Brittleness parameter direct inversion method and system based on reweighting

Abstract

The invention relates to a brittleness parameter direct inversion method and system based on reweighting, and belongs to the technical field of oil-gas exploration seismic inversion. The method comprises the following steps: performing angle superposition on pre-stack seismic data; establishing a forward modeling model based on the initial model, the difference matrix and the wavelet kernel matrix; on the basis of the forward modeling model, constructing a brittleness parameter inversion objective function based on reweighting; and inverting the objective function based on the seismic data obtained by angle superposition and the brittleness parameter based on reweighting to obtain an inversion result. According to the method, the accumulative error is reduced by adopting direct inversion, and the accumulative error is continuously increased by a conventional method for converting the brittleness index through pre-stack inversion, so that the prediction of the brittleness index is not accurate. According to the brittleness parameter direct inversion method, the problem can be well solved, the brittleness index is represented through direct inversion of E < rho >, accumulated errors are reduced to a great extent, and the brittleness index prediction precision is improved.

Description

Technical Field

[0001] The invention belongs to the technical field of oil and gas exploration seismic inversion, and in particular relates to a brittle parameter direct inversion method and system based on re-weighting. Background Art

[0002] There are two main methods for evaluating brittleness. One is to evaluate brittleness through mineral content or stress test results; the other is to express brittleness through elastic parameters. In terms of elastic parameters, the longitudinal and transverse wave velocities and densities were initially obtained through seismic prestack inversion and then converted into brittleness indexes. However, this method uses many empirical formulas, resulting in large cumulative errors. Later, Zong Zhaoyun et al. proposed the YPD formula, which provided a method for directly inverting Poisson's ratio and Young's modulus, which further reduced the error in calculating brittleness. Based on the YPD formula, Zhang Guangzhi et al. proposed an approximate equation for directly inverting elastic parameters Eρ and Poisson's ratio, providing a method for more accurate representation of brittleness. Seismic prestack inversion is an effective method for obtaining elastic parameters, but seismic inversion is a typical ill-posed problem with multiple solutions. Tikhonov showed that the problem can be solved and proposed Tikhonov regularization. Regularization constraints can solve this problem well. Wang et al. proposed a wave impedance inversion method based on L1 regularization, which proved the feasibility of this method in inversion. However, regularization ignores amplitude information and cannot better restore sparsity.

[0003] The conventional pre-stack inversion method for converting the brittleness index will cause the cumulative error to increase continuously, which directly leads to inaccurate prediction of the brittleness index. The direct inversion method of brittleness parameters can overcome this problem well. By directly inverting the elastic parameter Eρ and then characterizing the brittleness index, the cumulative error can be greatly reduced and the accuracy of brittleness index prediction can be improved. Therefore, it is urgent to provide a brittleness parameter inversion method to overcome the defects of the above inversion method. Summary of the invention

[0004] In view of the above problems, the present invention provides a direct inversion method and system for brittle parameters based on re-weighting. The regularization term is constrained by weights, and the weights are continuously updated with each round of iteration. The weights contain amplitude information, which can eliminate the influence of the amplitude of the regularization norm term on sparsity, so that the result has better sparsity than when there is no weighting, and can reduce the pathological nature of the inversion problem, improve the inversion accuracy, and reduce the cumulative error.

[0005] The first object of the present invention is to provide a direct inversion method for brittleness parameters based on reweighting, the inversion method comprising:

[0006] Angle stacking of pre-stack seismic data;

[0007] Based on the initial model, difference matrix and wavelet kernel matrix, a forward model is established;

[0008] Based on the forward model, the objective function of brittleness parameter inversion based on reweighting is constructed;

[0009] The inversion results are obtained based on the seismic data obtained by angle stacking and the inversion objective function based on the re-weighted brittle parameters.

[0010] In an embodiment of the present invention, the initial model is established based on well logging data.

[0011] In an embodiment of the present invention, the method of constructing a brittleness parameter inversion objective function based on a re-weighted method based on a forward model includes:

[0012] Based on the forward model, construct the inversion objective function;

[0013] Based on the inversion objective function, a re-weighted brittleness parameter inversion objective function is constructed.

