A seismic data processing method and device based on multiple types of prior information

Abstract

The application provides a seismic data processing method and device based on multiple types of prior information, and belongs to the technical field of oil and gas seismic exploration. The method comprises the following steps: based on original seismic data to be processed and corresponding well logging data, a seismic sparse representation target function coupled with multiple types of prior information is constructed, wherein the multiple types of prior information comprise low-frequency impedance prior information of the well logging data, seismic amplitude envelope prior information of the original seismic data, and support position prior information; a three-dimensional multi-channel Bayesian orthogonal matching pursuit algorithm is used to solve a maximum a posteriori probability density solution of the seismic sparse representation target function, wherein the maximum a posteriori probability density solution comprises a target reflection coefficient sequence of the original seismic data; a target wideband Ricker wavelet is obtained, and seismic data reconstruction is performed based on the target wideband Ricker wavelet and the target reflection coefficient sequence, so that target seismic data is obtained. According to the application, multiple types of prior information can be coupled to reconstruct seismic data, and the resolution of the seismic data is improved.

Description

Technical Field

[0001] This invention relates to the field of oil and gas seismic exploration technology, and in particular to a seismic data processing method and apparatus based on multiple types of prior information. Background Technology

[0002] As oil and gas exploration becomes increasingly sophisticated, the focus is shifting from conventional structural reservoirs to complex and concealed lithological reservoirs, placing ever higher demands on seismic data resolution. Given the complex surface, structure, and reservoir characteristics of oil and gas seismic exploration, the resolution of conventional band-limited seismic data is insufficient to accurately characterize thin interbedded layers, micro-fractures, and complex lithological traps. Therefore, broadening the seismic frequency band and improving seismic data resolution are crucial for enhancing the accuracy of reservoir description and quantitative interpretation. In recent years, domestic and international experts have researched various high-resolution seismic processing methods, which can be broadly categorized into three main types: deconvolution and inverse Q-filtering, compressed sensing and wavelet shaping, and deep learning super-resolution processing techniques. Deconvolution and inverse Q-filtering primarily improve seismic data resolution through inversion of compressed wavelets and seismic wave absorption attenuation compensation. Deep learning super-resolution processing methods utilize image processing and artificial intelligence technologies to directly learn the mapping relationship from low-resolution to high-resolution data, achieving high-definition processing of seismic images. Compressed sensing and wavelet shaping techniques are mainly used to reconstruct high-resolution seismic data by correcting broadband Ricker wavelets or broadband trapezoidal wavelets, under the premise of sparse seismic data representation.

[0003] Matching pursuit algorithms based on classical Morlet wavelets and Ricker wavelets have played a crucial role in time-frequency decomposition of non-stationary signals, seismic data regularization, high-resolution processing and inversion, and denoising. Matching pursuit-based high-resolution seismic processing is currently one of the most mainstream methods. However, matching pursuit algorithms based on greedy search strategies typically face challenges such as low computational efficiency, poor lateral continuity, and strong dependence on wavelet dictionaries. These characteristics present numerous challenges to high-resolution matching pursuit processing. Experts have primarily addressed these issues by developing multi-channel regularized matching pursuit algorithms, fast matching pursuit algorithms, and dynamic dictionary learning algorithms. However, current matching pursuit-based high-resolution seismic processing methods are generally data-driven, neglecting the instantaneous amplitude information contained in seismic data, failing to consider the high-frequency reflectivity and low-frequency prior information contained in well logging data, lacking quantitative optimization mechanisms for the effective frequency band, and requiring further improvement in the fidelity of high-resolution seismic data processing results. Therefore, there is an urgent need to develop high-fidelity seismic data sparse representation and high-resolution data processing techniques that couple multiple types of prior knowledge. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide a seismic data processing method and apparatus based on multiple types of prior information, which can couple multiple types of prior information to reconstruct seismic data and improve the resolution of seismic data.

[0005] According to one aspect of the present invention, a seismic data processing method based on multiple types of prior information is provided, the method comprising:

[0006] Based on the raw seismic data to be processed and the corresponding well logging data, a seismic sparse characterization objective function coupled with multiple types of prior information is constructed. The multiple types of prior information include the low-frequency impedance prior information of the well logging data, the seismic amplitude envelope prior information of the raw seismic data, and the support location prior information.

[0007] The maximum a posteriori probability density solution of the seismic sparse characterization objective function is obtained by using a three-dimensional multichannel Bayesian orthogonal matching pursuit algorithm. The maximum a posteriori probability density solution includes the target reflection coefficient sequence of the original seismic data.

[0008] The target broadband Ricker wavelet is acquired, and seismic data is reconstructed based on the target broadband Ricker wavelet and the target reflection coefficient sequence to obtain the target seismic data.

[0009] According to another aspect of the present invention, a seismic data processing apparatus based on multiple types of prior information is provided, the apparatus comprising:

[0010] The objective function construction unit is used to construct a seismic sparse characterization objective function coupled with multiple types of prior information based on the raw seismic data to be processed and the corresponding well logging data. The multiple types of prior information include low-frequency impedance prior information of the well logging data, seismic amplitude envelope prior information and support location prior information of the raw seismic data.

[0011] The solution unit is used to solve the maximum a posteriori probability density solution for the seismic sparse characterization objective function using a three-dimensional multichannel Bayesian orthogonal matching pursuit algorithm. The maximum a posteriori probability density solution includes the target reflection coefficient sequence of the original seismic data.

[0012] The reconstruction unit is used to acquire the target broadband Ricker wavelet, and to reconstruct the seismic data based on the target broadband Ricker wavelet and the target reflection coefficient sequence to obtain the target seismic data.

[0013] According to another aspect of the present invention, an electronic device is provided, comprising:

[0014] Processor; and

[0015] Stored program memory,

[0016] The program includes instructions that, when executed by the processor, cause the processor to perform the seismic data processing method based on multiple types of prior information.

[0017] According to another aspect of the present invention, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause a computer to execute the above-described seismic data processing method based on multiple types of prior information.

