Multi-channel reflection coefficient inversion method and device and computing equipment

Abstract

The invention discloses a multi-channel reflection coefficient inversion method, a multi-channel reflection coefficient inversion device and computing equipment. The method comprises the steps of generating an inversion objective function of a reflection coefficient according to an L2 norm of a residual error of actual seismic data and inversion seismic data, an Lp norm of the reflection coefficient and an L2-p group norm of a reflection coefficient rearrangement matrix; wherein 0 < p < 1; generating an iterative equation of a reflection coefficient based on the inversion objective function; acquiring collected original seismic data, generating seismic wavelets based on the original seismic data, and substituting the seismic wavelets and the original seismic data into the iterative equation to obtain a reflection coefficient inversion result. By adopting the scheme, the inversion of the multi-channel reflection coefficient can be realized without prior data, the stability of the inversion of the multi-channel reflection coefficient is improved, and the anti-noise capability and the inversion effect are improved.

Description

Technical Field

[0001] The present application relates to the field of exploration technology, and in particular to a multi-channel reflection coefficient inversion method, device, computing equipment, computer storage medium and computer program product. Background Art

[0002] Reflection coefficient inversion is an important part of seismic data processing. Currently, the commonly used reflection coefficient inversion methods include single-channel reflection coefficient inversion and multi-channel reflection coefficient inversion. Compared with single-channel reflection coefficient inversion, multi-channel reflection coefficient inversion has the characteristics of high stability and excellent lateral continuity, so it has many applications in the field of seismic data processing.

[0003] However, during the implementation process, the inventors found that the prior art has the following defects: the multi-channel reflection coefficient inversion method in the prior art is usually a multi-channel inversion based on structural constraints, which requires adding prior information such as formation dip or logging data to the framework of sparse inversion to construct a lateral constraint operator. This method has large uncertainty, insufficient noise resistance, and poor inversion effect. Summary of the invention

[0004] In view of the above problems, the present application is proposed to provide a multi-channel reflection coefficient inversion method, apparatus, computing device, computer storage medium and computer program product that overcome the above problems or at least partially solve the above problems.

[0005] According to a first aspect of the present application, a multi-channel reflection coefficient inversion method is provided, comprising:

[0006] Generate an inversion objective function of the reflection coefficient according to the L2 norm of the residual between the actual seismic data and the inverted seismic data, the Lp norm of the reflection coefficient, and the L2-p group norm of the reflection coefficient rearrangement matrix; wherein 0<p<1;

[0007] An iterative equation for generating a reflection coefficient based on the inversion objective function;

[0008] The collected original seismic data is obtained, a seismic wavelet is generated based on the original seismic data, and the seismic wavelet and the original seismic data are substituted into the iterative equation to obtain a reflection coefficient inversion result.

[0009] In an optional implementation, the inversion objective function is specifically:

[0010]

[0011] Where J represents the inversion objective function, λ and β represent regularization parameters, S represents the actual seismic data, R represents the reflection coefficient, and W represents the seismic wavelet matrix. represents the reflection coefficient rearrangement matrix, t represents the number of sampling points, and m represents the number of seismic traces.

[0012] In an optional implementation, the L2-p group norm of the reflection coefficient rearrangement matrix is ​​used for the lateral L2 norm constraint and the longitudinal p norm constraint.

[0013] In an optional implementation, the iterative equation is specifically:

[0014]

[0015] Among them, R k+1 represents the reflection coefficient of the k+1th iteration, R k represents the reflection coefficient of the kth iteration, W represents the seismic wavelet matrix, I represents the identity matrix, S represents the actual seismic data, λ and β represent the regularization parameters, ε represents the stability factor, Represents the reflection coefficient rearrangement matrix R of the kth iteration k The element in row i and column j in Represents the reflection coefficient rearrangement matrix R of the kth iteration k The sum of the jth column in .

[0016] In an optional embodiment, the method further includes:

[0017] Extracting part of the seismic data from the acquired original seismic data;

[0018] Generate multiple p parameter candidate values;

[0019] For any candidate p-parameter value, the reflection coefficient of the part of seismic data is inverted by using an iterative equation under the candidate p-parameter value, and an inversion effect evaluation index under the candidate p-parameter value is obtained according to the inversion result;

[0020] Determining an optimal p-parameter candidate value from the plurality of p-parameter candidate values ​​according to an inversion effect evaluation index corresponding to each p-parameter candidate value;

[0021] Assigning the optimal p parameter candidate value to the iterative equation to generate a final iterative equation;

[0022] Substituting the seismic wavelet and the original seismic data into the iterative equation to obtain the reflection coefficient inversion result includes: substituting the seismic wavelet and the original seismic data into the final iterative equation to obtain the reflection coefficient inversion result.

[0023] In an optional implementation, the inversion effect evaluation index obtained according to the inversion result under the candidate value of the p parameter includes:

[0024] Calculate the inversion error index based on the inversion results;

[0025] And / or, calculate the L0 norm of the inversion result.

