions / acts specified in one or more boxes of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, a programmable data processing apparatus, and / or other devices to operate in a particular manner, so that the computer-readable medium storing the instructions comprises a manufacture, which includes instructions that implement various aspects of the functions / acts specified in one or more boxes of the flowchart and / or block diagram.

[0204] The computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices, such that a series of operational steps are performed on the computer, other programmable data processing apparatus, or other devices to produce a computer-implemented process, so that the instructions executed on the computer, other programmable data processing apparatus, or other devices implement the functions / acts specified in one or more boxes of the flowchart and / or block diagram.

[0205] For other detailed descriptions of this exemplary embodiment, reference may be made to the corresponding descriptions in the foregoing embodiments, which will not be repeated here.

[0206] The embodiments of the present invention have been described above. The above description is exemplary and not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations are obvious to those of ordinary skill in the art in the technical field without departing from the scope and spirit of the described embodiments. The selection of the terms used herein is intended to best explain the principles of the embodiments, practical applications, or improvements to technologies in the market, or to enable other ordinary skilled persons in the technical field to understand the embodiments disclosed herein.

Claims

1. An optimized inversion method based on VTI, characterized in that, the method comprises: Step 1: Establish an inversion equation and an inversion objective function based on the VTI medium longitudinal wave anisotropic reflection coefficient formula; Step 2: Calculate the gradient of the inversion objective function; Step 3: Iteratively update the model variables of the inversion equation based on the gradient to obtain the final inversion result.

2. The method according to claim 1, characterized in that, the VTI medium longitudinal wave anisotropic reflection coefficient formula is: Among them, represents the VTI P-wave reflection coefficient, θ represents the P-wave incident angle, Δ represents the difference between the physical quantities below and above the interface, α represents the P-wave impedance, β represents the anisotropic shear modulus term of the S-wave, ξ represents the anisotropic P-wave velocity term, K represents the square of the ratio of the average S-wave velocity to the average P-wave velocity, which is a constant, Zp represents the P-wave impedance, σ represents the equivalent anisotropic parameter, μ represents the shear modulus of the S-wave, Vp represents the P-wave velocity, ε and δ represent the Thomsen anisotropic parameters, represents the average S-wave velocity, represents the average P-wave velocity.

3. The method according to claim 2, characterized in that, the inversion equation is: d = A(m) where d represents the data space, A represents the mapping function, m represents the elastic model variables, m = [α(t), β(t), ξ(t)], and t represents the model parameters of the t-th layer; the inversion objective function is: wherein, n is a random noise distribution, * represents a convolution operation, and ω represents a seismic wavelet matrix.

4. The method according to claim 3, characterized in that, the gradient is expressed as: Among them, represents the gradient; where, 5. The method according to claim 4, characterized in that, the gradient is solved by the following formula: the calculation result of the gradient is: where the Lagrange multiplier formula is: Among them, k 1 , k 2 , k 3 represent the coefficients of the reflection coefficient formula; λ represents the adjoint state variable; ω represents the wavelet function, and ι represents the Lagrangian multiplier formula; ι = f(t, θ) + C·λ[t], C represents the constraint condition.

6. The method according to claim 5, characterized in that, the model variables are iteratively updated using a limited memory quasi-Newton iterative optimization algorithm, and the iterative equation is: where n represents the nth iteration, and σ n represents the iteration step size, and H n represents the approximation of the Hessian matrix, where Among them, I represents the initial matrix, and y n represents the iterative difference matrix, and S n represents the gradient difference matrix.

7. The method according to claim 1, characterized in that, further comprising: Comparing the final inversion result with the actual logging data to determine the inversion accuracy.

8. An optimized inversion device based on VTI, characterized in that, comprising: A modeling module for establishing an inversion equation and an inversion objective function based on the VTI medium longitudinal wave anisotropic reflection coefficient formula; A calculation module for calculating the gradient of the inversion objective function; An inversion module for iteratively updating the model variables of the inversion equation based on the gradient to obtain the final inversion result.

9. A computer-readable storage medium, characterized in that, the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1-8 is implemented.

10. An electronic device, characterized in that, the electronic device comprises: A memory storing executable instructions; A processor, and the processor runs the executable instructions in the memory to implement the method according to any one of claims 1-8.

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