Seismic numerical simulation method and electronic equipment

Abstract

The invention discloses a seismic numerical simulation method and electronic equipment, and the method comprises the steps: obtaining an initial adaptive grid which is a finite difference regular grid; grid parameters corresponding to at least one area are determined according to simulation requirements, the at least one area is matched with the simulation requirements, the simulation requirements comprise simulation precision, and the grid parameters comprise grid step lengths; changing the grid size of at least one region in the initial adaptive grid according to the grid parameters of the at least one region to obtain an adaptive step size grid; and according to the self-adaptive step size grid, simulating a seismic numerical value to obtain a seismic numerical value simulation record. The method may re-determine a grid parameter, such as a grid step size, for a corresponding region based on the simulation accuracy. Therefore, the self-adaptive step size grid can be generated according to the simulation precision, so that the size of the grid can be changed in an area with low simulation precision requirement, for example, a medium grid is changed into a coarse grid, the computing resources can be reduced, and the simulation computing efficiency can be improved.

Description

Technical Field

[0001] This application relates to the field of geographic exploration technology, and in particular to a seismic numerical simulation method and electronic equipment. Background Technology

[0002] Seismic numerical simulation is a tool for analyzing oil and gas exploration and geological structures (e.g., rock layer distribution, fault location, and fluid enrichment zones).

[0003] Seismic numerical simulation can be achieved using adaptive meshing techniques. Adaptive meshing is a technique proposed for numerical simulation of flow fields involving multidimensional structures. An adaptive mesh consists of a series of nested, overlapping mesh sequences; a finer mesh is used in areas where detail is required, and a finer mesh is used in areas where detail is not required.

[0004] Currently, most adaptive mesh numerical simulations are applicable to the finite element method, but not the finite difference method. Furthermore, even when using the finite difference method, there are problems such as high computational resource consumption and low simulation efficiency. Summary of the Invention

[0005] This application provides an earthquake numerical simulation method and electronic device that can improve computational efficiency and accuracy while reducing computational resources.

[0006] To achieve the above objectives, the embodiments of this application provide the following solutions:

[0007] Firstly, an earthquake numerical simulation method is provided, which can be executed by an electronic device or an internal unit / functional module within an electronic device.

[0008] The method includes: obtaining an initial adaptive grid, which is a finite difference grid; determining grid parameters corresponding to at least one region according to simulation requirements, wherein the at least one region matches the simulation requirements, the simulation requirements including simulation accuracy, and the grid parameters including grid step size; changing the grid size of at least one region in the initial adaptive grid according to the grid parameters of at least one region to obtain an adaptive step size grid; and simulating seismic numerical data according to the adaptive step size grid to obtain a seismic numerical simulation record.

[0009] In this method, mesh parameters, such as the mesh step size, can be redefined for the corresponding region based on the simulation accuracy. For example, a longer step size is used in regions where simulation accuracy requirements are not high, while a shorter step size is used in regions where simulation accuracy requirements are high. In this way, an adaptive step size mesh can be generated according to the simulation accuracy, allowing the mesh size to be changed in regions where simulation accuracy requirements are not high, such as changing a medium mesh to a coarse mesh, thereby reducing computational resources and improving simulation efficiency.

[0010] In one possible implementation, determining the corresponding mesh parameters for at least one region based on simulation requirements includes: determining at least one velocity sorting box based on the velocity model structure; determining the velocity gradient of each region based on the at least one velocity sorting box; and determining the corresponding mesh parameters for at least one region based on the velocity gradient of each region.

[0011] This method determines the approximate range requiring fine-mesh calculations based on simulation accuracy. Within this range, velocity sorting boxes are set to identify at least one region. The velocity gradient for each region is then calculated to determine the corresponding mesh step size. This approach clearly identifies the meshes that need optimization.

[0012] In one possible implementation, if the target layers of the seismic model structure are concentrated, the velocity sorting box is a horizontal sorting box; if the target layers of the seismic model structure are dispersed, the velocity sorting box is a matrix sorting box.

