Multiple wave prediction method and device, computing device and storage medium

Abstract

The application discloses a multiple wave prediction method and device, a computing device and a storage medium. The method comprises the following steps: acquiring frequency domain data of any shot point at any seismic trace; generating a seismic data matrix according to the frequency domain data, the seismic data matrix being an upper triangular matrix with a main diagonal line of 0, and any non-first column of the seismic data matrix containing frequency domain data of at least one seismic trace of the same shot point; constructing a multiple wave matrix, the multiple wave matrix being an upper triangular matrix with a main diagonal line of 0, and any non-first column of the multiple wave matrix containing a multiple wave model of at least one seismic trace of the same shot point; establishing a functional relationship between the multiple wave matrix and the seismic data matrix; and obtaining model frequency domain data corresponding to any multiple wave model in the multiple wave matrix according to the functional relationship. According to the scheme, the prediction efficiency of the multiple wave can be improved under the premise of ensuring the prediction accuracy of the multiple wave.

Description

Technical Field

[0001] This invention relates to the field of exploration technology, specifically to a method, apparatus, computing device, and storage medium for predicting multiple waves. Background Technology

[0002] Seismic data processing is a crucial step in oil and gas exploration, and the results directly impact exploration outcomes. Multiples are a common type of interference data in seismic data. Multiples can affect migration velocity analysis, imaging of complex geological structures, detailed characterization of wavegroup features, and imaging of small faults. Therefore, multiple prediction is a key aspect of seismic data processing.

[0003] However, the inventors discovered the following drawbacks in the implementation process: the existing multiple wave prediction methods are relatively complex to implement and have low prediction efficiency. Summary of the Invention

[0004] In view of the above problems, the present invention is proposed to provide a method, apparatus, computing device and storage medium for predicting multiple waves that overcomes or at least partially solves the above problems.

[0005] According to one aspect of the present invention, a multiple wave prediction method is provided, comprising:

[0006] Obtain frequency domain data of any shot point in any seismic trace;

[0007] A seismic data matrix is ​​generated based on the frequency domain data; wherein the seismic data matrix is ​​an upper triangular matrix with a main diagonal of 0, and any non-first column of the seismic data matrix contains frequency domain data of at least one seismic trace from the same shot point.

[0008] Construct a multiple wave matrix; wherein the multiple wave matrix is ​​an upper triangular matrix with a main diagonal of 0, and any non-first column of the multiple wave matrix contains at least one seismic trace of the same shot point.

[0009] Establish the functional relationship between the wave multiple matrix and the seismic data matrix;

[0010] Based on the functional relationship, the model frequency domain data corresponding to any multiple wave model in the multiple wave matrix is ​​obtained.

[0011] In one optional implementation, the seismic data matrix specifically comprises:

[0012]

[0013] Among them, T (i,j) D is the element in the i-th row and j-th column of the seismic data matrix. (j-n,j-i)For the frequency domain data of the jn-th shot point in the ji seismic trace, D (1,j-i) This represents the frequency domain data of the first shot point in ji seismic traces, where n is the total number of seismic traces.

[0014] In one optional implementation, the multiple wave matrix is ​​specifically:

[0015]

[0016] Among them, P (i,j) M is the element in the i-th row and j-th column of the multiple wave matrix. (j-n,j-i) For the multiple wave model of the jn-th shot point in the j-th seismic trace, M (1,j-i) This is the multiple wave model of the first shot point in ji seismic traces, where n is the total number of seismic traces.

[0017] In an optional implementation, establishing the functional relationship between the wave multiple matrix and the seismic data matrix further includes:

[0018] Establish the following functional relationship:

[0019] P = T × T

[0020] Where P is the wave multiple matrix and T is the seismic data matrix.

[0021] In one optional implementation, obtaining the frequency domain data of any shot point in any seismic trace further includes:

[0022] Obtain time-domain data of any shot point in any seismic trace;

[0023] The frequency domain data is obtained by performing a Fourier transform on the time domain data.

[0024] In an optional implementation, after acquiring the time-domain data of any shot point in any seismic trace, the method further includes: for any time-domain data, acquiring the time-domain data of multiple neighboring sampling points in the longitudinal direction, calculating the corresponding L2 norm based on the time-domain data and the time-domain data of the multiple neighboring sampling points, and using the product of the L2 norm and the time-domain data as the corrected time-domain data.

