Marine seismic signal modeling method, equipment, medium and product

Abstract

The invention discloses a marine seismic signal modeling method and device, a medium and a product, and relates to the field of marine seismic signal simulation, and the method comprises the steps: determining a staggered grid finite difference operator according to a signal sampling and recovery theorem, and constructing a numerical error relational expression of the staggered grid finite difference operator; constructing a target function according to the numerical error relational expression; according to the objective function, a swarm intelligence algorithm combining a dung beetle optimization algorithm and a simulated annealing algorithm is adopted to determine an optimized staggered mesh finite difference operator; performing differential discretization on the elastic wave equation by using an optimized staggered grid finite difference operator, setting a convolution complete matching layer as an artificial boundary condition, and constructing a long-time modeling scheme of the marine seismic signal; determining an ocean medium model and boundary conditions of the modeling area, and performing long-time numerical simulation by using an ocean seismic signal long-time modeling scheme; according to the invention, high-efficiency and accurate long-time simulation of marine seismic signals can be realized, so that a foundation is laid for marine environment detection and monitoring.

Description

Technical Field

[0001] The present application relates to the field of marine seismic signal simulation, and in particular to a marine seismic signal modeling method, equipment, medium and product. Background Art

[0002] The ocean is vast and rich in resources. Fully understanding, utilizing and protecting marine resources is of great significance to the development of the national economy and national security. Among them, the marine environment is complex. Seismic waves caused by various natural activities (waves, seabed microseisms, etc.), biological activities (fish schools, etc.), and human activities (ships, etc.) are transmitted through the coupling effect with seawater, seabed sediments, and seabed bedrock. They contain very rich marine environment and sound source information. If they can be effectively utilized, they will be of great significance to promoting marine exploration and monitoring.

[0003] The existing method for earthquake detection is to extract effective information from marine seismic signals accompanied by complex environmental noise; among them, numerical simulation technology is the basic stage, and the use of numerical simulation methods to model marine seismic signals stimulated by various sound sources in the ocean (including ships, fish schools, seabed strata activities, etc.) helps to understand the characteristics of various types of seismic signals in the ocean, thereby realizing the processing and analysis of marine seismic signals.

[0004] Among them, the key to simulating marine seismic signals using wave equations mainly includes the following aspects: first, source expression. The vibration signals stimulated by different types of natural and human activities have different time-frequency domain characteristics, which require targeted research; second, boundary conditions for marine media conditions, including the treatment of fluid-solid interfaces and the treatment of undulating seabed topography; third, the use of appropriate numerical simulation methods, such as the finite element method, pseudo-spectral method, finite difference method, etc.; in comparison, the finite difference method has more advantages in balancing computational efficiency and simulation accuracy.

[0005] Therefore, in view of the vastness of the ocean, it is necessary to simulate marine seismic signals with long propagation distances. At the same time, in order to conduct subsequent detection and monitoring of the marine environment, it is also necessary to simulate the seismic waves excited by various marine activities for a long time. However, the current numerical simulation of seismic waves using the finite difference method is mostly short-term, and long-term simulation is prone to instability problems, which in turn leads to serious numerical errors and affects the subsequent analysis of marine seismic signals. For this reason, it is urgent to provide a long-term numerical simulation method for the marine environment to achieve efficient and accurate long-term simulation of marine seismic signals, which will provide a basis for marine environment detection and monitoring. Summary of the invention

[0006] The purpose of this application is to provide a method, device, medium and product for modeling marine seismic signals, which can realize efficient and accurate long-term simulation of marine seismic signals, thereby laying a foundation for marine environment detection and monitoring.

[0007] To achieve the above objectives, this application provides the following solutions:

[0008] In a first aspect, the present application provides a method for modeling marine seismic signals, the method comprising:

[0009] According to the signal sampling and recovery theorem, the staggered grid finite difference operator of the spatial first-order derivative is determined, and the numerical error relation of the staggered grid finite difference operator is constructed;

[0010] According to the numerical error relation of the staggered grid finite difference operator, an objective function including the staggered grid finite difference operator is constructed;

[0011] According to the objective function, a swarm intelligence algorithm combining the dung beetle optimization algorithm and the simulated annealing algorithm is used to determine the optimal staggered grid finite difference operator;

[0012] The elastic wave equation is differentially discretized using an optimized staggered grid finite difference operator, and a convolutional perfectly matched layer is set as an artificial boundary condition to construct a long-term modeling scheme for marine seismic signals.

