Interlayer multiple wave prediction method and device

Abstract

This invention discloses a method and apparatus for predicting inter-layer multiples. The method includes: splitting seismic data into upper and lower data; performing a first transformation on the upper and lower data respectively to obtain corresponding upper and lower frequency domain data; performing multiple product operations on the conjugate complex numbers of the lower and upper frequency domain data to obtain a product data volume; and performing a second transformation on the product data volume to obtain the predicted inter-layer multiple data. By splitting seismic data into upper and lower data, transforming it into corresponding upper and lower frequency domain data, and performing multiple product operations on the conjugate complex numbers of the lower and upper frequency domain data before transformation, the predicted inter-layer multiple data can be obtained. This reduces the interference of inter-layer multiples on seismic data and improves the imaging quality of seismic data.

Description

Technical Field

[0001] This invention relates to the field of marine petroleum geophysical exploration, specifically to a method and apparatus for predicting interlayer multiple waves. Background Technology

[0002] In the final stacked data volume of seismic data, there are often remnants of inter-layer multiples. Inter-layer multiples have always been a key focus of seismic data processing. Seismic data with strong reflection layers usually have more severe inter-layer multiple interference, which seriously affects the imaging quality of seismic data.

[0003] Therefore, there is an urgent need for a method to predict inter-layer multiples in order to solve the interference of inter-layer multiples on seismic data. Summary of the Invention

[0004] In view of the above problems, embodiments of the present invention are proposed to provide a method and apparatus for predicting inter-layer multiples that overcomes or at least partially solves the above problems.

[0005] According to one aspect of the present invention, an inter-layer multiple prediction method is provided, the method comprising:

[0006] The seismic data is split into upper and lower data.

[0007] The upper and lower data are subjected to the first transformation process to obtain the corresponding upper frequency domain data and lower frequency domain data.

[0008] The product data volume is obtained by multiplying the conjugate complex numbers of the lower frequency domain data and the upper frequency domain data multiple times.

[0009] The product data volume is then subjected to a second transformation to obtain the predicted inter-layer multiple wave data.

[0010] Optionally, the seismic data can be split into upper and lower data, which further include:

[0011] Based on the layers that generate interlayer multiples, the stacked seismic data volume is split to obtain the split upper and lower data.

[0012] Optionally, performing a first transformation on the upper and lower data respectively to obtain the corresponding upper frequency domain data and lower frequency domain data further includes:

[0013] The upper and lower data are processed by Fourier transform to obtain the corresponding upper and lower frequency domain data.

[0014] Optionally, Fourier transforms are performed on the upper and lower data respectively to obtain the corresponding upper and lower frequency domain data, specifically as follows:

[0015]

[0016]

[0017] Where D1 is the upper data, F1(f) is the upper frequency domain data, D2 is the lower data, F2(f) is the lower frequency domain data, f is the frequency, N is the number of frequency domain samples, the upper and lower frequency domain data are complex numbers, and j represents the imaginary part of the complex number.

[0018] Optionally, the product of the lower frequency domain data and the conjugate complex number of the upper frequency domain data is performed multiple times to obtain the product data volume, which further includes:

[0019] By performing complex conjugate processing on the upper frequency domain data, the conjugate complex number of the upper frequency domain data is obtained;

[0020] The lower frequency domain data is multiplied with the upper frequency domain data to obtain the frequency domain data volume.

[0021] The negative values ​​of the lower frequency domain data volume are multiplied by the lower frequency domain data volume to obtain the product data volume.

[0022] Optionally, the product data volume undergoes a second transformation to obtain the predicted inter-layer multiple wave data, which further includes:

[0023] The product data volume is processed by inverse Fourier transform to obtain the predicted inter-layer multiple wave data.

[0024] Optionally, the product data volume is subjected to inverse Fourier transform to obtain the predicted inter-layer multiple wave data, specifically:

[0025]

[0026] Among them, D m For the predicted interlayer multiple wave data, F m (f) represents the product data volume, f is the frequency, N is the number of frequency domain samples, and j represents the imaginary part of the complex number.

[0027] According to another aspect of the present invention, an inter-layer multiple wave prediction apparatus is provided, comprising:

[0028] The splitting module is suitable for splitting seismic data to obtain the split upper and lower data.

[0029] A first-stage transformation module is suitable for performing a first transformation process on the upper data and the lower data respectively to obtain the corresponding upper data in the frequency domain and the lower data in the frequency domain.

