A seismic spatial structure parameter prediction method, device and equipment and readable medium

Abstract

The application discloses a method for predicting seismic spatial structure parameters, comprising the following steps: reading observed seismic data; defining a prior probability density function of spatial structure parameters based on a variogram type of model parameters and the number of spatial structure parameters; defining a seismic forward operator and calculating a likelihood function between the spatial structure parameters and the observed seismic data based on the seismic forward operator; and calculating a posterior solution of a posterior probability density function of the spatial structure parameters based on the prior probability density function and the likelihood function through a Monte Carlo sampling method. The application also discloses a device for predicting seismic spatial structure parameters, a computer device and a readable storage medium. The application combines the Bayesian theory with the Monte Carlo inversion method, fully utilizes the volume constraint characteristics of seismic data, and simultaneously infers the longitudinal and lateral spatial structure of the underground medium in a data-driven manner.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of geological exploration, and particularly relates to a method and device for predicting seismic spatial structure parameters, equipment and readable medium. BACKGROUND

[0002] The underground medium can be regarded as a large spatial random field, each point in the space represents a random variable, and the points are related in time and space due to the influence of sedimentation, that is, the spatial structure. This correlation is described as a variogram in two-point geostatistics, as a training image in multi-point geostatistics, and as a model covariance matrix in mathematics.

[0003] In the field of geophysical inversion, whether deterministic inversion or stochastic inversion, the geological spatial structure information plays a crucial role. In deterministic inversion, the model covariance matrix is used to calculate the regularization term, control the resolution and stability of the inversion result, and also control the mathematical form features of the inversion solution, such as the smoothness and sparsity of the inversion result. In stochastic inversion, the variogram more significantly controls the geological form features of the stochastic simulation result, such as the lateral extension of the geological body, the vertical thickness variation, and the stratum dip angle. How to use the observation data to infer the spatial structure information of the underground medium has been one of the key problems that geostatistics has been trying to solve.

[0004] Geophysical observation data can be generally divided into two categories: point data and volume data. The point data refers to observation data obtained by directly measuring underground medium, such as logging data, which has a high sampling density in the vertical direction. The volume data refers to observation data obtained by the action of physical laws on underground medium, which is an indirect reflection of underground medium information, such as seismic data obtained by the propagation of seismic waves in underground medium, which has a wide horizontal coverage and can reflect the horizontal and vertical variation information of underground medium. In the estimation of vertical structure information, the experimental variogram is usually calculated by using logging data, and then the spatial structure parameters such as the range and the dip angle are determined by artificial adjustment or under the constraint of least squares criterion, so that the model variogram and the experimental variogram are best fitted (Deutsch and Journel, 1998). This method can better estimate the vertical structure information, but the estimation of horizontal structure information often faces the problems of few wells and uneven distribution. In the estimation of horizontal structure information, the industry currently mainly uses seismic data to extract attribute slices, and then analyzes the continuity of underground medium in different directions in the horizontal direction through slice data, so as to determine the horizontal variogram (Dubrule, 2003; Grana and Dvorkin, 2011; Yang Peijie, 2014). Since the seismic amplitude slices at different positions often have great differences, the horizontal structure information obtained by this method has strong subjective uncertainty. SUMMARY

[0005] Therefore, the embodiment of the present application aims to provide a seismic spatial structure parameter prediction method, device, equipment and readable medium, which combines the Bayesian theory and the Monte Carlo inversion method, fully utilizes the volume constraint characteristics of seismic data, and realizes the simultaneous inference of the vertical and horizontal spatial structures of underground medium in a data-driven manner.

[0006] Based on the above purpose, one aspect of the embodiment of the present application provides a seismic spatial structure parameter prediction method, which comprises the following steps: reading observation seismic data; defining a prior probability density function of spatial structure parameters based on the type of variogram of model parameters and the number of spatial structure parameters; defining a seismic forward operator and calculating a likelihood function between the spatial structure parameters and the observation seismic data based on the seismic forward operator; and calculating a posterior probability density function of the posterior solution of the spatial structure parameters based on the prior probability density function and the likelihood function by using the Monte Carlo sampling method.