[0013] The marine medium model and boundary conditions of the modeling area are determined respectively, and a long-term numerical simulation of the marine environment of the modeling area is performed using a long-term modeling scheme of marine seismic signals; the boundary conditions include: fluid-solid coupling boundary conditions.

[0014] Optionally, the numerical error relation E of the staggered grid finite difference operator is:

[0015]

[0016] Where Δx is the spatial sampling interval, c m is the staggered grid finite difference operator for the first-order spatial derivative, k x is the wave number, M is half the length of the staggered grid finite difference operator, and m is the spatial sampling position.

[0017] Optionally, constructing an objective function including a staggered grid finite difference operator according to a numerical error relation of the staggered grid finite difference operator specifically includes:

[0018] Using the formula Determine the objective function;

[0019] Among them, max takes the maximum value and ε is the set maximum error.

[0020] Optionally, the method of determining the optimized staggered grid finite difference operator based on the objective function by using a swarm intelligence algorithm combining a dung beetle optimization algorithm and a simulated annealing algorithm specifically includes:

[0021] Initialize the parameters of the dung beetle optimization algorithm; the parameters of the dung beetle optimization algorithm include: population size, maximum number of iterations and solution space range;

[0022] Randomly initialize the positions of dung beetle populations;

[0023] Determine the fitness value of each dung beetle in the dung beetle population according to the objective function;

[0024] Determine whether the optimal fitness value in the population meets the set conditions or whether the number of iterations reaches the maximum number of iterations; if the optimal fitness value meets the set conditions or the number of iterations reaches the maximum number of iterations, output the current position and use the current position as the optimal position of the dung beetle population; if the optimal fitness value does not meet the set conditions and the number of iterations does not reach the maximum number of iterations, update the position of the dung beetle population according to the four strategies of the dung beetle optimization algorithm; the four strategies include: rolling ball strategy, breeding strategy, foraging strategy and stealing strategy;

[0025] A simulated annealing algorithm is used to generate a neighborhood solution for each dung beetle in the dung beetle population;

[0026] The position of the dung beetle population is updated using the Metropolis acceptance criterion of the simulated annealing algorithm, and the step of determining the fitness value of each dung beetle in the dung beetle population according to the objective function is returned until the optimal position of the dung beetle population is determined;

[0027] The optimized staggered grid finite difference operator is determined based on the optimal position of the dung beetle population.

[0028] Optionally, both the breeding strategy and the foraging strategy introduce edge selection to dynamically adjust the breeding area and foraging area of ​​dung beetles.

[0029] Optionally, the ocean medium model includes: a longitudinal wave velocity model, a transverse wave velocity model and a density model.

[0030] In a second aspect, the present application provides a marine seismic signal modeling device, the marine seismic signal modeling device comprising:

[0031] A numerical error relation building module is used to determine the staggered grid finite difference operator of the spatial first-order derivative according to the signal sampling and recovery theorem, and to build the numerical error relation of the staggered grid finite difference operator;

[0032] An objective function construction module is used to construct an objective function including a staggered grid finite difference operator according to a numerical error relation of the staggered grid finite difference operator;

[0033] An optimized staggered grid finite difference operator determination module is used to determine the optimized staggered grid finite difference operator according to the objective function by using a swarm intelligence algorithm combining a dung beetle optimization algorithm and a simulated annealing algorithm;

[0034] The module for constructing a long-term modeling scheme for marine seismic signals is used to differentially discretize the elastic wave equation using an optimized staggered grid finite difference operator and set a convolutional fully matched layer as an artificial boundary condition to construct a long-term modeling scheme for marine seismic signals.