[0014] In the embodiment of the present invention, the inversion objective function is based on the inversion objective function, and the inversion objective function based on the reweighted brittleness parameter inversion objective function is constructed, including:

[0015] Add constraints to the inversion objective function;

[0016] The quadratic penalty term and dual term are introduced into the constrained inversion objective function, and the inversion objective function based on the heavily weighted brittle parameter is obtained.

[0017] In the embodiment of the present invention, the constraints include reweighting constraints, regularization constraints and initial model constraints.

[0018] In the embodiment of the present invention, the seismic data obtained based on the angle stacking and the inversion objective function based on the re-weighted brittleness parameter, to obtain the inversion result, includes:

[0019] The alternating direction multiplier method is used to decompose the heavily weighted brittle parameter inversion objective function into sub-objective functions;

[0020] Based on the seismic data obtained by angle stacking and the sub-objective function, the inversion result is obtained.

[0021] In an embodiment of the present invention, the sub-objective functions include a re-weighted elastic parameter inversion sub-objective function, a re-weighted Poisson's ratio parameter inversion sub-objective function, and a re-weighted density parameter inversion sub-objective function.

[0022] A second object of the present invention is to provide a brittleness parameter direct inversion system based on re-weighting, the system comprising:

[0023] The stacking module is used to perform angle stacking on pre-stack seismic data;

[0024] The forward model module is used to establish a forward model based on the initial model, difference matrix and wavelet kernel matrix;

[0025] The objective function construction module is used to construct a re-weighted brittle parameter inversion objective function based on the forward model;

[0026] The inversion module is used to invert the objective function based on the seismic data obtained by angle stacking and the brittleness parameters based on reweighting to obtain the inversion results.

[0027] In an embodiment of the present invention, the objective function construction module includes a first submodule and a second submodule;

[0028] The first submodule is used to construct an inversion objective function based on the forward model;

[0029] The second submodule is used to construct a re-weighted brittleness parameter inversion objective function based on the inversion objective function.

[0030] A third object of the present invention is to provide an electronic device, the electronic device comprising: a processor, the processor being coupled to a memory;

[0031] The memory is used to store computer programs;

[0032] The processor is used to execute the computer program stored in the memory so that the electronic device executes the method as described above.

[0033] A fourth object of the present invention is to provide a computer-readable storage medium, wherein the computer-readable storage medium includes a program or an instruction, and when the program or the instruction is executed on a computer, the computer executes the method as described above.

[0034] Beneficial effects of the present invention:

[0035] First, direct inversion reduces cumulative errors. The conventional pre-stack inversion method for converting the brittleness index will cause the cumulative error to increase continuously, which directly leads to inaccurate prediction of the brittleness index. The direct inversion method of brittleness parameters can overcome this problem well. By directly inverting Eρ and then characterizing the brittleness index, the cumulative error can be greatly reduced and the accuracy of brittleness index prediction can be improved.

[0036] Second, the inversion results are highly stable. Since seismic inversion is ill-posed, the regularization method can be used as a priori information to convert the ill-posed problem into a conditionally well-posed problem for solution, which reduces the uncertainty of the inversion and improves the stability of the inversion results. L1 regularization is widely used in seismic inversion. The L1 regularization method is based on convex optimization problems, and its approximate solution is sparse. Therefore, it can impose sparse constraints on the inversion objective function and effectively restore sparsity.

[0037] Third, the inversion results are highly accurate. Regularization ignores amplitude information and cannot restore sparsity better. The reweighted Lp constraint constrains the regularization term through weights. The weights are continuously updated with each iteration. They contain amplitude information and eliminate the influence of the amplitude of the regularization norm term on sparsity, making the result have better sparsity than without weighting, further improving the inversion accuracy.

[0038] Fourth, high computational efficiency. After constructing the complete inversion objective function, the alternating direction multiplier method (ADMM) is used to solve the objective function. The ADMM method is a computational framework for solving optimization problems and is suitable for solving distributed convex optimization problems. The algorithm can perform block optimization and converges quickly, thereby greatly improving the computational efficiency.