[0007] In some embodiments, calculating the likelihood function between the spatial structure parameter and the observed seismic data based on the seismic forward operator comprises: in response to a type of the variation function of the model parameter being a Gaussian variation function and noise being subject to a Gaussian distribution, calculating the likelihood function between the spatial structure parameter and the observed seismic data based on the following formula:

[0008]

[0009] wherein L(Ψ) is the likelihood function, k is a normalization constant, d obs is the observed seismic data, G is the seismic forward operator, m0 is an initial value of the model parameter, C Ψ is a covariance matrix of the parameter model, C n is a covariance matrix of noise.

[0010] In some embodiments, calculating the posterior solution of the posterior probability density function of the spatial structure parameter based on the prior probability density function and the likelihood function by the Monte Carlo sampling method comprises: sampling the prior probability density function, and disturbing a current state of the spatial structure parameter obtained by sampling by the sequential Gibbs algorithm to obtain a candidate state of the spatial structure parameter; calculating the likelihood function of the current state and the likelihood function of the candidate state of the spatial structure parameter respectively, and calculating the posterior solution of the posterior probability density function of the spatial structure parameter based on the likelihood function of the current state and the likelihood function of the candidate state of the spatial structure parameter.

[0011] In some embodiments, calculating the posterior solution of the posterior probability density function of the spatial structure parameter based on the likelihood function of the current state and the likelihood function of the candidate state of the spatial structure parameter comprises: calculating an acceptance probability based on the likelihood function of the current state and the likelihood function of the candidate state of the spatial structure parameter, and judging whether the acceptance probability reaches a preset threshold value; in response to the acceptance probability reaching the preset threshold value, accepting the spatial structure parameter corresponding to the candidate state as an iteration result.

[0012] In some embodiments, the method further comprises: in response to the acceptance probability not reaching the preset threshold value, resampling the prior probability density function, and returning to the step of disturbing the current state of the spatial structure parameter obtained by sampling by the sequential Gibbs algorithm to obtain the candidate state of the spatial structure parameter.

[0013] In some embodiments, calculating the acceptance probability based on the likelihood function of the current state and the likelihood function of the candidate state of the spatial structure parameter comprises: calculating the acceptance probability based on the following formula:

[0014] P Accept = min([1, L(Ψ Propose ) / L(ΨCurrent ])

[0015] where P Accept is the acceptance probability, Ψ Current is the current state of the spatial structure parameters, Ψ Propose is the candidate state of the spatial structure parameters, L(Ψ Current ) is the likelihood function of the current state of the spatial structure parameters, L(Ψ Propose ) is the likelihood function of the candidate state of the spatial structure parameters.

[0016] In some embodiments, the preset threshold is a number randomly selected in a uniformly distributed interval (0, 1).

[0017] In some embodiments, the method further comprises: determining whether the number of iterations is reached; and in response to the number of iterations being reached, outputting the result of each iteration as a sample set of the spatial structure parameters.

[0018] In some embodiments, the method further comprises: in response to the number of iterations not being reached, resampling the prior probability density function and returning to the step of perturbing the current state of the spatial structure parameters obtained by the sequential Gibbs algorithm to obtain the candidate state of the spatial structure parameters.

[0019] In some embodiments, the type of the variation function of the model parameters comprises: a Gaussian variation function, a spherical variation function, an exponential variation function, or a nested combination of different variation functions.

[0020] In some embodiments, the model parameters comprise wave impedance and / or P-wave velocity and / or S-wave velocity and / or density.

[0021] In some embodiments, defining the seismic forward operator comprises: defining the seismic forward operator according to a seismic wavelet and a reflection coefficient.

[0022] Another aspect of the embodiments of the present application also provides a device for predicting seismic spatial structure parameters, comprising: a reading data module configured to read observed seismic data; a first defining module configured to define a prior probability density function of spatial structure parameters based on a type of variation function of model parameters and a number of spatial structure parameters; a second defining module configured to define a seismic forward operator and calculate a likelihood function between the spatial structure parameters and the observed seismic data based on the seismic forward operator; and a sampling and predicting module configured to calculate a posterior solution of a posterior probability density function of the spatial structure parameters based on the prior probability density function and the likelihood function by a Monte Carlo sampling method.

[0023] Still further, an aspect of the present application provides a computer device, comprising: at least one processor; and a memory storing computer instructions executable on the processor, the instructions being executed by the processor to implement the steps of the above method.

[0024] Still further, an aspect of the present application provides a computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of the above method.