[0013] This method can determine at least one more accurate region, and ultimately obtain more accurate earthquake simulation values.

[0014] In one possible implementation, the velocity gradient corresponding to one region in at least one region is the average gradient of at least one velocity sorting box corresponding to one region.

[0015] In one possible implementation, determining the mesh parameters of at least one region based on the velocity gradient of at least one region includes:

[0016] For a first region in at least one region, the size of the grid and the difference order corresponding to the first region are determined based on the precision weight of the first region.

[0017] Precision weights can serve as a standard for determining the appropriate grid size. Therefore, determining the grid size and difference order for a region based on precision weights can minimize computational resources for that region while still meeting the required precision.

[0018] In one possible implementation, if the first region corresponds to the target layer in the overall seismic model, then the grid corresponding to the first region is a fine grid; if the accuracy weight of the first region is higher than the average gradient of the overall seismic model, then the grid corresponding to the first region is a medium grid; if the accuracy weight of the first region is lower than the average gradient of the overall seismic model, then the grid corresponding to the first region is a coarse grid; wherein, the coarse grid corresponds to the fourth-order difference, the medium grid corresponds to the eighth-order difference, and the fine grid corresponds to the sixteenth-order difference.

[0019] In one possible implementation, changing the mesh size of at least one region in the initial adaptive mesh according to the mesh parameters of at least one region includes: for a first region within the at least one region, reducing the number of mesh points within the first mesh according to the type of the first mesh corresponding to the first region to form a second mesh; performing finite difference calculations on each point within the second mesh to change the difference order of the second mesh, wherein the difference order of the second mesh is less than the difference order of the first mesh. The mesh types include coarse mesh, medium mesh, and fine mesh.

[0020] In this method, the initial grid (i.e., the first grid) corresponding to the first region is optimized based on the grid parameters of the first region to reduce the number of grid points in the first grid, thereby achieving adaptive change of grid type.

[0021] In one possible implementation, the seismic numerical simulation is performed based on an adaptive step-size grid to obtain a seismic numerical simulation record, including: simulating the seismic numerical simulation based on the adaptive step-size grid to obtain a first calculation result; performing amplitude compensation on the first calculation result to obtain a seismic numerical simulation record, wherein the ratio of the amplitude before and after compensation is inversely proportional to the ratio before and after the change in the number of network points.

[0022] In this scheme, the change in the number of grids leads to inconsistent amplitudes of sorting frames at different speeds. This scheme can ensure the accuracy of the calculation results by performing amplitude compensation on the calculation results.

[0023] In a second aspect, an electronic device is provided, comprising functional modules for performing the methods of the first aspect. For example, the communication device includes a processing unit (sometimes also called a processing module or processor) and / or a transceiver unit (sometimes also called a transceiver module or transceiver). These units (modules) can perform the corresponding functions in the method examples of the first aspect described above, as detailed in the method examples, and will not be repeated here.

[0024] The communication device can be the electronic device described in the first aspect above, or it can be a chip or chip system within the aforementioned electronic device. Optionally, the communication device further includes a memory. The memory stores computer programs, instructions, or data. The processing unit is coupled to the memory and the transceiver unit. When the processing unit reads the computer program, instructions, or data, it causes the communication device to execute the method performed by the electronic device in the above method embodiments.

[0025] Thirdly, this application provides a computer-readable storage medium storing a computer program that, when run, implements the method described in the first aspect above.