[0018] This invention proposes a high-resolution seismic data processing method based on a multi-channel Bayesian orthogonal matching pursuit algorithm coupled with multiple types of prior knowledge, within a sparse representation and Bayesian framework. First, based on the Bayesian maximum a posteriori probability solution, a sparse seismic representation objective function is constructed, coupling low-frequency impedance prior information, seismic amplitude envelope prior information, and supporting location prior information. Then, combining the matching pursuit framework, a multi-channel Bayesian orthogonal matching pursuit algorithm suitable for 3D seismic data is proposed. Finally, based on the sparse representation results, a broadband Ricker wavelet frequency and phase optimization method using particle swarm optimization is employed to achieve high-resolution seismic data processing with well logging constraints. Numerical results show that this method can improve the matching accuracy of seismic reflection coefficient location and amplitude. Application to actual 3D seismic data and stratigraphic slice analysis demonstrate the practicality and effectiveness of this method. Attached Figure Description

[0019] Further details, features, and advantages of the invention are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which:

[0020] Figure 1 A flowchart of a seismic data processing method based on multiple types of prior information according to an exemplary embodiment of the present invention is shown.

[0021] Figure 2 A schematic diagram of sparse reflection coefficients reconstructed using different matching pursuit methods in a noise-free environment according to an exemplary embodiment of the present invention is shown.

[0022] Figure 3 A schematic diagram of sparse reflection coefficients reconstructed using different matching pursuit methods is shown in the case of a signal-to-noise ratio of 2 according to an exemplary embodiment of the present invention.

[0023] Figure 4 A schematic diagram of the sparse characterization results of three-dimensional theoretical seismic data under noise-free conditions provided by an exemplary embodiment of the present invention is shown, wherein (a) corresponds to the original seismic data, (b) corresponds to the true reflection coefficient model, (c) corresponds to the sparse characterization results of the conventional orthogonal matching pursuit method, and (d) corresponds to the sparse characterization results of the present invention.

[0024] Figure 5A schematic diagram of the sparse characterization results of three-dimensional theoretical seismic data with a signal-to-noise ratio of 1 provided by an exemplary embodiment of the present invention is shown, wherein (a) corresponds to seismic data with a signal-to-noise ratio of 1, (b) corresponds to low-frequency impedance prior information, (c) corresponds to amplitude envelope prior information, and (d) corresponds to the sparse characterization results of the present invention.

[0025] Figure 6 The diagram illustrates the well-controlled high-resolution processing results of three-dimensional theoretical seismic data with a signal-to-noise ratio of 0.5, provided by an exemplary embodiment of the present invention. (a) corresponds to the P-wave impedance interpretation results of 15 pseudo-wells; (b) corresponds to the Pearson correlation coefficient calculated from the high-resolution seismic data and well logging data; and (c) corresponds to the data with a center frequency. Frequency integral upper limit , The high-resolution seismic spectrum, (d) corresponds to the high-resolution seismic data processed by this invention;

[0026] Figure 7 A schematic diagram of sparse characterization and high-resolution processing results of actual seismic data provided according to an exemplary embodiment of the present invention is shown, wherein (a) corresponds to the original seismic data, (b) corresponds to the seismic amplitude envelope, (c) corresponds to the sparse characterization result based on OMP, (d) corresponds to the high-resolution processing result based on OMP, (e) corresponds to the sparse characterization result of the present invention, and (f) corresponds to the high-resolution processing result of the present invention.

[0027] Figure 8 A schematic diagram comparing the spectra of seismic data before and after processing according to an exemplary embodiment of the present invention is shown, wherein (a) corresponds to the spectrum of the original seismic data, and (b) corresponds to the seismic spectrum after high-resolution processing;

[0028] Figure 9 A schematic diagram comparing stratigraphic slices of seismic data before and after processing according to an exemplary embodiment of the present invention is shown, wherein (a) corresponds to stratigraphic slice III-1 of the original seismic data, (b) corresponds to stratigraphic slice III-1 of the high-resolution seismic data, (c) corresponds to stratigraphic slice III-2 of the original seismic data, and (d) corresponds to stratigraphic slice III-2 of the high-resolution seismic data.

[0029] Figure 10 A schematic block diagram of a seismic data processing apparatus based on multiple types of prior information according to an exemplary embodiment of the present invention is shown.

[0030] Figure 11 A structural block diagram of an exemplary electronic device that can be used to implement embodiments of the present invention is shown. Detailed Implementation

[0031] Embodiments of the present invention will now be described in more detail with reference to the accompanying drawings. While some embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the invention. It should be understood that the accompanying drawings and embodiments are for illustrative purposes only and are not intended to limit the scope of protection of the invention.

[0032] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0033] The term "comprising" and its variations as used herein are open-ended, meaning "including but not limited to". The term "based on" means "at least partially based on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Definitions of other terms will be given in the following description. It should be noted that the concepts of "first", "second", etc., mentioned in this invention are used only to distinguish different devices, modules, or units, and are not intended to limit the order of functions performed by these devices, modules, or units or their interdependencies.

[0034] It should be noted that the terms "a" and "a plurality of" used in this invention are illustrative rather than restrictive. Those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0035] The names of the messages or information exchanged between the multiple devices in the embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of these messages or information.

[0036] This invention provides a seismic data processing method based on multiple types of prior information, which can be performed by a terminal, server, and / or other devices with processing capabilities. The method provided in this embodiment can be performed by any of the aforementioned devices, or by multiple devices working together; this invention does not limit this.

[0037] The following will refer to Figure 1 The flowchart of the seismic data processing method based on multi-type prior information is shown, and the method is introduced. The method includes the following steps 101-103.

[0038] Step 101: Based on the raw seismic data to be processed and the corresponding well logging data, construct a seismic sparse characterization objective function coupled with multiple types of prior information. The multiple types of prior information include low-frequency impedance prior information of well logging data, seismic amplitude envelope prior information of raw seismic data, and support location prior information.