[0026] According to a second aspect of the present application, a multi-channel reflection coefficient inversion device is provided, comprising:

[0027] The objective function construction module is used to generate an inversion objective function of the reflection coefficient according to the L2 norm of the residual between the actual seismic data and the inverted seismic data, the Lp norm of the reflection coefficient, and the L2-p group norm of the reflection coefficient rearrangement matrix; wherein 0<p<1;

[0028] An iterative equation generating module, used for generating an iterative equation of a reflection coefficient based on the inversion objective function;

[0029] The inversion module is used to obtain the collected original seismic data, generate seismic wavelets based on the original seismic data, and obtain the reflection coefficient inversion result after substituting the seismic wavelets and the original seismic data into the iterative equation.

[0030] In an optional implementation, the inversion objective function is specifically:

[0031]

[0032] Where J represents the inversion objective function, λ and β represent regularization parameters, S represents the actual seismic data, R represents the reflection coefficient, and W represents the seismic wavelet matrix. represents the reflection coefficient rearrangement matrix, t represents the number of sampling points, and m represents the number of seismic traces.

[0033] In an optional implementation, the L2-p group norm of the reflection coefficient rearrangement matrix is ​​used for the lateral L2 norm constraint and the longitudinal p norm constraint.

[0034] In an optional implementation, the iterative equation is specifically:

[0035]

[0036] Among them, R k+1 represents the reflection coefficient of the k+1th iteration, R k represents the reflection coefficient of the kth iteration, W represents the seismic wavelet matrix, I represents the identity matrix, S represents the actual seismic data, λ and β represent the regularization parameters, ε represents the stability factor, Represents the reflection coefficient rearrangement matrix R of the kth iteration k The element in row i and column j in Represents the reflection coefficient rearrangement matrix R of the kth iteration k The sum of the jth column in .

[0037] In an optional implementation, the device further includes: a p parameter determination module, configured to:

[0038] Extracting part of the seismic data from the acquired original seismic data;

[0039] Generate multiple p parameter candidate values;

[0040] For any candidate p-parameter value, the reflection coefficient of the part of seismic data is inverted by using an iterative equation under the candidate p-parameter value, and an inversion effect evaluation index under the candidate p-parameter value is obtained according to the inversion result;

[0041] Determining an optimal p-parameter candidate value from the plurality of p-parameter candidate values ​​according to an inversion effect evaluation index corresponding to each p-parameter candidate value;

[0042] Assigning the optimal p parameter candidate value to the iterative equation to generate a final iterative equation;

[0043] The inversion module is used to: substitute the seismic wavelet and the original seismic data into the final iterative equation to obtain the reflection coefficient inversion result.

[0044] In an optional implementation, the p parameter determination module is used to: calculate an inversion error index according to the inversion result;

[0045] And / or, calculate the L0 norm of the inversion result.

[0046] According to a third aspect of the present application, there is provided a computing device, comprising: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus;

[0047] The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute operations corresponding to the above-mentioned multi-channel reflection coefficient inversion method.

[0048] According to a fourth aspect of the present application, a computer storage medium is provided, wherein the storage medium stores at least one executable instruction, and the executable instruction enables a processor to perform operations corresponding to the above-mentioned multi-channel reflection coefficient inversion method.

[0049] According to a fifth aspect of the present application, a computer program product is provided, comprising at least one executable instruction, wherein the executable instruction enables a processor to perform operations corresponding to the above-mentioned multi-channel reflection coefficient inversion method.

[0050] The multi-channel reflection coefficient inversion method, apparatus, computing device, computer storage medium and computer program product provided in the embodiments of the present application add the L2-p group norm of the reflection coefficient rearrangement matrix in the inversion objective function, and can use the Lp norm to measure the vertical sparsity of the data while using the L2 norm to measure the lateral continuity of the data, thereby realizing the inversion of multi-channel reflection coefficients without the need for prior data, improving the stability of the multi-channel reflection coefficient inversion, improving the noise resistance and improving the inversion effect; and, the Lp norm of the reflection coefficient is also added to the inversion objective function, and the lateral continuity and longitudinal sparsity can be comprehensively adjusted in combination with the L2-p group norm of the reflection coefficient rearrangement matrix to improve the inversion effect; and, the embodiments of the present application add the L2 norm of the residual between the actual seismic data and the inverted seismic data in the inversion objective function, which can make the inversion result closer to the actual seismic data and improve the accuracy of the reflection coefficient inversion result.

[0051] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present application. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0053] Figure 1 A flow chart of a multi-channel reflection coefficient inversion method provided in Embodiment 1 of the present application is shown;

[0054] Figure 2a A schematic diagram of an original reflection coefficient generated based on a wedge model provided in the first embodiment of the present application is shown;

[0055] Figure 2b A schematic diagram of a noise-free synthetic seismic record generated based on a wedge model provided in the first embodiment of the present application is shown;

[0056] Figure 2c A schematic diagram of a noisy synthetic seismic record generated based on a wedge model provided in Example 1 of the present application is shown;

[0057] Figure 2d A schematic diagram of a single-channel inversion result based on a wedge model provided in Example 1 of the present application is shown;

[0058] Figure 2eA schematic diagram of a multi-channel inversion result based on a wedge model and structural constraints provided in the first embodiment of the present application is shown;