[0039] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0041] Figure 1 A flow chart of a brittleness parameter direct inversion method based on re-weighting according to an embodiment of the present invention is shown;

[0042] Figure 2 It shows a 0-10° seismic data volume obtained by angular stacking of seismic data of the A work area according to an embodiment of the present invention;

[0043] Figure 3 The 10-20° seismic data volume obtained by angular stacking of seismic data of the A work area according to an embodiment of the present invention is shown;

[0044] Figure 4 The 20-30° seismic data volume obtained by angular stacking of seismic data of the A work area according to an embodiment of the present invention is shown;

[0045] Figure 5 The 0-9° seismic data volume obtained by angular stacking of seismic data of the B work area according to an embodiment of the present invention is shown;

[0046] Figure 6The 9-18° seismic data volume obtained by angular stacking of seismic data of the B work area according to an embodiment of the present invention is shown;

[0047] Figure 7 The 18-27° seismic data volume obtained by angular stacking of seismic data of the B work area according to an embodiment of the present invention is shown;

[0048] Figure 8 The 0-9° seismic data volume obtained by angular stacking of seismic data of the C work area according to an embodiment of the present invention is shown;

[0049] Fig. 9 The 9-18° seismic data volume obtained by angular stacking of seismic data of the C work area according to an embodiment of the present invention is shown;

[0050] Fig.10 The 18-27° seismic data volume obtained by angular stacking of seismic data of the C work area according to an embodiment of the present invention is shown;

[0051] Fig.11 A schematic diagram of an initial model according to an embodiment of the present invention is shown;

[0052] Fig.12 The inversion result of elastic parameters based on re-weighting of the A work area according to an embodiment of the present invention is shown;

[0053] Fig.13 The inversion result of the Poisson's ratio parameter based on reweighting in the A work area according to an embodiment of the present invention is shown;

[0054] Fig.14 The density inversion result based on reweighting of the A work area according to an embodiment of the present invention is shown;

[0055] Fig.15 The inversion result of elastic parameters based on re-weighting of the B work area according to an embodiment of the present invention is shown;

[0056] Fig.16 The inversion result of the Poisson's ratio parameters based on reweighting in the B work area according to an embodiment of the present invention is shown;

[0057] Fig.17 The density inversion result based on reweighting of the B work area according to an embodiment of the present invention is shown;

[0058] Fig.18 The inversion result of elastic parameters based on re-weighting of the C work area according to an embodiment of the present invention is shown;

[0059] Fig.19 The inversion result of the Poisson's ratio parameter based on reweighting in the C work area according to an embodiment of the present invention is shown;

[0060] Fig. 20The density inversion result based on reweighting of the C work area according to an embodiment of the present invention is shown;

[0061] Fig.21 A comparison diagram of direct inversion results of brittleness parameters based on re-weighting in the A work area according to an embodiment of the present invention is shown;

[0062] Fig. 22 A comparison diagram of direct inversion results of brittleness parameters based on heavy weighting in work area B according to an embodiment of the present invention is shown;

[0063] Fig.23 A comparison diagram of direct inversion results of brittleness parameters based on heavy weighting in the C work area according to an embodiment of the present invention is shown;

[0064] Fig.24 A framework diagram of a brittleness parameter direct inversion system based on re-weighting according to an embodiment of the present invention is shown;

[0065] Fig.25 A framework diagram of an electronic device according to an embodiment of the present invention is shown;

[0066] In the figure:

[0067] Overlay module 1; forward model module 2; objective function construction module 3; inversion module 4; electronic device 300; processor 301; memory 302. DETAILED DESCRIPTION

[0068] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0069] like Figure 1 As shown, a brittleness parameter direct inversion method based on reweighting according to an embodiment of the present invention includes:

[0070] Step S1, performing angle stacking on pre-stack seismic data;

[0071] Step S2, establishing a forward model based on the initial model, the difference matrix and the wavelet kernel matrix;

[0072] Step S3, constructing a brittleness parameter inversion objective function based on a re-weighted method based on the forward model;

[0073] Step S4: invert the target function based on the seismic data obtained by angle stacking and the brittleness parameter based on re-weighting to obtain the inversion result.