[0025] The step of obtaining the frequency domain data by performing Fourier transform on the time domain data further includes: obtaining the frequency domain data by performing Fourier transform on the corrected time domain data.

[0026] In an optional implementation, after obtaining the model frequency domain data corresponding to any multiple model in the multiple matrix according to the functional relationship, the method further includes:

[0027] For any multiple wave model, the time domain data of the model is obtained by performing an inverse Fourier transform on the frequency domain data of the model corresponding to the multiple wave model.

[0028] According to another aspect of the present invention, a multiple wave prediction apparatus is provided, comprising:

[0029] The acquisition module is used to acquire frequency domain data of any shot point in any seismic trace;

[0030] A processing module is configured to generate a seismic data matrix based on the frequency domain data; wherein the seismic data matrix is ​​an upper triangular matrix with a main diagonal of 0, and any non-first column of the seismic data matrix contains frequency domain data of at least one seismic trace from the same shot point; and to construct a multiple matrix; wherein the multiple matrix is ​​an upper triangular matrix with a main diagonal of 0, and any non-first column of the multiple matrix contains a multiple model of at least one seismic trace from the same shot point; and to establish a functional relationship between the multiple matrix and the seismic data matrix.

[0031] The execution module is used to obtain the model frequency domain data corresponding to any multiple wave model in the multiple wave matrix according to the functional relationship.

[0032] In one optional implementation, the seismic data matrix specifically comprises:

[0033]

[0034] Among them, T (i,j) D is the element in the i-th row and j-th column of the seismic data matrix. (j-n,j-i) For the frequency domain data of the jn-th shot point in the ji seismic trace, D (1,j-i) This represents the frequency domain data of the first shot point in ji seismic traces, where n is the total number of seismic traces.

[0035] In one optional implementation, the multiple wave matrix is ​​specifically:

[0036]

[0037] Among them, P (i,j) M is the element in the i-th row and j-th column of the multiple wave matrix. (j-n,j-i) For the multiple wave model of the jn-th shot point in the j-th seismic trace, M (1,j-i) This is the multiple wave model of the first shot point in ji seismic traces, where n is the total number of seismic traces.

[0038] In one optional implementation, the processing module is used to: establish the following functional relationship:

[0039] P = T × T

[0040] Where P is the wave multiple matrix and T is the seismic data matrix.

[0041] In one optional implementation, the acquisition module is used to: acquire time-domain data of any shot point in any seismic trace;

[0042] The frequency domain data is obtained by performing a Fourier transform on the time domain data.

[0043] In one optional implementation, the acquisition module is configured to: for any time-domain data, acquire the time-domain data of multiple neighboring sampling points in the longitudinal direction of the time-domain data, calculate the corresponding L2 norm based on the time-domain data and the time-domain data of the multiple neighboring sampling points, and use the product of the L2 norm and the time-domain data as the corrected time-domain data.

[0044] The frequency domain data is obtained by performing a Fourier transform on the corrected time domain data.

[0045] In one optional implementation, the execution module is used to: for any multiple wave model, perform an inverse Fourier transform on the frequency domain data of the model corresponding to the multiple wave model to obtain the time domain data of the model.

[0046] According to another aspect of the present invention, a computing device is provided, 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 through the communication bus;

[0047] The memory is used to store at least one executable instruction, which causes the processor to perform the operation corresponding to the above-described multi-wave prediction method.

[0048] According to another aspect of the present invention, a computer storage medium is provided, the storage medium storing at least one executable instruction, the executable instruction causing a processor to perform the operation corresponding to the above-described multiple wave prediction method.

[0049] The multiple wave prediction method, apparatus, computing device, and storage medium provided by this invention construct an upper triangular matrix with a main diagonal of 0 from the frequency domain data of any shot point in any seismic trace, and construct a multiple wave matrix with the same structure, thereby converting multiple wave prediction into matrix operations, and improving the prediction efficiency of multiple waves while ensuring the accuracy of multiple wave prediction.

