Seismic reflection wave data extrapolation interpolation method, electronic device and storage medium

Abstract

A method for extrapolation and interpolation of seismic reflection wave data, an electronic device and a storage medium, belonging to the technical field of seismic data processing. In order to accurately extract seismic data, the present invention collects seismic data, selects a reference track gather based on the detection points shared by the shot point and the shot gather; generates a virtual reflection track gather based on the reference track gather through a high-order cumulant algorithm; performs convolution calculation on the obtained reference track gather and the obtained virtual reflection track gather to generate a reconstructed far-offset reflection track gather; applies a matched filter to the obtained reconstructed far-offset reflection track gather to generate a reconstructed reflection track gather that matches the original track gather. The present invention applies a high-order cumulant algorithm to the interferometric interpolation method, suppresses the side lobes, ensures the resolution of the reconstructed data by the interferometric interpolation method, has a good effect on noise suppression, effectively suppresses noise while realizing seismic data reconstruction, and improves the signal-to-noise ratio of seismic data.

Description

Technical Field

[0001] The invention belongs to the technical field of seismic data processing, and in particular relates to a seismic reflection wave data extrapolation and interpolation method, electronic equipment and storage medium. Background Art

[0002] In recent years, seismic exploration has been moving towards deeper and more complex geological conditions. In order to accurately locate oil and gas reservoirs, seismic algorithms have put forward higher and higher requirements on the density and quality of seismic data. However, due to the limitations of the actual environment and production conditions, seismic data may be missing. The existence of missing traces is usually caused by lakes, limited exploration costs, or building obstacles and obstacles at the receiver location. The lack of these data is prone to spatial aliasing, which has an adverse effect on subsequent processing such as migration and tomography.

[0003] At present, there are mainly interpolation methods based on transform domain, interpolation methods based on predictive filtering, interpolation methods based on sparse inversion, interpolation methods based on wave equations, and interpolation methods based on deep learning. However, the above methods focus on using the primary wave information in the data to reconstruct the missing data, and the multiple wave information is usually treated as noise. But multiple waves usually carry more information than the primary wave and can provide additional information about the underground structure. Therefore, more and more researchers are beginning to use multiple wave information to expand the quality and range of underground imaging.

[0004] Seismic interferometry is a seismic data processing method based on correlation and convolution reciprocity theorems in recent years. It has been widely used in first-arrival signal enhancement, seismic wave denoising, seismic data interpolation, etc. The interpolation method based on seismic interferometry successfully uses multiple waves to reconstruct missing data through the mutual conversion of primary waves and multiple waves. The traditional interferometry method is based on the correlation algorithm to calculate the time delay between different channels. The negative sidelobes of seismic wavelets have strong similarities. The correlation calculation will cause the reconstructed wavelets to produce two positive sidelobes. At present, the reconstructed seismic data is mainly shaped based on the deconvolution method to reduce the impact of sidelobes. However, the effect of the deconvolution method in suppressing sidelobes depends to a large extent on the accuracy of wavelet extraction. In low signal-to-noise ratio data, how to accurately extract seismic wavelets is also a great challenge. Summary of the invention

[0005] The problem to be solved by the present invention is to accurately extract seismic data, and a seismic reflection wave data extrapolation and interpolation method, electronic equipment and storage medium are proposed.

[0006] To achieve the above object, the present invention is implemented through the following technical solutions:

[0007] A seismic reflection wave data extrapolation interpolation method comprises the following steps:

[0008] S1. Collect seismic data based on shot points S and shot collections S n The common detection point R i Select reference gathers;

[0009] S2. Generate a virtual reflection gather based on the reference gather obtained in step S1 by using a high-order cumulant algorithm;

[0010] S3. Perform convolution calculation on the reference gather obtained in step S1 and the virtual reflection gather obtained in step S2 to generate a reconstructed far-offset reflection gather;

[0011] S4. Apply a matched filter to the reconstructed far-offset reflection gather obtained in step S3 to generate a reconstructed reflection gather that matches the original gather.

[0012] Furthermore, the specific implementation method of step S2 is to reset the positions of the shot points and the detection points based on the reference gather obtained in step S1, and set the detection point R i The virtual reflection gathers are generated by the shot points at which the virtual reflection gathers are generated. This step is implemented by the high-order cumulant algorithm, and the expression is:

[0013] Im[G(R j |R i ) v ]=cum{G(R j |S n ),G(R i |S n ),G(R i |S n )}=E[G(R j |S n )G(R i |S n )G(R i |S n )]

[0014] Among them, G(R j |R i ) v represents the reconstructed virtual reflection gather, G(R i |S n ) and G(R j |S n ) represent the n Excitation, R i and R j Received reflection gathers; cum means cumulative calculation, and E means mean calculation.