[0025] The present application has at least the following beneficial technical effects: the Bayesian theory is combined with the Monte Carlo inversion method, the volume constraint characteristics of seismic data are fully utilized, and the data-driven manner is adopted to simultaneously infer the longitudinal and lateral spatial structure of the underground medium. BRIEF DESCRIPTION OF DRAWINGS

[0026] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other embodiments can be obtained from these drawings without creative labor.

[0027] Figure 1 The schematic diagram of the embodiment of the seismic spatial structure parameter prediction method provided by the present application;

[0028] Figure 2 The flowchart of the embodiment of the seismic spatial structure parameter prediction method provided by the present application;

[0029] Figure 3 The test data of the horizontal stratum model and the inclined stratum model generated by random simulation;

[0030] Figure 4 The histogram distribution diagram of the spatial structure parameter prior model and the posterior model;

[0031] Figure 5 The comparison diagram of the predicted variogram and the real variogram;

[0032] Figure 6 The comparison diagram of the synthetic seismic record and the observed seismic record;

[0033] Figure 7 The schematic diagram of the embodiment of the seismic spatial structure parameter prediction device provided by the present application;

[0034] Figure 8 The schematic diagram of the embodiment of the computer device provided by the present application;

[0035] Figure 9A schematic diagram of an embodiment of the computer readable storage medium provided by the present application is shown. DETAILED DESCRIPTION

[0036] To make the objectives, technical solutions and advantages of the present application clearer, the embodiments of the present application are further described in detail below with reference to the drawings.

[0037] It should be noted that all the expressions of "first" and "second" in the embodiments of the present application are used to distinguish two same-named different entities or different parameters, and it can be seen that "first" and "second" are only used for the convenience of description and should not be understood as a limitation on the embodiments of the present application. The subsequent embodiments will not be described one by one.

[0038] From the published literature, there is no method of directly using seismic data and simultaneously inferring the lateral and vertical structural information of the underground medium in a data-driven manner. When the drilling is sparse or unevenly distributed, the traditional lateral spatial structure estimation method based on logging data and seismic attribute slice data is affected by human subjective factors, and the result has a large deviation.

[0039] In order to solve this problem, the present application combines the Bayesian theory with the Monte Carlo inversion method, fully utilizes the volume constraint characteristics of the seismic data, simultaneously infers the vertical and lateral spatial structure of the underground medium, and evaluates the uncertainty of the prediction result.

[0040] Based on the above purpose, in a first aspect, an embodiment of a seismic spatial structure parameter prediction method is provided. Figure 1 A schematic diagram of an embodiment of the seismic spatial structure parameter prediction method provided by the present application is shown. As shown in Figure 1 The seismic spatial structure parameter prediction method of the present embodiment comprises the following steps:

[0041] 001, reading observed seismic data;

[0042] 002, defining a prior probability density function of the spatial structure parameter based on the type of the model parameter's variation function and the number of the spatial structure parameter;

[0043] 003, defining a seismic forward operator and calculating a likelihood function between the spatial structure parameter and the observed seismic data based on the seismic forward operator; and

[0044] 004, calculating a posterior solution of the posterior probability density function of the spatial structure parameter based on the prior probability density function and the likelihood function by the Monte Carlo sampling method.

[0045] In the present embodiment, the geophysical forward problem has the following matrix expression: d obs =Gm+n. Wherein, dobs For observing seismic data, G is a seismic forward operator, m is a model parameter, and n is a noise term. The seismic forward operator is calculated from a wavelet and a reflection coefficient, and the model parameter is wave impedance, P-wave velocity, S-wave velocity, or density, etc. Meanwhile, the model parameter m is considered as a function of a spatial structure parameter Ψ, i.e., m = f(m0, σ 2 , Ψ), where m0 and σ 2 are the expectation and variance of the model parameter. The function model parameter of the spatial structure parameter is usually expressed as a Gaussian variogram, a spherical variogram, an exponential variogram, or a nested combination of different variograms in the theory of geostatistics.

[0046] When the model is one-dimensional, Ψ 1d = [r1]; when the model is two-dimensional, Ψ 2d = [r1, r2, θ1]; and when the model is three-dimensional, Ψ 3d = [r1, r2, r3, θ1, θ2, θ3]. Where r1, r2, and r3 represent the main range, the medium range, and the small range, respectively, and θ1, θ2, and θ3 represent the rotation angles of the main axis, the medium axis, and the short axis, respectively.