[0050] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0051] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0052] Figure 1 A flowchart illustrating a multiple wave prediction method provided by an embodiment of the present invention is shown;

[0053] Figure 2 A schematic diagram of a seismic data matrix provided in an embodiment of the present invention is shown;

[0054] Figure 3 A schematic diagram of yet another seismic data matrix provided by an embodiment of the present invention is shown;

[0055] Figure 4 A schematic diagram of a multiple wave matrix provided in an embodiment of the present invention is shown;

[0056] Figure 5 A schematic diagram of yet another type of wave multiple matrix provided by an embodiment of the present invention is shown;

[0057] Figure 6 A flowchart illustrating another wave multiple prediction method provided by an embodiment of the present invention is shown;

[0058] Figure 7 This figure shows a comparison between a forward model provided by an embodiment of the present invention and the multiple waves predicted by an embodiment of the present invention;

[0059] Figure 8 This figure shows a comparison between actual shot gather data and predicted multiple waves provided by an embodiment of the present invention;

[0060] Figure 9 A schematic diagram of the structure of a multiple wave prediction device provided in an embodiment of the present invention is shown;

[0061] Figure 10 A schematic diagram of the structure of a computing device provided in an embodiment of the present invention is shown. Detailed Implementation

[0062] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this invention will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.

[0063] Figure 1 A flowchart illustrating a multiple wave prediction method provided by an embodiment of the present invention is shown. Figure 1 As shown, the method specifically includes the following steps:

[0064] Step S110: Obtain the frequency domain data of any shot point in any seismic trace.

[0065] The acquired seismic data is obtained, and the seismic data of each shot point in each seismic trace is obtained from the acquired seismic data. The frequency domain representation of this seismic data is the frequency domain data of the corresponding shot point in the corresponding seismic trace.

[0066] Step S120: Generate a seismic data matrix based on the frequency domain data.

[0067] In this embodiment of the invention, multiple wave prediction is performed using frequency domain data, which simplifies the multiple wave prediction process and improves its efficiency. Furthermore, the multiple wave prediction process is transformed into a matrix operation process between the seismic data matrix and the multiple wave matrix, further enhancing the prediction efficiency.

[0068] Specifically, in this embodiment of the invention, the seismic data matrix and the wave multiple matrix are constructed based on the following formula 1.

[0069] M(s x ,g y f)=∫ k D(s x ,g k ,f)D(s x-k ,g y-k ,f)dk (Formula 1)

[0070] Among them, M(s) x ,g y f) represents the predicted shot point at s x Location, detector point at g y A multiple wave model; D(s) x ,g k f) indicates that the shot point is at s x Location, detector point at g k A set of earthquake data; D(s) x-k ,g y-k f) indicates that the shot point is at s x-k Location, detector point at g y-k This is a set of earthquake data. In Formula 1, f indicates that the data is in the frequency domain.

[0071] The constructed seismic data matrix contains frequency domain data for each shot point in each seismic trace. To improve the efficiency of multiple wave prediction, the seismic data matrix is ​​an upper triangular matrix with a main diagonal of 0, and any non-first column of the seismic data matrix contains frequency domain data for at least one seismic trace from the same shot point.

[0072] In one alternative implementation, the seismic data matrix can be specifically represented as shown in Formula 2 below:

[0073]

[0074] Where T is the seismic data matrix, T (i,j) D is the element in the i-th row and j-th column of the seismic data matrix. (j-n,j-i) For the frequency domain data of the jn-th shot point in the ji seismic trace, D (1,j-i) This represents the frequency domain data of the first shot point in ji seismic traces, where n is the total number of seismic traces.

[0075] Specifically, the total number of rows and columns in the seismic data matrix is ​​the same, and the total number of rows and columns is equal to the sum of the total number of shot points and the total number of seismic traces. For example, if there are m shot points and n seismic traces, the total number of rows and columns in the seismic data matrix is ​​m+n.