[0015] Furthermore, the specific implementation method of step S3 is to convert the primary wave into multiple waves to generate a reconstructed far-offset reflection gather G(R j |S)esv , the expression is:

[0016] G(R j |S) esv =2ik∫G(R j |R i ) v G(R i |S)d 2 R i

[0017] Here, i represents the imaginary part.

[0018] Furthermore, the specific implementation method of step S4 is to apply a matched filter to the virtual gather, based on the matched filter, to suppress the influence of the artifacts and generate a virtual reflection gather that matches the original gather, and the expression is:

[0019] ∈=|G pred *fG|+|f| 2

[0020] Among them, G pred represents the virtual gather, G represents the original gather, ∈ represents the fitting function, and when the value of ∈ is the smallest, the optimal matched filter f is obtained;

[0021] The optimal matched filter is used to match and generate the reconstructed reflection gather G' that matches the original gather. The expression is:

[0022] G pred *f=G'.

[0023] An electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of a seismic reflection wave data extrapolation and interpolation method when executing the computer program.

[0024] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the seismic reflection wave data extrapolation and interpolation method.

[0025] Beneficial effects of the present invention:

[0026] The seismic reflection wave data extrapolation and interpolation method described in the present invention effectively utilizes the multiple wave information in the original data, reconstructs the long-offset seismic data based on the mutual conversion of the multiple waves and the primary wave, and enriches the information contained in the seismic data.

[0027] The seismic reflection wave data extrapolation interpolation method described in the present invention applies a high-order cumulant algorithm to an interferometric interpolation method, suppresses side lobes, and ensures the resolution of data reconstructed by the interferometric interpolation method.

[0028] The seismic reflection wave data extrapolation and interpolation method described in the present invention has a good effect on noise suppression. While realizing seismic data reconstruction, it effectively suppresses noise and improves the signal-to-noise ratio of seismic data. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a flow chart of a seismic reflection wave data extrapolation and interpolation method according to the present invention;

[0030] Figure 2 The schematic diagram of the extrapolation and interpolation method of seismic reflection wave data described in the present invention is shown in FIG. 1 , wherein (a) is a virtual reflection gather generated by correlation calculation, and (b) is a super virtual reflection gather generated by convolution calculation.

[0031] Figure 3 Result comparison diagram of a seismic reflection wave data extrapolation interpolation method according to the present invention, wherein (a) is missing data, (b) is directly synthesized complete data, (c) is the reconstruction result of the traditional interferometric interpolation method, and (d) is the reconstruction result of Example 1;

[0032] Figure 4 1 is a comparison diagram of single-channel results of Example 1 of the present invention, wherein (a) is a single-channel result of the traditional interferometric interpolation method, and (b) is a single-channel result of Example 1;

[0033] Figure 5 This is a comparison diagram of the results of Example 2 of the present invention, where (a) is missing data, (b) is directly synthesized noisy data, (c) is the reconstruction result of the traditional interference interpolation method, and (d) is the reconstruction result of Example 2. DETAILED DESCRIPTION

[0034] In order to make the purpose, technical solution and advantages of the present invention more clear, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention, that is, the specific embodiments described are only part of the embodiments of the present invention, rather than all of the specific embodiments. The components of the specific embodiments of the present invention described and shown in the drawings herein can be arranged and designed in various different configurations, and the present invention can also have other embodiments.

[0035] Therefore, the following detailed description of the specific embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents the selected specific embodiments of the present invention. Based on the specific embodiments of the present invention, all other specific embodiments obtained by those skilled in the art without making creative work are within the scope of protection of the present invention.

[0036] In order to further understand the content, features and effects of the present invention, the following specific implementation methods are given as examples, and the attached Figure 1 -Attached Figure 5 The detailed instructions are as follows:

[0037] Embodiment 1:

[0038] A seismic reflection wave data extrapolation interpolation method comprises the following steps:

[0039] S1. Collect seismic data based on shot points S and shot collections S n The common detection point R i Select reference gathers;

[0040] S2. Generate a virtual reflection gather based on the reference gather obtained in step S1 by using a high-order cumulant algorithm;

[0041] Furthermore, the specific implementation method of step S2 is to reset the positions of the shot points and the detection points based on the reference gather obtained in step S1, and set the detection point R i The virtual reflection gathers are generated by the shot points at which the virtual reflection gathers are generated. This step is implemented by the high-order cumulant algorithm, and the expression is:

[0042] Im[G(R j |R i ) v ]=cum{G(R j |S n ),G(R i |S n ),G(R i |S n )}=E[G(R j |S n )G(R i |S n )G(R i |S n )]

[0043] Among them, G(R j |R i ) v represents the reconstructed virtual reflection gather, G(R i |S n ) and G(R j |S n ) represent the n Excitation, R i and R j Received reflection gathers; cum means cumulative calculation, E means mean calculation;

