A method, system, device, and storage medium for forward modeling analysis of seismic waves

Abstract

This application provides a method, system, device, and storage medium for seismic wave forward modeling analysis. The method includes: constructing forward modeling models based on different geological conditions, meshing all constructed forward modeling models, calculating mesh quality parameters for forward modeling models under different geological conditions, and performing overall statistical analysis of the model mesh quality parameters. This invention, by constructing models under different geological conditions, quantifying mesh quality parameters, and combining them with forward modeling results, achieves a quantitative analysis of the impact of mesh quality on simulation stability. This can guide reasonable mesh design, improve simulation efficiency and accuracy, and, through quantitative analysis of the impact of mesh quality, improve the reliability of seismic wave forward modeling, reduce errors and uncertainties in simulation results, and provide important scientific basis for mesh quality evaluation and forward modeling optimization.

Description

Technical Field

[0001] This application relates to the field of applied geophysical seismic wave forward modeling, and in particular to a seismic wave forward modeling analysis method, system, device and storage medium. Background Technology

[0002] Spectral element method (SEM) is a commonly used numerical simulation method in seismology to simulate the propagation of seismic waves in subsurface media. Based on classical finite element simulation theory, the SEM algorithm divides the overall geological model into discrete grid cells using a meshing method. Based on spectral decomposition, it achieves accurate solutions for wave field propagation within the discrete grid cells. Therefore, SEM can not only flexibly handle the discretization of complex models, but also achieves rapid convergence by drawing on spectral methods, making it an indispensable simulation algorithm for seismic wave simulation.

[0003] The discretization of geological models using the spectral element method is accomplished through effective mesh generation. The mesh generation results directly affect the performance of the spectral element simulation algorithm. For example, increasing the number of meshes can effectively improve the accuracy of the spectral element simulation, but it will also increase the overall simulation time. When maintaining a certain number of meshes, mesh density, element order, and mesh shape all affect the convergence of the spectral element simulation. Therefore, it can be said that a good and high-quality mesh generation is an important prerequisite for the successful implementation of the spectral element simulation algorithm. The quantitative analysis of the impact of mesh quality is to quantify the influence of mesh quality on the seismic wave forward modeling results. This includes evaluating the mesh quality, such as the fineness of the mesh generation and the regularity of the mesh shape, and then conducting numerical experiments or theoretical analysis.

[0004] Currently, a great deal of work has been done on existing mesh generation techniques for models, mainly focusing on improvements and enhancements to the mesh generation methods themselves, the judgment and evaluation of mesh generation quality, and universal adaptive adjustments to mesh generation for the finite element method. Based on mesh quality evaluation, traditional seismic wave mesh quality impact analysis methods lack methods to quantify the impact of mesh quality on forward modeling, which affects mesh quality evaluation. Therefore, it is urgent to establish a quantitative analysis method for mesh quality impact, so as to provide an efficient and reliable evaluation and analysis method for seismic wave simulation. Summary of the Invention

[0005] This application provides a seismic wave forward modeling analysis method, system, device, and storage medium to improve the problem that traditional seismic wave grid quality impact analysis methods lack a method to quantify the impact of grid quality on forward modeling, which affects grid quality evaluation.

[0006] To achieve the above objectives, this application adopts the following technical solution:

[0007] Firstly, a method for forward modeling and analyzing seismic waves, the method comprising:

[0008] Based on the construction forward model under different geological conditions, all forward models formed during construction are meshed. Then, the mesh quality parameters of the forward model under different geological conditions are calculated, and the overall mesh quality parameters of the models are statistically analyzed.

[0009] Select appropriate forward simulation parameters, perform forward simulation on the forward model, and statistically analyze the forward simulation results to obtain the mesh quality quantification relationship.