[0076] by Figure 2 For example, Figure 2 In this context, T(m_n) represents the seismic data matrix corresponding to m shot points and n seismic traces, and D... s1g1 D represents the frequency domain data of the first seismic trace at the first shot point. s4gn D represents the frequency domain data of the nth seismic trace at the 4th shot point. smgn This represents the frequency domain data of the nth seismic trace at the mth shot point… It can be seen that when i ≥ j, the elements in the seismic data matrix are 0, making the seismic data matrix an upper triangular matrix with 0 on the main diagonal. Furthermore, in the upper triangular region of the seismic data matrix, starting from column (n+1), each column contains the frequency domain data of one shot point for each seismic trace. Column (n+1) contains the frequency domain data of the first shot point for each seismic trace, column (n+2) contains the frequency domain data of the second shot point for each seismic trace, and so on, up to column (n+m) contains the frequency domain data of the mth shot point for each seismic trace. When arranging the frequency domain data of a corresponding shot point for each seismic trace in a column, the seismic traces are arranged in reverse order, meaning that the trace numbers of the non-zero elements in the column increase sequentially from bottom to top. For example, column (n+1) contains trace numbers D from top to bottom. s1gn , ...D s1g4 D s1g3 D s1g2 D s1g1This means arranging the frequency domain data of the first shot point in the nth seismic trace, the (n-1)th seismic trace, and so on, in the first seismic trace. In columns 2 to n, the frequency domain data of the first shot point is arranged in each column, and the number of columns increases, as does the number of frequency domain data arranged in that column.

[0077] by Figure 3 For example, Figure 3 In the matrix T(4_3), the seismic data matrix corresponds to 4 shot points and 3 seismic traces. s1g1 D represents the frequency domain data of the first seismic trace at the first shot point. s4g3 This represents the frequency domain data of the 4th shot point in the 3rd seismic trace... It can be seen that the main diagonal and lower triangular regions of the seismic data matrix are all 0; and when ji is greater than 3, the corresponding element is also 0. Columns 2 and 3 contain partial frequency domain data of the 1st shot point; columns 4 to 7 sequentially arrange all frequency domain data of the 1st to 4th shot points.

[0078] Step S130: Construct the multiple wave matrix.

[0079] The constructed multiple matrix contains multiple models for each shot point in each seismic trace. These multiple models can be understood as the corresponding multiple parameters. The multiple matrix is ​​an upper triangular matrix with a main diagonal of 0. Any column other than the first column of the multiple matrix contains the multiple model for at least one seismic trace from the same shot point.

[0080] In one alternative implementation, the multiple wave matrix can be specifically expressed as Equation 3 below:

[0081]

[0082] Where P is the multiple wave matrix, P (i,j) M is the element in the i-th row and j-th column of the multiple wave matrix. (j-n,j-i) For the multiple wave model of the jn-th shot point in the j-th seismic trace, M (1,j-i) This is the multiple wave model of the first shot point in ji seismic traces, where n is the total number of seismic traces.

[0083] Specifically, the total number of rows and columns in the seismic data matrix is ​​the same, and the total number of rows and columns is equal to the sum of the total number of shot points and the total number of seismic traces. For example, if there are m shot points and n seismic traces, the total number of rows and columns in the seismic data matrix is ​​m+n.

[0084] by Figure 4 For example, Figure 4 In this context, P(m_n) represents the multiple wave matrix corresponding to m shot points and n seismic traces, and M... s1g1 M represents the multiple wave model of the first seismic trace at the first shot point. s4gnM represents the multiple wave model of the nth seismic trace at the 4th shot point. smgn This represents the multiple wave model of the m-th shot point and the n-th seismic trace… It can be seen that when i≥j, the elements in the multiple wave matrix are 0, making the multiple wave matrix an upper triangular matrix with 0 on the main diagonal. Furthermore, in the upper triangular region of the multiple wave matrix, starting from column (n+1), each column arranges a multiple wave model of a shot point in each seismic trace. Column (n+1) arranges the multiple wave model of the first shot point in each seismic trace, column (n+2) arranges the multiple wave model of the second shot point in each seismic trace, and so on, up to column (n+m) arranges the multiple wave model of the m-th shot point in each seismic trace. When arranging the multiple wave models of the corresponding shot points in each seismic trace within a column, the seismic traces are arranged in reverse order, meaning that the trace numbers in the non-zero elements of the column increase sequentially from bottom to top. For example, column (n+1) is arranged from top to bottom as M… s1gn , ...M s1g4 M s1g3 M s1g2 M s1g1 This refers to arranging the multiple wave models of the first shot point in the nth seismic trace, the (n-1)th seismic trace, and so on, in the first seismic trace. In columns 2 to n, the multiple wave models of the first shot point are arranged in each column, and the number of columns increases, as does the number of multiple wave models arranged in that column.