[0026] Fourthly, a computer program product is provided, the computer program product comprising: computer program code, which, when run, causes the method in the first aspect to be executed. Attached Figure Description

[0027] Figure 1 A schematic flowchart of the earthquake numerical simulation method provided in the embodiments of this application;

[0028] Figure 2 This is a schematic diagram illustrating the velocity sorting of the experimental model provided in an embodiment of this application;

[0029] Figure 3 A schematic diagram illustrating the differences between different grids provided in the embodiments of this application;

[0030] Figure 4 A schematic diagram illustrating the adaptive step-size grid for model optimization provided in an embodiment of this application;

[0031] Figure 5 The salt dome velocity model provided for the embodiments of this application;

[0032] Figure 6 Comparison of single-shot records and wavefield snapshots obtained by the method provided in this application embodiment and the conventional method, respectively;

[0033] Figure 7 A comparison chart showing the time and memory usage for earthquake numerical simulation using the method provided in the embodiments of this application and conventional methods;

[0034] Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0035] The method provided in this application for simulating earthquake numerical values ​​can improve the efficiency and accuracy of simulation while reducing computational resources.

[0036] Seismic numerical simulation is a tool for analyzing oil and gas exploration and geological structures (e.g., strata distribution, fault locations, and fluid-rich zones). It simulates the propagation of seismic waves underground to predict potential seismic records under given geological conditions. For example, by simulating the propagation of seismic waves underground, it simulates seismic responses, records theoretical values ​​characterizing geological structures, and predicts potential seismic records based on these theoretical values.

[0037] Geological structures are typically complex, and therefore, the propagation patterns of seismic waves within them are highly complex. To obtain a more accurate understanding of geological structures, it is often necessary to simulate seismic data from multiple dimensions, focusing on thin and small granularities. "Thin" refers to stratifying the geological structure with a relatively small thickness, and "small" refers to analyzing it using small units as the granularity.

[0038] To address this, adaptive meshing technology is introduced. Adaptive meshing refers to a technique where the computational mesh automatically adjusts according to changes in the solution and requirements during numerical computation, thereby improving computational efficiency and accuracy. Adaptive meshing technology was proposed for numerical simulations of flow fields containing multidimensional structures. An adaptive mesh consists of a series of nested, overlapping mesh sequences, using a finer mesh in areas requiring detailed capture and a finer mesh in areas where detailed capture is not necessary. However, it suffers from high computational resource consumption and low simulation efficiency.

[0039] In view of this, the technical solution of the embodiments of this application is provided. The embodiments of this application propose an adaptive step-size grid, which can generate a matching step size for different accuracy requirements. For example, the step size is longer in areas with low accuracy requirements and shorter in areas with high accuracy requirements. The solution provided by the embodiments of this application can improve computational efficiency and accuracy while reducing computational resources.

[0040] To facilitate understanding of the solution provided in this application, before introducing the solution provided in the embodiments of this application, the process of conventional earthquake numerical simulation is first introduced, including the following steps:

[0041] (1) Generate regular mesh based on velocity model.

[0042] Velocity information representing earthquake numerical values ​​is input into a velocity model, or a velocity model is loaded, and the input data is stored as an array. The stored array is then discretized into a regular grid using a custom grid spacing. The discretized array can be saved using a single-precision floating-point (float 32) data type.

[0043] (2) Determine the vibration status of each grid point and record the observation information.

[0044] The earthquake model defines the hypocenter and receiver points, and uses numerical earthquake simulation to calculate the vibration at each discrete grid point under actual conditions. Observational information is recorded at the receiver points. The mathematical model for seismic wave propagation satisfies the following formula, which is applicable to the finite difference method:

[0045]

[0046] Where x and z are spatial coordinates, t is time, u(x,z,t) represents the pressure wave field value, v(x,z) represents the longitudinal wave velocity of the medium, and S(t) represents the source function.

[0047] (3) Obtain simulation parameters

[0048] Simulation parameters can be customized, including, for example, time sampling interval Dt; spatial sampling intervals Dx and Dz; spatial order; number of time sampling points SampNum; snapshot output time SnapPos; number of boundary absorption layers PML; and source wavelet frequency F.

[0049] Assuming the earthquake source wavelet is a Ricker wavelet, then the source function satisfies the following formula:

[0050]

[0051] Where t represents time, f represents the center frequency, and r represents the parameter controlling the frequency range.

[0052] (4) Determine the computational stability of earthquake numerical simulation.