[0010] Based on the construction forward modeling for different geological conditions, all constructed forward modeling models were meshed. Mesh quality parameters of the forward modeling under different geological conditions were calculated, and the overall mesh quality parameters were statistically analyzed. Specifically, this included: using a horizontal surface forward model as the standard model and a two-dimensional Gaussian surface as the top surface, the standard model was distorted; different degrees of surface distortion were used to quantify different mesh qualities, where the distortion quantification was represented by the standard deviation of a two-dimensional Gaussian function. The specific carrier of mesh quality is the forward model. Meshing was performed on the constructed forward model. During the meshing process, the horizontal X / Y direction mesh was uniformly divided, and the mesh edge angle in the Z direction was slid to fit the top Gaussian surface of the model, thereby quantifying the quality. Different top surface distortion degrees can be transferred to the bottom of the model to obtain models with different mesh qualities. Based on the completion of the forward modeling with different top surface distortion degrees, the mesh quality of the mesh after meshing is calculated. The evaluation standard of mesh quality is specifically quantified by the commonly used mesh skewness. The calculation process can be completed with the help of general mesh quality evaluation software. Since the model meshing is made to fit the top surface of the model by sliding the Z-direction mesh boundary angle, the Z-direction mesh boundary angle of the same model is different from the top surface to the ground surface. This leads to different meshing quality at different locations of the same model. Therefore, after calculating the mesh quality parameters of the forward modeling under different geological conditions, it is necessary to further perform overall statistical analysis on the model meshing quality parameters.

[0011] Optionally, appropriate forward simulation parameters can be selected, including: after completing the model meshing, performing forward simulation of the model. For the forward simulation parameters, since this application aims to obtain universal quantization relationships, the source wavelet is selected using commonly used forward simulation parameters. The source is selected as a 25Hz Ricker wavelet, and the source location is selected as the model center. This setting can ensure that all forward models are stable in the initial stage of simulation.

[0012] Optionally, forward modeling is performed on the forward model and the forward modeling results are statistically analyzed to obtain the quantitative relationship of mesh quality, including: the quantitative relationship of the influence of mesh quality on forward modeling results under the corresponding simulation conditions can be obtained by comparing the stability of different forward modeling results with the statistical correspondence of the overall mesh quality parameters of the model.

[0013] Secondly, a seismic wave forward modeling analysis system is configured as follows:

[0014] Based on the construction forward model under different geological conditions, all forward models formed during construction are meshed. Then, the mesh quality parameters of the forward model under different geological conditions are calculated, and the overall mesh quality parameters of the models are statistically analyzed.

[0015] Select appropriate forward simulation parameters, perform forward simulation on the forward model, and statistically analyze the forward simulation results to obtain the mesh quality quantification relationship.

[0016] Optionally, based on the construction of forward models under different geological conditions, all constructed forward models are meshed, and the mesh quality parameters of the forward models under different geological conditions are calculated. The overall mesh quality parameters are then statistically analyzed. Specifically, this includes: using a horizontal surface forward model as the standard model and a two-dimensional Gaussian surface as the top surface, the standard model is distorted; different degrees of surface distortion are used to quantify different mesh qualities, where the distortion quantification is represented by the standard deviation of a two-dimensional Gaussian function. The specific carrier of mesh quality is the forward model. The constructed forward model is meshed, and during the meshing process, the horizontal X / Y direction mesh is uniformly divided, and the mesh edge angle in the Z direction is slid to fit the top Gaussian surface of the model. Different degrees of top surface distortion can be transferred to the bottom of the model to obtain models with different mesh qualities. Based on the meshing of the forward model with different degrees of top surface distortion, the mesh quality of the mesh is calculated. The evaluation standard of mesh quality is specifically quantified by the commonly used mesh skewness. The calculation process can be completed with the help of general mesh quality evaluation software. Since the model mesh is meshed by sliding the Z-direction mesh boundary angle to fit the top surface of the model, the Z-direction mesh boundary angle of the same model is different from the top surface to the ground surface. This leads to different mesh meshing quality at different locations of the same model. Therefore, after calculating the mesh quality parameters of the forward model under different geological conditions, it is necessary to further perform overall statistical analysis on the model mesh meshing quality parameters.

[0017] Optionally, appropriate forward simulation parameters can be selected, including: after completing the model meshing, performing forward simulation of the model. For the forward simulation parameters, since this application aims to obtain universal quantization relationships, the source wavelet is selected using commonly used forward simulation parameters. The source is selected as a 25Hz Ricker wavelet, and the source location is selected as the model center. This setting can ensure that all forward models are stable in the initial stage of simulation.

[0018] Optionally, forward modeling is performed on the forward model and the forward modeling results are statistically analyzed to obtain the quantitative relationship of mesh quality, including: the quantitative relationship of the influence of mesh quality on forward modeling results under the corresponding simulation conditions can be obtained by comparing the stability of different forward modeling results with the statistical correspondence of the overall mesh quality parameters of the model.

