Adaptive structural constraint seismic wave impedance inversion method, medium and device

Abstract

This invention relates to the field of seismic impedance inversion technology, specifically to an adaptive structural constraint seismic wave impedance inversion method, medium, and equipment. The method includes: calculating multiple inversion results based on multiple different structural constraint intensities; calculating the average structural similarity between each inversion result and all inversion results, selecting the structural constraint weight used by the inversion result with the highest average similarity as the absolute constraint weight of the structural constraint term; calculating the structural sensitivity index based on the inversion results with multiple different structural constraint intensities; normalizing each structural sensitivity index; differentiating the normalized structural sensitivity indices to obtain the relative constraint weight corresponding to each impedance parameter, assigning the relative constraint weight to the relative constraint intensity matrix; and calculating the final inversion result. This invention can overcome the loss of detail and structural artifacts caused by unreasonable structural constraint intensity settings, wavelet bandwidth limitations, and unreliable structural information.

Description

Technical Field

[0001] This invention relates to the field of seismic impedance inversion technology, and in particular to an adaptive structurally constrained seismic wave impedance inversion method, medium, and equipment. Background Technology

[0002] Seismic impedance inversion is an effective technique for identifying the properties of subsurface rocks and fluids from post-stack reflection seismic data. Based on the convolution model, reflection seismic data is the result of the convolution of reflection coefficients and wavelets. In this theory, the seismic reflection data in each trace is only related to the corresponding impedance parameter. Therefore, traditional seismic impedance inversion is performed trace-by-trace. Although single-trace inversion is computationally efficient, the spatial continuity of the inversion profile is poor due to the different noise levels of each trace and the lack of spatial constraints between impedance parameters of different traces. To enhance the quality of seismic imaging, the structural information contained in the post-stack seismic data is used as a geometric constraint on the wave impedance structure; this method is called structurally constrained inversion. Existing structurally constrained inversion methods typically use plane wave analysis or structural tensor techniques to calculate local dip angles from post-stack seismic records, construct local gradient operators based on the local dip angle information, and use the wave impedance gradient as a regularization constraint in the objective function of multi-trace wave impedance inversion. In multichannel inversion with structural constraints, the wave impedance parameters of multiple channels are coupled into a whole by the structural constraints. Since the local gradient operator in the regularization constraint contains structural information, the wave impedance parameters distributed along the local tilt direction will tend to be consistent, and the continuity of wave impedance parameters and structural characteristics between adjacent gathers are thus enhanced.

[0003] Seismic structure-constrained impedance retrieval utilizes the structural information inherent in post-stack seismic data to constrain impedance retrieval, aiming to enhance the continuity of impedance structure and improve the resolution and interpretability of seismic imaging. However, due to the limited bandwidth of seismic wavelets and the low resolution of post-stack seismic data, the structural features extracted from post-stack seismic data corresponding to complex geological structures are not necessarily the structural features of impedance parameters. Therefore, using this unreliable structural information to constrain impedance retrieval is unreasonable. Furthermore, traditional structural constraint retrieval methods apply the same structural constraint strength to all impedance parameters. When the constraint strength is small, the structural constraint cannot sufficiently suppress the interference of data noise on the retrieval; when the constraint strength is large, inaccurate structural information will impair the accuracy and resolution of impedance retrieval imaging.

[0004] Therefore, it can be seen that traditional seismic structural constraint wave impedance inversion has the following two drawbacks: The strength of the constraints is difficult to determine: structural constraints exist in the inversion objective function as regularization terms, but their weights are entirely dependent on human selection. When the weights are set too small, the effect of the structural constraints is very weak, and the constraint strength on the inversion results is insufficient to achieve the target effect; while when the weights are set too large, although the large-scale wave impedance structural features can be enhanced, small-scale details will be smoothed out, and even false anomalies that do not exist will be generated in areas where the structural information is inaccurate.

[0005] The irrationality of uniform constraint strength distribution: The constraint strength of existing structural constraints in the inversion is determined solely by the weight of the regularization term, resulting in all wave impedance parameters experiencing the same structural constraint strength. As demonstrated above, the structural information extracted from post-stack seismic data is not entirely accurate, and applying the same strength of structural constraints to regions with inaccurate structural information is unreasonable. Summary of the Invention

[0006] The main objective of this invention is to provide an adaptive structural constraint seismic impedance inversion method, medium, and device, aiming to solve the technical problems of difficulty in determining the constraint strength and unreasonable uniform distribution of constraint strength in existing seismic structural constraint impedance inversion methods.