[0039] In one possible implementation, based on Bayesian inference and the theory of sparse seismic data representation, low-frequency impedance prior information reflecting the macroscopic stability characteristics of the formation is first extracted from well logging data. Then, the amplitude envelope prior information indicating the location of the reflection interface is obtained from the original seismic data through Hilbert transform, and the supporting location prior information is quantified by combining the lateral continuity characteristics of the formation. Next, the three types of prior information are transformed into corresponding likelihood function constraints or prior probability distributions, which are then integrated into a sparse representation model based on the L0 norm. A joint objective function of "data fitting error + sparsity constraint + multi-type prior constraint" is constructed, so that the objective function not only fits the characteristics of the observation data but is also constrained by the real physical laws of the formation. This lays the mathematical foundation for subsequent accurate solutions to seismic sparse representation results and improves the accuracy of reflection coefficient matching.

[0040] Specifically, the objective function construction process in step 101 above can be as follows:

[0041] The L0 norm-based sparse representation of earthquakes is to transform the raw earthquake data... Decomposed into an atomic dictionary A weighted linear superposition of a series of atoms. Atom dictionary. It is usually constructed based on classical Morlet wavelets, classical Ricker wavelets, or well-calibrated seismic wavelets.

[0042] The problem of sparse characterization of 3D seismic data can be represented as determining a sparse sequence of 3D seismic reflection coefficients from a redundant atomic dictionary, controlling the sparsity of the sequence (i.e. minimizing the number of non-zero values) through L0 norm regularization, and accurately characterizing the stratigraphic reflection features in the most concise form while ensuring the accuracy of data fitting.

[0043] Therefore, a target reflection coefficient sequence to be solved can be constructed based on the raw seismic data to be processed. As shown in equation (1):

[0044] (1)

[0045] In the formula, Represents the raw three-dimensional seismic data. Represents a three-dimensional seismic reflection coefficient sequence. Represents an atomic dictionary. These are the weighting coefficients for L0 norm regularization. This represents the L0 norm of the three-dimensional seismic reflection coefficient sequence.

[0046] In order to more easily couple multiple types of prior information, in equation (1) It can be expressed as follows (2):

[0047] (2)

[0048] In the formula, This indicates the number of atoms in the atom dictionary. Indicates the location Earthquake Channel i The sparse characterization coefficient of each atom, , Indicates the first i The seismic reflection coefficient of an individual atom. It is the first in the atomic dictionary i One atom, It is Gaussian random noise with a mean of 0.

[0049] Based on equations (1) and (2) above, the seismic data likelihood function in the Bayesian framework can be constructed. As shown in equation (3):

[0050] (3)

[0051] In the formula, This indicates that the mean is 0 and the covariance is... The Gaussian distribution.

[0052] To improve the stability of sparse representations of seismic data, low-frequency impedance prior information can be constructed based on well logging data corresponding to the original seismic data. likelihood function As shown in equation (4):

[0053] (4)

[0054] In the formula, Let be an integral matrix. This is the covariance matrix of the low-frequency impedance in the well logging data.

[0055] Based on raw seismic data and well logging data, a three-dimensional seismic reflection coefficient sequence can be constructed. The prior information is as follows (5):

[0056] (5)

[0057] In the formula, It is the standard deviation of the seismic reflection coefficient of the i-th atom. It is the first iStandard deviation of well logging data reflection coefficients estimated based on generalized Gaussian distribution at individual atomic locations. It is the first i The absolute value of the seismic reflection amplitude at each sampling point.

[0058] Equation (5) incorporates high-frequency information from well logging data and mid-frequency amplitude information from seismic data, which helps improve the matching accuracy of weighting coefficients.

[0059] Furthermore, based on the prior information of seismic amplitude envelope and support location from the original seismic data, a sparse characterization coefficient sequence can be constructed. Prior probability density distribution As shown in equation (6):

[0060] (6)

[0061] In the formula, Indicates that it follows the parameter. Bernoulli distribution, Indicates the first in the atomic dictionary i The probability of an atom being selected As shown in equation (7):

[0062] (7)

[0063] In the formula, These are the probability normalization coefficients. Represents raw seismic data The Hilbert transform.

[0064] By combining equations (3) to (7), a seismic sparse representation objective function coupling multiple types of prior knowledge is constructed, as shown in equation (8):

[0065] (8)

[0066] In the formula, This represents the sequence of sparse representation coefficients of the target to be solved.

[0067] For the optimization problem shown in equation (8), we developed an orthogonal matching pursuit algorithm to solve the target reflection coefficient sequence. and target sparse characterization coefficient sequence The maximum a posteriori probability density solution.

[0068] Step 102: Using the three-dimensional multichannel Bayesian orthogonal matching pursuit algorithm, the maximum a posteriori probability density solution is obtained for the seismic sparse characterization objective function. The maximum a posteriori probability density solution includes the target reflection coefficient sequence of the original seismic data.

[0069] In one possible implementation, the algorithm uses Bayesian maximum a posteriori probability inference as its core framework, combining the lateral continuity characteristics of 3D multichannel data with the greedy search logic of orthogonal matching pursuit to solve the seismic sparse representation objective function constructed in step 101. The algorithm initializes the seismic data residuals, the target reflection coefficient sequence, and the set of matching atoms. In each iteration, based on the correlation between the wavelet atoms and the current residual, it selects the optimal atoms using regularization parameters that support location priors. It then integrates regularization weight coefficients from the low-frequency impedance prior information of well logging data, updates the sparse representation coefficients (i.e., the increment of the target reflection coefficients) and the matching atom weight coefficients using corresponding formulas, and simultaneously smooths the 3D multichannel residuals to ensure lateral consistency and redefines the atom selection probability. This iteration is repeated until a preset number of iterations is reached or the residual is less than a threshold, ultimately outputting the maximum a posteriori probability density solution containing the target reflection coefficient sequence, thus achieving a high-fidelity sparse representation of the seismic data.

[0070] Specifically, the solution process for step 102 above can be as follows:

[0071] Assume the total number of iterations for the 3D multichannel Bayesian orthogonal matching pursuit algorithm is... M Based on equation (8), the first... n The next iteration, the... i The target sparse characterization coefficients at each sampling point , No. n Index of the optimal atom position in the next iteration i * and the n The target reflection coefficient sequence of the next iteration .

[0072] No. n The next iteration, the... i The target sparse characterization coefficients at each sampling point As shown in equation (9):

[0073] (9)

[0074] In the formula, , No. n Seismic data residuals after -1 iteration .