[0030] The product processing module is suitable for performing multiple product processing on the conjugate complex numbers of the lower frequency domain data and the upper frequency domain data to obtain the product data volume;

[0031] The second transformation module is suitable for performing a second transformation on the product data volume to obtain the predicted inter-layer multiple wave data.

[0032] 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;

[0033] The memory is used to store at least one executable instruction, which causes the processor to perform the operation corresponding to the above-described interlayer multiple wave prediction method.

[0034] According to another aspect of the present invention, a computer storage medium is provided, the storage medium storing at least one executable instruction that causes a processor to perform an operation corresponding to the interlayer multiple wave prediction method described above.

[0035] The inter-layer multiple prediction method and apparatus provided by embodiments of the present invention decompose seismic data into upper and lower data. A first transformation is performed on the upper and lower data respectively to obtain corresponding upper and lower frequency domain data. The conjugate complex numbers of the lower and upper frequency domain data are multiplied multiple times to obtain a product data volume. The product data volume is then subjected to a second transformation to obtain the predicted inter-layer multiple data. By decomposing seismic data into upper and lower data, transforming it into corresponding upper and lower frequency domain data, and performing multiple product transformations on the conjugate complex numbers of the lower and upper frequency domain data before further transformation, predicted inter-layer multiple data can be obtained. This reduces the interference of inter-layer multiples on seismic data and improves the imaging quality of seismic data.

[0036] The above description is merely an overview of the technical solutions of the embodiments of the present invention. In order to better understand the technical means of the embodiments of the present invention and to implement them in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the embodiments of the present invention more obvious and understandable, specific implementation methods of the embodiments of the present invention are described below. Attached Figure Description

[0037] 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 embodiments of the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0038] Figure 1 A flowchart of an inter-layer multiple wave prediction method according to an embodiment of the present invention is shown;

[0039] Figure 2a A schematic diagram of the stacked data volume of seismic data is shown;

[0040] Figure 2b This diagram illustrates how an overlay data volume can be split into different data sets.

[0041] Figure 2c A schematic diagram of the original stacked profile of the seismic data is shown;

[0042] Figure 2d A schematic diagram of the interlayer multiple waves predicted according to the present invention is shown;

[0043] Figure 3 A schematic diagram of an inter-layer multiple wave prediction device according to an embodiment of the present invention is shown;

[0044] Figure 4 A schematic diagram of the structure of a computing device according to an embodiment of the present invention is shown. Detailed Implementation

[0045] 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.

[0046] Figure 1 A flowchart of an inter-layer multiple wave prediction method according to an embodiment of the present invention is shown, as follows: Figure 1 As shown, the method includes the following steps:

[0047] Step S101: The seismic data is split to obtain the split upper and lower data.

[0048] Seismic data can be acquired using instruments such as seismic survey instruments. Seismic data contains stacked data volumes, such as... Figure 2aAs shown, the superimposed data volume contains remnants of inter-layer multiples, which affect the propagation of seismic waves and present challenges in identification and suppression. In this embodiment, the layers in the seismic data that generate inter-layer multiples can be determined first. Based on these layers, the superimposed data volume is split into upper and lower data, as shown below. Figure 2a The image shows a stacked volume of seismic data, containing two distinct lines, t1 and t2, representing different stacked strata. Decomposing the stacked volume yields... Figure 2b The data consists of two parts, where t1 is the line corresponding to the upper data and t2 is the line corresponding to the lower data. Figure 2a and Figure 2b For illustrative purposes only, the specific splitting can be set according to the implementation situation, and no limitation is made here.

[0049] Step S102: Perform the first transformation process on the upper data and the lower data respectively to obtain the corresponding upper data and lower data in the frequency domain.

[0050] After splitting the data into upper and lower parts, a first transformation process is performed based on the upper and lower data. In this embodiment, the first transformation process takes Fourier transform as an example, performing Fourier transform on the upper and lower data respectively to obtain the corresponding upper and lower frequency domain data. Specifically, based on the frequency and the number of frequency domain samples, the corresponding upper and lower frequency domain data are calculated using Fourier transform, as shown in the following formula:

[0051]

[0052]

[0053] Where D1 represents the upper data, F1(f) represents the upper frequency domain data, D2 represents the lower frequency domain data, F2(f) represents the lower frequency domain data, f represents the frequency, N represents the number of frequency domain samples, and n ranges from 1 to N-1. Both the upper and lower frequency domain data are complex numbers, and j represents the imaginary part of the complex number. The above formula is for illustrative purposes only; specific transformation processing can be implemented according to the actual situation, and no limitations are imposed here.