[0007] To achieve the above objectives, this invention proposes an adaptive structurally constrained seismic wave impedance inversion method, comprising the following steps: S1. Obtain post-stack seismic data and seismic wavelets; S2. Extract seismic slope information from seismic data using plane wave analysis methods; S3. Construct a structure-oriented model constraint matrix using seismic slope information; S4. Construct a multichannel impedance forward modeling operator using seismic data; S5. Based on the multi-channel impedance forward modeling operator, construct the inversion objective function based on adaptive structural constraints; S6. Select the weights of the reference model terms for the inversion objective function; S7. Select multiple structural constraint strengths based on the weights of the reference model terms, and calculate multiple inversion results based on the multiple structural constraint strengths; S8. Calculate the average structural similarity between each inversion result and all inversion results, and select the structural constraint weights used by the inversion result with the highest average structural similarity as the absolute constraint weights. S9. Calculate the structural sensitivity index based on the inversion results of multiple different structural constraint weights; S10. Normalize each structural sensitivity index to obtain the normalized structural sensitivity index. S11. Differentiate the normalized structural sensitivity index to obtain the relative constraint weights corresponding to each impedance parameter, and assign the relative constraint weights to the relative constraint strength matrix. S12. The final inversion result is obtained by calculating the weights of the reference model terms, the structural constraint weights, and the relative constraint strength matrix.

[0008] A further improvement of the adaptive structurally constrained seismic impedance inversion method of the present invention lies in the following expression for the inversion objective function: ; in, The natural logarithm of the multichannel impedance. As a reference model, The relative constraint strength matrix, For reference model items The weight, For the weights of structural constraint terms, For multichannel seismic data, For multiple forward modeling operators, For gradient operators in directions parallel to the local tilt angle, The objective function is... Solution of the inverted objective function The expression is as follows: ; in, This is the transpose of the matrix. It is an identity matrix.

[0009] A further improvement of the adaptive structurally constrained seismic impedance inversion method of the present invention is that the specific step S6 is: to... Set to 0 to select multiple items from smallest to largest. Calculate different The root mean square error of the inverted data term, and from The optimal value is determined using the L-curve method from the curve plot of root mean square error. .

[0010] A further improvement of the adaptive structurally constrained seismic impedance inversion method of the present invention is that the specific step of S7 is: using the data obtained in S6... The relative constraint strength matrix Set as an identity matrix, select multiple from smallest to largest. Each [function] is calculated based on the expression of the solution to the inversion objective function. Corresponding inversion results , For the final selection The number of.

[0011] A further improvement of the adaptive structural constraint seismic impedance inversion method of the present invention lies in the following expression for the average structural similarity: ; in, For the inversion results The corresponding average structural similarity, SSIM For multi-scale structural similarity functions; select ASSIM The absolute constraint weight corresponding to the inversion result with the largest value As the final weight, This is the inversion result corresponding to the constraint strength.

[0012] A further improvement of the adaptive structural constraint seismic impedance inversion method of the present invention lies in the following expression for the structural sensitivity index: ; in, For the first i wave impedance parameters The corresponding structural sensitivity index, For all inversion results obtained in S8, the first The average value of each wave impedance parameter, For the first Inversion results corresponding to each constraint strength The first in Each wave impedance parameter.

[0013] A further improvement of the adaptive structural constraint seismic impedance inversion method of the present invention is that, when normalizing multiple structural sensitivity indices, the following expression is used for calculation: ; in, For all wave impedance parameters The minimum value in, For all wave impedance parameters The maximum value in, For the normalized first i wave impedance parameters The corresponding structural sensitivity.

[0014] A further improvement of the adaptive structurally constrained seismic impedance inversion method of the present invention is that the specific steps of S11 are as follows: The normalized structural sensitivity index obtained in S10 is differentiated using the following formula to obtain the relative constraint weight of each wave impedance parameter. ; ; in, It is a constant between 0 and 1. It is a positive constant. It is a natural constant; Use the following formula to Assign the relative constraint strength matrix W: ; in, The relative constraint weights for the first wave impedance parameter. For the total number of Dao collections, This represents the number of impedance parameters in a single channel.