[0019] A seismic wave forward modeling and analysis device, characterized in that the device comprises:

[0020] The data construction module is used to build forward models for different geological conditions.

[0021] The data partitioning and data processing module is used to perform grid partitioning on all forward models formed during construction, then calculate the grid quality parameters of forward models under different geological conditions, and perform overall statistics on the grid quality parameters of the models.

[0022] The simulation parameter selection module and the simulation module are used to select appropriate forward simulation parameters, perform forward simulation on the forward model, and statistically analyze the forward simulation results to obtain the mesh quality quantification relationship.

[0023] A fourth aspect of this invention provides an electronic device, the electronic device comprising:

[0024] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method proposed in the first aspect of the present invention.

[0025] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method as described in the first aspect of the present invention.

[0026] In summary, the above method and apparatus have the following technical effects:

[0027] This invention proposes a seismic wave forward modeling analysis method. By constructing models with different geological conditions, quantifying grid quality parameters, and combining the forward modeling results, it achieves a quantitative analysis of the impact of grid quality on simulation stability. This method can guide reasonable grid design, improve simulation efficiency and accuracy, and enhance the reliability of seismic wave forward modeling by quantitatively analyzing the impact of grid quality, thereby reducing simulation errors and uncertainties. This method solves the problem of the lack of means to quantify the impact of grid quality on forward modeling in existing technologies, providing a scientific basis for grid quality evaluation and forward modeling optimization. Attached Figure Description

[0028] Figure 1 Flowchart of a method for quantitative analysis of the impact of grid quality on seismic wave forward modeling:

[0029] Figure 2 Forward modeling for different geological conditions;

[0030] Figure 3 Calculation of mesh quality parameters for forward modeling under the same geological conditions;

[0031] Figure 4 For overall statistics of model mesh parameters;

[0032] Figure 5 The results of forward modeling are shown. Detailed Implementation

[0033] 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 some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0034] Example 1

[0035] This application proposes a seismic wave forward modeling analysis method, the method comprising:

[0036] Establish forward modeling models for different geological conditions, and perform mesh generation on all forward modeling models;

[0037] Calculate the mesh quality parameters of the forward model under different geological conditions, and perform overall statistics on the mesh quality parameters.

[0038] Select appropriate forward simulation parameters, perform forward simulation on the forward model, and statistically analyze the forward simulation results to obtain the mesh quality quantification relationship.

[0039] Optionally, based on the construction of forward models under different geological conditions, all constructed forward models are meshed, and the mesh quality parameters of the forward models under different geological conditions are calculated. The overall mesh quality parameters are then statistically analyzed. Specifically, this includes: using a horizontal surface forward model as the standard model and a two-dimensional Gaussian surface as the top surface, the standard model is distorted; different degrees of surface distortion are used to quantify different mesh qualities, where the distortion quantification is represented by the standard deviation of a two-dimensional Gaussian function. The specific carrier of mesh quality is the forward model. The constructed forward model is meshed, and during the meshing process, the horizontal X / Y direction mesh is uniformly divided, and the mesh edge angle in the Z direction is slid to fit the top Gaussian surface of the model. Different degrees of top surface distortion can be transferred to the bottom of the model to obtain models with different mesh qualities. Based on the meshing of the forward model with different degrees of top surface distortion, the mesh quality of the mesh is calculated. The evaluation standard of mesh quality is specifically quantified by the commonly used mesh skewness. The calculation process can be completed with the help of general mesh quality evaluation software. Since the model mesh is meshed by sliding the Z-direction mesh boundary angle to fit the top surface of the model, the Z-direction mesh boundary angle of the same model is different from the top surface to the ground surface. This leads to different mesh meshing quality at different locations of the same model. Therefore, after calculating the mesh quality parameters of the forward model under different geological conditions, it is necessary to further perform overall statistical analysis on the model mesh meshing quality parameters.

[0040] Understandable: The standard forward model implemented, such as Figure 2 As shown in model1, the model's length, width, and height are 4000m / 2000m / 4000m respectively. Figure 2 bf represents the forward model with different degrees of distortion at the top, with the distortion gradually increasing. Figure 3 The results of mesh generation and mesh quality parameter calculation for the forward model show that, from 3a to f, the overall mesh quality decreases as the top surface distortion of the model gradually increases; Figure 3 The overall statistical parameters of the model mesh were statistically analyzed, and the statistical results are as follows: Figure 4 As shown, Figure 4 The statistical results of the mesh quality of the AF models are as follows: the mesh quality of model1 is 1. As a standard reference, the mesh quality of model2 is between 0.9 and 1, the mesh quality of model3 is between 0.8 and 1, the mesh quality of model4 is between 0.6 and 1, the mesh quality of model5 is between 0.4 and 1, and the mesh quality of model6 is between 0.2 and 1.