[0047] According to the Bayesian theory, the probabilistic inversion problem of the spatial structure parameter Ψ is solved by seismic observation data d obs , which can be expressed as: σ(Ψ) = kρ(Ψ)L(Ψ). Where ρ(Ψ) is the prior probability density function of the spatial structure parameter, and k is a normalization constant.

[0048] Since the spatial structure parameter Ψ has a multi-dimensional characteristic, the analytical expression of the prior probability density function ρ(Ψ) cannot be calculated, and therefore the analytical expression of the posterior probability density function σ(Ψ) cannot be directly solved. However, according to the Monte Carlo inversion theory, once a large number of implementations of the prior probability can be obtained, and the likelihood function is known, the posterior solution in a high-dimensional space can be obtained by the Monte Carlo sampling method. There are various kinds of Monte Carlo sampling methods, and the Extended-Metropolis algorithm is adopted in the present application. The advantage of this method is that the prior probability can be directly disturbed by using the sequential Gibbs algorithm to obtain a candidate state, thereby avoiding the shortcoming of the traditional Monte Carlo algorithm that cannot calculate the expression of the proposal distribution in a high-dimensional space. Through this method, the random implementation solution of the to-be-solved structure parameter Ψ can be obtained.

[0049] In this embodiment, within the framework of Bayesian theory, a posterior probability expression for solving spatial structure parameters is established, and the likelihood function expression of spatial structure parameters and seismic data is derived. When solving the posterior probability of high-dimensional spatial structure parameters, a sequential Gibbs sampling algorithm is introduced to perturb the current state and obtain candidate states, overcoming the shortcoming of the traditional Metropolis algorithm in being unable to calculate the proposal distribution expression in high-dimensional space.

[0050] In some embodiments of the present invention, the calculation of the likelihood function between spatial structure parameters and observed seismic data based on the seismic forward modeling operator includes: in response to the fact that the variation function of the model parameters is of the Gaussian variation function type and the noise follows a Gaussian distribution, the likelihood function between the spatial structure parameters and observed seismic data is calculated based on the following formula:

[0051]

[0052] Where L(Ψ) is the likelihood function, k is the normalization constant, and d obs To observe seismic data, G is the seismic forward modeling operator, m0 is the initial value of the model parameters, and C... Ψ Let C be the covariance matrix of the parametric model. n Let be the covariance matrix of the noise.

[0053] In this embodiment, based on Bayesian theory, using seismic observation data d obs The probabilistic inversion problem of solving the spatial structure parameter Ψ can be expressed as: σ(Ψ)=kρ(Ψ)L(Ψ). Here, ρ(Ψ) is the prior probability density function of the spatial structure parameter, and k is a normalization constant. It is assumed that the model parameters and noise both follow a Gaussian distribution, i.e. To highlight the relationship between model parameter m and spatial structure parameter Ψ, C m Use C Ψ To represent, that is The two are equivalent. In this case, the expression for the likelihood function is:

[0054]

[0055] Where L(Ψ) is the likelihood function, k is the normalization constant, and d obs To observe seismic data, G is the seismic forward modeling operator, m0 is the initial value of the model parameters, and C... Ψ Let C be the covariance matrix of the parametric model. n Let be the covariance matrix of the noise.

[0056] In some embodiments of the present application, the posterior solution of the posterior probability density function of the spatial structure parameter is calculated based on the prior probability density function and the likelihood function by a Monte Carlo sampling method, comprising: sampling the prior probability density function, and disturbing the current state of the spatial structure parameter obtained by sampling to obtain a candidate state of the spatial structure parameter by a sequential Gibbs algorithm; calculating the likelihood function of the current state and the candidate state of the spatial structure parameter respectively, and calculating the posterior solution of the posterior probability density function of the spatial structure parameter based on the likelihood function of the current state and the candidate state of the spatial structure parameter.

[0057] In some embodiments of the present application, the posterior solution of the posterior probability density function of the spatial structure parameter is calculated based on the likelihood function of the current state and the candidate state of the spatial structure parameter, comprising: calculating the acceptance probability based on the likelihood function of the current state and the candidate state of the spatial structure parameter, and judging whether the acceptance probability reaches a preset threshold; in response to the acceptance probability reaching the preset threshold, accepting the spatial structure parameter corresponding to the candidate state as an iteration result.