[0054] Step S103: Perform multiple product processing on the conjugate complex numbers of the lower frequency domain data and the upper frequency domain data to obtain the product data volume.

[0055] After obtaining the upper and lower frequency domain data, the upper frequency domain data is processed by performing complex conjugate processing to obtain its conjugate complex number. For example, if the upper frequency domain data is x + yj, its conjugate complex number is x - yj. The real parts of both are equal, and their imaginary parts are opposites. Multiplying the lower and upper frequency domain data yields the frequency domain data volume, which can be represented by the following formula:

[0056] F v =F2*F1 *

[0057] Among them, F v F1 is the frequency domain data volume, F2 is the lower frequency domain data, and F1 is the lower frequency domain data volume. * For the conjugate complex number of the upper part of the frequency domain data, the product processing can be performed using operations such as one-dimensional convolution multiplication.

[0058] The lower frequency domain data is then multiplied with the negative value of the obtained frequency domain data volume to obtain the product data volume, as shown in the following formula:

[0059] F m =-F v F2

[0060] Among them, F v F1 is the frequency domain data volume, F2 is the lower frequency domain data, F m For a product data volume, product processing can be performed using operations such as one-dimensional convolution multiplication.

[0061] Step S104: Perform a second transformation on the product data volume to obtain the predicted inter-layer multiple wave data.

[0062] The resulting product data volume undergoes a second transformation, which can be performed using an inverse Fourier transform, to obtain the predicted inter-layer multiple wave data, as shown in the following formula:

[0063]

[0064] Among them, D m For the predicted interlayer multiple wave data, F m (f) represents the product data volume, where f is the frequency, N is the number of frequency domain samples, n ranges from 1 to N-1, and j represents the imaginary part of the complex number. The above formula is for illustrative purposes only; specific transformation processing can be set according to the implementation situation, which is not limited here.

[0065] like Figure 2c As shown, Figure 2c This is the original stacked profile of the seismic data, from Figure 2cStrata A and strata B can be clearly seen in the middle, that is Figure 2c Apart from the lines where A and B are located, Figure 2c It also includes the generation of first-order inter-wave multiples C-axis and second-order inter-wave multiples D-axis, i.e. Figure 2c The points indicated by arrows C and D. Figure 2d To analyze the interlayer multiples data predicted based on the upper and lower seismic data obtained after splitting the seismic data into layers A and B according to this embodiment, the interlayer multiples data are then analyzed. Figure 2d The arrow indicates the predicted interlayer multiples, which are... Figure 2c The C-axis of the first-level interlayer multiples and the D-axis of the second-level interlayer multiples coincide. Based on this embodiment, interlayer multiples in seismic data can be accurately predicted. The predicted interlayer multiples in seismic data can reduce interference with seismic data and improve the imaging quality of seismic data.

[0066] According to the inter-layer multiple prediction method provided in this embodiment of the invention, seismic data is split into upper and lower data. A first transformation is performed on the upper and lower data respectively to obtain corresponding upper and lower frequency domain data. The conjugate complex numbers of the lower and upper frequency domain data are multiplied multiple times to obtain a product data volume. The product data volume is then subjected to a second transformation to obtain the predicted inter-layer multiple data. By splitting seismic data into upper and lower data, transforming it into corresponding upper and lower frequency domain data, and performing multiple product transformations on the conjugate complex numbers of the lower and upper frequency domain data before further transformation, predicted inter-layer multiple data can be obtained. This reduces the interference of inter-layer multiples on seismic data and improves the imaging quality of seismic data.

[0067] Figure 3 A schematic diagram of the structure of the inter-layer multiple wave prediction device provided in an embodiment of the present invention is shown. Figure 3 As shown, the device includes:

[0068] The splitting module 310 is suitable for splitting seismic data to obtain the split upper and lower data.

[0069] The first transformation module 320 is adapted to perform a first transformation process on the upper data and the lower data respectively to obtain the corresponding upper data and lower data in the frequency domain.

[0070] The product processing module 330 is adapted to perform multiple product processing on the conjugate complex number of the lower frequency domain data and the upper frequency domain data to obtain the product data volume;

[0071] The second transformation module 340 is adapted to perform a second transformation on the product data volume to obtain the predicted inter-layer multiple wave data.

