Improved prestack inversion method and device based on second-order oscillation particle swarm optimization

Abstract

The present invention provides a prestack inversion method and device of improved second-order oscillation particle swarm optimization, which relates to the technical field of prestack inversion, including: obtaining prestack angle gather data, layer data and elastic parameters of target well logging in a target work area; determining the solution space of elastic parameters in the target work area based on elastic parameters and layer data; constructing a first objective function for inverting elastic parameters in the target work area based on prestack angle gather data and elastic parameters of target well logging; optimizing the first objective function based on the improved second-order oscillation particle swarm optimization algorithm and angle gather data of target seismic trace under the constraint of solution space to obtain the optimal solution of elastic parameters of target seismic trace; constructing elastic parameter profile of target work area based on the optimal solution of elastic parameters of multiple seismic traces. The method adopts four different second-order oscillation factors in the oscillation link, so that the global and local search capabilities of particle swarm algorithm can be adjusted, and the inversion accuracy is guaranteed.

Description

Technical Field

[0001] The invention relates to the technical field of prestack inversion, and in particular to a prestack inversion method and device for improving second-order oscillation particle swarm optimization. Background Art

[0002] Seismic inversion is a highly nonlinear problem. In the absence of a reliable initial model, it is difficult for local (gradient-based) optimization algorithms to achieve a global optimal solution. Particle swarm optimization, as an intelligent optimization algorithm inspired by bird flocks in nature, has been used to solve geophysical inversion problems. Although particle swarm optimization is easy to implement and converges quickly, its standard version still has the risk of premature convergence. When it converges prematurely, it will find a local optimal solution, which will lead to being trapped in the local optimal solution and unable to jump out.

[0003] In summary, existing seismic inversion methods are prone to fall into local optimality and have the technical problem of being unable to guarantee the accuracy of inversion results. Summary of the invention

[0004] The purpose of the present invention is to provide a pre-stack inversion method and device for improving second-order oscillation particle swarm optimization, so as to alleviate the technical problems of existing seismic inversion methods that they are prone to fall into local optimum and cannot guarantee the accuracy of inversion results.

[0005] In a first aspect, the present invention provides a pre-stack inversion method of improved second-order oscillation particle swarm optimization, comprising: obtaining pre-stack angle gather data, layer data and elastic parameters of a target well logging in a target work area; wherein the pre-stack angle gather data is a collection of angle gather data of multiple seismic traces; the target well logging represents any well logging in the target work area; based on the elastic parameters of the target well logging and the layer data of the target work area, determining the solution space of the elastic parameters in the target work area; based on the pre-stack angle gather data and the elastic parameters of the target well logging, constructing a first objective function for inverting the elastic parameters in the target work area; under the constraints of the solution space, optimizing the first objective function based on the improved second-order oscillation particle swarm optimization algorithm and the angle gather data of the target seismic trace to obtain the optimal solution of the elastic parameters of the target seismic trace; wherein the improved second-order oscillation particle swarm optimization algorithm includes four different second-order oscillation factors; the target seismic trace represents any seismic trace among the multiple seismic traces; based on the optimal solution of the elastic parameters of the multiple seismic traces, constructing the elastic parameter profile of the target work area.

[0006] In an optional embodiment, based on the pre-stack angle gather data and the elastic parameters of the target well logging, a first objective function for inverting the elastic parameters in the target work area is constructed, including: extracting seismic wavelet data from the pre-stack angle gather data based on a preset incident angle set; determining a forward operator based on a preset incident angle set, the seismic wavelet data and a first-order difference matrix in the Aki-Richard formula; calculating the mean value and covariance matrix of the elastic parameters based on the elastic parameters of the target well logging; constructing a first objective function for inverting the elastic parameters in the target work area based on the forward operator, the mean value of the elastic parameters, the covariance matrix and a specified regularization parameter.

[0007] In an optional embodiment, the method further includes: constructing an initial function for inverting the elastic parameters of the target well logging based on the pre-stack angle gather data of the target well logging, the initial regularization parameter, the forward operator, the average value of the elastic parameters and the covariance matrix; randomly initializing a first initial particle swarm for solving the elastic parameters of the target well logging under the constraints of the solution space; wherein, in the first initial particle swarm, the position of each particle corresponds one-to-one to an optional solution of the elastic parameters of the target well logging; optimizing the initial function using an improved second-order oscillating particle swarm optimization algorithm, and taking the optimal solution that reaches a preset number of iterations as the initial solution of the elastic parameters of the target well logging; when it is determined that the error between the elastic parameters of the target well logging and the initial solution does not meet the preset error requirement, adjusting the initial regularization parameter, and returning to execute the previous step until the error between the initial solution and the elastic parameters of the target well logging meets the preset error requirement, and the specified regularization parameter is obtained.

[0008] In an optional embodiment, under the constraints of the solution space, the first objective function is optimized based on an improved second-order oscillation particle swarm optimization algorithm and the angular trace data of the target seismic track, including: substituting the angular trace data of the target seismic track into the first objective function to obtain a second objective function for inverting the elastic parameters of the target seismic track; under the constraints of the solution space, randomly initializing a second initial particle swarm for solving the elastic parameters of the target seismic track; wherein, in the second initial particle swarm, the position of each particle corresponds one-to-one to an optional solution of the elastic parameters of the target seismic track; and optimizing the second objective function using an improved second-order oscillation particle swarm optimization algorithm, and taking the optimal solution that reaches a preset number of iterations as the elastic parameters of the target seismic track.