[0075] No. n Index of matching atom positions in the next iteration i * As shown in equation (10):

[0076] (10)

[0077] In the formula, the first n The target reflection coefficient sequence of the next iteration The Middle i Seismic reflection coefficient of matching atoms , It is a regularization parameter used to measure prior information about the atomic position.

[0078] No. n The target reflection coefficient sequence of the next iteration As shown in equation (11):

[0079] (11)

[0080] In the formula, , For the process n The set of matching atoms after the next iteration. , , This represents the regularization weighting coefficient for low-frequency impedance prior information in well logging data.

[0081] Using a three-dimensional multichannel Bayesian orthogonal matching pursuit algorithm, matching atoms are selected in each iteration based on equations (9), (10), and (11), and the target sparse characterization coefficient sequence and the target reflection coefficient sequence are jointly updated. n The seismic data residuals after the next iteration are , No. i The probability of an atom being selected can be redefined based on the residuals of seismic data. , express The Hilbert transform.

[0082] Until after M Sub-iteration or seismic data residuals If the value is less than a preset iteration threshold, the maximum a posteriori probability density solution is obtained. Based on this, sparse representation and reconstruction of seismic data can be stably achieved.

[0083] Step 103: Obtain the target broadband Ricker wavelet, and reconstruct the seismic data based on the target broadband Ricker wavelet and the target reflection coefficient sequence to obtain the target seismic data.

[0084] In one possible implementation, the target broadband Ricker wavelet and the target reflection coefficient sequence are used as core inputs. Based on the convolution generation principle of seismic data "wavelet-reflection coefficient-seismic record", the feature fusion of the two is achieved through time-domain convolution operations. The target broadband Ricker wavelet, as the signal carrier of seismic wave propagation, carries the frequency band and phase characteristics of stratum reflection. The target reflection coefficient sequence, as a mathematical representation of stratum lithological changes, accurately characterizes the location and intensity of the reflection interface. After convolution, the stratum reflection information is transformed into a signal form that conforms to the law of seismic propagation. Finally, the target seismic data with high resolution and fidelity, which can accurately reflect the true characteristics of the underground strata, is reconstructed.

[0085] Optionally, in order to improve the matching degree between the processed high-resolution seismic data (i.e., the target seismic data) and the actual seismic data, the particle swarm optimization algorithm can be used to optimize the frequency integral boundary and phase correction of the basic broadband Ricker wavelet to obtain the target broadband Ricker wavelet, so that the target broadband Ricker wavelet matches the real reflection characteristics of the strata and the actual seismic propagation law.

[0086] In one possible implementation, the core objective is to improve the matching degree between the processed target seismic data and the actual seismic data. Leveraging the global optimization capability of the particle swarm optimization (PSO) algorithm, optimization is performed on the frequency integration boundary (determining the wavelet bandwidth) and phase correction (compensating for propagation phase distortion) of the basic broadband Ricker wavelet (constructed from the integral of a conventional band-limited Ricker wavelet). During the optimization process, the actual formation reflection characteristics (characterized by the formation reflection coefficient calculated from well logging data) and the actual seismic propagation laws (reflected by actual seismic data from the wellbore access) serve as dual constraints. The optimal parameter combination is iteratively searched using the PSO algorithm to ensure that the frequency band characteristics of the target broadband Ricker wavelet accurately match the full-band information of formation reflection while maintaining consistency with the actual propagation phase of the seismic wave through phase correction. This ultimately yields a target broadband Ricker wavelet that combines fidelity and adaptability, laying a crucial foundation for subsequent high-resolution seismic data reconstruction.

[0087] Specifically, the correction process for the broadband Ricker wavelet can be as follows:

[0088] Based on the integral of the conventional band-limited Ricker wavelet, a basic broadband Ricker wavelet is constructed as shown in equations (12) and (13) below:

[0089] (12)

[0090] (13)

[0091] In the formula, and Let represent the integral boundaries of the center frequency of the conventional band-limited Ricker wavelet, where Set to the preset frequency, usually set to .

[0092] To improve the matching degree between the processed target seismic data and the actual seismic data, we introduce the complex domain Ricker wavelet to correct the phase difference.

[0093] Based on equations (12) and (13), the complex domain Ricker wavelet is constructed as shown in equation (14):

[0094] (14)

[0095] In the formula, It is the corrected phase of the Ricker wavelet in the complex domain. Denotes the real part of the Ricker wavelet in the complex field. This represents the Hilbert transformation operator.

[0096] For hyperparameters and phase To optimize this approach, we developed a hyperparameter optimization strategy based on particle swarm optimization, whose objective functional is constructed using the correlation between seismic data and well logging data at the well site.

[0097] Specifically, based on the correlation between seismic data and well logging data at the well location, an integral boundary for the target frequency is constructed. and target phase correction amount The objective functional is as follows (15):

[0098] (15)

[0099] In the formula, This represents the target seismic data after phase correction. , This refers to the target reflectance coefficient sequence. It is a sequence of formation reflection coefficients calculated based on well logging data. This indicates the actual seismic data from the well bypass. The Pearson correlation coefficient, representing the sequence of formation reflection coefficients calculated from target seismic data and well logging data, is primarily used to optimize hyperparameters. . The Pearson correlation coefficient, representing the target seismic data and the actual seismic data in the wellbore, is mainly used to optimize the phase correction coefficient. . and It is the regularization weight coefficient.

[0100] The particle swarm optimization algorithm is used to solve the target functional, and the target frequency integral boundary is obtained. and target phase correction amount Based on the target frequency integral boundary Target phase correction amount The basic broadband Ricker wavelet is corrected to obtain the corrected target broadband Ricker wavelet.

[0101] Subsequently, high-resolution seismic data reconstruction can be achieved by performing time-domain convolution operations based on the target broadband Ricker wavelet and the target reflection coefficient sequence, thus obtaining the target seismic data.

[0102] This high-resolution seismic data processing method can still maintain good lateral continuity, phase consistency, and denoising characteristics even under low signal-to-noise ratio conditions.

[0103] To further illustrate the feasibility of this invention, a numerical test and a practical example are provided below:

[0104] Example 1: Theoretical model testing, see details. Figure 2 , Figure 3 , Figure 4 , Figure 5 , Figure 6 .