[0041] Optionally, appropriate forward simulation parameters are selected, including: after completing the model meshing, forward simulation is performed. For the forward simulation parameters, since this application aims to obtain universal quantification relationships, commonly used forward simulation parameters are selected for the source wavelet. The source is selected as a 25Hz Ricker wavelet, and the source location is selected as the model center. This setting can ensure that all forward models are stable in the initial stage of simulation. Forward simulation is performed on the forward model, and the forward simulation results are statistically analyzed to obtain the quantification relationship of mesh quality. This includes: based on the stability of different forward simulation results and the statistical correspondence of the overall mesh quality parameters of the model, the quantification relationship of the influence of mesh quality on the forward simulation results under the corresponding simulation conditions can be obtained.

[0042] Understandable: Based on the overall statistical quality of the mesh, a forward modeling simulation is performed, and the simulation results obtained are as follows... Figure 5 As shown, by comparison Figure 5 and Figure 4 The quantitative relationship of the influence of mesh quality can be obtained: when the skewness of the mesh is greater than 0.8, the simulation stability is not affected; when the skewness drops to 0.6, the simulation of the absorbing boundary wave field begins to show instability; and when the skewness is further reduced to 0.4 or below, the forward modeling of the wave field shows serious non-convergence.

[0043] Example 2

[0044] Embodiments of this application also propose a seismic wave forward modeling analysis system, which is configured as follows:

[0045] Establish forward modeling models for different geological conditions, and perform mesh generation on all forward modeling models;

[0046] Calculate the mesh quality parameters of the forward model under different geological conditions, and perform overall statistics on the mesh quality parameters.

[0047] Select appropriate forward simulation parameters, perform forward simulation on the forward model, and statistically analyze the forward simulation results to obtain the mesh quality quantification relationship.

[0048] Optionally, based on the construction of forward models under different geological conditions, all constructed forward models are meshed, and the mesh quality parameters of the forward models under different geological conditions are calculated. The overall mesh quality parameters are then statistically analyzed. Specifically, this includes: using a horizontal surface forward model as the standard model and a two-dimensional Gaussian surface as the top surface, the standard model is distorted; different degrees of surface distortion are used to quantify different mesh qualities, where the distortion quantification is represented by the standard deviation of a two-dimensional Gaussian function. The specific carrier of mesh quality is the forward model. The constructed forward model is meshed, and during the meshing process, the horizontal X / Y direction mesh is uniformly divided, and the mesh edge angle in the Z direction is slid to fit the top Gaussian surface of the model. Different degrees of top surface distortion can be transferred to the bottom of the model to obtain models with different mesh qualities. Based on the meshing of the forward model with different degrees of top surface distortion, the mesh quality of the mesh is calculated. The evaluation standard of mesh quality is specifically quantified by the commonly used mesh skewness. The calculation process can be completed with the help of general mesh quality evaluation software. Since the model mesh is meshed by sliding the Z-direction mesh boundary angle to fit the top surface of the model, the Z-direction mesh boundary angle of the same model is different from the top surface to the ground surface. This leads to different mesh meshing quality at different locations of the same model. Therefore, after calculating the mesh quality parameters of the forward model under different geological conditions, it is necessary to further perform overall statistical analysis on the model mesh meshing quality parameters.

[0049] Optionally, appropriate forward simulation parameters can be selected, including: after completing the model meshing, performing forward simulation of the model. For the forward simulation parameters, since this application aims to obtain universal quantization relationships, the source wavelet is selected using commonly used forward simulation parameters. The source is selected as a 25Hz Ricker wavelet, and the source location is selected as the model center. This setting can ensure that all forward models are stable in the initial stage of simulation.

[0050] Optionally, forward modeling is performed on the forward model and the forward modeling results are statistically analyzed to obtain the quantitative relationship of mesh quality, including: the quantitative relationship of the influence of mesh quality on forward modeling results under the corresponding simulation conditions can be obtained by comparing the stability of different forward modeling results with the statistical correspondence of the overall mesh quality parameters of the model.