[0053] To ensure the proper functioning of earthquake numerical simulations, it is necessary to assess their computational stability. For example, if the following conditions are met, the computational stability of the earthquake numerical simulation can be considered good, and the simulation can proceed:

[0054]

[0055] Where dt represents the time sampling interval, c max dx represents the maximum medium velocity. min This indicates the minimum horizontal sampling interval.

[0056] (5) Perform a Taylor expansion on the mathematical model of seismic wave propagation to solve for the wave field value at each discrete grid point at each time step. The Taylor expansion of the mathematical model of seismic wave propagation yields:

[0057]

[0058] in, This represents the wave field value at point (i, j) in space at time k.

[0059] Adding the two formulas in (5) and omitting higher-order minor quantities, we can obtain the second-order derivative at time k (i.e., the time derivative equation), as follows:

[0060]

[0061] Similarly, the spatial derivative equation can be obtained:

[0062]

[0063] Substituting the obtained time and space derivative equations into the original mathematical model of seismic wave propagation, we can obtain the difference schemes of the acoustic wave equations with second-order time and second-order space accuracy:

[0064]

[0065] Similarly, we can obtain the fourth, eighth, and sixteenth order difference schemes for space, which will not be elaborated here.

[0066] (6) Start the forward numerical modeling of earthquake.

[0067] By using a nested time-space loop, the wavefield value of each discrete grid point at each time step is solved. After the field value is updated, the seismic record and wavefield snapshot are saved and output to complete the seismic numerical simulation.

[0068] The solutions provided in the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0069] The method provided in this application can be applied to electronic devices with computing capabilities. This application does not limit the type of electronic device. For example, the electronic device can be a computer, PC, server, cloud server, cloud computing device, or other terminal device.

[0070] Please see Figure 1 , Figure 1 This is a flowchart illustrating the earthquake numerical simulation method provided in an embodiment of this application. Figure 1 As shown, the process of this method includes the following steps.

[0071] S101. Obtain the initial adaptive mesh.

[0072] An initial adaptive grid can be used for earthquake numerical simulation. In the embodiments of this application, the initial adaptive grid adopts a finite difference grid, which can change the grid point spacing and position according to the actual needs of the model, thereby reducing computational resources and improving simulation efficiency.

[0073] In this embodiment, the finite difference mesh can be either a finite difference orthogonal mesh or a finite difference irregular mesh. In a finite difference orthogonal mesh, the spacing between any two points is the same; while in a finite difference irregular mesh, the spacing between different points can be different. Therefore, the finite difference mesh is more flexible than the finite difference orthogonal mesh because it allows for changes in the spacing and position of mesh points according to the actual needs of the model.

[0074] S102. Determine the grid parameters for at least one region based on the simulation requirements.

[0075] Simulation requirements include simulation accuracy. It is understood that different simulation accuracies require different mesh coarseness. For example, higher simulation accuracy requirements necessitate a finer mesh to obtain more accurate simulation results; lower accuracy requirements allow for a coarser mesh to reduce computational resources. Mesh coarseness can be considered as the granularity of the mesh. In this application's embodiments, three mesh granularities / types are involved: coarse mesh, medium mesh, and fine mesh. It is understood that in a coarse mesh, the spacing / step size between mesh points is larger, and the difference order is lower; in a medium mesh, compared to a coarse mesh, the spacing / step size between mesh points is smaller, and the difference order is higher; in a fine mesh, compared to a medium mesh, the spacing / step size between mesh points is even smaller, and the difference order is higher.

[0076] In this embodiment, the approximate range requiring fine-mesh simulation can be initially selected based on the simulation accuracy. For example, at least one velocity sorting box can be determined based on the velocity model structure, and then at least one region can be determined based on the at least one velocity sorting box. This at least one region can be considered as the region where the network to be optimized is located, where "optimization" refers to changing the network size.