[0074] In step S1, the angle stacking of the pre-stack seismic data is performed as follows:

[0075] According to the range of incident angle, partial stacking is performed at different angles to form three partial angle stacking bodies. For example, taking the inversion result of the A work area as an example, the angle of the pre-stack seismic data of the A work area is 0-30°, and partial stacking is performed at different angles to form three partial angle stacking bodies, which are 0-10° (see Figure 2 )、10-20°(see Figure 3 )、20-30°(see Figure 4 );

[0076] The present invention does not limit the specific stacking angle during angle stacking, which can be set according to specific actual operations. The angle of the pre-stack seismic data of the B work area is 0-27°, and partial stacking is performed to form three partial angle stacking bodies, which are 0-9° (see Figure 5 )、9-18°(see Figure 6 ), 18-27°(see Figure 7 );

[0077] The angle of the pre-stack seismic data of the C area is 0-27°, and partial stacking is performed to form three partial angle stacking bodies, which are 0-9° (see Figure 8 )、9-18°(see Fig. 9 ), 18-27°(see Fig.10 );

[0078] In step S1, the initial model is established according to the well logging data, and the specific steps are as follows:

[0079] Interpolation and filtering are performed based on the well logging data and geological layers to construct a low-frequency model, namely the initial model, such as Fig.11 As shown;

[0080] The forward model is established by multiplying the difference matrix, angle matrix, wavelet kernel matrix and natural logarithm matrix, where the difference matrix D is shown in formula (1);

[0081]

[0082] The wavelet kernel matrix W is shown in formula (2).

[0083]

[0084] The forward model established is shown in formula (3).

[0085]

[0086] In formula (3), M represents the number of angles; L Eρis the natural logarithm of Eρ (elastic performance parameter); L σ is the natural logarithm of Poisson's ratio; L ρ is the natural logarithm of density;

[0087]

[0088]

[0089]

[0090] Equations (4)-(6) are explanations of a(θ), b(θ), and c(θ) in equation (3). In equations (4)-(6), (θ) is the incident angle; and k is the ratio of the transverse and longitudinal wave velocities.

[0091] In step S2, based on the forward model, a re-weighted brittleness parameter inversion objective function is constructed, including:

[0092] Step A1: constructing an inversion objective function based on the forward model;

[0093] Step A2: Based on the inversion objective function, a re-weighted brittleness parameter inversion objective function is constructed.

[0094] In step A1, the inversion objective function is constructed based on the forward model, specifically: the forward model is simplified to obtain the inversion objective function,

[0095] Formula (3) can be simplified to formula (7):

[0096] S=WBDL=AL (7)

[0097] In formula (7), A represents the product of matrices W, B, and D; L represents Eρ, Poisson's ratio, and density natural logarithm matrix.

[0098] Subtract the two sides of equation (7) and find the minimum value to obtain the inversion objective function, as shown in equation (8).

[0099]

[0100] In formula (8), S represents the earthquake record.

[0101] In step A2, based on the inversion objective function, constructing a re-weighted brittleness parameter inversion objective function includes:

[0102] Step B1, adding constraints to the inversion objective function;

[0103] Step B2: introducing a quadratic penalty term and a dual term into the constrained inversion objective function to obtain an inversion objective function based on heavily weighted brittleness parameters.

[0104] In step B1, the constraints include reweighting constraints, regularization constraints and initial model constraints. It can be seen that adding constraints to the inversion objective function means adding reweighting constraints, regularization constraints and initial model constraints to the inversion objective function (shown in formula (5));

[0105] Then, step B2 is performed, that is, a quadratic penalty term and a dual term are introduced into the constrained inversion objective function. The final brittleness parameter inversion objective function based on reweighting is specifically shown in formula (9).

[0106]

[0107] The weight update formula is shown in formula (10):

[0108]

[0109] In formula (9)-formula (10), ||||1 is the L1 norm, |||| p is the Lp norm; μ is the regularization parameter of the initial model; λ is the regularization factor; w i is the weight; η is the Lagrangian constraint parameter of the difference; C is the dual term

[0110] In step S4, the seismic data obtained based on the angle stacking and the inversion objective function based on the re-weighted brittleness parameter are used to obtain the inversion result, including:

[0111] Step C1, using an alternating direction multiplier method to decompose the re-weighted brittle parameter inversion objective function into sub-objective functions;

[0112] Step C2: based on the seismic data obtained by angle stacking and the sub-objective function, an inversion result is obtained.