[0035] The long-term numerical simulation module is used to determine the ocean medium model and boundary conditions of the modeling area respectively, and use the long-term modeling scheme of marine seismic signals to perform long-term numerical simulation of the ocean environment in the modeling area; the boundary conditions include: fluid-solid coupling boundary conditions.

[0036] In a third aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the marine seismic signal modeling method.

[0037] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which implements the marine seismic signal modeling method when executed by a processor.

[0038] In a fifth aspect, the present application provides a computer program product, including a computer program, which implements the marine seismic signal modeling method when executed by a processor.

[0039] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0040] The present application provides a method, device, medium and product for modeling marine seismic signals. By constructing an objective function including a staggered grid finite difference operator according to the numerical error relation of the staggered grid finite difference operator, the numerical error caused by the differential approximation is reduced as much as possible, the error accumulation during long-term simulation is reduced, the accuracy of the simulation is improved, and the stability of the simulation is ensured. A swarm intelligence algorithm (Hybrid DBO-SA) combining a dung beetle optimization algorithm (DBO) and a simulated annealing algorithm (SA) is used to obtain an optimized staggered grid finite difference operator suitable for long-term numerical simulation. The present application numerically simulates seismic signals excited by pulse-like sources in a marine environment, and efficiently and accurately realizes modeling of marine seismic signals, laying a foundation for the subsequent analysis of the time-frequency domain characteristics of seismic signals excited by various types of natural and human activities in the ocean, and also provides a strong foundation for marine environment perception and monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0042] Figure 1 This is an application environment diagram of a marine seismic signal modeling method in one embodiment of the present application;

[0043] Figure 2 A schematic diagram of a flow chart of a method for modeling marine seismic signals provided in one embodiment of the present application;

[0044] Figure 3 This is a flow chart of the swarm intelligence algorithm that combines the dung beetle optimization algorithm and the simulated annealing algorithm;

[0045] Figure 4 Schematic diagram of numerical dispersion curves for optimizing staggered grid finite difference operators using a swarm intelligence algorithm combining the dung beetle optimization algorithm and the simulated annealing algorithm;

[0046] Figure 5 This is a schematic diagram of the ocean medium model;

[0047] Figure 6 Schematic diagram of modeling results of marine seismic signals (x component);

[0048] Figure 7 Schematic diagram of the modeling results of marine seismic signals (z component). DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0050] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0051] The marine seismic signal modeling method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, or it can be integrated on the server 104, or it can be placed on the cloud or other servers. The terminal 102 can send the ocean medium model, source wavelet type and main frequency, boundary conditions, etc. of the area to be modeled to the server 104. After the server 104 receives the ocean medium model, source wavelet type and main frequency, boundary conditions, etc. of the area to be modeled, for the area to be modeled, the server 104 determines the staggered grid finite difference operator of the first-order derivative in space according to the signal sampling and recovery theorem, and constructs the numerical error relationship of the staggered grid finite difference operator; according to the numerical error relationship of the staggered grid finite difference operator, construct an objective function containing the staggered grid finite difference operator. ; According to the objective function, a swarm intelligence algorithm combining a dung beetle optimization algorithm and a simulated annealing algorithm is used to determine an optimized staggered grid finite difference operator; the elastic wave equation is differentially discretized using the optimized staggered grid finite difference operator, and a convolutional fully matched layer is set as an artificial boundary condition, thereby constructing a long-term modeling scheme for marine seismic signals; the boundary conditions of the modeling area are determined, and the long-term modeling scheme for marine seismic signals is used to perform a long-term numerical simulation of the marine environment in the modeling area; the server 104 can feed back the obtained long-term numerical simulation signal to the terminal 102. In addition, in some embodiments, the marine seismic signal modeling method can also be implemented by the server 104 or the terminal 102 alone, such as the terminal 102 can directly process the area to be modeled, or the server 104 can obtain the marine medium model of the area to be modeled from the data storage system, and perform long-term modeling of marine seismic signals for the area to be modeled.