[0105] This section presents methodological tests based on sparse characterization of one-dimensional and three-dimensional seismic data. Figure 2 and Figure 3 The figures show the sparse reflection coefficient sequences reconstructed using the orthogonal matching pursuit (OMP) method, amplitude envelope-constrained Bayesian OMP, and the present invention, respectively, under noise-free and signal-to-noise ratio (SNR) conditions of 2. As can be seen from the figures, even though different methods reconstruct the seismic data well, the sparse characterization results of the stratigraphic reflection coefficients differ significantly. The conventional OMP algorithm exhibits large errors in both the location and numerical values ​​of the reflection coefficients. When amplitude envelope information is introduced into the OMP algorithm, we find that the matching accuracy of the location information is effectively improved. When low-frequency impedance prior information is introduced, the numerical values ​​of the sparse reflection coefficients are effectively recovered. Therefore, we conclude that amplitude envelope information is mainly used to improve the matching accuracy of location information, and the introduction of low-frequency impedance prior information helps to improve the matching accuracy of the weighting coefficients.

[0106] Figure 4 and Figure 5The figures show the multichannel sparse characterization results of 3D seismic data under noise-free and signal-to-noise ratio (SNR) conditions, respectively. As can be seen from the figures, even in the noise-free condition, the characterization results of the conventional BOMP (Bayesian Orthogonal Matching Pursuit) method exhibit lateral discontinuities. Due to the introduction of seismic amplitude envelope information and low-frequency prior information, the matching pursuit results of this invention exhibit better lateral continuity. The low-frequency impedance prior information in the seismic amplitude envelope and well logging data is less affected by noise; even with an SNR of 1, this invention still maintains good lateral continuity. Figure 6 The high-resolution three-dimensional seismic processing results obtained using this invention are presented under the conditions of a signal-to-noise ratio of 0.5 and a dominant frequency of 25Hz. Using equations (12)-(15), frequency and phase corrections are performed on the sparse characterization results via broadband Ricker wavelets. Based on the particle swarm optimization algorithm, the upper limit of the frequency integral is equal to the value of the center frequency, and the phase correction amount is equal to the value of the original seismic data, resulting in clean, high-resolution seismic data with phase consistency with the original seismic data. The effective frequency band of the seismic data after noise removal and high-resolution processing reaches 2-92Hz, verifying the effectiveness of the method.

[0107] Example 2: Actual data testing, see details. Figure 7 , Figure 8 , Figure 9 .

[0108] This section uses actual 3D seismic data to test the practicality of the method. Figure 7 and Figure 8 The results are: seismic sparsity characterization of actual seismic data, high-resolution processing results, and a comparison of seismic spectra before and after processing. Figure 7 c and Figure 7 d represents the sparse characterization result based on the OMP method and the high-resolution processing result, respectively. We found that the sparse characterization result of the conventional OMP method has poor lateral continuity. The sparse characterization result and high-resolution seismic data of this invention have better lateral continuity while maintaining high resolution, such as... Figure 7 The location is indicated by the black rectangle in the middle. Additionally, via... Figure 8 By comparing the frequencies, we found that the effective frequency band of the original seismic data was 6.0Hz-54.0Hz, while the effective frequency band of the seismic data after high-resolution processing was 3.0Hz-72.0Hz. This shows that the present invention can broaden the high-frequency information of seismic data while also effectively enriching the low-frequency information of seismic data. Figure 9 This image shows a comparison of stratigraphic slices of seismic data before and after processing. As can be seen in the image, the processed stratigraphic slices depict richer channel sand bodies, such as... Figure 9As shown in the red rectangle, the original stratigraphic slices, which were difficult to characterize due to seismic resolution limitations, showed better reconstruction of hidden channel sand bodies in the processed stratigraphic slices. This phenomenon demonstrates that the present invention helps improve the reservoir characterization accuracy of complex and concealed oil and gas reservoirs, and has good prospects for practical application.

[0109] This embodiment can achieve the following beneficial effects:

[0110] This invention proposes a high-resolution seismic data processing method based on a multi-channel Bayesian orthogonal matching pursuit algorithm coupled with multiple types of prior knowledge, within a sparse representation and Bayesian framework. First, based on the Bayesian maximum a posteriori probability solution, a sparse seismic representation objective function is constructed, coupling low-frequency impedance prior information, seismic amplitude envelope prior information, and supporting location prior information. Then, combining the matching pursuit framework, a multi-channel Bayesian orthogonal matching pursuit algorithm suitable for 3D seismic data is proposed. Finally, based on the sparse representation results, a broadband Ricker wavelet frequency and phase optimization method using particle swarm optimization is employed to achieve high-resolution seismic data processing with well logging constraints. Numerical results show that this method can improve the matching accuracy of seismic reflection coefficient location and amplitude. Application to actual 3D seismic data and stratigraphic slice analysis demonstrate the practicality and effectiveness of this method.

[0111] This invention provides a seismic data processing apparatus based on multiple types of prior information. This apparatus is used to implement the aforementioned seismic data processing method based on multiple types of prior information, such as... Figure 10 As shown, the seismic data processing device 1000 includes: an objective function construction unit 1001, a solution unit 1002, and a reconstruction unit 1003.

[0112] The objective function construction unit 1001 is used to construct a seismic sparse characterization objective function coupled with multiple types of prior information based on the raw seismic data to be processed and the corresponding well logging data. The multiple types of prior information include the low-frequency impedance prior information of the well logging data, the seismic amplitude envelope prior information of the raw seismic data, and the support location prior information.

[0113] Solver 1002 is used to solve the maximum a posteriori probability density solution for the seismic sparse characterization objective function using a three-dimensional multichannel Bayesian orthogonal matching pursuit algorithm. The maximum a posteriori probability density solution includes the target reflection coefficient sequence of the original seismic data.

[0114] Reconstruction unit 1003 is used to acquire the target broadband Ricker wavelet, and to reconstruct the seismic data based on the target broadband Ricker wavelet and the target reflection coefficient sequence to obtain the target seismic data.