[0058] In some embodiments of the present application, the method further comprises: in response to the acceptance probability not reaching the preset threshold, resampling the prior probability density function, and returning to the step of disturbing the current state of the spatial structure parameter obtained by sampling to obtain a candidate state of the spatial structure parameter by a sequential Gibbs algorithm.

[0059] In some embodiments of the present application, the acceptance probability is calculated based on the likelihood function of the current state and the candidate state of the spatial structure parameter, comprising: calculating the acceptance probability based on the following formula:

[0060] P Accept =min([1,L(Ψ Propose ) / L(Ψ Current )])

[0061] Wherein, P Accept is the acceptance probability, Ψ Current is the current state of the spatial structure parameter, Ψ Propose is the candidate state of the spatial structure parameter, L(Ψ Current ) is the likelihood function of the current state of the spatial structure parameter, and L(Ψ Propose ) is the likelihood function of the candidate state of the spatial structure parameter.

[0062] In some embodiments of the present application, the preset threshold is a number randomly selected in the interval (0, 1) of uniform distribution.

[0063] In some embodiments of the present application, the method further comprises: determining whether the number of iterations is reached; and in response to the number of iterations being reached, outputting the result of each iteration as a sample set of the spatial structure parameters.

[0064] In some embodiments of the present application, the method further comprises: in response to the number of iterations not being reached, resampling the prior probability density function and returning to the step of perturbing the current state of the spatial structure parameters obtained by sampling to obtain a candidate state of the spatial structure parameters by the sequential Gibbs algorithm.

[0065] In some embodiments of the present application, the type of the variation function of the model parameters comprises: a Gaussian variation function, a spherical variation function, an exponential variation function, or a nested combination of different variation functions.

[0066] In some embodiments of the present application, the model parameters comprise wave impedance and / or P-wave velocity and / or S-wave velocity and / or density.

[0067] In some embodiments of the present application, defining the seismic forward operator comprises: defining the seismic forward operator according to a seismic wavelet and a reflection coefficient.

[0068] The specific embodiments of the present application are further described below according to specific embodiments. Figure 2 An embodiment of the method for predicting seismic spatial structure parameters provided by the present application is shown in the flowchart shown in FIG. 1. Figure 2 As shown in FIG. 1, the flow comprises:

[0069] Step 1: reading observed seismic data;

[0070] Step 2: defining a prior model of the spatial structure parameters ρ(Ψ);

[0071] Step 3: defining a seismic forward operator G according to a seismic wavelet and a reflection coefficient;

[0072] Step 4: sampling the prior model ρ(Ψ) to obtain a current state of the spatial structure parameters Ψ Current ;

[0073] Step 5: perturbing the current state Ψ Current to obtain a candidate state Ψ Propose of the spatial structure parameters by the sequential Gibbs algorithm;

[0074] Step 6: calculating the likelihood functions L(Ψ Current ) and L(Ψ Propose ) of the current state and the candidate state of the spatial structure parameters with the observed seismic data according to the seismic forward operator;

[0075] Step 7: calculating the acceptance probability P Accept = min([1, L(Ψ Propose) / L(Ψ Current )]);

[0076] Step 8: Randomly select a number u from the interval (0,1) according to a uniform distribution. If P Accept If u ≥ 0, then accept the candidate state to obtain the i-th iteration result Ψ of the spatial structure parameters. i =Ψ Propose Otherwise Ψ i =Ψ Current Repeat steps 4-7 until P. Accept ≥u;

[0077] Step 9: Repeat steps 4-8 to obtain the converged spatial structure parameter sample set {Ψ1,Ψ2,...,Ψ}. n}

[0078] To verify the effectiveness of the spatial structure parameter prediction method, horizontal and inclined strata models were designed for validation. Figure 3 The figures shown are test data from horizontal and inclined stratigraphic models generated using unconditional random simulation. Figure 3 In the reference models shown in (a) and (c), the mean and standard deviation of the logarithmic wave impedance are set to 9 (m / s·kg / m²). 3 ) and 0.2 (m / s·kg / m 3 The horizontal stratigraphic model includes two spatial structure parameters: lateral range and longitudinal range, set to 70 (traces) and 9 (ms) respectively; the inclined stratigraphic model includes three spatial structure parameters: lateral range, longitudinal range, and dip angle (the angle with the vertical direction), set to 70 (traces), 9 (ms), and 80° respectively. Figure 3 Figures (b) and (d) show the synthetic seismic data corresponding to the reference model, calculated using a 30Hz Ricker wavelet and logarithmic domain wave impedance reference model, with 10% random noise added. The corresponding problem is then to... Figure 3 Using the noisy seismic data in (b) and (d) as input, the spatial structure parameters of the horizontal stratigraphic model and the inclined stratigraphic model are estimated respectively by the present invention.