[0059] Figure 2f A schematic diagram of a multi-channel inversion result obtained by an inversion method of the present application based on a wedge model provided in Example 1 of the present application is shown;

[0060] Figure 3a A schematic diagram of an original reflection coefficient generated based on a nappe reflection coefficient model provided in the first embodiment of the present application is shown;

[0061] Figure 3b A schematic diagram of a noisy synthetic seismic record generated based on a nappe reflection coefficient model provided in the first embodiment of the present application is shown;

[0062] Figure 3c A schematic diagram of a multi-channel inversion result based on a structural constraint and based on a nappe reflection coefficient model provided in the first embodiment of the present application is shown;

[0063] Figure 3d A schematic diagram of a multi-channel inversion result obtained by an inversion method of the present application based on a nappe reflection coefficient model provided in the first embodiment of the present application is shown;

[0064] Figure 4a The present invention shows a kind of original seismic data applied to a practical scenario provided by the first embodiment of the present application;

[0065] Figure 4b A schematic diagram of a multi-channel inversion result obtained by an inversion method of the present application applied to an actual scene provided in the first embodiment of the present application is shown;

[0066] Figure 5 A flow chart of a multi-channel reflection coefficient inversion method provided in Embodiment 2 of the present application is shown;

[0067] Figure 6 A structural diagram of a multi-channel reflection coefficient inversion device provided in Embodiment 3 of the present application is shown;

[0068] Figure 7 A schematic diagram of the structure of a computing device provided in Embodiment 4 of the present application is shown. DETAILED DESCRIPTION

[0069] The exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present application and to fully convey the scope of the present application to those skilled in the art.

[0070] Embodiment 1

[0071] Figure 1 FIG. 4 is a flow chart of a multi-channel reflection coefficient inversion method provided in Example 1 of the present application. Figure 1 As shown, the method comprises the following steps:

[0072] Step S101, generating an inversion objective function of the reflection coefficient according to the L2 norm of the residual between the actual seismic data and the inverted seismic data, the Lp norm of the reflection coefficient and the L2-p group norm of the reflection coefficient rearrangement matrix; wherein 0<p<1.

[0073] In the process of multi-channel reflection sparse inversion, it is first necessary to construct the inversion objective function on which the inversion depends. Among them, the inversion objective function constructed in the embodiment of the present application consists of the following three parts:

[0074] The first part is: the L2 norm of the residual of the actual seismic data and the inverted seismic data. Among them, the actual seismic data is the real measured seismic trace data; the inverted seismic data is the seismic data generated based on the reflection coefficient obtained by inversion. The residual of the actual seismic data and the inverted seismic data is specifically the difference between the actual seismic data and the inverted seismic data. Then the L2 norm of the residual of the actual seismic data and the inverted seismic data can be shown as formula 1:

[0075] Part1=‖S-WR‖2 (Formula 1)

[0076] Where S represents the actual seismic data, S = [s1,…,s j …s m ],s j represents the jth seismic channel data, m represents the number of seismic channels; R represents the reflection coefficient, R = [r1, ..., r j …r m ],r j represents the reflection coefficient corresponding to the j-th seismic trace; W represents the seismic wavelet matrix, W=kron(I,w) is a block diagonal matrix, the seismic wavelet is located on the diagonal, and the other elements of the matrix are zero, kron represents the Kronecker product, I represents the unit matrix, w represents the seismic wavelet; WR is the inverted seismic data.

[0077] By adding this Part 1 constraint to the inversion objective function, it can ensure that the final inversion result is closer to the real result and improve the inversion effect.

[0078] The second part is: the Lp norm of the reflection coefficient. The reflection coefficient in the inversion objective function in the embodiment of the present application refers to the inverted reflection coefficient variable. Then the second part can be expressed as the following formula 2:

[0079] Part 2 = ‖R‖p ; 0<p<1 (Formula 2)

[0080] By adding this part of the Part2 constraint to the inversion objective function, the data can be constrained vertically to ensure the sparsity of the results, and it can be combined with the subsequent third part to comprehensively adjust the lateral continuity and vertical sparsity.

[0081] The third part is: the L2-p group norm of the reflection coefficient rearrangement matrix. The reflection coefficient rearrangement matrix is ​​a matrix that rearranges the reflection coefficients into t rows and m columns, where t represents the number of seismic sampling points and m represents the number of seismic channels. The L2-p group norm of the reflection coefficient rearrangement matrix is ​​used for the horizontal L2 norm constraint and the vertical p norm constraint.

[0082] Then the third part can be expressed as the following formula 3:

[0083]

[0084] in, represents the reflection coefficient rearrangement matrix, t represents the number of sampling points in each seismic trace, and m represents the number of seismic traces.

[0085] The above formula 3 is specifically expressed as the following formula 4:

[0086]

[0087] in, Represents the reflection coefficient rearrangement matrix The jth element in the i-th row; p represents the p parameter in the L2-p group norm.

[0088] By adding this part Part3 to the inversion objective function, the Lp norm can be used to measure the vertical sparsity of the data and the L2 norm can be used to measure the lateral continuity of the data.