[0115] Optionally, the objective function construction unit 1001 is used for:

[0116] Based on the raw seismic data to be processed, a target reflection coefficient sequence to be solved is constructed. As shown in equation (1):

[0117] (1)

[0118] In the formula, Represents the raw three-dimensional seismic data. Represents a three-dimensional seismic reflection coefficient sequence. Represents an atomic dictionary. These are the weighting coefficients for L0 norm regularization. The L0 norm of the three-dimensional seismic reflection coefficient sequence;

[0119] in, It can be expressed as follows (2):

[0120] (2)

[0121] In the formula, This indicates the number of atoms in the atom dictionary. Indicates the location Earthquake Channel i The sparse characterization coefficient of each atom, , Indicates the first i The seismic reflection coefficient of an individual atom. It is the first in the atomic dictionary i One atom, It is Gaussian random noise with a mean of 0;

[0122] Based on equations (1) and (2) above, a seismic data likelihood function is constructed. As shown in equation (3):

[0123] (3)

[0124] In the formula, This indicates that the mean is 0 and the covariance is... Gaussian distribution;

[0125] Based on the well logging data corresponding to the original seismic data, low-frequency impedance prior information is constructed. likelihood function As shown in equation (4):

[0126] (4)

[0127] In the formula, Let be the integral matrix. This represents the covariance matrix of low-frequency impedance in well logging data.

[0128] Based on the original seismic data and the well logging data, a three-dimensional seismic reflection coefficient sequence is constructed. The prior information is as follows (5):

[0129] (5)

[0130] In the formula, It is the standard deviation of the seismic reflection coefficient of the i-th atom. It is the first i Standard deviation of well logging data reflection coefficients estimated based on generalized Gaussian distribution at individual atomic locations. It is the first i The absolute value of the seismic reflection amplitude at each sampling point;

[0131] Based on the prior information of seismic amplitude envelope and support location from the original seismic data, a sparse characterization coefficient sequence is constructed. Prior probability density distribution As shown in equation (6):

[0132] (6)

[0133] In the formula, Indicates that it follows the parameter. Bernoulli distribution, Indicates the first in the atomic dictionary i The probability of an atom being selected As shown in equation (7):

[0134] (7)

[0135] In the formula, These are the probability normalization coefficients. The original seismic data Hilbert transform;

[0136] By combining equations (3) to (7), a seismic sparse representation objective function coupling multiple types of prior knowledge is constructed, as shown in equation (8):

[0137] (8)

[0138] In the formula, This represents the sequence of sparse characterization coefficients of the target to be solved.

[0139] Optionally, the solving unit 1002 is used for:

[0140] Assume the total number of iterations for the 3D multichannel Bayesian orthogonal matching pursuit algorithm is... MBased on equation (8), the first... n The next iteration, the... i The target sparse characterization coefficients at each sampling point , No. n Index of the optimal atom position in the next iteration i * and the n The target reflection coefficient sequence of the next iteration ;

[0141] No. n The next iteration, the... i The target sparse characterization coefficients at each sampling point As shown in equation (9):

[0142] (9)

[0143] In the formula, , No. n Seismic data residuals after -1 iteration ;

[0144] No. n Index of matching atom positions in the next iteration i * As shown in equation (10):

[0145] (10)

[0146] In the formula, the first n The target reflection coefficient sequence of the next iteration The Middle i Seismic reflection coefficient of matching atoms , It is a regularization parameter used to measure prior information about the atomic position;

[0147] No. n The target reflection coefficient sequence of the next iteration As shown in equation (11):

[0148] (11)

[0149] In the formula, , For the process n The set of matching atoms after the next iteration. , , This represents the regularization weighting coefficient for low-frequency impedance prior information in well logging data.

[0150] Using a three-dimensional multichannel Bayesian orthogonal matching pursuit algorithm, matching atoms are selected in each iteration based on equations (9), (10), and (11), and the target sparse characterization coefficient sequence and the target reflection coefficient sequence are jointly updated until the process is completed. M Sub-iteration or seismic data residuals If the value is less than the preset iteration threshold, the maximum posterior probability density solution is obtained.

[0151] Optionally, the reconstruction unit 1003 is used for:

[0152] The frequency integral boundary and phase correction of the basic broadband Ricker wavelet are optimized using the particle swarm optimization algorithm to obtain the target broadband Ricker wavelet, so that the target broadband Ricker wavelet matches the real reflection characteristics of the strata and the actual earthquake propagation law.

[0153] Optionally, the reconstruction unit 1003 is used for:

[0154] Based on the integral of the conventional band-limited Ricker wavelet, a basic broadband Ricker wavelet is constructed as shown in equations (12) and (13) below:

[0155] (12)

[0156] (13)

[0157] In the formula, and Let represent the integral boundaries of the center frequency of the conventional band-limited Ricker wavelet, where Set to preset frequency;

[0158] Based on equations (12) and (13), the complex domain Ricker wavelet is constructed as shown in equation (14):

[0159] (14)

[0160] In the formula, It is the corrected phase of the Ricker wavelet in the complex domain. Denotes the real part of the Ricker wavelet in the complex field. Represents the Hilbert transform operator;

[0161] Based on the correlation between seismic data and well logging data at the well location, an integral boundary for the target frequency is constructed. and target phase correction amount The objective functional is as follows (15):

[0162] (15)

[0163] In the formula, This represents the target seismic data after phase correction. , This refers to the target reflectance coefficient sequence. It is a sequence of formation reflection coefficients calculated based on well logging data. This indicates the actual seismic data of the well bypass. The Pearson correlation coefficient represents the sequence of formation reflection coefficients calculated from the target seismic data and well logging data. The Pearson correlation coefficient represents the correlation between the target seismic data and the actual seismic data in the wellbore. and These are the regularization weight coefficients;

[0164] The target functional is solved using the particle swarm optimization algorithm to obtain the target frequency integral boundary. and target phase correction amount ;

[0165] Based on the target frequency integral boundary The target phase correction amount The base broadband Ricker wavelet is corrected to obtain the corrected target broadband Ricker wavelet.