[0079] Figure 4 The figure shows the spatial structure parameter prediction results obtained after 50,000 iterations of sampling. The gray histogram represents the defined prior model of spatial structure parameters, and the black histogram represents the posterior model of spatial structure parameters calculated by this invention. Figure 4 Figures (a) and (b) show the predicted lateral and longitudinal ranges of the horizontal stratigraphic model. Figure 4(c), (d) and (e) respectively show the lateral variation, the vertical variation and the dip angle prediction results of the inclined stratum model. It can be seen that the posterior distribution is more concentrated than the prior distribution given as a uniform distribution, and the prediction result of the lateral variation is 56 (m), and the prediction result of the vertical variation is 10 (ms), which is consistent with the true lateral and vertical variations. Meanwhile, it can be seen from (e) that the stratum dip angle has the maximum posterior probability at 78°, which is also consistent with the true value. Figure 4

[0080] Figure 5 It is shown that the comparison between the variogram calculated according to the predicted spatial structure parameters and the variogram calculated according to the true spatial structure parameters, wherein Figure 5 (a) shows the variogram prediction results of the spatial structure parameters of the horizontal stratum model in the horizontal direction and the vertical direction, Figure 5 (b) shows the variogram prediction results of the spatial structure parameters of the inclined stratum model in the main variation direction (along the dip angle direction), and it can be seen that the predicted variogram is consistent with the true variogram, wherein the vertical variogram is closer to the true variogram, and the lateral variogram has a small difference from the true variogram.

[0081] Figure 6 It is shown that the comparison between the synthetic seismic record and the observed seismic record, and the virtual waveform is the synthetic seismic record according to the predicted spatial structure parameters and the seismic forward operator, and the realization waveform area homodromous axis is the input seismic record. From Figure 6 It can be seen from the comparison between the synthetic seismic record and the true seismic record that the synthetic seismic record is consistent with the true seismic record, which proves the correctness of the estimated spatial structure parameter results.

[0082] It should be particularly pointed out that each step in each embodiment of the above-mentioned seismic spatial structure parameter prediction method can be crossed, replaced, added, deleted and reduced, and therefore, these reasonable permutation and combination transformations also belong to the protection scope of the present application, and the protection scope of the present application should not be limited to the embodiments.

[0083] Based on the above purpose, a second aspect of the embodiments of the present application provides a seismic spatial structure parameter prediction device. Figure 7 It is shown that the schematic diagram of the embodiment of the seismic spatial structure parameter prediction device provided by the present application. As shown in Figure 7 ​As shown, the seismic spatial structure parameter prediction device of the embodiment of the present application comprises the following modules: a reading data module 011 configured to read observed seismic data; a first defining module 012 configured to define a prior probability density function of a spatial structure parameter based on a type of a model parameter's variation function and a number of spatial structure parameters; a second defining module 013 configured to define a seismic forward operator and calculate a likelihood function between the spatial structure parameter and the observed seismic data based on the seismic forward operator; and a sampling prediction module 014 configured to calculate a posteriori solution of a posteriori probability density function of the spatial structure parameter based on the prior probability density function and the likelihood function by a Monte Carlo sampling method.

[0084] Based on the above purpose, a third aspect of the embodiment of the present application provides a computer device. Figure 8 As shown is a schematic diagram of an embodiment of the computer device provided by the present application. Figure 8 As shown, the computer device of the embodiment of the present application comprises the following devices: at least one processor 021; and a memory 022, the memory 022 storing computer instructions 023 executable on the processor, and the instructions are executed by the processor to implement the steps of the above method.