[0015] The present invention also provides a readable storage medium storing a computer program adapted to be loaded by a processor and executed as described above for the adaptive structural constraint seismic impedance inversion method.

[0016] The present invention also provides a computer device, the computer device including a memory and a processor, the memory storing a computer program, which, when executed by the processor, runs the adaptive structural constraint seismic impedance inversion method as described above.

[0017] The technical solution of the present invention has the following beneficial effects: The adaptive structurally constrained seismic impedance inversion method of the present invention determines the absolute constraint strength in the inversion based on the inversion results of a series of uniform structural constraints and multi-scale structural similarity indices. The method involves defining the relative constraint strength of each wave impedance parameter based on the standard deviation of each wave impedance parameter in the inversion results of a series of uniform structural constraints. The method adaptively differentiates the structural constraint strength received by the wave impedance using absolute constraint strength and relative constraint strength, thus solving the technical problems of difficulty in determining the constraint strength obtained by wave impedance inversion of existing seismic structural constraints and unreasonable uniform distribution of constraint strength.

[0018] This invention improves the structural constraint strategy in seismic impedance inversion with structural constraints. In the absence of completely accurate structural information, it adaptively determines the absolute and relative constraint strengths of the structural constraints based on the distortion degree of the inversion results under different structural constraint strengths, and uses them to adjust the constraint strength in continuous and discontinuous regions of the wave impedance. While enhancing large-scale structural features, it also takes into account the recovery of fine-scale features, overcoming the loss of details and structural artifacts caused by unreasonable setting of structural constraint strength, wavelet bandwidth limitation, and unreliability of structural information.

[0019] This invention adaptively adjusts the structural constraint strength on the wave impedance parameters, avoiding interference from artificially set structural constraint weights and inaccurate structural information on wave impedance imaging. This not only enhances the continuity of large-scale structures but also ensures the recovery of fine-scale features, overcoming the loss of detail and false artifacts caused by unreasonable structural constraint strength, wavelet bandwidth limitations, and unreliable structural information. This invention can be applied to: ① Oil and gas exploration: for exploring oil and gas reservoirs, determining the location, thickness, and properties of underground oil and gas reservoirs, and understanding underground structures and geological features; ② Underground structure detection: such as the survey and study of underground structures like strata, faults, and structural lines. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0021] Figure 1 This is a flowchart of the adaptive structural constraint seismic wave impedance inversion method of the present invention; Figure 2 This is a schematic diagram of the actual impedance model; Figure 3 A schematic diagram of synthetic seismic data (signal-to-noise ratio = 2); Figure 4 This is a schematic diagram of the earthquake dip angle; Figure 5 This is a schematic diagram of the inversion results for a traditional uniform structure constraint. Figure 6 for Figure 5 The graphs shown represent the root mean square error, average structural similarity, and structural similarity of the real model corresponding to the inversion results. Figure 7 for Figure 5 A schematic diagram of the normalized structural sensitivity index corresponding to the inversion result shown; Figure 8 This is a schematic diagram of the wave impedance inversion results of the adaptive structural constraints of the present invention; Figure 9 for Figure 8 The graphs show the root mean square error, average structural similarity, and structural similarity of the inversion results to the actual model. Figure 10 for Figure 8 The diagram shows the normalized structure sensitivity index corresponding to the inversion result. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0023] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indication will also change accordingly.

[0024] Furthermore, in this invention, descriptions involving "first," "second," etc., are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0025] In this invention, unless otherwise explicitly specified and limited, the terms "connection," "fixed," etc., should be interpreted broadly. For example, "fixed" can mean a fixed connection, a detachable connection, or an integral part; it can mean a mechanical connection or an electrical connection; it can mean a direct connection or an indirect connection through an intermediate medium; it can mean the internal communication of two components or the interaction between two components, unless otherwise explicitly limited. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0026] Existing structurally constrained impedance inversion techniques apply uniform structural constraints to all impedance parameters using structural features extracted from post-stack seismic data. The inversion results are highly dependent on the manually set constraint strength, limiting their practicality. This invention achieves adaptive adjustment of structural constraints, significantly reducing the dependence of the inversion results on the manually selected constraint strength.