[0009] In an optional implementation, in the improved second-order oscillating particle swarm optimization algorithm, the update equation of the particle velocity is expressed as: Among them, 3f represents the particle dimension, f represents the number of sampling points in each seismic trace, represents the velocity of the i-th particle in the k-th iteration, v i,3f =[v i,1 v i,2 …v i,3f ] 1×3f , ω represents the inertia weight, represents the velocity of the i-th particle in the k-1th iteration, c1 represents the individual learning factor, c2 represents the social learning factor, r1 and r2 represent two independent uniform random numbers between [0,1]. represents the optimal position of the current i-th particle, ξ1, ξ2, ξ3, ξ4 represent different second-order oscillation factors, represents the position of the i-th particle in the k-1th iteration, represents the position of the i-th particle in the k-2-th iteration, Indicates the optimal particle position in the current population.

[0010] In an optional implementation, before specifying the number of iterations, the value range of the second-order oscillation factor is: 0<ξ1<1,0<ξ3<1; after specifying the number of iterations, the value range of the second-order oscillation factor is: ξ1<ξ2-1,ξ3<ξ4-1,0<ξ2<1,0<ξ4<1.

[0011] In an optional implementation, the first objective function is expressed as: Wherein, d represents the pre-stack angle gather data of the area to be inverted, G represents the forward operator, m represents the elastic parameter to be solved, λ represents the specified regularization parameter, represents the average value of the elastic parameters of the target well logging, C m The covariance matrix representing the elastic parameters of the target well log.

[0012] In a second aspect, the present invention provides a pre-stack inversion device for improved second-order oscillation particle swarm optimization, comprising: an acquisition module, used to acquire pre-stack angle gather data, stratum data and elastic parameters of target well logging in a target work area; wherein the pre-stack angle gather data is a collection of angle gather data of multiple seismic channels; the target well logging represents any well logging in the target work area; a determination module, used to determine the solution space of the elastic parameters in the target work area based on the elastic parameters of the target well logging and the stratum data of the target work area; a first construction module, used to construct a solution space of the elastic parameters in the target work area based on the pre-stack angle gather data and the elastic parameters of the target well logging A first objective function for inverting elastic parameters in the target work area; a first optimization module for optimizing the first objective function based on an improved second-order oscillation particle swarm optimization algorithm and angular trace data of a target seismic trace under the constraints of the solution space to obtain an optimal solution for the elastic parameters of the target seismic trace; wherein the improved second-order oscillation particle swarm optimization algorithm includes four different second-order oscillation factors; the target seismic trace represents any one of the multiple seismic traces; and a second construction module for constructing an elastic parameter profile of the target work area based on the optimal solution for the elastic parameters of the multiple seismic traces.

[0013] In a third aspect, the present invention provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, the steps of the improved second-order oscillation particle swarm optimization prestack inversion method described in any of the aforementioned embodiments are implemented.

[0014] In a fourth aspect, the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and when the computer instructions are executed by a processor, the improved second-order oscillation particle swarm optimization pre-stack inversion method described in any one of the aforementioned embodiments is implemented.

[0015] The present invention provides a pre-stack inversion method of improved second-order oscillation particle swarm optimization. After determining the first objective function of the elastic parameters in the solution space and the inversion target work area, the method optimizes the first objective function by using the improved second-order oscillation particle swarm optimization algorithm and the angle track gather data of the target seismic trace. Since the improved algorithm adopts four different second-order oscillation factors in the oscillation link, the global and local search capabilities of the particle swarm algorithm can be effectively adjusted, avoiding the problem that the existing method is easy to fall into the local minimum, thereby ensuring the accuracy of the inversion result. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 A flowchart of a prestack inversion method for improving second-order oscillation particle swarm optimization provided by an embodiment of the present invention;

[0018] Figure 2 A flow chart of a method for inverting elastic parameters of a target seismic trace provided by an embodiment of the present invention;

[0019] Figure 3 A functional module diagram of a prestack inversion device for improved second-order oscillation particle swarm optimization provided by an embodiment of the present invention;

[0020] Figure 4 A schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0022] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] Some embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0024] Embodiment 1

[0025] Figure 1 A flowchart of a prestack inversion method for improving second-order oscillation particle swarm optimization provided by an embodiment of the present invention is shown in FIG. Figure 1 As shown, the method specifically comprises the following steps:

[0026] Step S102, obtaining pre-stack angle gather data, layer data and elastic parameters of target logging in the target work area.

[0027] Specifically, the target work area is the geographical area where prestack inversion is to be performed. Given that the prestack angle gather data has more and more accurate information about the underground medium and has a higher signal-to-noise ratio, in order to perform prestack inversion on the target work area, an embodiment of the present invention selects the prestack angle gather data of the target work area as the actual observation data, wherein the prestack angle gather data is a collection of angle gather data of multiple seismic channels.

[0028] In addition to obtaining pre-stack angle gather data, it is also necessary to obtain the layer data of the work area and the elastic parameters of the target well logging, wherein the target well logging represents any well logging in the target work area. In the embodiment of the present invention, the elastic parameters include: longitudinal wave impedance Z P , shear wave impedance Z S That is to say, to perform prestack inversion on the target work area, a well logging interpretation must be performed on a well in the area in advance to obtain the elastic parameters of the well logging.

[0029] Step S104, based on the elastic parameters of the target well logging and the stratigraphic data of the target work area, determine the solution space of the elastic parameters in the target work area.