[0085] by Figure 5 For example, Figure 5 In the matrix T(4_3), the multiple wave matrix corresponds to the four shot points and three seismic traces. s1g1 M represents the multiple wave model of the first seismic trace at the first shot point. s4g3 This represents the multiple wave model of the 4th shot point in the 3rd seismic trace... It can be seen that the main diagonal and lower triangular regions of the multiple wave model matrix are all 0; and when ji is greater than 3, the corresponding elements are also 0. Columns 2 and 3 contain some of the multiple wave models of the 1st shot point; columns 4 to 7 sequentially arrange all the multiple wave models of the 1st to 4th shot points.

[0086] Furthermore, it can be seen that the multiple wave matrix and the seismic data matrix have the same pattern in the arrangement of elements.

[0087] Step S140: Establish the functional relationship between the multiple wave matrix and the seismic data matrix.

[0088] Establish a functional relationship between the multiple wave matrix and the seismic data matrix so that each multiple wave model in the multiple wave matrix satisfies Formula 1 above, thereby ensuring the accuracy of multiple wave prediction.

[0089] In one alternative implementation, the functional relationship between the wave multiple matrix and the seismic data matrix is ​​shown in Equation 4:

[0090] P = T × T (Formula 4)

[0091] Where P is the wave multiple matrix and T is the seismic data matrix.

[0092] This shows that by converting the prediction process of multiples into matrix operations, the model data of each multiple model can be solved using a simple algorithm of matrix operations.

[0093] Step S150: Obtain the model frequency domain data corresponding to any multiple wave model in the multiple wave matrix according to the functional relationship.

[0094] Matrix operations can be used to obtain the frequency domain data of any multiple wave model, that is, to represent each multiple wave model using the frequency domain data of the shot point in the seismic trace. Taking the determination of the multiple wave model of the 3rd shot point in the 3rd seismic trace as an example, M can be obtained using the above formula 1. s3g3 =D s3g1 *D s2g2+ D s3g2 *D s1g1 Through formula 4, Figure 3 and Figure 5 M can be obtained s3g3 = (0, 0, 0, D) s1g1 D s2g2 D s3g3 )╳(0, 0, D s3g3 D s3g2 D s3g1 ,0,0) T =D s3g1 *D s2g2+ D s3g2 *D s1g1 The two predictions yield the same result, thus improving the prediction efficiency of multiple waves while ensuring the accuracy of multiple wave prediction in this method.

[0095] Therefore, the multiple wave prediction method provided in this embodiment of the invention constructs an upper triangular matrix with a main diagonal of 0 from the frequency domain data of any shot point in any seismic trace, and constructs a multiple wave matrix with the same structure, thereby converting multiple wave prediction into matrix operations, and improving the prediction efficiency of multiple waves while ensuring the accuracy of multiple wave prediction.

[0096] Figure 6 The diagram shows a flowchart of another multiple wave prediction method provided by an embodiment of the present invention.

[0097] like Figure 6 As shown, the method specifically includes the following steps:

[0098] Step S610: Obtain time-domain data of any shot point in any seismic trace.

[0099] Typically, the initially acquired seismic data is time-domain data, so this step obtains the time-domain data of each shot point for each seismic trace.

[0100] Step S620: For any given time-domain data, preprocess the time-domain data to obtain corrected time-domain data.

[0101] In actual implementation, since multiple waves are mainly generated in strong reflective layers, in order to simplify the prediction process of multiple waves and reduce noise interference, this embodiment of the invention further preprocesses the time-domain data after obtaining it, so that subsequent multiple wave prediction can be mainly based on the data of strong reflective layers, thereby improving the prediction effect.

[0102] In one optional preprocessing method, the time-domain data of multiple neighboring sampling points in the longitudinal direction of the time-domain data are obtained. The corresponding L2 norm is calculated based on the time-domain data and the time-domain data of the multiple neighboring sampling points. The product of the L2 norm and the time-domain data is used as the corrected time-domain data. In this preprocessing method, the L2 norm is used as the weight of the time-domain data, thereby increasing the weight of the strong reflection layer data. Specifically, the corrected time-domain data can be obtained through the following formula 5:

[0103] x(t)=‖c‖2*d(t) (Formula 5)

[0104] Where d(t) is the original time-domain data, x(t) is the corrected time-domain data, and c is the set of sampling points corresponding to d(t), c = {d(tn), d(t-n+1), ..., d(t), ..., d(t+n-1), d(t+n)}, and n is the weighted number of sampling points. That is, taking the sampling point corresponding to d(t) as the center, n neighboring sampling points are selected upwards and n neighboring sampling points are selected downwards. The selected neighboring sampling points and the sampling point corresponding to d(t) are used as the set c. The L2 norm of the data in the set c is calculated, and the product of the L2 norm and d(t) is used as the corrected time-domain data.