[0089] In an optional implementation, the inversion objective function is specifically a function that minimizes the weighted sum of the L2 norm of the residual between the actual seismic data and the inverted seismic data, the Lp norm of the reflection coefficient, and the L2-p group norm of the reflection coefficient rearrangement matrix. Then the inversion objective function can be expressed by the following formula 5:

[0090]

[0091] Among them, J represents the inversion objective function, λ and β represent regularization parameters (by adjusting λ and β, the proportion of the above three parts in the inversion objective function can be adjusted to balance the longitudinal sparsity and lateral continuity), S represents the actual seismic data, R represents the reflection coefficient, and W represents the seismic wavelet matrix. represents the reflection coefficient rearrangement matrix, t represents the number of sampling points, and m represents the number of seismic traces.

[0092] Step S102: generating an iterative equation of the reflection coefficient based on the inversion objective function.

[0093] With the minimum value of the inversion objective function as the goal, an iterative solution algorithm is used to generate an iterative equation of the reflection coefficient. That is, after the inversion objective function is differentiated with respect to the reflection coefficient, the solution when its derivative is equal to zero is the optimal solution, and the optimal solution is solved by an iterative algorithm.

[0094] In an optional implementation, the iterative equation may be specifically shown as Formula 6:

[0095]

[0096] Among them, R k+1 represents the reflection coefficient of the k+1th iteration, R k represents the reflection coefficient of the kth iteration, W represents the seismic wavelet matrix, I represents the unit matrix, S represents the actual seismic data, λ and β represent the regularization parameters, and ε represents the stability factor (in order to avoid a minimum value of the matrix singularity caused by the denominator being zero, such as 10 -8 wait), Represents the reflection coefficient rearrangement matrix R of the kth iteration k The element in row i and column j in Represents the reflection coefficient rearrangement matrix R of the kth iteration k The sum of the elements in the jth column of .

[0097] Step S103, acquiring the collected original seismic data, generating seismic wavelets based on the original seismic data, substituting the seismic wavelets and the original seismic data into the iterative equation to obtain the reflection coefficient inversion result.

[0098] Step S101 and step S102 are the iterative equation construction process, and S and W in step S101 and step S102 refer to actual seismic data and seismic wavelet matrix. This step is the process of inverting the actual acquisition data using the constructed iterative equation. In the specific implementation process, the original seismic data is substituted into S in the iterative equation, and the seismic wavelet is substituted into W in the iterative equation, so as to perform continuous iteration. The iteration is terminated when the iteration termination condition is met, and the reflection coefficient R updated when the iteration is terminated is the final reflection coefficient inversion result. Afterwards, the final high-resolution result and other post-processing processes can also be obtained by convolving the reflection coefficient inversion result with the broadband seismic wavelet.

[0099] The following Figure 2a-2f , Figure 3a-3d , Figure 4a-4bThe technical effects achieved by the multi-channel reflection coefficient inversion method (hereinafter referred to as the inversion method of the present application) provided in the embodiment of the present application are explained in detail:

[0100] Figure 2a-2f Based on the wedge model, Figure 2a A schematic diagram of the original reflection coefficient generated based on the wedge model. Figure 2b is Figure 2a Schematic diagram of noise-free synthetic seismic records generated based on the above; Figure 2c is Figure 2a Schematic diagram of noisy synthetic seismic records generated by adding 50% random noise to the basis; among them, the number of seismic channels is 30, the sampling interval is 2ms, and the pulse interval of the seismic channels increases from 2ms to 60ms one by one. In order to better simulate the characteristics of sandstone in field seismic data, the reflection coefficient amplitudes corresponding to the upper and lower reflection interfaces are taken as 0.3 and -0.3 respectively, and the initial sub-wave is the Ricker sub-wave with a main frequency of 30Hz; Figure 2d is Figure 2c A schematic diagram of an inversion result obtained by using a single-channel inversion method of the prior art; Figure 2e is Figure 2c A schematic diagram of the inversion results obtained by using the existing technology based on the structural constraint method; Figure 2f is Figure 2c Schematic diagram of the inversion results obtained by using the multi-channel reflection coefficient inversion method of this application. Figure 2d , Figure 2e , Figure 2f It can be seen that the single-channel inversion has poor noise resistance and it is difficult to maintain the lateral relationship of seismic data, especially in thin layer areas (1-15 channels, i.e., less than 30ms); the traditional multi-channel inversion method based on structural constraints will produce illusions and affect the amplitude preservation when thin layers are affected by noise; the multi-channel reflection coefficient inversion method provided in the present application has good noise resistance and can maintain the lateral relationship of data well. At the same time, relatively accurate inversion results can still be obtained in thin layer areas (less than 12ms), and it has a higher vertical resolution.