[0166] Optionally, the reconstruction unit 1003 is used for:

[0167] Time-domain convolution is performed based on the target broadband Ricker wavelet and the target reflection coefficient sequence to reconstruct high-resolution seismic data and obtain the target seismic data.

[0168] This embodiment can achieve the following beneficial effects:

[0169] This invention proposes a high-resolution seismic data processing method based on a multi-channel Bayesian orthogonal matching pursuit algorithm coupled with multiple types of prior knowledge, within a sparse representation and Bayesian framework. First, based on the Bayesian maximum a posteriori probability solution, a sparse seismic representation objective function is constructed, coupling low-frequency impedance prior information, seismic amplitude envelope prior information, and supporting location prior information. Then, combining the matching pursuit framework, a multi-channel Bayesian orthogonal matching pursuit algorithm suitable for 3D seismic data is proposed. Finally, based on the sparse representation results, a broadband Ricker wavelet frequency and phase optimization method using particle swarm optimization is employed to achieve high-resolution seismic data processing with well logging constraints. Numerical results show that this method can improve the matching accuracy of seismic reflection coefficient location and amplitude. Application to actual 3D seismic data and stratigraphic slice analysis demonstrate the practicality and effectiveness of this method.

[0170] An exemplary embodiment of the present invention also provides an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor. The memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to cause the electronic device to perform a method according to an embodiment of the present invention.

[0171] An exemplary embodiment of the present invention also provides a non-transitory computer-readable storage medium storing a computer program, wherein the computer program, when executed by a computer's processor, is used to cause the computer to perform a method according to an embodiment of the present invention.

[0172] An exemplary embodiment of the present invention also provides a computer program product, including a computer program, wherein, when executed by a computer's processor, the computer program is used to cause the computer to perform a method according to an embodiment of the present invention.

[0173] refer to Figure 11 The present invention will now be described in the form of a structural block diagram of an electronic device 1100 that can serve as a server or client of the present invention, which is an example of a hardware device that can be applied to various aspects of the present invention. The term "electronic device" is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0174] like Figure 11 As shown, the electronic device 1100 includes a computing unit 1101, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 1102 or a computer program loaded into a random access memory (RAM) 1103 from a storage unit 1108. The RAM 1103 may also store various programs and data required for the operation of the device 1100. The computing unit 1101, ROM 1102, and RAM 1103 are interconnected via a bus 1104. An input / output (I / O) interface 1105 is also connected to the bus 1104.

[0175] Multiple components in electronic device 1100 are connected to I / O interface 1105, including: input unit 1106, output unit 1107, storage unit 1108, and communication unit 1109. Input unit 1106 can be any type of device capable of inputting information to electronic device 1100. Input unit 1106 can receive input digital or text information and generate key signal inputs related to user settings and / or function control of electronic device. Output unit 1107 can be any type of device capable of presenting information and may include, but is not limited to, a display, speaker, video / audio output terminal, vibrator, and / or printer. Storage unit 1108 may include, but is not limited to, disk and optical disk. Communication unit 1109 allows electronic device 1100 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers and / or chipsets, such as Bluetooth devices, Wi-Fi devices, WiMax devices, cellular communication devices, and / or the like.

[0176] The computing unit 1101 can be various general-purpose and / or dedicated processing components with processing and computing capabilities. Some examples of the computing unit 1101 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 1101 performs the various methods and processes described above. For example, in some embodiments, the above-described seismic data processing method based on multiple types of prior information can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as storage unit 1108. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 1100 via ROM 1102 and / or communication unit 1109. In some embodiments, the computing unit 1101 can be configured to perform the above-described seismic data processing method based on multiple types of prior information by any other suitable means (e.g., by means of firmware).

[0177] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0178] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0179] As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, device, and / or apparatus (e.g., disk, optical disk, memory, programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including machine-readable media that receive machine instructions as machine-readable signals. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0180] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0181] The systems and technologies described herein can be implemented in computing systems that include back-end components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0182] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other.