[0077] Velocity sorting boxes include horizontal sorting boxes and matrix sorting boxes. The horizontal length of the horizontal sorting box is the total horizontal distance of the model, and the vertical length can be a custom length. The horizontal and vertical lengths of the rectangular sorting box are not fixed and can be adaptively generated according to the structural shape. The rectangular sorting box is more flexible in use than the horizontal sorting box and can be used for complex structural areas, high-speed / low-speed anomalies, velocity reversals, bulges, and sharp points, and other special structural areas.

[0078] To ensure that no errors occur during the calculation, the horizontal and vertical lengths in the two-dimensional velocity model must satisfy the following formula:

[0079] x≥2*λ max +o max y≥2*λ max +o max

[0080] Where, λ max o is the maximum step size for adaptive sampling. max It is the order of the maximum difference.

[0081] The choice of velocity selection boxes varies depending on the structure of the velocity model. According to sedimentological principles, subsurface geological structures typically exist in a layered structure. In this embodiment, if the target layers of the seismic model structure are concentrated, the velocity selection box is a horizontal selection box; if the target layers of the seismic model structure are dispersed and the layered structure is complex and not obvious, the velocity selection box is a small-area matrix selection box. The structural features within each velocity selection box and whether it falls within the target layer range require auxiliary constraints.

[0082] Understandably, earthquake numerical simulation focuses on exploring the propagation patterns of seismic waves underground, obtaining subsurface geological structures by analyzing the characteristics of various seismic waves. Because of the differences in velocity and density among different subsurface media, wave impedance is formed, causing seismic waves to be reflected when they reach the stratigraphic interfaces. In two-dimensional acoustic media, if only velocity is considered and the influence of density is ignored, stratigraphic layers can be distinguished based on velocity differences. Therefore, the velocity gradient of a region can characterize the complexity of its geological structure. A larger gradient indicates more dramatic tectonic changes, and the required accuracy of the numerical simulation also increases accordingly.

[0083] Therefore, after determining at least one velocity sorting box, the velocity gradient of the region corresponding to that sorting box can be determined. For example, the gradient operator can be used to solve for the gradient at each point. Assume the two-dimensional spatial wavefield gradient operator satisfies the following formula:

[0084]

[0085] Where i and j are the vector components of the model in the x and y directions, and the partial derivatives in the two directions are:

[0086]

[0087] Therefore, the average gradient within a velocity sorting box is:

[0088]

[0089] Where x and y are the horizontal and vertical coordinates of the region.

[0090] In this embodiment of the application, the average gradient within at least one velocity sorting box can be used as the accuracy weight of the corresponding region, and then the grid size and difference order used for the corresponding region can be determined based on the accuracy weight. Figure 2 The diagram illustrates the differences between different grids. Taking the first region as an example, if the first region corresponds to the target layer in the overall seismic model, then the grid corresponding to the first region is a fine grid; if the accuracy weight of the first region is higher than the average gradient of the overall seismic model, then the grid corresponding to the first region is a medium grid; if the accuracy weight of the first region is lower than the average gradient of the overall seismic model, then the grid corresponding to the first region is a coarse grid. Specifically, the coarse grid corresponds to fourth-order finite difference, the medium grid corresponds to eighth-order finite difference, and the fine grid corresponds to sixteenth-order finite difference. This can also be understood as evaluating the grid selection based on the target layer and the accuracy weight obtained from solving the average gradient. For the target layer, a high-order, high-precision grid is used; for other areas, if the accuracy weight of the sorting zone is higher than the average gradient of the overall model, a medium grid is used; if it is lower, a coarse grid is used.

[0091] S103. Change the mesh size of at least one region in the initial adaptive mesh according to the mesh parameters of at least one region to obtain an adaptive step size mesh.