[0051] Example 3

[0052] Embodiments of this application also propose a seismic wave forward modeling analysis apparatus, the apparatus comprising:

[0053] The data construction module is used to build forward models for different geological conditions.

[0054] The data partitioning and data processing module is used to perform grid partitioning on all forward models formed during construction, then calculate the grid quality parameters of forward models under different geological conditions, and perform overall statistics on the grid quality parameters of the models.

[0055] The simulation parameter selection module and the simulation module are used to select appropriate forward simulation parameters, perform forward simulation on the forward model, and statistically analyze the forward simulation results to obtain the mesh quality quantification relationship.

[0056] Example 4

[0057] This application also proposes an electronic device, which includes:

[0058] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the methods of the embodiments of this application.

[0059] Example 5

[0060] Furthermore, to achieve the above objectives, embodiments of this application also propose a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method of the embodiments of this application.

[0061] The following is a detailed introduction to the various components of the electronic device:

[0062] In this context, the processor is the control center of the electronic device. It can be a single processor or a collective term for multiple processing elements. For example, a processor can be one or more central processing units (CPUs), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention, such as one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs).

[0063] Alternatively, the processor can perform various functions of the electronic device by running or executing software programs stored in memory and by calling data stored in memory.

[0064] The memory is used to store the software program that executes the solution of the present invention, and the execution is controlled by the processor. The specific implementation method can be referred to the above method embodiment, and will not be repeated here.

[0065] Optionally, the memory can be read-only memory (ROM) or other types of static storage devices capable of storing static information and instructions, random access memory (RAM) or other types of dynamic storage devices capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory can be integrated with the processor or exist independently and coupled to the processor through an interface circuit of an electronic device; this embodiment of the invention does not specifically limit this.

[0066] A transceiver is used to communicate with network devices or with terminal devices.

[0067] Optionally, the transceiver may include a receiver and a transmitter. The receiver is used to implement the receiving function, and the transmitter is used to implement the sending function.

[0068] Optionally, the transceiver can be integrated with the processor or exist independently and coupled to the processor through the router's interface circuit. This embodiment of the invention does not specifically limit this.

[0069] Furthermore, the technical effects of the electronic device can be referred to the technical effects of the data transmission method described in the above method embodiments, and will not be repeated here.

[0070] It should be understood that the processor in the embodiments of the present invention can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0071] It should also be understood that the memory in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0072] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0073] It should be understood that the term "and / or" in this article merely describes the relationship between related objects in a method for quantitative analysis of the impact of seismic wave forward modeling grid quality. It indicates that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it may also indicate an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0074] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0075] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0076] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

Claims

1. A seismic wave forward modeling analysis method, characterized in that: The method includes: Based on the construction forward model under different geological conditions, all forward models formed during construction are meshed. Then, the mesh quality parameters of the forward model under different geological conditions are calculated, and the overall mesh quality parameters of the models are statistically analyzed. Select appropriate forward simulation parameters, perform forward simulation on the forward model, and statistically analyze the forward simulation results to obtain the mesh quality quantification relationship.

2. The method of claim 1, wherein, Based on the construction forward modeling for different geological conditions, all constructed forward modeling models were meshed. Mesh quality parameters of the forward modeling under different geological conditions were calculated, and the overall mesh quality parameters were statistically analyzed. Specifically, this included: using a horizontal surface forward model as the standard model and a two-dimensional Gaussian surface as the top surface, the standard model was distorted; different degrees of surface distortion were used to quantify different mesh qualities, where the distortion quantification was represented by the standard deviation of a two-dimensional Gaussian function. The specific carrier of mesh quality is the forward model. Meshing was performed on the constructed forward model. During the meshing process, the horizontal X / Y direction mesh was uniformly divided, and the mesh edge angle in the Z direction was slid to fit the top Gaussian surface of the model, thereby quantifying the quality. Different top surface distortion degrees can be transferred to the bottom of the model to obtain models with different mesh qualities. Based on the completion of the forward modeling with different top surface distortion degrees, the mesh quality of the mesh after meshing is calculated. The evaluation standard of mesh quality is specifically quantified by the commonly used mesh skewness. The calculation process can be completed with the help of general mesh quality evaluation software. Since the model meshing is made to fit the top surface of the model by sliding the Z-direction mesh boundary angle, the Z-direction mesh boundary angle of the same model is different from the top surface to the ground surface. This leads to different meshing quality at different locations of the same model. Therefore, after calculating the mesh quality parameters of the forward modeling under different geological conditions, it is necessary to further perform overall statistical analysis on the model meshing quality parameters.