[0113] In step C1, the sub-objective functions include a re-weighted elastic parameter inversion sub-objective function, a re-weighted Poisson's ratio parameter inversion sub-objective function, and a re-weighted density parameter inversion sub-objective function;

[0114] It can be seen that on the basis of equations (9) and (10), the alternating direction multiplier (ADMM) method is used to decompose the reweighted brittle parameter inversion objective function, and the sub-objective functions J(L), J(R) and J(C) are obtained, as shown in equations (11)-(13).

[0115]

[0116]

[0117]

[0118] In equations (11)-(13), R=DL and C is the dual term.

[0119] In step C2, the inversion result is obtained based on the seismic data obtained by angle stacking and the sub-objective function, namely:

[0120] Substituting the seismic data obtained by angle stacking into the sub-objective function, the inversion results are obtained, specifically: the data in step S1 is input into equations (11)-(13), and the inversion results of elastic parameters based on reweighting, the inversion results of Poisson's ratio parameters based on reweighting, and the inversion results of Poisson's ratio parameters based on reweighting are obtained respectively;

[0121] The inversion results for the A work area are as follows: Figure 12-14 ,in, Fig.12 The inversion results of elastic parameters based on reweighting in the A work area are shown in Figure 2. Fig.13 The inversion results of the Poisson's ratio parameters based on reweighting in the A work area are shown in Figure 2. Fig.14 This is the density inversion result based on reweighting in the A work area;

[0122] The inversion results for the B area are as follows: Figure 15-17 ,in, Fig.15 The inversion results of elastic parameters based on reweighting in the B area are shown in Figure 2. Fig.16 The inversion results of Poisson's ratio parameters based on reweighting in the B area are shown in Figure 2. Fig.17 This is the density inversion result based on reweighting in the B area;

[0123] The inversion results for the C area are as follows: Figure 18-Figure 20 ,in, Fig.18 The inversion results of elastic parameters based on reweighting in the C area are shown in Figure 2. Fig.19 The inversion results of the Poisson's ratio parameters based on reweighting in the C area are shown in Figure 2. Fig. 20 This is the density inversion result based on reweighting in the C area.

[0124] In order to verify the inversion result, in the embodiment of the present invention, the logging curve and the inversion result are put into the same figure for comparison, as shown in the specific figure. Figure 21-23 As shown, Fig.21 This is a comparison chart of the inversion results based on the reweighted brittleness parameters of the A work area. Fig. 22 This is a comparison chart of the inversion results based on the heavily weighted brittleness parameters in the B area. Fig.23 This is a comparison chart of the inversion results based on the heavy-weighted brittleness parameters in the C area;

[0125] Figure 21-23 In the figure, Eρ represents the comparison chart corresponding to the inversion results of elastic performance parameters, σ represents the comparison chart corresponding to the inversion results of Poisson's ratio parameters, and ρ represents the comparison chart corresponding to the inversion results of density;

[0126] from Figure 21-23From the single-channel inversion results, it can be seen that the inversion result curve of the reweighted (Lp) method is consistent with the true model curve, that is, the method used in the present invention is close to the true inversion result, has high accuracy, and is more stable.

[0127] like Fig.24 In some embodiments of the present invention, a brittleness parameter direct inversion system based on reweighting is provided, the system comprising:

[0128] The stacking module 1 is used to perform angle stacking on pre-stack seismic data;

[0129] The forward model module 2 is used to establish a forward model based on the initial model, the difference matrix and the wavelet kernel matrix;

[0130] The objective function construction module 3 is used to construct a brittleness parameter inversion objective function based on a re-weighted method based on the forward model;

[0131] The inversion module 4 is used to invert the target function based on the seismic data obtained by angle stacking and the brittleness parameter based on reweighting to obtain the inversion result.

[0132] In the embodiment of the present invention, the objective function construction module 3 includes a first submodule and a second submodule;

[0133] The first submodule is used to construct an inversion objective function based on the forward model;

[0134] The second submodule is used to construct a re-weighted brittleness parameter inversion objective function based on the inversion objective function.