[0092] This application embodiment can determine the mesh parameters (e.g., mesh size / step size) of at least one region according to simulation requirements, and then change the initial adaptive mesh according to the mesh parameters to obtain an adaptive step size mesh. For example, the parameter information of the region corresponding to each velocity sorting box can be determined. For coarse and medium mesh regions, resampling is performed with an adaptive mesh step size, while the fine mesh region remains unchanged. The resampling process for coarse and medium mesh regions involves reducing the number of mesh points within that mesh region. Taking the first region as an example, the number of mesh points inside the first mesh can be reduced according to the type of the first mesh corresponding to the first region to form a second mesh; finite difference calculation is performed on each point in the second mesh to change the difference order of the second mesh, where the difference order of the second mesh is less than that of the first mesh.

[0093] For easier understanding, please refer to Figure 3 This shows the initial adaptive mesh and the adaptive step-size mesh. From Figure 3 It can be seen that changing from the initial adaptive mesh to the adaptive step size mesh can reduce memory resources by nearly 9 times in the coarse mesh region and by nearly 4 times in the medium mesh region, and the calculation speed can also be significantly improved.

[0094] S104. Perform earthquake numerical simulation based on the adaptive step size grid to obtain earthquake numerical simulation records.

[0095] After obtaining the adaptive step-size grid, seismic numerical simulations can be performed based on the adaptive step-size grid. The process of simulating seismic numerical data based on the adaptive step-size grid is similar to that based on the initial adaptive grid, and will not be elaborated upon here.

[0096] Understandably, since the number of grid cells changes compared to the initial adaptive grid, this leads to inconsistent amplitudes in different regions. Therefore, amplitude compensation is needed to ensure the accuracy of the simulation results. In this embodiment, the ratio of amplitude before and after compensation is inversely proportional to the ratio before and after the change in the number of grid cells. Assuming the ratio of amplitude before and after compensation is C, then:

[0097]

[0098] Among them, A ini As the initial amplitude, A con N represents the changed amplitude. ini N is the initial number of grid cells. con This represents the changed grid number. For coarse grids, the grid number changes to one-ninth of its previous value; therefore, For the medium grid, the number of grid cells changes to one-quarter of what it used to be. For finer meshes, since the number of meshes remains unchanged, Amplitude compensation is performed on each sorting zone before outputting the results to ensure the accuracy of the results.

[0099] The scheme provided in the above-described embodiments of this application uses gradient operators to guide the generation of adaptive step-size grids. For different geological structures and their required accuracy, it suggests matching step sizes and finite order. The grid density can be adjusted according to changes in the physical field, reducing computational resources and improving simulation efficiency. Furthermore, matching the grid density / step size with the simulation accuracy allows for more precise tracking and simulation of the source wavefield dynamics, improving the accuracy of numerical simulations.

[0100] The method provided in this application can also be applied to finite difference numerical simulation, and is not limited to geological structures and seismic environments, thus having a wider range of applications.

[0101] Taking salt dome geology as an example, the salt dome velocity model, such as Figure 5 As shown, the basic parameters of the model are as follows: dimensions (number of sample points): Nx = 762, Nz = 610; 100 layers of PML absorbing boundary; numerical simulation parameters: time sampling interval is 0.5ms; spatial sampling interval dx = dz = 5m; recording time is 2.5s; snapshot time is 1s; the source uses a 30Hz Ricker wavelet; the source coordinates are (Nx / 2, 0); the receiver is located at Nz = 0, the channel spacing is 5m, and the receiver is fully arranged; the spatial-temporal second-order difference is used.

[0102] Seismic numerical simulations of salt dome geology were performed using both traditional grids and adaptive step-size grids provided in the embodiments of this application. The results are as follows: Figure 6 As shown. Among them, Figure 6 The images, from left to right, show 2.5s seismic records and 1s wavefield snapshots obtained using conventional grids and the methods provided in the embodiments of this application, as well as the difference between the two. Figure 6 As can be seen, the maximum relative error of the seismic record before and after optimization is only 0.17% compared with the traditional grid provided in this application embodiment. It can be seen that the method provided in this application embodiment ensures the accuracy of the simulation calculation results.