[0101] Figure 3a-3d Based on the nappe reflection coefficient model, Figure 3a Schematic diagram of the original reflection coefficient generated by the SEG / EAGE nappe reflection coefficient model; Figure 3b is Figure 3a Schematic diagram of seismic records generated by convolving the reflection coefficient with a Ricker wavelet of 30 Hz main frequency and then adding 10% random noise, where the record length is 240 ms and the sampling interval is 2 ms. Figure 3c is Figure 3b The schematic diagram of the inversion result obtained by using the structural constraint method in the prior art is shown in FIG. Figure 3d is Figure 3b Schematic diagram of the inversion results obtained by using the multi-channel reflection coefficient method of this application. Figure 3c and Figure 3d It can be seen that the inversion results obtained in this application have better advantages in noise resistance and weak signal recognition (such as the area indicated by the arrow), and have better continuity for areas with large structural fluctuations (such as the area indicated by the circle). It is worth noting that the SEG / EAGE nappe model is a non-sparse model, and the multi-channel reflection coefficient inversion method provided in this application can still provide relatively accurate inversion results when the reflection coefficient is non-sparse.

[0102] Figure 4a-4b It is the data applied to the actual scene (post-stack data of a certain work area). Figure 4a It is the original seismic data applied to actual scenarios, specifically a field seismic profile containing 600 seismic traces and a sampling interval of 2ms. The research goal is to distinguish the period of the steep slope fan (where the arrow points). Figure 4b This is a schematic diagram of the inversion results obtained after inversion using the multi-channel reflection coefficient inversion method provided by the present application. From the figure, it can be seen that the present application has a higher resolution and can clearly distinguish the stages of the steep slope fan. The lateral structural continuity of the steep slope fan is well maintained, and the small faults inside the fan body are clearer.

[0103] It can be seen that the multi-channel reflection coefficient inversion method provided in the embodiment of the present application adds the L2-p group norm of the reflection coefficient rearrangement matrix in the inversion objective function, and can use the Lp norm to measure the vertical sparsity of the data while using the L2 norm to measure the lateral continuity of the data, thereby realizing the inversion of multi-channel reflection coefficients without the need for prior data, improving the stability of the multi-channel reflection coefficient inversion, improving the noise resistance and improving the inversion effect; and, the Lp norm of the reflection coefficient is also added to the inversion objective function, which can be combined with the L2-p group norm of the reflection coefficient rearrangement matrix to comprehensively adjust the lateral continuity and vertical sparsity, thereby improving the inversion effect; and the embodiment of the present application adds the L2 norm of the residual between the actual seismic data and the inverted seismic data in the inversion objective function, which can make the inversion result closer to the actual seismic data and improve the accuracy of the reflection coefficient inversion result.

[0104] Embodiment 2

[0105] Figure 5 The flowchart of a multi-channel reflection coefficient inversion method provided in the second embodiment of the present application is shown. In particular, this embodiment provides a process for determining the optimal solution of the p parameter.

[0106] Specifically, Figure 5 As shown, the method comprises the following steps:

[0107] Step S501, extracting part of the seismic data from the collected original seismic data.

[0108] This part of seismic data is a small part of the original seismic data, and this part of seismic data is used to determine the optimal value of the p parameter. In the actual implementation process, the amount of data of the extracted part of the seismic data can be comprehensively selected based on the determination accuracy and processing efficiency of the optimal value of the p parameter. The embodiment of the present application does not limit the specific extraction method. For example, seismic data of 10% of the total area can be extracted as part of the seismic data for subsequent processing.

[0109] Step S502: Generate multiple p-parameter candidate values.

[0110] The p parameter in the embodiment of the present application specifically refers to the p parameter in the Lp norm of the reflection coefficient and the L2-p group norm of the reflection coefficient rearrangement matrix, corresponding to p in formulas such as Formula 4 and Formula 6.

[0111] In actual implementation, the p parameter selection range may be set to be, for example, 0.4-0.8 or 0-1, and multiple p parameter candidate values ​​may be generated within the range. For example, p parameter candidate values ​​may be selected within the range according to corresponding step sizes.

[0112] Step S503: for any p-parameter candidate value, use the iterative equation under the p-parameter candidate value to perform reflection coefficient inversion on part of the seismic data, and obtain an inversion effect evaluation index under the p-parameter candidate value according to the inversion result.

[0113] For each p-parameter candidate value, the p-parameter candidate value is substituted into the iteration equation to obtain the corresponding specific iteration equation, and then the reflection coefficient of the partial seismic data is inverted using the specific iteration equation (specifically, the partial seismic data and the seismic wavelet obtained based on the partial seismic data are substituted into the specific iteration equation) to obtain the corresponding inversion result. That is, each p-parameter candidate value corresponds to a corresponding inversion result.

[0114] Furthermore, according to the inversion results corresponding to each p-parameter candidate value, the inversion effect evaluation index under each p-parameter candidate value is calculated.

[0115] In an optional implementation, an inversion error index may be calculated based on the inversion result; and / or the L0 norm of the inversion result may be calculated. The inversion error index may reflect the inversion error, and the L0 norm of the inversion result may reflect the number of pulses of the inversion result, thereby reflecting the data sparsity or signal-to-noise ratio.

[0116] Among them, the inversion error index can be obtained in the following way: forward modeling the inversion result corresponding to any p-parameter candidate value with the seismic wavelet obtained from part of the seismic data (i.e., R0W0, R0 is the inversion result under a certain p-parameter candidate value, and W0 is the seismic wavelet obtained based on part of the seismic data), and calculating the error value between the forward modeling result and part of the seismic data (such as error parameter values ​​such as root mean square error), and this error value can be used as the inversion error index corresponding to the p-parameter candidate value.