[0030] When the horizon data of the target work area and the elastic parameters of the target logging are known, the initial model of the elastic parameters in the target work area can be obtained through interpolation and extrapolation, that is, the initial model of the longitudinal wave impedance, shear wave impedance and density. Next, the range of values ​​of the preset offset value floating up and down with the initial model as the center is used as the solution space of the elastic parameters in the target work area. For example, the initial model ±30% is used as the solution space of the elastic parameters in the target work area, and the solution space is also the search space of the optimization algorithm.

[0031] Taking the initial model ±30% as an example, if the shear wave impedance at a sampling point in the initial model is Z S , then in the solution space, the transverse wave impedance value range of this sampling point is (1-0.3)*Z S to (1+0.3)*Z S .

[0032] Step S106: constructing a first objective function for inverting the elastic parameters in the target work area based on the pre-stack angle gather data and the elastic parameters of the target well logging.

[0033] The embodiment of the present invention uses the pre-stack angle gather data as the actual observation data. It is known that the forward modeling data can be synthesized through the convolution model. Therefore, the goal of the pre-stack inversion in the embodiment of the present invention is to further obtain the synthetic forward modeling data through the convolution model based on the elastic parameters obtained by the pre-stack inversion, and the deviation between the synthetic forward modeling data and the actual observation data is minimized. On this basis, the regularization term is introduced, and the elastic parameters of the target logging are used as the reference data for the inverted elastic parameters of the target work area, thereby constructing the first objective function for inverting the elastic parameters in the target work area.

[0034] Step S108, under the constraints of the solution space, the first objective function is optimized based on the improved second-order oscillation particle swarm optimization algorithm and the angle gather data of the target seismic trace to obtain the optimal solution of the elastic parameters of the target seismic trace.

[0035] The improved second-order oscillation particle swarm optimization algorithm includes four different second-order oscillation factors; the target seismic trace represents any seismic trace among the multiple seismic traces.

[0036] When inverting the elastic parameters in the target work area, the embodiment of the present invention adopts a single-channel form, that is, each seismic channel in the seismic profile is inverted separately, and finally the inversion results of all seismic channels are synthesized to obtain the entire inversion parameter profile.

[0037] If the number of sampling points of the target seismic trace is f, and the elastic parameters of each sampling point include three parameters [Z P Z S ρ], then it can be known that the data dimension of the elastic parameters of the target seismic channel is 3f. The embodiment of the present invention uses an improved second-order oscillation particle swarm optimization algorithm to solve the elastic parameters of the target seismic channel. Therefore, first, a particle swarm should be randomly initialized under the constraint of the solution space, and the dimension of each particle should be 3f, and the position of each particle represents a potential solution for the elastic parameters of the target seismic channel. Then, the improved second-order oscillation particle swarm optimization algorithm is used to iteratively optimize the population. When optimizing, the first objective function is used as the fitness function of the particle, and the angle track data of the target seismic channel is used as the actual observation data in the current first objective function to solve the fitness function value. The fitness function value is continuously reduced and converged through population iteration until the convergence condition is reached (the preset convergence accuracy is met / the preset number of iterations is reached), and the optimal solution of the elastic parameters of the target seismic channel is determined.

[0038] The improved second-order oscillation particle swarm optimization algorithm, as the name suggests, is an improvement on the traditional second-order oscillation particle swarm optimization algorithm. The improved second-order oscillation particle swarm optimization algorithm introduces four different second-order oscillation factors into the particle velocity update equation to increase the diversity of particle position updates, so that more information is considered in the process of particle position updates, thereby improving the limitation of the traditional particle swarm algorithm that may converge prematurely, that is, reducing the possibility of falling into local optimality and increasing the possibility of global convergence.

[0039] Step S110, constructing an elastic parameter profile of the target work area based on the optimal solutions of the elastic parameters of multiple seismic traces.

[0040] An embodiment of the present invention provides a pre-stack inversion method of improved second-order oscillation particle swarm optimization. After determining the first objective function of the solution space and the elastic parameters in the inversion target work area, the method optimizes the first objective function using the improved second-order oscillation particle swarm optimization algorithm and the angular trace data of the target seismic trace. Since the improved algorithm uses four different second-order oscillation factors in the oscillation link, it can effectively adjust the global and local search capabilities of the particle swarm algorithm, avoiding the problem that the existing method is prone to falling into local minima, thereby ensuring the accuracy of the inversion result.

[0041] In an optional implementation, the above step S106, based on the pre-stack angle gather data and the elastic parameters of the target well logging, constructs a first objective function for inverting the elastic parameters in the target work area, specifically including the following steps:

[0042] Step S1061, extracting seismic wavelet data from pre-stack angle gather data based on a preset incident angle set.

[0043] Specifically, to obtain seismic wavelet data, first determine the t incident angles θ of the prestack angle gather data: u , (u=1,2,…t) (i.e., a preset incident angle set), and then stack the pre-stack angle gather data by angle to obtain post-stack seismic data. Then, the seismic wavelet data W(θ) of the target area can be extracted from the post-stack seismic data, where θ=[θ1 θ2 … θ t ] T .

[0044] Step S1062, determining a forward operator based on a preset incident angle set, seismic wavelet data and a first-order difference matrix in the Aki-Richard formula.

[0045] Known Where S represents synthetic data. According to the Aki-Richard formula, c1(θ)=1+tan 2 θ, c2(θ) = -8γ 2tan 2 θ, γ represents the ratio of the shear wave velocity vs to the longitudinal wave velocity vp, which is optional. The value of γ is a constant of 0.5. L represents the first-order difference matrix in the Aki-Richard formula. I P =ln(Z P ), I S =ln(Z S ), I D =ln(ρ), Z P , Z S , ρ represent longitudinal wave impedance, shear wave impedance and density respectively.