[0027] like Figure 1 As shown, this invention proposes an adaptive structurally constrained seismic wave impedance inversion method, comprising the following steps: S1. Obtain post-stack seismic data and seismic wavelets; S2. Seismic slope information is extracted from seismic data using plane wave analysis. Seismic slope information characterizes the structural information of the subsurface medium, and plane wave analysis (plane wave analysis technique) is the main tool for extracting local seismic slopes from seismic profiles. This method considers the propagation relationships between all seismic traces and tends to generate stable slope estimates.

[0028] The expression for the relationship between the wave field and slope of a plane wave is as follows: ; in, For a plane wave field, For the local slope, the first Earthquake data It can be done through the first Earthquake data and the corresponding local slope To predict, a robust estimate of the local slope of the entire seismic profile can be obtained by minimizing the residual between the measured seismic record and the predicted seismic record.

[0029] S3. Construct a structure-oriented model constraint matrix using seismic slope information; traditional inversion typically applies first-order gradient constraints to the model in both horizontal and vertical directions, and the corresponding gradient operator can be expressed as follows: and This invention, combining the seismic slope information obtained from S2, can use the following formula to calculate the gradient operator in the horizontal direction. Transform the gradient operator into a direction parallel to the local tilt angle. : ; in, and The calculation is performed using the following formula: ; ; in, For the first Local tilt angle of each parameter, For the total number of Dao collections, This represents the number of impedance parameters in a single channel.

[0030] S4. Construct a multichannel impedance forward modeling operator using seismic data; Based on the convolution model, the forward model of multichannel seismic impedance can be expressed as: ; in, For multichannel seismic data, For multiple forward modeling operators, It is the natural logarithm of the multichannel impedance parameter.

[0031] S5. Based on the multi-channel impedance forward modeling operator, construct the inversion objective function based on adaptive structural constraints; the expression of the inversion objective function is as follows: ; in, The natural logarithm of the multichannel impedance. As a reference model, The relative constraint strength matrix, For reference model items The weight, The weights of the structural constraint terms are referred to in this invention as... For absolute constraint weights, For multichannel seismic data, For gradient operators in directions parallel to the local tilt angle, The objective function is... Solution of the inverted objective function The expression is as follows: ; in, This can be represented as the transpose of a matrix. Represents the identity matrix.

[0032] S6. Select the weights of the reference model terms for the inversion objective function; specifically: ... Set to 0, select a series from smallest to largest. Calculate different The root mean square error of the inverted data term, and from The optimal value is determined using the L-curve method from the curve plot of root mean square error. .

[0033] S7. Select the weights of multiple structural constraint terms based on the weights of the reference model terms, and calculate multiple inversion results based on the weights of the multiple structural constraint terms; specifically, use the results obtained in S6. The relative constraint strength matrix Set as an identity matrix, and select a series of values ​​from smallest to largest. , You can start with a very small number, such as 0.001, and increase it in increments of 2. It cannot increase indefinitely; you can first calculate the current... The root mean square error of the inverted data terms is minimized, which determines the largest... The root mean square error corresponding to the value cannot exceed a relative threshold for that value, such as 15%.

[0034] Each solution is calculated based on the expression of the inversion objective function. Corresponding inversion results , For the final selection The number of elements. In this embodiment, bold characters are used as model vectors to represent the inversion results; thin characters are used to represent parameters.

[0035] S8. Calculate the average structural similarity between each inversion result and all inversion results; the expression for the average structural similarity is as follows: ; in, For the inversion results The corresponding average structural similarity, SSIM This is a multi-scale structural similarity function used to evaluate the structural similarity between two inversion results; [Selection] ASSIM The absolute constraint weight corresponding to the inversion result with the largest value As the final weight, This is the inversion result corresponding to the constraint strength.

[0036] S9. Based on all of S8 K Multiple structural sensitivity indices are calculated from the inversion results; the expressions for the structural sensitivity indices are as follows: ; in, For the first i wave impedance parameters The corresponding structural sensitivity index, For all inversion results obtained in S8, the first The average value of each wave impedance parameter, For the first Inversion results corresponding to each constraint strength The first in Each wave impedance parameter is characterized by its sensitivity to structural constraints. A smaller value indicates a lower likelihood of distortion in the wave impedance parameter as the structural constraint weight changes, while a larger value indicates that the wave impedance parameter is more susceptible to distortion due to structural constraints.