[0117] Step S504: determining an optimal p-parameter candidate value from a plurality of p-parameter candidate values ​​according to the inversion effect evaluation index corresponding to each p-parameter candidate value.

[0118] Specifically, for each candidate value of the p parameter, the inversion error index and the L0 norm index corresponding to the candidate value of the p parameter are weighted and summed to obtain the corresponding value, and the final inversion effect score is obtained according to the value and score mapping table, and the weighted sum value is negatively correlated with the final inversion effect score, that is, the smaller the inversion error index, the smaller the L0 norm, and the smaller the corresponding weighted sum value, the higher the final inversion effect score. Among them, the smaller the inversion error index, the higher the inversion accuracy, the smaller the L0 norm, the less pulse fluctuation, the higher the signal-to-noise ratio, and the higher the sparsity, so that the final inversion effect score is higher.

[0119] The p-parameter candidate value with the smallest weighted sum value or the highest final inversion effect score is selected as the optimal p-parameter candidate value.

[0120] Step S505, assigning the optimal p parameter candidate value to the iterative equation to generate a final iterative equation.

[0121] The iterative equation obtained after assigning the optimal p-parameter candidate value obtained in step S504 to the p parameter in the iterative equation is called the final iterative equation.

[0122] Step S506, substituting the seismic wavelet and the original seismic data into the final iterative equation to obtain the reflection coefficient inversion result.

[0123] The final iterative equation is used to perform reflection coefficient inversion on the original seismic data of the entire area to obtain the final reflection coefficient inversion result.

[0124] It can be seen that the multi-channel reflection coefficient inversion method provided in the embodiment of the present application first uses part of the seismic data to perform inversion under different p-parameter candidate values ​​to obtain the inversion result, and then calculates the inversion effect evaluation index under different p-parameter candidate values, selects the optimal p-parameter candidate value based on the inversion effect evaluation index, and finally uses the final iterative equation under the optimal p-parameter candidate value to perform multi-channel reflection coefficient inversion for the entire area, thereby improving the rationality of the p-parameter and enhancing the final inversion effect.

[0125] Embodiment 3

[0126] Figure 6 FIG. 4 shows a structural diagram of a multi-channel reflection coefficient inversion device provided in Embodiment 3 of the present application. Figure 6 As shown, the device 600 includes: an objective function building module 610, an iterative equation generating module 620 and an inversion module 630.

[0127] The objective function construction module 610 is used to generate an inversion objective function of the reflection coefficient according to the L2 norm of the residual between the actual seismic data and the inverted seismic data, the Lp norm of the reflection coefficient, and the L2-p group norm of the reflection coefficient rearrangement matrix; wherein 0<p<1;

[0128] An iterative equation generating module 620, used to generate an iterative equation of a reflection coefficient based on the inversion objective function;

[0129] The inversion module 630 is used to obtain the collected original seismic data, generate seismic wavelets based on the original seismic data, and obtain a reflection coefficient inversion result after substituting the seismic wavelets and the original seismic data into the iterative equation.

[0130] In an optional implementation, the inversion objective function is specifically:

[0131]

[0132] Where J represents the inversion objective function, λ and β represent regularization parameters, S represents the actual seismic data, ε represents the reflection coefficient, and W represents the seismic wavelet matrix. represents the reflection coefficient rearrangement matrix, t represents the number of sampling points, and m represents the number of seismic traces.

[0133] In an optional implementation, the L2-p group norm of the reflection coefficient rearrangement matrix is ​​used for the lateral L2 norm constraint and the longitudinal p norm constraint.

[0134] In an optional implementation, the iterative equation is specifically:

[0135]

[0136] Among them, R k+1 represents the reflection coefficient of the k+1th iteration, R k represents the reflection coefficient of the kth iteration, W represents the seismic wavelet matrix, I represents the identity matrix, S represents the actual seismic data, λ and β represent the regularization parameters, ε represents the stability factor, Represents the reflection coefficient rearrangement matrix R of the kth iteration k The element in row i and column j in Represents the reflection coefficient rearrangement matrix R of the kth iteration k The sum of the jth column in .

[0137] In an optional implementation, the device further includes: a p parameter determination module (not shown in the figure), configured to:

[0138] Extracting part of the seismic data from the acquired original seismic data;

[0139] Generate multiple p parameter candidate values;

[0140] For any candidate p-parameter value, the reflection coefficient of the part of seismic data is inverted by using an iterative equation under the candidate p-parameter value, and an inversion effect evaluation index under the candidate p-parameter value is obtained according to the inversion result;

[0141] Determining an optimal p-parameter candidate value from the plurality of p-parameter candidate values ​​according to an inversion effect evaluation index corresponding to each p-parameter candidate value;

[0142] Assigning the optimal p parameter candidate value to the iterative equation to generate a final iterative equation;

[0143] The inversion module 630 is used to substitute the seismic wavelet and the original seismic data into the final iterative equation to obtain the reflection coefficient inversion result.

[0144] In an optional implementation, the p parameter determination module is used to: calculate an inversion error index according to the inversion result;

[0145] And / or, calculate the L0 norm of the inversion result.