[0052] In an exemplary embodiment, Figure 2As shown, a method for modeling marine seismic signals is provided. The method is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used as an example to illustrate, including the following S201 to S206. Among them:

[0053] S201, determining a staggered grid finite difference operator of a spatial first-order derivative according to a signal sampling and recovery theorem, and constructing a numerical error relation of the staggered grid finite difference operator;

[0054] S201 specifically includes:

[0055] According to the sampling and recovery theorem, the sinc function is used to interpolate the uniformly sampled discrete sequence and restore it to the original continuous signal:

[0056]

[0057] Where f(x) represents a continuous function, f m is a discrete sequence obtained by uniformly sampling a continuous function, f m =f(mΔx), Δx is the spatial sampling interval.

[0058] Use the function value at the adjacent sampling point to calculate the spatial derivative value at the spatial 1 / 2 sampling point, and take the derivative of the left and right sides of the above formula respectively. Substituting in and truncating to 2M points, we can obtain the staggered grid finite difference operator for the spatial first-order derivative:

[0059]

[0060] in, c m represents the staggered grid finite difference operator for the first-order spatial derivatives, w(m) is the cutoff function.

[0061] From formula (2), it can be seen that using the staggered grid finite difference operator instead of the differential operator will lead to numerical errors, and long-term numerical simulation will cause error accumulation, leading to instability. Therefore, in order to minimize the numerical error and improve the stability of long-term simulation, the left and right ends of formula (2) are Fourier transformed to optimize the staggered grid finite difference operator, and the following staggered grid finite difference operator error relation E (dispersion relation) is obtained:

[0062]

[0063] Where Δx is the spatial sampling interval, c m is the staggered grid finite difference operator for the first-order spatial derivative, k xis the wave number, M is half the length of the staggered grid finite difference operator, and m is the spatial sampling position.

[0064] S202, constructing an objective function including a staggered grid finite difference operator according to a numerical error relation of the staggered grid finite difference operator;

[0065] In a specific embodiment, the objective function takes the following infinite norm form:

[0066]

[0067] Among them, max takes the maximum value and ε is the set maximum error.

[0068] In addition, the objective function can also be constructed using the 1-norm, 2-norm, etc.

[0069] S203, according to the objective function, using a swarm intelligence algorithm combining a dung beetle optimization algorithm and a simulated annealing algorithm to determine an optimized staggered grid finite difference operator;

[0070] DBO simulates the behaviors of dung beetles such as rolling balls, dancing, foraging, reproduction, and stealing. The target update strategy constructed based on this has a more powerful exploration and optimization capability, and has shown fast and efficient convergence performance in various optimization problems. The DBO algorithm has poor ability to avoid local optimality, especially for high-dimensional, multi-modal, and ill-conditioned problems. Therefore, in order to meet the needs of staggered grid finite difference operator optimization, combined with the random elements and acceptable probability mechanism of the SA algorithm and the fast optimization convergence performance of the DBO algorithm, a swarm intelligence algorithm based on the DBO algorithm and the SA algorithm (Hybrid DBO-SA) was constructed. Figure 3 As shown, the specific implementation is as follows:

[0071] S1, initializing the parameters of the dung beetle optimization algorithm; the parameters of the dung beetle optimization algorithm include: population size, maximum number of iterations and solution space range;

[0072] S2, randomly initialize the position of the dung beetle population (target solution);

[0073] S3, determining the fitness value of each dung beetle in the dung beetle population according to the objective function;

[0074] S4, judging whether the optimal fitness value in the population meets the set conditions or judging whether the number of iterations reaches the maximum number of iterations; if the optimal fitness value meets the set conditions or the number of iterations reaches the maximum number of iterations, outputting the current position and taking the current position as the optimal position of the dung beetle population; if the optimal fitness value does not meet the set conditions and the number of iterations does not reach the maximum number of iterations, updating the position of the dung beetle population according to the four strategies of the dung beetle optimization algorithm; the four strategies include: rolling ball strategy, breeding strategy, foraging strategy and stealing strategy;

[0075] The rolling ball strategy modeling is expressed as follows, when σ<0.9, it is barrier-free mode, and when σ≥0.9, it is barrier mode:

[0076]

[0077] in, represents the position of the i-th dung beetle at the t-th iteration (target solution). g represents the natural coefficient that determines whether the dung beetle changes direction. A value of 1 indicates no change in direction, and a value of -1 indicates a change in direction. r1∈(0,0.2) is a constant, r2∈(0,1) is a deviation coefficient, and X w Indicates the worst position of the dung beetle. In the obstacle mode, the dung beetle avoids obstacles by dancing, γ = tanθ, θ∈(0,π).