[0046] It can be seen from the above description that the inversion of the embodiment of the present invention adopts a single-channel form. Therefore, the above convolution model is rewritten into the following matrix form: (j=1,2,…n); where S j represents the synthetic forward modeling data of the jth seismic trace, j ranges from 1 to n, n represents the total number of seismic traces, G represents the forward modeling operator, Based on this expression, it can be seen that the forward operator can be determined according to the preset incident angle set, seismic wavelet data and the first-order difference matrix.

[0047] It is known that each seismic trace has f sampling points, so I P It can be expressed as I S , I D And so on. According to the forward expression It can be seen that in the embodiment of the present invention, the elastic parameter obtained by inversion is actually the result of taking the logarithm of the elastic parameter [I P I S I D ], therefore, the inversion is completed and the following mathematical relationship formula needs to be used: Convert the result to [Z P Z S ρ] is enough.

[0048] Step S1063, calculating the mean value and covariance matrix of the elastic parameters based on the elastic parameters of the target well logging.

[0049] In an embodiment of the present invention, Therefore, after obtaining the elastic parameters of the target well logging [Z P Z S ρ], we can first calculate the logarithm of each parameter based on the above expression, and then take the average value of the logarithm as the average value of the elastic parameter (Z P Z S ρ respectively), and the covariance matrix The elements on the diagonal of the covariance matrix are [I P I S I D ], that is, I P The variance of I S The variance of I D Variance, the off-diagonal elements are the covariances between the two variables, for example, I P and I S The covariance between .

[0050] Step S1064, constructing a first objective function for inverting the elastic parameters in the target work area based on the forward modeling operator, the average value of the elastic parameters, the covariance matrix and the specified regularization parameter.

[0051] In this embodiment of the present invention, the first objective function is expressed as: Where d represents the prestack angle gather data of the area to be inverted, G represents the forward operator, m represents the elastic parameter to be solved, and λ represents the specified regularization parameter. represents the average value of the elastic parameters of the target well, C m Represents the covariance matrix of the elastic parameters of the target well logging. That is to say, when inverting the optimal solution of elastic parameters of a certain area based on the first objective function, d in the first objective function should be brought into the prestack angle gather data of the area.

[0052] The construction principle of the first objective function is explained below:

[0053] The goal of prestack inversion is to estimate the elastic parameter m given the actual observed data d, and to minimize the deviation between the synthetic forward data obtained based on the elastic parameter convolution and the actual observed data d. The inversion is performed under the Bayesian framework. Since the elastic parameter m has very strong multi-solutions during the inversion process, the prior information is used as a regularization term to constrain the inversion process: Among them, P(m|d) is the posterior probability, P(d|m) is the likelihood function, P(d) is the marginal probability, if d is known, then P(d) is a constant value, and P(m) is the prior probability function of the elasticity parameter.

[0054] Assuming there is random noise e, the likelihood function P(d|m) can be expressed as: Among them, σ e Represents the standard deviation of the noise.

[0055] P(m) provides prior information of unknown parameters to constrain the inversion. Since elastic parameters have strong statistical correlation, the prior information in the target well log is introduced to alleviate uncertainty.

[0056] From this we can see that the maximum a posteriori probability can be expressed as:

[0057] Given that the posterior probability is used to characterize the acceptability of the inverted elastic parameters, the larger the posterior probability, the higher the acceptability of the inverted parameters and the better the inversion results. Given that the exponent of exp in the formula of the maximum posterior probability is negatively signed, the posterior probability is negatively correlated with the absolute value of the exponent in its formula, that is, the smaller the absolute value of the exponent of exp, the larger the posterior probability. Therefore, the first objective function, that is, the fitness function, is as follows: The smaller the fitness function value is, the smaller the absolute value of the exponent representing exp is. In the first objective function, λ is a specified regularization parameter, which is used to balance the weight between the first residual term and the second constraint term in the first objective function.

[0058] If the specified regularization parameter in the first objective function is set to a fixed value based on experience, the accuracy of the inversion result will be limited to a certain extent. Therefore, the embodiment of the present invention adopts the following operations to find the optimal regularization parameter (that is, the specified regularization parameter). In an optional embodiment, the method of the present invention further includes the following steps:

[0059] Step S201, constructing an initial function for inverting the elastic parameters of the target well logging based on the pre-stack angle gather data of the target well logging, the initial regularization parameter, the forward operator, the average value and the covariance matrix of the elastic parameters.

[0060] Specifically, an embodiment of the present invention utilizes the pre-stack angle gather data of the target well logging to invert the elastic parameters (predicted values) of the target well logging, and uses the elastic parameters (actual values) of the target well logging obtained in step S102 as control data for the inversion result, thereby providing a reference for optimizing the initial regularization parameters in the initial function.

[0061] The initial function is expressed as: Where d′ represents the prestack angle gather data of the target well logging, and λ0 represents the initial regularization parameter.

[0062] Step S202, under the constraint of the solution space, randomly initialize a first initial particle group for solving the elastic parameters of the target well logging; wherein, in the first initial particle group, the position of each particle corresponds one-to-one to an optional solution of the elastic parameters of the target well logging.

[0063] The first initial particle group includes multiple particles, and the position of each particle is an optional solution (that is, a potential solution) of the target well logging elastic parameter. The more particles there are in the first initial particle group, the stronger the corresponding global search capability is, but the longer the required calculation time is. The embodiment of the present invention does not specifically limit the size of the population, and the user can set it according to actual conditions.