[0146] It can be seen that the multi-channel reflection coefficient inversion device provided in the embodiment of the present application adds the L2-p group norm of the reflection coefficient rearrangement matrix in the inversion objective function, and can use the Lp norm to measure the vertical sparsity of the data while using the L2 norm to measure the lateral continuity of the data, thereby realizing the inversion of multi-channel reflection coefficients without the need for priori data, improving the stability of the multi-channel reflection coefficient inversion, improving the noise resistance and improving the inversion effect; and, the Lp norm of the reflection coefficient is also added to the inversion objective function, which can be combined with the L2-p group norm of the reflection coefficient rearrangement matrix to comprehensively adjust the lateral continuity and vertical sparsity, thereby improving the inversion effect; and the embodiment of the present application adds the L2 norm of the residual between the actual seismic data and the inverted seismic data in the inversion objective function, which can make the inversion result closer to the actual seismic data and improve the accuracy of the reflection coefficient inversion result.

[0147] Embodiment 4

[0148] Figure 7The schematic diagram of the structure of a computing device provided in the fourth embodiment of the present application is shown. The specific embodiments of the present application do not limit the specific implementation of the computing device.

[0149] like Figure 7 As shown, the computing device may include: a processor (processor) 702 , a communications interface (Communications Interface) 704 , a memory (memory) 706 , and a communication bus 708 .

[0150] The processor 702, the communication interface 704, and the memory 706 communicate with each other via a communication bus 708. The communication interface 704 is used to communicate with other devices such as a client or other server network elements. The processor 702 is used to execute a program 710, which can specifically execute the relevant steps in the embodiment of the multi-channel reflection coefficient inversion method for calculating the device.

[0151] Specifically, the program 710 may include program codes, which include computer operation instructions.

[0152] The processor 702 may be a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application. The one or more processors included in the computing device may be processors of the same type, such as one or more CPUs; or may be processors of different types, such as one or more CPUs and one or more ASICs.

[0153] The memory 706 is used to store the program 710. The memory 706 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory. The program 710 may be specifically used to enable the processor 702 to perform the operations in the above method embodiment.

[0154] Embodiment 5

[0155] Embodiment 5 of the present application provides a non-volatile computer storage medium, which stores at least one executable instruction or computer program, and the executable instruction or computer program can enable a processor to perform operations corresponding to the multi-channel reflection coefficient inversion method in any of the above method embodiments.

[0156] Embodiment 6

[0157] Embodiment 6 of the present application provides a computer program product, which includes at least one executable instruction or computer program, and the executable instruction or computer program can enable a processor to execute operations corresponding to the multi-channel reflection coefficient inversion method in any of the above method embodiments.

[0158] In summary, according to the computing device, computer storage medium and computer program product provided by this embodiment, the L2-p group norm of the reflection coefficient rearrangement matrix is ​​added to the inversion objective function, and the Lp norm can be used to measure the vertical sparsity of the data while using the L2 norm to measure the lateral continuity of the data, thereby realizing the inversion of multi-channel reflection coefficients without the need for prior data, improving the stability of multi-channel reflection coefficient inversion, improving the noise resistance and improving the inversion effect; and, the Lp norm of the reflection coefficient is also added to the inversion objective function, which can be combined with the L2-p group norm of the reflection coefficient rearrangement matrix to comprehensively adjust the lateral continuity and vertical sparsity, thereby improving the inversion effect; and the embodiment of the present application adds the L2 norm of the residual between the actual seismic data and the inverted seismic data to the inversion objective function, which can make the inversion result closer to the actual seismic data and improve the accuracy of the reflection coefficient inversion result.

[0159] The algorithm or display provided here are not inherently related to any specific computer, virtual system or other equipment. Various general systems can also be used together with the teaching based on this. According to the above description, it is obvious to construct the structure required for this type of system. In addition, the present application embodiment is not directed to any specific programming language yet. It should be understood that various programming languages ​​can be utilized to realize the content of the present application described here, and the above description of specific languages ​​is to disclose the best mode of implementation of the present application.

[0160] In the description provided herein, a large number of specific details are described. However, it is understood that the embodiments of the present application can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

[0161] Similarly, it should be understood that in order to streamline the present application and help understand one or more of the various inventive aspects, in the above description of the exemplary embodiments of the present application, the various features of the embodiments of the present application are sometimes grouped together into a single embodiment, figure, or description thereof. However, the disclosed method should not be interpreted as reflecting the following intention: the claimed application requires more features than the features clearly stated in each claim. More specifically, as reflected in the claims below, the inventive aspects are less than all the features of the single embodiment disclosed above. Therefore, the claims following the specific embodiment are hereby expressly incorporated into the specific embodiment, wherein each claim itself serves as a separate embodiment of the present application.

[0162] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and in addition they may be divided into a plurality of submodules or subunits or subcomponents. Except that at least some of such features and / or processes or units are mutually exclusive, all features disclosed in this specification (including the accompanying claims, abstracts and drawings) and all processes or units of any method or device disclosed in this manner may be combined in any combination. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstracts and drawings) may be replaced by an alternative feature providing the same, equivalent or similar purpose.