[0085] The present application further provides a computer readable storage medium. Figure 9 As shown is a schematic diagram of an embodiment of the computer readable storage medium provided by the present application. Figure 9 As shown, the computer readable storage medium 031 stores a computer program 032 which is executed by the processor to execute the above method.

[0086] Finally, it needs to be explained that those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by a computer program to instruct related hardware, and the program of the seismic spatial structure parameter prediction method can be stored in a computer readable storage medium, and when the program is executed, it can include the processes of the above-mentioned embodiment methods. The storage medium of the program can be a magnetic disc, an optical disc, a read-only memory (ROM) or a random access memory (RAM) and the like. The above-mentioned computer program embodiment can achieve the same or similar effect as any of the above-mentioned method embodiments.

[0087] In addition, the method disclosed by the embodiment of the present application can also be implemented as a computer program executed by a processor, which can be stored in a computer readable storage medium. When the computer program is executed by the processor, the above-mentioned functions defined in the method disclosed by the embodiment of the present application are executed.

[0088] Furthermore, the method steps and system elements described above can also be implemented using a controller and a computer readable storage medium for storing a computer program which causes the controller to implement the steps or element functions described above.

[0089] Those of skill would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the disclosure herein can be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans can implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present embodiments.

[0090] In one or more exemplary designs, the functions described can be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions can be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media can be any available media that can be accessed by a general purpose or special purpose computer. By way of example, and not limitation, such computer-readable media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code means in the form of instructions or data structures and that can be accessed by a general-purpose or special-purpose computer, or a general-purpose or special-purpose processor. Also, any connection is properly termed a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or other wire-based, fiber-optic based, or wireless technologies, then the coaxial cable, fiber optic cable, twisted pair, DSL, or other wire-based, fiber-optic based, or wireless technologies are included in the definition of medium. Disk and disc, as used herein, includes compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and blu-ray disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.

[0091] The above are exemplary embodiments disclosed by the present application, but it should be noted that various changes and modifications can be made without departing from the scope of the embodiments disclosed by the present application defined by the claims. The functions, steps and / or actions of the method claims described herein need not be performed in any particular order. Furthermore, although elements of the embodiments disclosed by the present application can be described or claimed in individual forms, they can also be implemented together unless explicitly restricted otherwise.

[0092] It should be understood that, as used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising", or "includes" and / or "including" when used herein, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0093] The above-mentioned embodiment number of the embodiments of the present application is only for description, not representing the advantages or disadvantages of the embodiments.

[0094] Those skilled in the art can understand that all or part of the steps of the above-mentioned embodiments can be completed by hardware, or by program instructing relevant hardware to complete, and the program can be stored in a computer readable storage medium, which can be read-only memory, magnetic disk or optical disk, etc.

[0095] Those skilled in the art should understand that the above discussion of any embodiment is only exemplary, and is not intended to imply that the scope of the embodiments disclosed by the present application (including the claims) is limited to these examples; under the idea of the embodiments of the present application, the above embodiments or technical features in different embodiments can also be combined, and there are many other changes of the above different aspects of the embodiments of the present application. In order to be brief, they are not provided in details. Therefore, any omission, modification, equivalent replacement, improvement, etc. made in the spirit and principle of the embodiments of the present application shall be included in the protection scope of the embodiments of the present application.

Claims

1. A method for predicting spatial structure parameters of an earthquake, characterized in that, The method comprises the following steps: reading observed seismic data; defining a prior probability density function of a spatial structure parameter based on a type of a variogram of a model parameter and a number of spatial structure parameters; defining a seismic forward operator and calculating a likelihood function between the spatial structure parameter and the observed seismic data based on the seismic forward operator; and calculating a posterior solution of a posterior probability density function of the spatial structure parameter based on the prior probability density function and the likelihood function by a Monte Carlo sampling method, wherein the calculating the likelihood function between the spatial structure parameter and the observed seismic data based on the seismic forward operator comprises: in response to the type of the variogram of the model parameter being a Gaussian variogram and noise obeying a Gaussian distribution, calculating the likelihood function between the spatial structure parameter and the observed seismic data based on the following formula: wherein, is a likelihood function, is a normalization constant, is observed seismic data, is a seismic forward operator, is an initial value of a model parameter, is a covariance matrix of a parameter model, is a covariance matrix of noise.