[0064] Step S203, optimizing the initial function using an improved second-order oscillation particle swarm optimization algorithm, and taking the optimal solution that reaches a preset number of iterations as the initial solution of the elastic parameters of the target well logging.

[0065] The position of each particle represents a solution of the initial function (that is, the fitness function of the particle), and the corresponding function value can be obtained by substituting it into the initial function. It can be seen from the above description that in the embodiment of the present invention, the smaller the fitness function value, the greater the posterior probability, which represents the better position of the particle. Therefore, based on the improved second-order oscillation particle swarm optimization algorithm, the population is continuously updated and the particle position is changed, so that the fitness function is continuously reduced until the preset number of iterations converges, and the optimal solution of the elastic parameter corresponding to the initial regularization parameter is obtained, and it is used as the initial solution of the elastic parameter of the target logging.

[0066] Step S204, when it is determined that the error between the elastic parameter of the target well logging and the initial solution does not meet the preset error requirement, adjust the initial regularization parameter, and return to execute the previous step until the error between the initial solution and the elastic parameter of the target well logging meets the preset error requirement, and obtain the specified regularization parameter.

[0067] If the error between the initial solution of the elastic parameters of the target well logging obtained by inversion based on the initial regularization parameter and the actual elastic parameters of the target well logging obtained through well logging interpretation (the data obtained in step S102) does not meet the preset error requirements, it means that the value of the initial regularization parameter is not set appropriately, and the value of the regularization parameter in the initial function should be adjusted, that is, the initial function should be updated, and then based on the updated initial function, the particle swarm is iteratively optimized again using the improved second-order oscillation particle swarm optimization algorithm, and the corresponding optimal solution is determined until the error between the optimal solution and the actual elastic parameters of the target well logging meets the preset error requirements, then the adjustment of the regularization parameter is stopped, and the regularization parameter in the latest initial function is used as the designated regularization parameter.

[0068] It should be noted that [Z P Z S ρ] and [I P I S I D] are essentially elastic parameters. Therefore, when calculating the error between the inverted elastic parameters and the actual elastic parameters, the error before taking the logarithm can be uniformly calculated, and the error before taking the logarithm can also be uniformly calculated. The embodiment of the present invention does not specifically limit it, as long as the magnitude of the data is unified.

[0069] In an optional implementation, the above step S108, under the constraint of the solution space, optimizes the first objective function based on the improved second-order oscillation particle swarm optimization algorithm and the angle gather data of the target seismic trace, and specifically includes the following steps:

[0070] Step S1081, bringing the angle gather data of the target seismic trace into the first objective function to obtain a second objective function for inverting the elastic parameters of the target seismic trace.

[0071] Step S1082: Under the constraints of the solution space, a second initial particle swarm is randomly initialized for solving the elastic parameters of the target seismic trace.

[0072] Among them, in the second initial particle group, the position of each particle corresponds one-to-one to the optional solution of the elastic parameter of the target seismic trace.

[0073] Step S1083, optimizing the second objective function using an improved second-order oscillation particle swarm optimization algorithm, and taking the optimal solution that reaches a preset number of iterations as the elastic parameter of the target seismic trace.

[0074] In the above-described method flow of how to determine the specified regularization parameter, the process of how to invert the elastic parameters of the target well logging when the regularization parameter is determined has been introduced in detail. By analogy, Figure 2 A flow chart of a method for inverting elastic parameters of a target seismic trace provided by an embodiment of the present invention is shown in FIG. Figure 2 As shown in the figure, to invert the elastic parameters of the target seismic trace, the value of d in the first objective function is the angle gather data of the target seismic trace, thereby obtaining the second objective function. Next, the second initial particle swarm is randomly initialized, and then the particle swarm is iteratively optimized based on the improved second-order oscillation particle swarm optimization algorithm until the particle that can minimize the function value of the second objective function is determined, that is, the optimal solution of the second objective function, and it is used as the elastic parameter of the target seismic trace.

[0075] In an optional implementation, in the improved second-order oscillating particle swarm optimization algorithm, the update equation of the particle velocity is expressed as: Among them, 3f represents the particle dimension, f represents the number of sampling points in each seismic trace, represents the velocity of the i-th particle in the k-th iteration, v i,3f =[v i,1 v i,2…v i,3f ] 1×3f , ω represents the inertia weight, represents the velocity of the i-th particle in the k-1th iteration, c1 represents the individual learning factor, c2 represents the social learning factor, r1 and r2 represent two independent uniform random numbers between [0,1]. represents the optimal position of the current i-th particle, ξ1, ξ2, ξ3, ξ4 represent different second-order oscillation factors, represents the position of the i-th particle in the k-1th iteration, represents the position of the i-th particle in the k-2-th iteration, Indicates the optimal particle position in the current population.

[0076] The update equation for the particle position is the traditional one: Based on the above particle velocity update equation, it can be seen that the improved second-order oscillation particle swarm optimization algorithm uses four different second-order oscillation factors ξ1, ξ2, ξ3, and ξ4. If the relationship and value range of the four parameters are obtained, the diversity of the group can be improved, the global convergence of the algorithm can be improved, and the accuracy can be improved.

[0077] Specifically, consider the update equation of the particle velocity as v k =v k-1 +a×Δk, let ω=1, combined with the position update formula Here a is similar to the acceleration in the distance speed time relationship, that is, the second-order derivative of the distance with respect to time. The number of iterations k here can be regarded as the time in the conventional relationship. It can be obtained: Simplifying it, we can get:

[0078] The simplified formula above is a second-order differential equation. According to the solution formula of the second-order differential equation, two characteristic roots are obtained: in,

[0079] In order to make the function oscillation of the particle position converge, it is necessary that Δ<0 and Known When 0<ξ2,ξ4<1, Established. To make Δ<0, if: Then it holds, and after simplification, we can get the relationship: 0<ξ1<1,0<ξ3<1.