[0037] S10. Normalize the multiple structural sensitivity indices to obtain the normalized structural sensitivity indices; the following expression is used for calculation when normalizing multiple structural sensitivity indices: ; in, For all wave impedance parameters The minimum value in, For all wave impedance parameters The maximum value in, For the normalized first i wave impedance parameters The corresponding structural sensitivity.

[0038] S11. Differentiate the normalized structural sensitivity index to obtain multiple relative constraint weights, and assign these relative constraint weights to the relative constraint strength matrix; specifically, use the following formula to differentiate the normalized structural sensitivity index obtained in S10 to obtain the relative constraint weight for each wave impedance parameter. ; ; in, It is a constant between 0 and 1. Its value can be determined based on the proportion of larger values ​​in the NSCS statistical results. It determines the starting point of the decay of the normalized structural sensitivity index. The smaller the value, the more the normalized structural sensitivity index is scaled to close to 0, that is, the fewer the number of wave impedance parameters constrained by the structure. The larger the value, the fewer the normalized structural sensitivity index is scaled to close to 0, that is, the more the number of wave impedance parameters constrained by the structure. It is a positive constant that controls the decay rate. It is a natural constant; Use the following formula to Assign the relative constraint strength matrix W: ; in, The relative constraint weights for the first wave impedance parameter. For the total number of Dao collections, This represents the number of impedance parameters in a single channel.

[0039] S12. Calculate the final inversion result based on the weights of the reference model terms, the structural constraint weights, and the relative constraint strength matrix. Specifically, this involves using the results obtained in the above steps... , The inversion result m is calculated by substituting the relative constraint strength matrix W into the expression of the solution of the inversion objective function. To further improve the sparsity of the result, sparse regularization can be applied to m for denoising. The denoised result is then... The reference model is assigned to the expression of the solution to the inversion objective function. And repeat S6 to S12 again to determine the optimal solution in each iteration. , And W, and this adaptive step can be performed once in each iteration, using the expression of the solution to the inverted objective function and sparse regularization, according to and The calculation is performed iteratively, and the final inversion result is output after a preset number of iterations (e.g., 10). .

[0040] The present invention also provides a readable storage medium storing a computer program adapted to be loaded by a processor and executed as described above for the adaptive structural constraint seismic impedance inversion method.

[0041] The present invention also provides a computer device, the computer device including a memory and a processor, the memory storing a computer program, which, when executed by the processor, runs the adaptive structural constraint seismic impedance inversion method as described above.

[0042] Figure 2 The actual impedance model used in this embodiment is employed. Synthetic seismic data is obtained by convolving the reflection coefficient corresponding to this model with a Ricker wavelet with a dominant frequency of 30Hz. Random noise with a signal-to-noise ratio of 2 is added to the synthetic data to simulate the measured post-stack data. The noisy data is shown below. Figure 3 As shown, the local dip angle of this data was calculated using plane wave decomposition techniques, and the results are as follows. Figure 3 As shown, the region with a local dip angle of 60 degrees corresponds to a discontinuous region similar to a fault in the wave impedance model, but the area of ​​the discontinuous region in the wave impedance model is much smaller. Figure 4 The area shown indicates that the structural information estimation near discontinuous regions is inaccurate. This structural estimation error is due to the inherent characteristic of the limited bandwidth of the wavelet, and it cannot be corrected at present.

[0043] Figure 5 The results of traditional uniform structural constraint inversion under different structural constraint strengths are shown by the arrows. When the structural constraint strength is large, due to the inaccuracy of structural information, the region of discontinuous wave impedance will be over-smoothed, resulting in wave impedance artifacts that do not exist.

[0044] according to Figure 5 The 16 inversion results shown are used to calculate the average structural similarity of each inversion result and the structural similarity between each result and the real model. The results are as follows: Figure 6 As shown, the two trends are consistent with the change of absolute constraint strength, which proves the effectiveness of the average structural similarity index in evaluating the strength of structural constraint application. In this embodiment, the absolute constraint strength is determined to be 5.12. Figure 5 The normalized structure sensitivity index corresponding to the inversion result shown is as follows: Figure 7 As shown, the regions with higher structural sensitivity indices correspond precisely to... Figure 5As indicated by the arrows, under the constraint of a traditional uniform structure, the inversion results are prone to distortion in these regions. This proves that the structural sensitivity index can characterize regions that are prone to distortion under the guidance of erroneous structural information.