Claims

1. A seismic data processing method based on multiple types of prior information, characterized in that, The method includes: Based on the raw seismic data to be processed and the corresponding well logging data, a seismic sparse characterization objective function coupled with multiple types of prior information is constructed. The multiple types of prior information include the low-frequency impedance prior information of the well logging data, the seismic amplitude envelope prior information of the raw seismic data, and the support location prior information. The maximum a posteriori probability density solution of the seismic sparse characterization objective function is obtained by using a three-dimensional multichannel Bayesian orthogonal matching pursuit algorithm. The maximum a posteriori probability density solution includes the target reflection coefficient sequence of the original seismic data. The target broadband Ricker wavelet is acquired, and seismic data is reconstructed based on the target broadband Ricker wavelet and the target reflection coefficient sequence to obtain the target seismic data; The construction of a seismic sparse representation objective function based on the raw seismic data to be processed and the corresponding well logging data, coupled with multiple types of prior knowledge, includes: Based on the raw seismic data to be processed, a target reflection coefficient sequence to be solved is constructed. As shown in equation (1): （1） In the formula, Represents the raw three-dimensional seismic data. Represents a three-dimensional seismic reflection coefficient sequence. Represents an atomic dictionary. These are the weighting coefficients for L0 norm regularization. The L0 norm of the three-dimensional seismic reflection coefficient sequence; in, It can be expressed as follows (2): （2） In the formula, This indicates the number of atoms in the atom dictionary. Indicates the location Earthquake Channel i The sparse characterization coefficient of each atom, , Indicates the first i The seismic reflection coefficient of an individual atom. It is the first in the atomic dictionary i One atom, It is Gaussian random noise with a mean of 0; Based on equations (1) and (2) above, a seismic data likelihood function is constructed. As shown in equation (3): （3） In the formula, This indicates that the mean is 0 and the covariance is... Gaussian distribution; Based on the well logging data corresponding to the original seismic data, low-frequency impedance prior information is constructed. likelihood function As shown in equation (4): （4） In the formula, Let be an integral matrix. This represents the covariance matrix of low-frequency impedance in well logging data. Based on the original seismic data and the well logging data, a three-dimensional seismic reflection coefficient sequence is constructed. The prior information is as follows (5): （5） In the formula, It is the standard deviation of the seismic reflection coefficient of the i-th atom. It is the first i Standard deviation of well logging data reflection coefficients estimated based on generalized Gaussian distribution at individual atomic locations. It is the first i The absolute value of the seismic reflection amplitude at each sampling point; Based on the prior information of seismic amplitude envelope and support location from the original seismic data, a sparse characterization coefficient sequence is constructed. Prior probability density distribution As shown in equation (6): （6） In the formula, Indicates that it follows the parameter. Bernoulli distribution, Indicates the first in the atomic dictionary i The probability of an atom being selected As shown in equation (7): （7） In the formula, These are the probability normalization coefficients. The original seismic data Hilbert transform; By combining equations (3) to (7), a seismic sparse representation objective function coupling multiple types of prior knowledge is constructed, as shown in equation (8): （8） In the formula, This represents the sequence of sparse representation coefficients of the target to be solved; The method of using a three-dimensional multi-channel Bayesian orthogonal matching pursuit algorithm to solve for the maximum a posteriori probability density solution of the seismic sparse characterization objective function includes: Assume the total number of iterations for the 3D multichannel Bayesian orthogonal matching pursuit algorithm is... M Based on equation (8), the first... n The next iteration, the... i The target sparse characterization coefficients at each sampling point , No. n Index of the optimal atom position in the next iteration i * and the n The target reflection coefficient sequence of the next iteration ; No. n The next iteration, the... i The target sparse characterization coefficients at each sampling point As shown in equation (9): （9） In the formula, , No. n Seismic data residuals after -1 iteration ; No. n Index of matching atom positions in the next iteration i * As shown in equation (10): （10） In the formula, the first n The target reflection coefficient sequence of the next iteration The Middle i Seismic reflection coefficient of matching atoms , It is a regularization parameter used to measure prior information about the atomic position; No. n The target reflection coefficient sequence of the next iteration As shown in equation (11): （11） In the formula, , For the process n The set of matching atoms after the next iteration. , , This represents the regularization weighting coefficient for low-frequency impedance prior information in well logging data. Using a three-dimensional multichannel Bayesian orthogonal matching pursuit algorithm, matching atoms are selected in each iteration based on equations (9), (10), and (11), and the target sparse characterization coefficient sequence and the target reflection coefficient sequence are jointly updated until the process is completed. M Sub-iteration or seismic data residuals If the value is less than the preset iteration threshold, the maximum posterior probability density solution is obtained.

2. The method according to claim 1, characterized in that, The acquisition of the target broadband Ricker subwavelength includes: The frequency integral boundary and phase correction of the basic broadband Ricker wavelet are optimized using the particle swarm optimization algorithm to obtain the target broadband Ricker wavelet, so that the target broadband Ricker wavelet matches the real reflection characteristics of the strata and the actual earthquake propagation law.

3. The method according to claim 2, characterized in that, The process of optimizing the frequency integration boundary and phase correction of the fundamental broadband Ricker wavelet using the particle swarm optimization algorithm to obtain the target broadband Ricker wavelet includes: Based on the integral of the conventional band-limited Ricker wavelet, a basic broadband Ricker wavelet is constructed as shown in equations (12) and (13) below: （12） （13） In the formula, and Let represent the integral boundaries of the center frequency of the conventional band-limited Ricker wavelet, where Set to preset frequency; Based on equations (12) and (13), the complex domain Ricker wavelet is constructed as shown in equation (14): （14） In the formula, It is the corrected phase of the Ricker wavelet in the complex domain. Denotes the real part of the Ricker wavelet in the complex field. Represents the Hilbert transform operator; Based on the correlation between seismic data and well logging data at the well location, an integral boundary for the target frequency is constructed. and target phase correction amount The objective functional is as follows (15): （15） In the formula, This represents the target seismic data after phase correction. , This refers to the target reflectance coefficient sequence. It is a sequence of formation reflection coefficients calculated based on well logging data. This indicates the actual seismic data of the well bypass. The Pearson correlation coefficient represents the sequence of formation reflection coefficients calculated from the target seismic data and well logging data. The Pearson correlation coefficient represents the correlation between the target seismic data and the actual seismic data in the wellbore. and These are the regularization weight coefficients; The target functional is solved using the particle swarm optimization algorithm to obtain the target frequency integral boundary. and target phase correction amount ; Based on the target frequency integral boundary The target phase correction amount The base broadband Ricker wavelet is corrected to obtain the corrected target broadband Ricker wavelet.

4. The method according to claim 1, characterized in that, The process of reconstructing seismic data based on the target broadband Ricker wavelet and the target reflection coefficient sequence to obtain target seismic data includes: Time-domain convolution is performed based on the target broadband Ricker wavelet and the target reflection coefficient sequence to reconstruct high-resolution seismic data and obtain the target seismic data.

5. A seismic data processing device based on multiple types of prior information, characterized in that, The apparatus is used to implement the method as described in claim 1, the apparatus comprising: The objective function construction unit is used to construct a seismic sparse characterization objective function coupled with multiple types of prior information based on the raw seismic data to be processed and the corresponding well logging data. The multiple types of prior information include low-frequency impedance prior information of the well logging data, seismic amplitude envelope prior information and support location prior information of the raw seismic data. The solution unit is used to solve the maximum a posteriori probability density solution for the seismic sparse characterization objective function using a three-dimensional multichannel Bayesian orthogonal matching pursuit algorithm. The maximum a posteriori probability density solution includes the target reflection coefficient sequence of the original seismic data. The reconstruction unit is used to acquire the target broadband Ricker wavelet, and to reconstruct the seismic data based on the target broadband Ricker wavelet and the target reflection coefficient sequence to obtain the target seismic data.

6. The apparatus according to claim 5, characterized in that, The reconstruction unit is used for: The frequency integral boundary and phase correction of the basic broadband Ricker wavelet are optimized using the particle swarm optimization algorithm to obtain the target broadband Ricker wavelet, so that the target broadband Ricker wavelet matches the real reflection characteristics of the strata and the actual earthquake propagation law.

7. An electronic device, characterized in that, The electronic device includes: Processor; and Stored program memory, The program includes instructions that, when executed by the processor, cause the processor to perform the method according to any one of claims 1-4.

8. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-4.

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