[0078] The edge selection method is introduced into the breeding strategy to dynamically adjust the breeding area of ​​dung beetles:

[0079]

[0080] Among them, Lb p , Ub p are the upper and lower limits of the breeding strategy position update, X p is the optimal position of the current iteration. is the parameter of the edge selection strategy, T is the maximum number of iterations. Lb and Ub are the upper and lower limits of the target solution space respectively. a1, a2∈(0,1) are two independent random coefficients.

[0081] The foraging strategy also introduces edge selection to dynamically adjust the foraging area:

[0082]

[0083] Among them, Lb b ,Ub b are the upper and lower limits of the foraging strategy position update, X b is the current optimal position of the dung beetle population. b1,b2∈(0,1) are two independent random coefficients.

[0084] The stealing strategy is:

[0085]

[0086] Among them, c is a constant and h is a random quantity that satisfies the normal distribution.

[0087] S5, using a simulated annealing algorithm to generate a neighborhood solution for each dung beetle in the dung beetle population;

[0088] S6, using the Metropolis acceptance criterion of the simulated annealing algorithm to update the position of the dung beetle population, and return to S3 until the optimal position of the dung beetle population is determined; the specific update strategy is:

[0089]

[0090] Among them, fit represents the fitness value corresponding to the current position of the dung beetle, fit new represents the fitness value corresponding to its neighborhood position. T0 is the current temperature. When the neighborhood solution is better, that is, fit new <fit, use the neighboring position instead of the current position; and when the neighboring solution is worse than the current solution, that is, fit new ≥fit, then calculate the probability P and determine whether P is greater than a random number between 0 and 1. If so, update the current position, otherwise do not update.

[0091] like Figure 4 As shown in the figure, the dashed lines are the numerical dispersion curves of the 8th, 12th, 16th and 20th order optimized staggered grid finite difference operators, and the solid lines are the numerical dispersion curves of the conventional staggered grid finite difference operators. Figure 2 Part (b) is the absolute error curve after magnification 1000 times. By comparison, it is found that the optimized staggered grid finite difference coefficients have a higher spectrum coverage while ensuring an accuracy error of 1‰, which can effectively control the error accumulation during numerical simulation and avoid the instability problem of long-term numerical simulation.

[0092] S7, determining the optimized staggered grid finite difference operator according to the optimal position of the dung beetle population.

[0093] S204, using an optimized staggered grid finite difference operator to differentially discretize the elastic wave equation, and setting a convolutional perfectly matched layer (CPML) as an artificial boundary condition, thereby constructing a long-term modeling scheme for marine seismic signals; setting a convolutional perfectly matched layer can avoid the influence of the artificial truncation boundary caused by the limited calculation area on the simulation results, and absorb the wave field energy propagating to the artificial boundary;

[0094] The first-order velocity stress form of the elastic wave equation is expressed as follows:

[0095]

[0096] Among them, vx , v z represents the particle vibration velocity, τ xx , τ zz , τ xz is the stress component. λ, μ are Lame constants, ρ is density. x, z represent spatial position, and t represents time.

[0097] The discretized equation is expressed as follows:

[0098]

[0099] Among them, Δx and Δz represent the spatial sampling interval, Δt represents the time step, i and j represent the spatial grid points, and n represents the discrete time point.

[0100] by As an example, the implementation of CPML boundary conditions can be expressed as follows:

[0101]

[0102] in, is the memory variable, κ x Generally, it can be set to 1, x From 0 to α max =πf0 is a linear function, where f0 represents the main frequency of the source wavelet. is the decay function, δ x It represents the thickness of CPML layer, R is the theoretical reflection coefficient, and N is the exponential factor, which is generally taken as 2.