[0045] Figure 7 To obtain the inversion results under different absolute constraint weights after adding relative constraint weights using the method proposed in this invention, these results no longer contain the following: Figure 5 The arrow indicates the structural distortion. For example... Figure 10 As shown, the structural sensitivity index corresponding to most wave impedance parameters in the inversion results is approximately 0. This indicates that after improvement with relative constraint weights, even with large absolute constraint weights, the distortion of the wave impedance structure is effectively suppressed. Furthermore, as shown in the figure... Figure 9 As shown, compared to Figure 6 After the improvement of relative constraint weights, the root mean square error of the data, the average structural similarity, and the structural similarity index with the real model all show a significant reduction in the variation with absolute constraint strength. This proves the effectiveness of adjusting the relative constraint weights between wave impedance parameters using the structural sensitivity index. Meanwhile, from Figure 9 As can be seen from the average structural similarity curve, the index will decrease when the absolute constraint weight is greater than 5.12. In order to ensure the purity of the inversion imaging and enhance the structural features, the inversion result corresponding to the absolute constraint weight of 5.12 is the final wave impedance inversion result.

[0046] The above description is merely a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural transformations made using the contents of the present invention's specification and drawings under the inventive concept of the present invention, or direct / indirect applications in other related technical fields, are included within the patent protection scope of the present invention.

Claims

1. A method of adaptive structural constraint seismic wave impedance inversion, characterized by, Includes the following steps: S1. Obtain post-stack seismic data and seismic wavelets; S2. Extract seismic slope information from seismic data using plane wave analysis methods; S3. Construct a structure-oriented model constraint matrix using seismic slope information; S4. Construct a multichannel impedance forward modeling operator using seismic data; S5. Based on the multi-channel impedance forward modeling operator, construct the inversion objective function based on adaptive structural constraints; S6. Select the weights of the reference model terms for the inversion objective function; S7. Select multiple structural constraint strengths based on the weights of the reference model terms, and calculate multiple inversion results based on the multiple structural constraint strengths; S8. Calculate the average structural similarity between each inversion result and all inversion results, and select the structural constraint weights used by the inversion result with the highest average structural similarity as the absolute constraint weights. S9. Calculate the structural sensitivity index based on the inversion results of multiple different structural constraint weights; S10. Normalize each structural sensitivity index to obtain the normalized structural sensitivity index. S11. Differentiate the normalized structural sensitivity index to obtain the relative constraint weights corresponding to each impedance parameter, and assign the relative constraint weights to the relative constraint strength matrix. S12. The final inversion result is obtained by calculating the weights of the reference model terms, the structural constraint weights, and the relative constraint strength matrix.

2. The method of adaptive structural constraint seismic wave impedance inversion of claim 1, wherein, The expression for the inversion objective function is as follows: ； in, The natural logarithm of the multichannel impedance. As a reference model, The relative constraint strength matrix, For reference model items The weight, For the weights of structural constraint terms, For multichannel seismic data, For multiple forward modeling operators, For gradient operators in directions parallel to the local tilt angle, The objective function is... Solution of the inverted objective function The expression is as follows: ； in, This is the transpose of the matrix. It is an identity matrix.

3. The adaptive structurally constrained seismic wave impedance inversion method as described in claim 2, characterized in that, The specific steps of S6 are as follows: ... Set to 0 to select multiple items from smallest to largest. Calculate different The root mean square error of the inverted data term, and from The optimal value is determined using the L-curve method from the curve plot of root mean square error. .

4. The adaptive structurally constrained seismic wave impedance inversion method as described in claim 3, characterized in that, The specific steps in S7 are: using the information obtained in S6. The relative constraint strength matrix Set as an identity matrix, select multiple from smallest to largest. Each [function] is calculated based on the expression of the solution to the inversion objective function. Corresponding inversion results , For the final selection The number of.

5. A readable storage medium, characterized in that, The readable storage medium stores a computer program adapted to be loaded by a processor and executed by the adaptive structural constraint seismic impedance inversion method according to any one of claims 1-4.

6. A computer device, characterized in that, The computer device includes a memory and a processor. The memory stores a computer program, which, when executed by the processor, runs the adaptive structural constraint seismic impedance inversion method according to any one of claims 1-4.

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