[0080] In order to make the function of particle position converge asymptotically, it is necessary that Δ>0 and Known When 0<ξ2,ξ4<1, Established. To make Δ>0, if: Then it holds, and after simplification we can get the relationship: ξ1<ξ2-1,ξ3<ξ4-1,0<ξ2<1,0<ξ4<1.

[0081] Therefore, the embodiment of the present invention sets that before specifying the number of iterations, the value range of the second-order oscillation factor is: 0<ξ1<1,0<ξ3<1; after specifying the number of iterations, the value range of the second-order oscillation factor is: ξ1<ξ2-1,ξ3<ξ4-1,0<ξ2<1,0<ξ4<1.

[0082] Based on the above setting of the value range of the second-order oscillation factor, the improved second-order oscillation particle swarm optimization algorithm can have a strong exploration ability in the early stage to obtain suitable particles, and a high development ability in the later stage to accelerate the convergence speed. The improved second-order oscillation particle swarm optimization algorithm improves the diversity of the group and improves the global convergence by selecting different ξ1, ξ2, ξ3, ξ4 and determining a more accurate value range. In the process of the fitness function continuously decreasing, the second-order oscillation factor makes the next movement vector of the particle point to a better angle range, promotes the particle to jump out of the local optimum, and improves the global optimization ability.

[0083] In summary, the embodiment of the present invention adopts an improved second-order oscillation particle swarm algorithm as an optimization algorithm for prestack inversion. Since four different second-order oscillation factors are used in the oscillation link to adjust the global and local search capabilities of the particle swarm algorithm, a given solution space can be searched globally, which can effectively avoid the problem of the particle swarm algorithm falling into a local minimum due to premature convergence, thereby obtaining a more accurate inversion result, improving the resolution and accuracy of the prestack inversion, and significantly enhancing the anti-noise ability, and having stronger stability.

[0084] Embodiment 2

[0085] An embodiment of the present invention also provides a pre-stack inversion device of improved second-order oscillation particle swarm optimization, which is mainly used to execute the pre-stack inversion method of improved second-order oscillation particle swarm optimization provided in the above-mentioned embodiment one. The following is a specific introduction to the pre-stack inversion device of improved second-order oscillation particle swarm optimization provided in the embodiment of the present invention.

[0086] Figure 3 is a functional module diagram of a prestack inversion device for improved second-order oscillation particle swarm optimization provided by an embodiment of the present invention, such as Figure 3 As shown, the device mainly includes: an acquisition module 10, a determination module 20, a first construction module 30, a first optimization module 40, and a second construction module 50, wherein:

[0087] The acquisition module 10 is used to obtain pre-stack angle gather data, layer data and elastic parameters of target well logging in the target work area; wherein the pre-stack angle gather data is a collection of angle gather data of multiple seismic channels; and the target well logging represents any well logging in the target work area.

[0088] The determination module 20 is used to determine the solution space of the elastic parameters in the target work area based on the elastic parameters of the target well logging and the layer data of the target work area.

[0089] The first construction module 30 is used to construct a first objective function for inverting elastic parameters in a target work area based on pre-stack angle gather data and elastic parameters of target well logging.

[0090] The first optimization module 40 is used to optimize the first objective function based on the improved second-order oscillation particle swarm optimization algorithm and the angular trace data of the target seismic trace under the constraints of the solution space, so as to obtain the optimal solution of the elastic parameters of the target seismic trace; wherein the improved second-order oscillation particle swarm optimization algorithm includes four different second-order oscillation factors; the target seismic trace represents any seismic trace among multiple seismic traces.

[0091] The second construction module 50 is used to construct an elastic parameter profile of the target work area based on the optimal solution of the elastic parameters of multiple seismic traces.

[0092] An embodiment of the present invention provides a pre-stack inversion device of improved second-order oscillation particle swarm optimization. After determining the first objective function of the solution space and the elastic parameters in the inversion target work area, the device optimizes the first objective function using the improved second-order oscillation particle swarm optimization algorithm and the angular trace data of the target seismic trace. Since the improved algorithm uses four different second-order oscillation factors in the oscillation link, it can effectively adjust the global and local search capabilities of the particle swarm algorithm, avoiding the problem that the existing method is prone to falling into local minima, thereby ensuring the accuracy of the inversion result.

[0093] Optionally, the first building module 30 is specifically used for:

[0094] Seismic wavelet data are extracted from pre-stack angle gather data based on a preset incident angle set.

[0095] The forward modeling operator is determined based on a preset incident angle set, seismic wavelet data and the first-order difference matrix in the Aki-Richard formula.

[0096] The mean value and covariance matrix of the elastic parameters are calculated based on the elastic parameters of the target well logging.

[0097] Based on the forward operator, the mean value of the elastic parameters, the covariance matrix and the specified regularization parameters, a first objective function for inverting the elastic parameters in the target work area is constructed.

[0098] Optionally, the device is also used for:

[0099] Based on the pre-stack angle gather data of the target well logging, the initial regularization parameter, the forward operator, the mean value and the covariance matrix of the elastic parameters, the initial function for inverting the elastic parameters of the target well logging is constructed.

[0100] Under the constraint of the solution space, a first initial particle group for solving the elastic parameters of the target well logging is randomly initialized; wherein, in the first initial particle group, the position of each particle corresponds one-to-one to an optional solution of the elastic parameters of the target well logging.