[0044] S3. Perform convolution calculation on the reference gather obtained in step S1 and the virtual reflection gather obtained in step S2 to generate a reconstructed far-offset reflection gather;

[0045] Furthermore, the specific implementation method of step S3 is to convert the primary wave into multiple waves to generate a reconstructed far-offset reflection gather G(R j |S) esv , the expression is:

[0046] G(R j |S) esv =2ik∫G(R j |R i ) v G(R i |S)d 2 R i

[0047] Where i represents the imaginary part;

[0048] S4. Applying a matched filter to the reconstructed far-offset reflection gather obtained in step S3 to generate a reconstructed reflection gather that matches the original gather;

[0049] Furthermore, the specific implementation method of step S4 is to apply a matched filter to the virtual gather, based on the matched filter, to suppress the influence of the artifacts and generate a virtual reflection gather that matches the original gather, and the expression is:

[0050] ∈=|G pred *fG|+|f| 2

[0051] Among them, G pred represents the virtual gather, G represents the original gather, ∈ represents the fitting function, and when the value of ∈ is the smallest, the optimal matched filter f is obtained;

[0052] The optimal matched filter is used to match and generate the reconstructed reflection gather G' that matches the original gather. The expression is:

[0053] G pred *f=G'.

[0054] Furthermore, the method of this embodiment is applied to the extrapolation of a single-layer horizontal medium. The parameters of the forward modeling process are set as follows: the main frequency of the seismic wavelet is 30 Hz, the sampling interval is 2 ms, and the trace spacing is 10 m. This embodiment compares the results of the traditional interferometric interpolation method and the interferometric interpolation method based on high-order cumulants. Figure 3 As shown in (c) and (d), the far-offset data of the missing data (Figure (a)) are successfully extrapolated, but the seismic wave event axes of the traditional interferometric interpolation method are obviously coarse. This is because side lobes with stronger energy are generated on both sides of the main lobe of the reflected wave.

[0055] pass Figure 4 The comparison of single channel results can show the above results more clearly. Figure 4 In (a), the black dotted line is the directly synthesized single-channel data of the far-offset distance, and the black solid line is the single-channel result of the traditional interferometric interpolation method. Figure 4 The black solid line in (b) is the single-channel result of the warning cumulant type interferometric interpolation method. The comparison shows that the high-order cumulant type interferometric extrapolation method can better suppress the generation of side lobes, and on the basis of realizing the reconstruction of reflection wave data, it can also ensure the resolution of the reconstructed seismic data.

[0056] Embodiment 2:

[0057] A seismic reflection wave data extrapolation interpolation method comprises the following steps:

[0058] S1. Collect seismic data based on shot points S and shot collections S n The common detection point R i Select reference gathers;

[0059] S2. Generate a virtual reflection gather based on the reference gather obtained in step S1 by using a high-order cumulant algorithm;

[0060] Furthermore, the specific implementation method of step S2 is to reset the positions of the shot points and the detection points based on the reference gather obtained in step S1, and set the detection point R i The virtual reflection gathers are generated by the shot points at which the virtual reflection gathers are generated. This step is implemented by the high-order cumulant algorithm, and the expression is:

[0061] Im[G(R j |R i ) v ]=cum{G(R j |S n ),G(R i |S n ),G(R i |S n )}=E[G(R j |S n )G(R i |S n )G(R i |S n )]

[0062] Among them, G(R j |R i ) v represents the reconstructed virtual reflection gather, G(R i |S n ) and G(R j |S n ) represent then Excitation, R i and R j Received reflection gathers; cum means cumulative calculation, E means mean calculation;

[0063] S3. Perform convolution calculation on the reference gather obtained in step S1 and the virtual reflection gather obtained in step S2 to generate a reconstructed far-offset reflection gather;

[0064] Furthermore, the specific implementation method of step S3 is to convert the primary wave into multiple waves to generate a reconstructed far-offset reflection gather G(R j |S) esv , the expression is:

[0065] G(R j |S) esv =2ik∫G(R j |R i ) v G(R i |S)d 2 R i

[0066] Where i represents the imaginary part;

[0067] S4. Applying a matched filter to the reconstructed far-offset reflection gather obtained in step S3 to generate a reconstructed reflection gather that matches the original gather;

[0068] Furthermore, the specific implementation method of step S4 is to apply a matched filter to the virtual gather, based on the matched filter, to suppress the influence of the artifacts and generate a virtual reflection gather that matches the original gather, and the expression is:

[0069] ∈=|G pred *fG|+|f| 2

[0070] Among them, G pred represents the virtual gather, G represents the original gather, ∈ represents the fitting function, and when the value of ∈ is the smallest, the optimal matched filter f is obtained;

[0071] The optimal matched filter is used to match and generate the reconstructed reflection gather G' that matches the original gather. The expression is:

[0072] G pred *f=G'.