[0105] In another alternative preprocessing method, for any given time-domain data, the absolute value of the time-domain data is used as the weight, and the product of the weight and the time-domain data is used as the corrected time-domain data.

[0106] Step S630: Perform Fourier transform on the corrected time-domain data to obtain the frequency domain data of any shot point in any seismic trace.

[0107] The corresponding frequency domain data is obtained by performing a Fourier transform on the time domain data. Specifically, for the corrected time domain data of any shot point in any seismic trace, the corresponding frequency domain data is obtained by performing a Fourier transform on the time domain data.

[0108] For example, Fourier transform can be performed using the following formula 6:

[0109]

[0110] Where D(f) is the frequency domain data, x(t) is the corrected time domain data, f is the frequency, and N is the number of frequency domain samples.

[0111] Step S640: Generate a seismic data matrix based on the frequency domain data, construct a multiple matrix, and establish a functional relationship between the multiple matrix and the seismic data matrix.

[0112] Step S650: Obtain the model frequency domain data corresponding to any multiple wave model in the multiple wave matrix according to the functional relationship.

[0113] The specific implementation process of steps S640-S650 can be found in [reference needed]. Figure 1 The descriptions in the embodiments are not repeated here.

[0114] Step S660: For any multiple wave model, perform an inverse Fourier transform on the frequency domain data of the model corresponding to the multiple wave model to obtain the time domain data of the model.

[0115] The multiple wave model obtained in step S650 is represented by frequency domain data. This step can be processed by inverse Fourier transform to obtain a multiple wave model represented by time domain data.

[0116] For example, the inverse Fourier transform can be performed using the following formula 7:

[0117]

[0118] Where M(f) represents the model frequency domain data, m(t) represents the model time domain data, f represents the frequency, and N represents the number of frequency domain samples.

[0119] The following is Figure 7 and Figure 8 The following example illustrates the predictive effect of the embodiments of the present invention:

[0120] Figure 7 The diagram shows a comparison between a forward model provided by an embodiment of the present invention and the multiple waves predicted by an embodiment of the present invention. Figure 7 Region A represents shot gather data generated using a forward model. The wave indicated by the arrow in Region A is the multiple wave under the effective reflector layer. Region B represents the multiple wave predicted by the method provided in this embodiment of the invention. The forward model and this embodiment of the invention use the same original data. It can be seen that the multiple wave predicted by the method provided in this embodiment of the invention is very close to the multiple wave generated by the forward model, and has a good prediction effect.

[0121] Figure 8 This diagram shows a comparison between actual shot gather data and multiple waves predicted by an embodiment of the present invention. Figure 8 The C region is a schematic diagram of the actual shot gather data. The wave indicated by the arrow in the C region is the multiple wave under the effective reflector layer. The D region is the multiple wave predicted by the method provided in the embodiment of the present invention. The actual shot gather data and the embodiment of the present invention use the same original data. It can be seen that the multiple wave predicted by the method provided in the embodiment of the present invention is very close to the multiple wave generated by the actual shot gather data, and has a good prediction effect.

[0122] Therefore, in the multiple wave prediction method provided by the embodiments of the present invention, time-domain data of any shot point in any seismic trace is collected, and the corresponding frequency-domain data is obtained through Fourier transform. Then, multiple wave prediction is performed based on the frequency-domain data, thereby improving the multiple wave prediction efficiency. Moreover, in the embodiments of the present invention, the time-domain data can be corrected by L2 norm and other methods to obtain corrected time-domain data. Then, the frequency-domain data is obtained based on the corrected time-domain data. This allows multiple wave prediction to mainly rely on data from the strong reflection layer, thereby improving the prediction effect and efficiency of multiple waves.

[0123] Figure 9 A schematic diagram of a multiple wave prediction device provided in an embodiment of the present invention is shown. Figure 9 As shown, the multiple wave prediction device 900 includes: an acquisition module 910, a processing module 920, and an execution module 930.