[0101] The initial function is optimized using an improved second-order oscillatory particle swarm optimization algorithm, and the optimal solution that reaches a preset number of iterations is used as the initial solution of the elastic parameters of the target logging.

[0102] When it is determined that the error between the elastic parameter of the target well logging and the initial solution does not meet the preset error requirement, the initial regularization parameter is adjusted, and the previous step is returned to be executed until the error between the initial solution and the elastic parameter of the target well logging meets the preset error requirement, thereby obtaining the specified regularization parameter.

[0103] Optionally, the first optimization module 40 is specifically used for:

[0104] The angle gather data of the target seismic trace is brought into the first objective function to obtain the second objective function for inverting the elastic parameters of the target seismic trace.

[0105] Under the constraint of the solution space, a second initial particle group for solving the elastic parameters of the target seismic trace is randomly initialized; wherein, in the second initial particle group, the position of each particle corresponds one-to-one to an optional solution of the elastic parameters of the target seismic trace.

[0106] The second objective function is optimized using an improved second-order oscillation particle swarm optimization algorithm, and the optimal solution that reaches a preset number of iterations is taken as the elastic parameter of the target seismic trace.

[0107] Optionally, in the improved second-order oscillating particle swarm optimization algorithm, the update equation of the particle velocity is expressed as: Among them, 3f represents the particle dimension, f represents the number of sampling points in each seismic trace, represents the velocity of the i-th particle in the k-th iteration, v i,3f =[v i,1 v i,2 … v i,3f ] 1×3f , ω represents the inertia weight, represents the velocity of the i-th particle in the k-1th iteration, c1 represents the individual learning factor, c2 represents the social learning factor, r1 and r2 represent two independent uniform random numbers between [0,1]. represents the optimal position of the current i-th particle, ξ1, ξ2, ξ3, ξ4 represent different second-order oscillation factors, represents the position of the i-th particle in the k-1th iteration, represents the position of the i-th particle in the k-2-th iteration, Indicates the optimal particle position in the current population.

[0108] Optionally, before specifying the number of iterations, the second-order oscillation factor can be in the range of: 0<ξ1<1,0<ξ3<1; after specifying the number of iterations, the value range of the second-order oscillation factor is: ξ1<ξ2-1,ξ3<ξ4-1,0<ξ2<1,0<ξ4<1.

[0109] Optionally, the first objective function is expressed as: Where d represents the prestack angle gather data of the area to be inverted, G represents the forward operator, m represents the elastic parameter to be solved, and λ represents the specified regularization parameter. represents the average value of the elastic parameters of the target well, C m Represents the covariance matrix of the elastic parameters of the target well log.

[0110] Embodiment 3

[0111] See also Figure 4 An embodiment of the present invention provides an electronic device, which includes: a processor 60, a memory 61, a bus 62 and a communication interface 63, wherein the processor 60, the communication interface 63 and the memory 61 are connected via the bus 62; the processor 60 is used to execute an executable module stored in the memory 61, such as a computer program.

[0112] The memory 61 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 63 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0113] The bus 62 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0114] Among them, the memory 61 is used to store programs, and the processor 60 executes the program after receiving the execution instruction. The method executed by the device defined by the process disclosed in any embodiment of the above-mentioned embodiment of the present invention can be applied to the processor 60 or implemented by the processor 60.

[0115] The processor 60 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 60. The above processor 60 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in the embodiments of the present invention can be directly embodied as a hardware decoding processor to be executed, or the hardware and software modules in the decoding processor can be executed. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 61, and the processor 60 reads the information in the memory 61 and completes the steps of the above method in combination with its hardware.

[0116] An embodiment of the present invention provides a computer program product of an improved pre-stack inversion method and apparatus for second-order oscillation particle swarm optimization, comprising a computer-readable storage medium storing a non-volatile program code executable by a processor, wherein the instructions included in the program code can be used to execute the method described in the foregoing method embodiment. For specific implementation, please refer to the method embodiment, which will not be described in detail here.

[0117] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0118] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0119] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0120] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inside", "outside", etc. indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, or the positions or positional relationships in which the product of the invention is usually placed when in use. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific position, be constructed and operated in a specific position, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", "third", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0121] In addition, the terms "horizontal", "vertical", "overhanging" and the like do not mean that the components are required to be absolutely horizontal or overhanging, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0122] In the description of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0123] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A prestack inversion method based on improved second-order oscillation particle swarm optimization, characterized in that: include: Acquire pre-stack angle gather data, layer data and elastic parameters of target well logging in the target work area; wherein the pre-stack angle gather data is a collection of angle gather data of multiple seismic traces; the target well logging represents any well logging in the target work area; Determining a solution space of elastic parameters in the target work area based on the elastic parameters of the target well logging and the horizon data of the target work area; Based on the pre-stack angle gather data and the elastic parameters of the target well logging, construct a first objective function for inverting the elastic parameters in the target work area; Under the constraints of the solution space, the first objective function is optimized based on the improved second-order oscillation particle swarm optimization algorithm and the angle track gather data of the target seismic trace to obtain the optimal solution of the elastic parameters of the target seismic trace; wherein the improved second-order oscillation particle swarm optimization algorithm includes four different second-order oscillation factors; the target seismic trace represents any seismic trace among the multiple seismic traces; Based on the optimal solutions of the elastic parameters of the multiple seismic traces, an elastic parameter profile of the target work area is constructed.