[0135] like Fig.25 As shown, in some embodiments of the present invention, an electronic device 300 is provided, comprising: a processor 301, wherein the processor 301 is coupled to a memory 302;

[0136] The memory 302 is used to store computer programs;

[0137] The processor 301 is configured to execute the computer program stored in the memory 302 so that the electronic device executes the method in the above embodiment.

[0138] In some embodiments of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a program or an instruction. When the program or the instruction is executed on a computer, the computer is caused to execute the method in the above-mentioned embodiments.

[0139] According to an embodiment of the present invention, the computer-readable storage medium may be a non-volatile computer-readable computer-readable storage medium, for example, may include but is not limited to: 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 portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, apparatus, or device.

[0140] Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent substitutions for some of the technical features therein; and these modifications or substitutions do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A direct inversion method for brittle parameters based on reweighting, characterized in that: include: Angle stacking of pre-stack seismic data; Based on the initial model, difference matrix and wavelet kernel matrix, a forward model is established; Based on the forward model, the objective function of brittleness parameter inversion based on reweighting is constructed; The inversion results are obtained based on the seismic data obtained by angle stacking and the inversion objective function based on the re-weighted brittle parameters.

2. The method for direct inversion of brittleness parameters based on reweighting according to claim 1, characterized in that: The initial model is established based on well logging data.

3. The method for direct inversion of brittleness parameters based on reweighting according to claim 1, characterized in that: The method of constructing a re-weighted brittleness parameter inversion objective function based on the forward model includes: Based on the forward model, construct the inversion objective function; Based on the inversion objective function, a re-weighted brittleness parameter inversion objective function is constructed.

4. The method for direct inversion of brittleness parameters based on reweighting according to claim 1, characterized in that: The method of constructing a re-weighted brittleness parameter inversion objective function based on the inversion objective function includes: Add constraints to the inversion objective function; The quadratic penalty term and dual term are introduced into the constrained inversion objective function, and the inversion objective function based on the heavily weighted brittle parameter is obtained.

5. The method for direct inversion of brittle parameters based on reweighting according to claim 4 is characterized in that: The constraints include reweighting constraints, regularization constraints and initial model constraints.

6. The method for direct inversion of brittleness parameters based on reweighting according to any one of claims 1 to 5, characterized in that: The inversion results obtained based on the seismic data obtained by angle stacking and the inversion objective function based on the re-weighted brittleness parameters include: The alternating direction multiplier method is used to decompose the heavily weighted brittle parameter inversion objective function into sub-objective functions; Based on the seismic data obtained by angle stacking and the sub-objective function, the inversion result is obtained.

7. The method for direct inversion of brittle parameters based on reweighting according to claim 6, characterized in that: The sub-objective functions include a re-weighted elastic parameter inversion sub-objective function, a re-weighted Poisson's ratio parameter inversion sub-objective function and a re-weighted density parameter inversion sub-objective function.

8. A brittleness parameter direct inversion system based on reweighting, characterized in that: include: The stacking module is used to perform angle stacking on pre-stack seismic data; The forward model module is used to establish a forward model based on the initial model, difference matrix and wavelet kernel matrix; The objective function construction module is used to construct a re-weighted brittle parameter inversion objective function based on the forward model; The inversion module is used to invert the objective function based on the seismic data obtained by angle stacking and the brittleness parameters based on reweighting to obtain the inversion results.

9. The re-weighted brittleness parameter direct inversion system according to claim 8, characterized in that: The objective function construction module includes a first submodule and a second submodule; The first submodule is used to construct an inversion objective function based on the forward model; The second submodule is used to construct a re-weighted brittleness parameter inversion objective function based on the inversion objective function.

10. An electronic device, characterized in that: include: a processor coupled to the memory; The memory is used to store computer programs; The processor is configured to execute the computer program stored in the memory, so that the electronic device executes the method according to any one of claims 1 to 7.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a program or an instruction. When the program or the instruction is executed on a computer, the computer is caused to perform the method according to any one of claims 1 to 7.

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