[0073] In this embodiment, the effect of suppressing noise is tested. In the forward simulation, the main frequency of the seismic wave is set to 30 Hz and the sampling interval is 2 ms. Figure 5 (a) is the seismic data with missing tracks 51 to 81. Figure 5(b) It can be seen that after adding strong noise, the reflection wave data at far offset distances are basically covered up, making it difficult to extract effective information. Figure 5 (c) and Figure 5 (d) are the results of the traditional interferometric interpolation method and the interferometric interpolation method based on high-order cumulants. Both methods can effectively suppress noise and improve the signal-to-noise ratio of seismic data. However, by comparison, it can be found that the correlation calculation in the traditional interferometric interpolation method will strengthen the deformation of the wavelet and lead to the generation of false event axes, while the interferometric extrapolation method based on high-order cumulants can effectively suppress the generation of side lobes and has a more accurate reconstruction result.

[0074] Embodiment 3:

[0075] An electronic device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of a seismic reflection wave data extrapolation and interpolation method described in either Example 1 or Example 2 are implemented.

[0076] The computer device of the present invention may be a device including a processor and a memory, such as a single chip microcomputer including a central processing unit, etc. Moreover, the processor is used to implement the steps of the above-mentioned seismic reflection wave data extrapolation interpolation method when executing the computer program stored in the memory.

[0077] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0078] The memory may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0079] Embodiment 4:

[0080] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the method for extrapolating and interpolating seismic reflection wave data described in Example 1 or Example 2 is implemented.

[0081] The computer-readable storage medium of the present invention can be any form of storage medium that can be read by a processor of a computer device, including but not limited to non-volatile memory, volatile memory, ferroelectric memory, etc. A computer program is stored on the computer-readable storage medium. When the processor of the computer device reads and executes the computer program stored in the memory, the steps of the above-mentioned seismic reflection wave data extrapolation and interpolation method can be implemented.

[0082] The computer program includes computer program code, which may be in source code form, object code form, executable file or some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer readable medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer readable media do not include electric carrier signals and telecommunication signals.

[0083] It should be noted that relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0084] Although the present application has been described above with reference to specific embodiments, various modifications may be made thereto and parts thereof may be replaced with equivalents without departing from the scope of the present application. In particular, as long as there is no structural conflict, the various features in the specific embodiments disclosed in the present application may be used in combination with each other in any manner, and the fact that these combinations are not exhaustively described in this specification is only for the sake of omitting space and saving resources. Therefore, the present application is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A seismic reflection wave data extrapolation interpolation method, characterized in that: The steps include: S1. Collect seismic data based on shot points S and shot collections S n The common detection point R i Select reference gathers; S2. Generate a virtual reflection gather based on the reference gather obtained in step S1 by using a high-order cumulant algorithm; The specific implementation method of step S2 is to reset the positions of the shot points and the detection points based on the reference gathers obtained in step S1, and set the detection point R i The virtual reflection gathers are generated by the shot points at which the virtual reflection gathers are generated. This step is implemented by the high-order cumulant algorithm, and the expression is: Im[G(R j |R i ) v ]=cum{G(R j |S n ),G(R i |S n ),G(R i |S n )}=E[G(R j |S n )G(R i |S n )G(R i |S n )] Among them, G(R j |R i ) v represents the reconstructed virtual reflection gather, G(R i |S n ) and G(R j |S n ) represent the n Excitation, R i and R j Received reflection gathers; cum means cumulative calculation, E means mean calculation; S3. Perform convolution calculation on the reference gather obtained in step S1 and the virtual reflection gather obtained in step S2 to generate a reconstructed far-offset reflection gather; The specific implementation method of step S3 is to convert the primary wave into multiple waves to generate a reconstructed far-offset reflection gather G(R j |S) esv , the expression is: G(R j |S) esv =2ik∫G(R j |R i ) v G(R i |S)d 2 R i Where i represents the imaginary part; S4. Applying a matched filter to the reconstructed far-offset reflection gather obtained in step S3 to generate a reconstructed reflection gather that matches the original gather; The specific implementation method of step S4 is to apply a matched filter to the virtual gather, based on the matched filter, to suppress the influence of artifacts and generate a virtual reflection gather that matches the original gather. The expression is: ∈=|G esv *f-G|+|f| 2 Among them, G esv represents the virtual gather, G represents the original gather, ∈ represents the fitting function, and when the value of ∈ is the smallest, the optimal matched filter f is obtained; The optimal matched filter is used to match and generate the reconstructed reflection gather G′ that matches the original gather. The expression is: G esv *f=G′。 2. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the seismic reflection wave data extrapolation and interpolation method as claimed in claim 1 when executing the computer program.

3. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for extrapolating and interpolating seismic reflection wave data as claimed in claim 1 is implemented.

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