[0103] Furthermore, the method provided in this application embodiment can reduce computing resources and improve computing efficiency compared to traditional methods. For example, please refer to... Figure 7 The diagram illustrates the memory and computation time required for earthquake numerical simulation using conventional methods and the methods provided in the embodiments of this application. Figure 7 It can be seen that the method provided in this application embodiment is about 2.5 times faster than the traditional method; and the method provided in this application embodiment requires 30% less memory than the traditional method.

[0104] Based on the same inventive concept, embodiments of this application provide an electronic device. Please refer to... Figure 8 This application provides a schematic block diagram of an electronic device 800. The electronic device 800 can be a computer, a terminal device such as a computer, or a chip (system) within a terminal device. In this application embodiment, the chip system can be composed of chips or may include chips and other discrete components. Specific functions can be found in the descriptions of the above method embodiments.

[0105] The electronic device 800 includes one or more processors 801 for implementing or supporting the electronic device 800 in implementing the functions of the electronic device in the methods provided in the embodiments of this application. For details, please refer to the detailed description in the method examples, which will not be repeated here. The processor 801 can also be called a processing unit or processing module, and can implement certain control functions. The processor 801 can be a general-purpose processor or a dedicated processor, etc. For example, it includes: a baseband processor, a central processing unit, an application processor, a modem processor, a graphics processor, an image signal processor, a digital signal processor, a video codec processor, a controller, a memory, and / or a neural network processor, etc. The baseband processor can be used to process communication protocols and communication data. The central processing unit can be used to control the electronic device 800 (e.g., a terminal device), execute software programs, and / or process data. Different processors can be independent devices or integrated into one or more processors, for example, integrated on one or more application-specific integrated circuits.

[0106] In one design, processor 801 may include program 803 (sometimes also referred to as code or instructions), which can be executed on processor 801 to cause communication device 800 to perform the methods described in the following embodiments. In yet another possible design, communication device 800 includes circuitry (…). Figure 8 (Not shown), the circuit is used to implement the electronic device functions in the above embodiments.

[0107] In one design, the communication device 800 may include one or more memories 802 storing a program 804 (sometimes referred to as code or instructions), which can be run on the processor 801 to cause the electronic device 800 to perform the methods described in the above method embodiments.

[0108] In one design, the processor 801 and / or memory 802 may include an artificial intelligence (AI) module 807 and an AI module 808, which are used to implement AI-related functions. The AI ​​module may be implemented through software, hardware, or a combination of both. For example, the AI ​​module may include a RAN intelligent controller (RIC) module. For example, the AI ​​module may be a near real-time RIC or a non-real-time RIC.

[0109] In one possible design, the processor 801 and / or memory 802 may also store data. The processor and memory may be configured separately or integrated together.

[0110] The electronic devices in the above embodiments can be terminal devices, circuits, chips applied in electronic devices, or other combined devices or components having the above-mentioned electronic devices. When the communication device is an electronic device, the processing module can be a processor, such as a CPU. When the communication device is a component having the functions of the above-mentioned electronic devices, the processing module can be a processor. When the communication device is a chip system, the electronic device can be an FPGA, a dedicated ASIC, a system-on-chip (SoC), a CPU, a network processor (NP), a DSP, a microcontroller unit (MCU), a programmable logic device (PLD), or other integrated chips. The processing module can be the processor of the chip system. The communication interface can be the input / output interface or interface circuit of the chip system. For example, the interface circuit can be a code / data read / write interface circuit. The interface circuit can be used to receive code instructions (the code instructions are stored in memory and can be read directly from memory or through other devices) and transmit them to the processor; the processor can be used to run the code instructions to execute the methods in the above-mentioned method embodiments. For another example, the interface circuit can also be a signal transmission interface circuit between the processor and other devices.

[0111] This application also provides a computer-readable storage medium, including instructions that, when executed on a computer, cause the computer to perform... Figure 1 Methods executed by electronic devices.

[0112] This application also provides a computer program product, including instructions that, when run on a computer, cause the computer to perform... Figure 1Methods executed by electronic devices.