[0124] The acquisition module 910 is used to acquire frequency domain data of any shot point in any seismic trace;

[0125] Processing module 920 is configured to generate a seismic data matrix based on the frequency domain data; wherein the seismic data matrix is ​​an upper triangular matrix with a main diagonal of 0, and any non-first column of the seismic data matrix contains frequency domain data of at least one seismic trace from the same shot point; and to construct a multiple matrix; wherein the multiple matrix is ​​an upper triangular matrix with a main diagonal of 0, and any non-first column of the multiple matrix contains a multiple model of at least one seismic trace from the same shot point; and to establish a functional relationship between the multiple matrix and the seismic data matrix.

[0126] The execution module 930 is used to obtain the model frequency domain data corresponding to any multiple wave model in the multiple wave matrix according to the functional relationship.

[0127] In one optional implementation, the seismic data matrix specifically comprises:

[0128]

[0129] Among them, T (i,j) D is the element in the i-th row and j-th column of the seismic data matrix. (j-n,j-i) For the frequency domain data of the jn-th shot point in the ji seismic trace, D (1,j-i) This represents the frequency domain data of the first shot point in ji seismic traces, where n is the total number of seismic traces.

[0130] In one optional implementation, the multiple wave matrix is ​​specifically:

[0131]

[0132] Among them, P (i,j) M is the element in the i-th row and j-th column of the multiple wave matrix. (j-n,j-i) For the multiple wave model of the jn-th shot point in the j-th seismic trace, M (1,j-i) This is the multiple wave model of the first shot point in ji seismic traces, where n is the total number of seismic traces.

[0133] In one optional implementation, the processing module is used to: establish the following functional relationship:

[0134] P = T × T

[0135] Where P is the wave multiple matrix and T is the seismic data matrix.

[0136] In one optional implementation, the acquisition module is used to: acquire time-domain data of any shot point in any seismic trace;

[0137] The frequency domain data is obtained by performing a Fourier transform on the time domain data.

[0138] In one optional implementation, the acquisition module is configured to: for any time-domain data, acquire the time-domain data of multiple neighboring sampling points in the longitudinal direction of the time-domain data, calculate the corresponding L2 norm based on the time-domain data and the time-domain data of the multiple neighboring sampling points, and use the product of the L2 norm and the time-domain data as the corrected time-domain data.

[0139] The frequency domain data is obtained by performing a Fourier transform on the corrected time domain data.

[0140] In one optional implementation, the execution module is used to: for any multiple wave model, perform an inverse Fourier transform on the frequency domain data of the model corresponding to the multiple wave model to obtain the time domain data of the model.

[0141] Therefore, the multiple wave prediction device provided in this embodiment of the invention constructs an upper triangular matrix with a main diagonal of 0 from the frequency domain data of any shot point in any seismic trace, and constructs a multiple wave matrix with the same structure, thereby converting multiple wave prediction into matrix operations, and improving the prediction efficiency of multiple waves while ensuring the accuracy of multiple wave prediction.

[0142] This invention provides a non-volatile computer storage medium storing at least one executable instruction that can execute the multiple wave prediction method in any of the above method embodiments.

[0143] Figure 10 A schematic diagram of a computing device according to an embodiment of the present invention is shown. The specific embodiments of the present invention do not limit the specific implementation of the computing device.

[0144] like Figure 10 As shown, the computing device may include: a processor 1002, a communications interface 1004, a memory 1006, and a communications bus 1008.

[0145] The processor 1002, communication interface 1004, and memory 1006 communicate with each other via communication bus 1008. Communication interface 1004 is used to communicate with other network elements such as clients or other servers. The processor 1002 executes program 1010, specifically performing the relevant steps described above in the embodiment of the multiple wave prediction method.

[0146] Specifically, program 1010 may include program code that includes computer operation instructions.

[0147] The processor 1002 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The computing device includes one or more processors, which may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0148] Memory 1006 is used to store program 1010. Memory 1006 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device. Program 1010 can specifically be used to cause processor 1002 to perform the operations described in the method embodiments above.

[0149] The algorithms or displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings herein. The required structure for constructing such systems is apparent from the above description. Furthermore, the embodiments of the present invention are not directed to any particular programming language. It should be understood that the content of the invention described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of the invention.