3. The method of claim 2, wherein, Selecting appropriate forward modeling parameters includes: after completing the model meshing, performing forward modeling simulation. For forward modeling parameters, since this application aims to obtain universal quantization relationships, the source wavelet is selected using commonly used forward modeling parameters. The source is selected as a 25Hz Ricker wavelet, and the source location is selected as the model center. This setting can ensure that all forward modeling models are stable in the initial stage of simulation.

4. The method of claim 3, wherein, Forward modeling is performed, and the results are statistically analyzed to obtain the quantitative relationship of mesh quality. This includes obtaining the quantitative relationship of the influence of mesh quality on the forward modeling results under the corresponding simulation conditions based on the stability of different forward modeling results and the statistical correspondence of the overall mesh quality parameters of the model.

5. A seismic wave forward modeling analysis system, characterized in that, The system is configured as follows: Based on the construction forward model under different geological conditions, all forward models formed during construction are meshed. Then, the mesh quality parameters of the forward model under different geological conditions are calculated, and the overall mesh quality parameters of the models are statistically analyzed. Select appropriate forward simulation parameters, perform forward simulation on the forward model, and statistically analyze the forward simulation results to obtain the mesh quality quantification relationship.

6. The seismic wave forward modeling analysis system of claim 5, wherein, Based on the construction forward modeling for different geological conditions, all constructed forward modeling models were meshed. Mesh quality parameters of the forward modeling under different geological conditions were calculated, and the overall mesh quality parameters were statistically analyzed. Specifically, this included: using a horizontal surface forward model as the standard model and a two-dimensional Gaussian surface as the top surface, the standard model was distorted; different degrees of surface distortion were used to quantify different mesh qualities, where the distortion quantification was represented by the standard deviation of a two-dimensional Gaussian function. The specific carrier of mesh quality is the forward model. Meshing was performed on the constructed forward model. During the meshing process, the horizontal X / Y direction mesh was uniformly divided, and the mesh edge angle in the Z direction was slid to fit the top Gaussian surface of the model, thereby quantifying the quality. Different top surface distortion degrees can be transferred to the bottom of the model to obtain models with different mesh qualities. Based on the completion of the forward modeling with different top surface distortion degrees, the mesh quality of the mesh after meshing is calculated. The evaluation standard of mesh quality is specifically quantified by the commonly used mesh skewness. The calculation process can be completed with the help of general mesh quality evaluation software. Since the model meshing is made to fit the top surface of the model by sliding the Z-direction mesh boundary angle, the Z-direction mesh boundary angle of the same model is different from the top surface to the ground surface. This leads to different meshing quality at different locations of the same model. Therefore, after calculating the mesh quality parameters of the forward modeling under different geological conditions, it is necessary to further perform overall statistical analysis on the model meshing quality parameters.

7. The seismic wave forward modeling analysis system of claim 6, wherein, Selecting appropriate forward modeling parameters includes: after completing the model meshing, performing forward modeling simulation. For forward modeling parameters, since this application aims to obtain universal quantization relationships, the source wavelet is selected using commonly used forward modeling parameters. The source is selected as a 25Hz Ricker wavelet, and the source location is selected as the model center. This setting can ensure that all forward modeling models are stable in the initial stage of simulation.

8. A seismic wave forward modeling analysis device, characterized by, The device includes: The data construction module is used to build forward models for different geological conditions. The data partitioning and data processing module is used to perform grid partitioning on all forward models formed during construction, then calculate the grid quality parameters of forward models under different geological conditions, and perform overall statistics on the grid quality parameters of the models. The simulation parameter selection module and the simulation module are used to select appropriate forward simulation parameters, perform forward simulation on the forward model, and statistically analyze the forward simulation results to obtain the mesh quality quantification relationship.

9. An electronic device, comprising: The electronic device includes: processor; A memory storing computer-readable instructions, which, when executed by the processor, implement the seismic wave forward modeling analysis method as described in any one of claims 1 to 4.

10. A computer readable storage medium, characterized in that, The computer-readable storage medium contains program code, which can be called by a processor to execute the seismic wave forward modeling analysis method as described in any one of claims 1 to 4.

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