[0113] This application provides a chip system including a processor and potentially a memory, for implementing the functions of the electronic device described in the aforementioned method. The chip system may be composed of chips or may include chips and other discrete devices.

[0114] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0115] In the embodiments of the present invention, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0116] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple grid units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0117] Furthermore, the functional units in the embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0118] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or grid device, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes: Universal Serial Bus flash disk (USB), portable hard disk, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk, and other media capable of storing program code. Obviously, those skilled in the art can make various modifications and variations to this application without departing from the scope of this application. Therefore, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application also intends to include these modifications and variations.

Claims

1. A method for numerical simulation of earthquakes, characterized in that, include: Obtain an initial adaptive mesh, wherein the initial adaptive mesh is a finite difference mesh; Based on the simulation requirements, at least one region is determined to have corresponding grid parameters. The at least one region matches the simulation requirements, which include simulation accuracy, and the grid parameters include grid step size. By changing the mesh size of at least one region in the initial adaptive mesh according to the mesh parameters of at least one region, an adaptive step mesh is obtained; The earthquake numerical simulation is performed based on the adaptive step-size grid to obtain the earthquake numerical simulation record.

2. The method as described in claim 1, characterized in that, Determine the corresponding mesh parameters for at least one region based on simulation requirements, including: Determine at least one velocity sorting box based on the velocity model structure; Based on the at least one velocity sorting box, a velocity gradient of at least one region is determined, and the at least one region is associated with the at least one velocity sorting box; The mesh parameters corresponding to at least one region are determined based on the velocity gradient of the at least one region.

3. The method as described in claim 2, characterized in that, If the target layers of the seismic model structure are concentrated, the velocity sorting box is a horizontal sorting box; if the target layers of the seismic model structure are dispersed, the velocity sorting box is a matrix sorting box.

4. The method as described in claim 2, characterized in that, The velocity gradient corresponding to one of the at least one regions is the average gradient of at least one velocity sorting box corresponding to that region.

5. The method according to any one of claims 1-4, characterized in that, Determining the mesh parameters of at least one region based on the velocity gradient of the at least one region includes: For a first region within the at least one region, the size and difference order of the grid corresponding to the first region are determined based on the precision weight of the first region, wherein the grid parameters include the size and difference order.

6. The method as described in claim 5, characterized in that, If the first region corresponds to the target layer in the overall earthquake model, then the grid corresponding to the first region is a fine grid; If the accuracy weight of the first region is higher than the average gradient of the overall earthquake model, then the grid corresponding to the first region is a medium grid. If the accuracy weight of the first region is lower than the average gradient of the overall earthquake model, then the grid corresponding to the first region is a coarse grid. The coarse mesh corresponds to the fourth-order difference, the medium mesh corresponds to the eighth-order difference, and the fine mesh corresponds to the sixteenth-order difference.

7. The method as described in claim 6, characterized in that, Changing the mesh size of at least one region in the initial adaptive mesh according to the mesh parameters of at least one region includes: For a first region within the at least one region, the number of grid points within the first grid is reduced according to the type of the first grid corresponding to the first region to form a second grid; wherein, the grid type includes coarse grid, medium grid, and fine grid; Finite difference calculations are performed on each point within the second grid to change the difference order of the second grid, wherein the difference order corresponding to the second grid is less than the difference order corresponding to the first grid.

8. The method as described in claim 1, characterized in that, Seismic numerical simulations were performed using an adaptive step-size grid to obtain a seismic numerical simulation record, including: The earthquake numerical simulation was performed using an adaptive step-size grid to obtain the first calculation results; Amplitude compensation is applied to the first calculation result to obtain an earthquake numerical simulation record, wherein the ratio of the amplitude before and after compensation is inversely proportional to the ratio before and after the change in the number of network points.

9. A communication device, characterized in that, The communication device includes a processor and a memory, the memory being used to store a computer program, and the processor being used to execute the computer program stored in the memory, causing the communication device to perform the method as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store a computer program that, when run on a computer, causes the computer to perform the method as described in any one of claims 1 to 8.

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