[0150] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0151] Similarly, it should be understood that, in order to simplify the invention and aid in understanding one or more of the various inventive aspects, features of the embodiments of the invention are sometimes grouped together in a single embodiment, figure, or description thereof in the above description of exemplary embodiments of the invention. However, this disclosure should not be construed as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as reflected in the following claims, inventive aspects lie in fewer than all features of a single foregoing disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into this detailed description, wherein each claim itself is a separate embodiment of the invention.

[0152] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0153] Furthermore, those skilled in the art will understand that although some embodiments herein include certain features included in other embodiments but not others, combinations of features from different embodiments are intended to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments can be used in any combination.

[0154] The various component embodiments of the present invention can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some or all of the components according to the embodiments of the present invention. The present invention can also be implemented as a device or apparatus program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such programs implementing the present invention can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0155] It should be noted that the above embodiments are illustrative of the invention and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be construed as limiting the order of execution.

Claims

1. A method for predicting multiple waves, characterized in that, include: Obtain frequency domain data of any shot point in any seismic trace; A seismic data matrix is ​​generated based on the frequency domain data; wherein the seismic data matrix is ​​an upper triangular matrix with a main diagonal of 0, and any non-first column of the seismic data matrix contains frequency domain data of at least one seismic trace from the same shot point. Construct a multiple wave matrix; wherein the multiple wave matrix is ​​an upper triangular matrix with a main diagonal of 0, and any non-first column of the multiple wave matrix contains at least one seismic trace of the same shot point. Establish the functional relationship between the wave multiple matrix and the seismic data matrix; Based on the functional relationship, the model frequency domain data corresponding to any multiple wave model in the multiple wave matrix is ​​obtained.

2. The method according to claim 1, characterized in that, The earthquake data matrix is ​​specifically as follows: in, Let be the element in the i-th row and j-th column of the earthquake data matrix. For the frequency domain data of the jn-th shot point in the ji seismic trace, This represents the frequency domain data of the first shot point in ji seismic traces, where n is the total number of seismic traces.

3. The method according to claim 2, characterized in that, The specific multiple wave matrix is ​​as follows: in, Let be the element in the i-th row and j-th column of the multiple wave matrix. For the multiple wave model of the jn-th shot point in the j-th seismic trace, This is the multiple wave model of the first shot point in ji seismic traces, where n is the total number of seismic traces.

4. The method according to claim 3, characterized in that, The establishment of the functional relationship between the wave multiple matrix and the seismic data matrix further includes: Establish the following functional relationship: Where P is the wave multiple matrix and T is the seismic data matrix.

5. The method according to any one of claims 1-4, characterized in that, The acquisition of frequency domain data of any shot point in any seismic trace further includes: Obtain time-domain data of any shot point in any seismic trace; The frequency domain data is obtained by performing a Fourier transform on the time domain data.

6. The method according to claim 5, characterized in that, After obtaining the model frequency domain data corresponding to any multiple model in the multiple matrix according to the functional relationship, the method further includes: For any multiple wave model, the time domain data of the model is obtained by performing an inverse Fourier transform on the frequency domain data of the model corresponding to the multiple wave model.

7. A multiple wave prediction device, characterized in that, include: The acquisition module is used to acquire frequency domain data of any shot point in any seismic trace; The processing module is used to generate a seismic data matrix based on the frequency domain data. The seismic data matrix is ​​an upper triangular matrix with a main diagonal of 0. Any non-first column of the seismic data matrix contains frequency domain data of at least one seismic trace from the same shot point. The method includes constructing a multiple matrix, which is an upper triangular matrix with a main diagonal of 0, and a multiple model in which any non-first column of the multiple matrix contains at least one seismic trace from the same shot point; and establishing a functional relationship between the multiple matrix and the seismic data matrix. The execution module is used to obtain the model frequency domain data corresponding to any multiple wave model in the multiple wave matrix according to the functional relationship.

8. A computing device, characterized in that, include: The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus. The memory is used to store at least one executable instruction that causes the processor to perform the operation corresponding to the multi-wave prediction method as described in any one of claims 1-6.

9. A computer storage medium, characterized in that, The storage medium stores at least one executable instruction that causes the processor to perform the operation corresponding to the multiple wave prediction method as described in any one of claims 1-6.

Images