2. The prestack inversion method of improved second-order oscillation particle swarm optimization according to claim 1 is characterized in that: Based on the pre-stack angle gather data and the elastic parameters of the target well logging, a first objective function for inverting the elastic parameters in the target work area is constructed, including: Extracting seismic wavelet data from the prestack angle gather data based on a preset incident angle set; Determining a forward operator based on a preset incident angle set, the seismic wavelet data, and a first-order difference matrix in the Aki-Richard formula; Calculating the mean value and covariance matrix of the elastic parameters based on the elastic parameters of the target well logging; Based on the forward operator, the average value of the elastic parameters, the covariance matrix and a specified regularization parameter, a first objective function for inverting the elastic parameters in the target work area is constructed.

3. The prestack inversion method of improved second-order oscillation particle swarm optimization according to claim 2 is characterized in that: The method further comprises: Based on the pre-stack angle gather data of the target well logging, the initial regularization parameter, the forward operator, the average value of the elastic parameters and the covariance matrix, construct an initial function for inverting the elastic parameters of the target well logging; Under the constraint of the solution space, a first initial particle group for solving the elastic parameter of the target well logging is randomly initialized; wherein, in the first initial particle group, the position of each particle corresponds one-to-one to an optional solution of the elastic parameter of the target well logging; The initial function is optimized using an improved second-order oscillating particle swarm optimization algorithm, and the optimal solution that reaches a preset number of iterations is used as the initial solution of the elastic parameters of the target well logging; When it is determined that the error between the elastic parameter of the target well logging and the initial solution does not meet the preset error requirement, the initial regularization parameter is adjusted, and the previous step is returned to be executed until the error between the initial solution and the elastic parameter of the target well logging meets the preset error requirement, thereby obtaining the specified regularization parameter.

4. The prestack inversion method of improved second-order oscillation particle swarm optimization according to claim 1 is characterized in that: Under the constraints of the solution space, the first objective function is optimized based on an improved second-order oscillation particle swarm optimization algorithm and angle gather data of the target seismic trace, including: Substituting the angle gather data of the target seismic trace into the first objective function to obtain a second objective function for inverting the elastic parameters of the target seismic trace; Under the constraint of the solution space, a second initial particle group for solving the elastic parameters of the target seismic trace is randomly initialized; wherein, in the second initial particle group, the position of each particle corresponds one-to-one to an optional solution of the elastic parameters of the target seismic trace; The second objective function is optimized using an improved second-order oscillation particle swarm optimization algorithm, and the optimal solution that reaches a preset number of iterations is used as the elastic parameter of the target seismic trace.

5. The prestack inversion method of improved second-order oscillation particle swarm optimization according to claim 1, characterized in that: In the improved second-order oscillating particle swarm optimization algorithm, the update equation of particle velocity is expressed as: Among them, 3f represents the particle dimension, f represents the number of sampling points in each seismic trace, represents the velocity of the i-th particle in the k-th iteration, v i,3f =[v i,1 v i,2 …v i,3f ] 1×3f , ω represents the inertia weight, represents the velocity of the i-th particle in the k-1th iteration, c1 represents the individual learning factor, c2 represents the social learning factor, r1 and r2 represent two independent uniform random numbers between [0,1]. represents the optimal position of the current i-th particle, ξ1, ξ2, ξ3, ξ4 represent different second-order oscillation factors, represents the position of the i-th particle in the k-1th iteration, represents the position of the i-th particle in the k-2-th iteration, Indicates the optimal particle position in the current population.

6. The prestack inversion method of improved second-order oscillation particle swarm optimization according to claim 5 is characterized in that: Before specifying the number of iterations, the value range of the second-order oscillation factor is: 0<ξ1<1,0<ξ3<1; After specifying the number of iterations, the value range of the second-order oscillation factor is: ξ1<ξ2-1,ξ3<ξ4-1,0<ξ2<1,0<ξ4<1.

7. The prestack inversion method of improved second-order oscillation particle swarm optimization according to claim 2, characterized in that: The first objective function is expressed as: Wherein, d represents the pre-stack angle gather data of the area to be inverted, G represents the forward operator, m represents the elastic parameter to be solved, λ represents the specified regularization parameter, represents the average value of the elastic parameters of the target well logging, C m The covariance matrix representing the elastic parameters of the target well log.

8. An improved second-order oscillation particle swarm optimization prestack inversion device, characterized in that: include: An acquisition module is used to acquire pre-stack angle gather data, layer data and elastic parameters of target well logging in a target work area; wherein the pre-stack angle gather data is a collection of angle gather data of multiple seismic traces; and the target well logging represents any well logging in the target work area; A determination module, used to determine a solution space of elastic parameters in the target work area based on the elastic parameters of the target well logging and the layer data of the target work area; A first construction module is used to construct a first objective function for inverting the elastic parameters in the target work area based on the pre-stack angle gather data and the elastic parameters of the target well logging; A first optimization module is used to optimize the first objective function based on an improved second-order oscillation particle swarm optimization algorithm and the angle track gather data of the target seismic trace under the constraints of the solution space to obtain an optimal solution for the elastic parameters of the target seismic trace; wherein the improved second-order oscillation particle swarm optimization algorithm includes four different second-order oscillation factors; and the target seismic trace represents any seismic trace among the multiple seismic traces; The second construction module is used to construct the elastic parameter profile of the target work area based on the optimal solution of the elastic parameters of the multiple seismic traces.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, wherein: When the processor executes the computer program, the steps of the prestack inversion method of improved second-order oscillation particle swarm optimization according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and when the computer instructions are executed by a processor, the prestack inversion method of improved second-order oscillation particle swarm optimization according to any one of claims 1 to 7 is implemented.

Images