[0103] S205, respectively determining the marine medium model and boundary conditions of the modeling area, and using the long-term modeling scheme of marine seismic signals to perform long-term numerical simulation of the marine environment in the modeling area; the boundary conditions include: fluid-solid coupling boundary conditions. The marine medium model includes: a longitudinal wave velocity model, a shear wave velocity model and a density model.

[0104] S205 specifically includes:

[0105] Establish an ocean medium model based on the actual conditions of the modeling area;

[0106] Set appropriate boundary conditions according to the actual terrain conditions of the region;

[0107] According to actual modeling requirements, determine the sub-wave type and main frequency for specific seismic sources such as seabed stratum activities and ship activities;

[0108] The long-term modeling scheme of marine seismic signals is used to conduct long-term numerical simulation of the marine environment in the modeling area.

[0109] In an exemplary embodiment, the following is set Figure 5 The ocean medium model shown has a model size of 12km*0.5km, and the grid spacing in the lateral and depth directions is 1m. According to the condition of continuous positive stress at the fluid-solid boundary, the medium model parameters are corrected. A simulated source is set at a position of 1km in the lateral direction and 50m in the water depth. The source function uses a Ricker wavelet with a main frequency of 25Hz. The time step is set to 0.1ms. Receivers are set at intervals of 500m along the seabed interface to receive seabed seismic signals.

[0110] The results of marine seismic signal modeling are as follows Figure 6 , Figure 7 As shown, they are x component and z component respectively. It can be seen that the seabed seismic signals are well recorded at each receiving point on the seabed, and the modeled signals converge stably in the long-term simulation. It can be seen that the long-term modeling scheme of marine seismic signals constructed in this application can realize efficient and accurate long-term numerical simulation of the marine environment.

[0111] Based on the same inventive concept, the embodiment of the present application also provides a marine seismic signal modeling device for implementing the marine seismic signal modeling method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more marine seismic signal modeling device embodiments provided below can refer to the limitations of the marine seismic signal modeling method above, and will not be repeated here.

[0112] In an exemplary embodiment, a marine seismic signal modeling device is provided, comprising:

[0113] A numerical error relation building module is used to determine the staggered grid finite difference operator of the spatial first-order derivative according to the signal sampling and recovery theorem, and to build the numerical error relation of the staggered grid finite difference operator;

[0114] An objective function construction module is used to construct an objective function including a staggered grid finite difference operator according to a numerical error relation of the staggered grid finite difference operator;

[0115] An optimized staggered grid finite difference operator determination module is used to determine the optimized staggered grid finite difference operator according to the objective function by using a swarm intelligence algorithm combining a dung beetle optimization algorithm and a simulated annealing algorithm;

[0116] The module for constructing a long-term modeling scheme for marine seismic signals is used to differentially discretize the elastic wave equation using an optimized staggered grid finite difference operator and set a convolutional fully matched layer as an artificial boundary condition to construct a long-term modeling scheme for marine seismic signals.

[0117] The long-term numerical simulation module is used to determine the ocean medium model and boundary conditions of the modeling area respectively, and use the long-term modeling scheme of marine seismic signals to perform long-term numerical simulation of the ocean environment in the modeling area; the boundary conditions include: fluid-solid coupling boundary conditions.

[0118] In an exemplary embodiment, a computer device is provided, which may be a server or a terminal. The computer device includes a processor, a memory, an input / output interface (I / O for short) and a communication interface. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface is connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a method for modeling marine seismic signals is implemented.

[0119] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0120] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0121] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0122] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0123] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0124] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0125] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for modeling marine seismic signals, characterized in that: The marine seismic signal modeling method comprises: According to the signal sampling and recovery theorem, the staggered grid finite difference operator of the spatial first-order derivative is determined, and the numerical error relation of the staggered grid finite difference operator is constructed; According to the numerical error relation of the staggered grid finite difference operator, an objective function including the staggered grid finite difference operator is constructed; According to the objective function, a swarm intelligence algorithm combining the dung beetle optimization algorithm and the simulated annealing algorithm is used to determine the optimal staggered grid finite difference operator; The elastic wave equation is differentially discretized using an optimized staggered grid finite difference operator, and a convolutional perfectly matched layer is set as an artificial boundary condition to construct a long-term modeling scheme for marine seismic signals. The marine medium model and boundary conditions of the modeling area are determined respectively, and a long-term numerical simulation of the marine environment of the modeling area is performed using a long-term modeling scheme of marine seismic signals; the boundary conditions include: fluid-solid coupling boundary conditions.