[0163] In addition, those skilled in the art will appreciate that, although some embodiments herein include certain features included in other embodiments but not other features, the combination of features of different embodiments is meant to be within the scope of the present application and form different embodiments. For example, in the claims below, any one of the claimed embodiments may be used in any combination.

[0164] The various component embodiments of the present application can be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. It should be understood by those skilled in the art that a microprocessor or digital signal processor (DSP) can be used in practice to implement some or all functions of some or all components according to the embodiments of the present application. The application can also be implemented as a device or apparatus program (e.g., computer program and computer program product) for executing a part or all of the methods described herein. Such a program implementing the present application can be stored on a computer-readable medium, or can have the form of one or more signals. Such a signal can be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0165] It should be noted that the above embodiments illustrate the present application rather than limit the present application, and that those skilled in the art may design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference symbol between brackets shall not be constructed as a limitation on the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "one" or "an" preceding an element does not exclude the presence of multiple such elements. The present application may be implemented by means of hardware including several different elements and by means of a suitably programmed computer. In a unit claim that lists several devices, several of these devices may be embodied by the same hardware item. The use of the words first, second, and third, etc. does not indicate any order. These words may be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be understood as limitations on the order of execution.

Claims

1. A multi-channel reflection coefficient inversion method, characterized in that: include: Generate an inversion objective function of the reflection coefficient according to the L2 norm of the residual between the actual seismic data and the inverted seismic data, the Lp norm of the reflection coefficient, and the L2-p group norm of the reflection coefficient rearrangement matrix; wherein 0<p<1; An iterative equation for generating a reflection coefficient based on the inversion objective function; The collected original seismic data is obtained, a seismic wavelet is generated based on the original seismic data, and the seismic wavelet and the original seismic data are substituted into the iterative equation to obtain a reflection coefficient inversion result.

2. The method according to claim 1, characterized in that: The inversion objective function is specifically: Where J represents the inversion objective function, λ and β represent regularization parameters, S represents the actual seismic data, R represents the reflection coefficient, and W represents the seismic wavelet matrix. represents the reflection coefficient rearrangement matrix, t represents the number of sampling points, and m represents the number of seismic traces.

3. The method according to claim 2, characterized in that The L2-p group norm of the reflection coefficient rearrangement matrix is ​​used for the lateral L2 norm constraint and the longitudinal p norm constraint.

4. The method according to claim 3, characterized in that: The iterative equation is specifically: Among them, R k+1 represents the reflection coefficient of the k+1th iteration, R k represents the reflection coefficient of the kth iteration, W represents the seismic wavelet matrix, I represents the identity matrix, S represents the actual seismic data, λ and β represent the regularization parameters, ε represents the stability factor, Represents the reflection coefficient rearrangement matrix R of the kth iteration k The element in row i and column j in Represents the reflection coefficient rearrangement matrix R of the kth iteration k The sum of the jth column in .

5. The method according to any one of claims 1 to 4, characterized in that The method further comprises: Extracting part of the seismic data from the acquired original seismic data; Generate multiple p parameter candidate values; For any candidate p-parameter value, the reflection coefficient of the part of seismic data is inverted by using an iterative equation under the candidate p-parameter value, and an inversion effect evaluation index under the candidate p-parameter value is obtained according to the inversion result; Determining an optimal p-parameter candidate value from the plurality of p-parameter candidate values ​​according to an inversion effect evaluation index corresponding to each p-parameter candidate value; Assigning the optimal p parameter candidate value to the iterative equation to generate a final iterative equation; Substituting the seismic wavelet and the original seismic data into the iterative equation to obtain the reflection coefficient inversion result includes: substituting the seismic wavelet and the original seismic data into the final iterative equation to obtain the reflection coefficient inversion result.

6. The method according to claim 5, characterized in that The inversion effect evaluation index obtained according to the inversion result under the candidate value of the p parameter includes: Calculate the inversion error index based on the inversion results; And / or, calculate the L0 norm of the inversion result.

7. A multi-channel reflection coefficient inversion device, characterized in that: include: The objective function construction module is used to generate an inversion objective function of the reflection coefficient according to the L2 norm of the residual between the actual seismic data and the inverted seismic data, the Lp norm of the reflection coefficient, and the L2-p group norm of the reflection coefficient rearrangement matrix; wherein 0<p<1; An iterative equation generating module, used for generating an iterative equation of a reflection coefficient based on the inversion objective function; The inversion module is used to obtain the collected original seismic data, generate seismic wavelets based on the original seismic data, and obtain the reflection coefficient inversion result after substituting the seismic wavelets and the original seismic data into the iterative equation.

8. A computing device, characterized in that include: A processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to perform operations corresponding to the multi-channel reflection coefficient inversion method according to any one of claims 1-6.

9. A computer storage medium, characterized in that: The storage medium stores at least one executable instruction, and the executable instruction enables the processor to execute operations corresponding to the multi-channel reflection coefficient inversion method according to any one of claims 1-6.

10. A computer program product, characterized in that The method comprises at least one executable instruction, wherein the executable instruction enables a processor to execute operations corresponding to the multi-channel reflection coefficient inversion method according to any one of claims 1 to 6.

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