2. The method of claim 1, wherein, the calculating the posterior solution of the posterior probability density function of the spatial structure parameter based on the prior probability density function and the likelihood function by the Monte Carlo sampling method comprises: sampling the prior probability density function and perturbing a current state of the spatial structure parameter sampled to obtain a candidate state of the spatial structure parameter by a sequential Gibbs algorithm; calculating a likelihood function of the current state and a likelihood function of the candidate state of the spatial structure parameter respectively and calculating the posterior solution of the posterior probability density function of the spatial structure parameter based on the likelihood function of the current state and the likelihood function of the candidate state of the spatial structure parameter.

3. The method of predicting seismic spatial structure parameters according to claim 2, characterized in that, the calculating the posterior solution of the posterior probability density function of the spatial structure parameter based on the likelihood function of the current state and the likelihood function of the candidate state of the spatial structure parameter comprises: calculating an acceptance probability based on the likelihood function of the current state and the likelihood function of the candidate state of the spatial structure parameter and judging whether the acceptance probability reaches a preset threshold value; in response to the acceptance probability reaching the preset threshold value, accepting the spatial structure parameter corresponding to the candidate state as an iteration result.

4. The method for predicting seismic spatial structure parameters according to claim 3, characterized in that, further comprising: in response to the acceptance probability not reaching the preset threshold value, resampling the prior probability density function and returning to the step of perturbing the current state of the spatial structure parameter sampled to obtain the candidate state of the spatial structure parameter by the sequential Gibbs algorithm.

5. The method of claim 3, wherein, the calculating the acceptance probability based on the likelihood function of the current state and the likelihood function of the candidate state of the spatial structure parameter comprises: calculating the acceptance probability based on the following formula: wherein, is the acceptance probability, is the current state of the spatial structure parameters, is the candidate state of the spatial structure parameters, is the likelihood function of the current state of the spatial structure parameters, is the likelihood function of the candidate state of the spatial structure parameters.

6. The method of predicting seismic spatial structure parameters according to claim 3, wherein, the preset threshold value is a number randomly drawn in an interval (0, 1) of a uniform distribution.

7. The method of claim 3, wherein, further comprising: judging whether an iteration number is reached; in response to the iteration number being reached, outputting each iteration result as a spatial structure parameter sample set.

8. The method for predicting seismic spatial structure parameters according to claim 7, characterized in that, further comprising: in response to the iteration number not being reached, resampling the prior probability density function and returning to the step of perturbing the current state of the spatial structure parameter sampled to obtain the candidate state of the spatial structure parameter by the sequential Gibbs algorithm.

9. The method of predicting seismic spatial structure parameters according to claim 1, wherein, the type of the variogram of the model parameter comprises a Gaussian variogram, a spherical variogram, an exponential variogram or a nested combination of different variograms.

10. The method of claim 1, wherein, the model parameter comprises wave impedance and / or P-wave velocity and / or S-wave velocity and / or density.

11. The method of predicting seismic spatial structure parameters according to claim 1, wherein, The defining seismic forward operator comprises: The seismic forward operator is defined according to a seismic wavelet and a reflection coefficient.

12. A device for predicting seismic spatial structure parameters, characterized in that it comprises: The method comprises: a reading data module configured to read observed seismic data; a first defining module configured to define a prior probability density function of a spatial structure parameter based on a type of a variation function of a model parameter and a number of spatial structure parameters; a second defining module configured to define a seismic forward operator and calculate a likelihood function between the spatial structure parameter and the observed seismic data based on the seismic forward operator; and a sampling prediction module configured to calculate a posterior solution of a posterior probability density function of the spatial structure parameter based on the prior probability density function and the likelihood function by a Monte Carlo sampling method, wherein the second defining module is configured to calculate the likelihood function between the spatial structure parameter and the observed seismic data based on the following formula in response to the type of the variation function of the model parameter being a Gaussian variation function and noise obeying a Gaussian distribution: wherein, is a likelihood function, is a normalization constant, is observed seismic data, is a seismic forward operator, is an initial value of a model parameter, is a covariance matrix of a parameter model, is a covariance matrix of noise.

13. A computer device, characterized by The method comprises: at least one processor; and a memory storing computer instructions executable on the processor, the instructions being executed by the processor to implement the steps of the method of any one of claims 1-11.

14. A computer-readable storage medium, the computer-readable storage medium storing a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method of any one of claims 1-11.

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