2. The marine seismic signal modeling method according to claim 1, characterized in that: The numerical error relation E of the staggered grid finite difference operator is: Where Δx is the spatial sampling interval, c m is the staggered grid finite difference operator for the first-order spatial derivative, k x is the wave number, M is half the length of the staggered grid finite difference operator, and m is the spatial sampling position.

3. The marine seismic signal modeling method according to claim 2, characterized in that: The objective function including the staggered grid finite difference operator is constructed according to the numerical error relation of the staggered grid finite difference operator, specifically including: Using the formula Determine the objective function; Among them, max takes the maximum value and ε is the set maximum error.

4. The marine seismic signal modeling method according to claim 1, characterized in that: The method of optimizing the staggered grid finite difference operator by using a swarm intelligence algorithm combining a dung beetle optimization algorithm and a simulated annealing algorithm according to the objective function specifically includes: Initialize the parameters of the dung beetle optimization algorithm; the parameters of the dung beetle optimization algorithm include: population size, maximum number of iterations and solution space range; Randomly initialize the positions of dung beetle populations; Determine the fitness value of each dung beetle in the dung beetle population according to the objective function; Determine whether the optimal fitness value in the population meets the set conditions or whether the number of iterations reaches the maximum number of iterations; if the optimal fitness value meets the set conditions or the number of iterations reaches the maximum number of iterations, output the current position and use the current position as the optimal position of the dung beetle population; if the optimal fitness value does not meet the set conditions and the number of iterations does not reach the maximum number of iterations, update the position of the dung beetle population according to the four strategies of the dung beetle optimization algorithm; the four strategies include: rolling ball strategy, breeding strategy, foraging strategy and stealing strategy; A simulated annealing algorithm is used to generate a neighborhood solution for each dung beetle in the dung beetle population; The position of the dung beetle population is updated using the Metropolis acceptance criterion of the simulated annealing algorithm, and the step of determining the fitness value of each dung beetle in the dung beetle population according to the objective function is returned until the optimal position of the dung beetle population is determined; The optimized staggered grid finite difference operator is determined based on the optimal position of the dung beetle population.

5. The marine seismic signal modeling method according to claim 4, characterized in that: Both the breeding strategy and foraging strategy introduce edge selection to dynamically adjust the breeding area and foraging area of ​​dung beetles.

6. The marine seismic signal modeling method according to claim 1, characterized in that: The ocean medium model includes: a longitudinal wave velocity model, a transverse wave velocity model and a density model.

7. A marine seismic signal modeling device, characterized in that: The marine seismic signal modeling device comprises: A numerical error relation building module is used to determine the staggered grid finite difference operator of the spatial first-order derivative according to the signal sampling and recovery theorem, and to build the numerical error relation of the staggered grid finite difference operator; An objective function construction module is used to construct an objective function including a staggered grid finite difference operator according to a numerical error relation of the staggered grid finite difference operator; An optimized staggered grid finite difference operator determination module is used to determine the optimized staggered grid finite difference operator according to the objective function by using a swarm intelligence algorithm combining a dung beetle optimization algorithm and a simulated annealing algorithm; The module for constructing a long-term modeling scheme for marine seismic signals is used to differentially discretize the elastic wave equation using an optimized staggered grid finite difference operator and set a convolutional fully matched layer as an artificial boundary condition to construct a long-term modeling scheme for marine seismic signals. The long-term numerical simulation module is used to determine the ocean medium model and boundary conditions of the modeling area respectively, and use the long-term modeling scheme of marine seismic signals to perform long-term numerical simulation of the ocean environment in the modeling area; the boundary conditions include: fluid-solid coupling boundary conditions.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the marine seismic signal modeling method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the marine seismic signal modeling method described in any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the marine seismic signal modeling method described in any one of claims 1 to 6 is implemented.

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