[0072] Optionally, the split module 310 further includes:

[0073] Based on the layers that generate interlayer multiples, the stacked seismic data volume is split to obtain the split upper and lower data.

[0074] Optionally, the primary conversion module 320 is further adapted to:

[0075] The upper and lower data are processed by Fourier transform to obtain the corresponding upper and lower frequency domain data.

[0076] Optionally, the primary conversion module 320 is further adapted to:

[0077]

[0078]

[0079] Where D1 is the upper data, F1(f) is the upper frequency domain data, D2 is the lower data, F2(f) is the lower frequency domain data, f is the frequency, N is the number of frequency domain samples, the upper and lower frequency domain data are complex numbers, and j represents the imaginary part of the complex number.

[0080] Optionally, the product processing module 330 is further adapted to:

[0081] By performing complex conjugate processing on the upper frequency domain data, the conjugate complex number of the upper frequency domain data is obtained;

[0082] The lower frequency domain data is multiplied with the upper frequency domain data to obtain the frequency domain data volume.

[0083] The negative values ​​of the lower frequency domain data volume are multiplied by the lower frequency domain data volume to obtain the product data volume.

[0084] Optionally, the secondary transformation module 340 is further adapted to:

[0085] The product data volume is processed by inverse Fourier transform to obtain the predicted inter-layer multiple wave data.

[0086] Optionally, the secondary transformation module 340 is further adapted to:

[0087]

[0088] Among them, D m For the predicted interlayer multiple wave data, F m(f) represents the product data volume, f is the frequency, N is the number of frequency domain samples, and j represents the imaginary part of the complex number.

[0089] The descriptions of the above modules refer to the corresponding descriptions in the method embodiments, and will not be repeated here.

[0090] According to the inter-layer multiple prediction device provided in this embodiment of the invention, seismic data is split into upper and lower data. A first transformation is performed on the upper and lower data respectively to obtain corresponding upper and lower frequency domain data. The conjugate complex numbers of the lower and upper frequency domain data are multiplied multiple times to obtain a product data volume. The product data volume is then subjected to a second transformation to obtain the predicted inter-layer multiple data. By splitting seismic data into upper and lower data, transforming it into corresponding upper and lower frequency domain data, and performing multiple product operations on the conjugate complex numbers of the lower and upper frequency domain data before further transformation, predicted inter-layer multiple data can be obtained. This reduces the interference of inter-layer multiples on seismic data and improves the imaging quality of seismic data.

[0091] This invention also provides a non-volatile computer storage medium storing at least one executable instruction that can execute the interlayer multiple prediction method in any of the above method embodiments.

[0092] Figure 4 The diagram illustrates the structure of a computing device according to an embodiment of the present invention. The specific embodiments of the present invention do not limit the specific implementation of the computing device.

[0093] like Figure 4 As shown, the computing device may include: a processor 402, a communications interface 404, a memory 406, and a communications bus 408.

[0094] Its features are:

[0095] The processor 402, communication interface 404, and memory 406 communicate with each other via communication bus 408.

[0096] Communication interface 404 is used to communicate with other network elements such as clients or other servers.

[0097] The processor 402 is used to execute program 410, which can specifically execute the relevant steps in the above-described embodiment of the interlayer multiple wave prediction method.

[0098] Specifically, program 410 may include program code that includes computer operation instructions.

[0099] Processor 402 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.

[0100] Memory 406 is used to store program 410. Memory 406 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0101] Specifically, program 410 can be used to cause processor 402 to execute the inter-layer multiple prediction method in any of the above method embodiments. The specific implementation of each step in program 410 can be found in the corresponding descriptions of the steps and units in the above inter-layer multiple prediction embodiments, and will not be repeated here. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the devices and modules described above can be referred to the corresponding process descriptions in the foregoing method embodiments, and will not be repeated here.

[0102] 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 embodiments of the present invention described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing preferred embodiments of the present invention.

[0103] 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.

[0104] Similarly, it should be understood that, in order to streamline the embodiments of 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 embodiments of the invention require 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 that detailed description, characterized in that each claim itself is a separate embodiment of the invention.

[0105] 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.

[0106] 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.

[0107] 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 embodiments of the present invention can also be implemented as device or apparatus programs (e.g., computer programs and computer program products) for performing part or all of the methods described herein. Such programs implementing the embodiments of 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.

[0108] It should be noted that the above embodiments are illustrative of the present invention and not restrictive of the invention, 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. Embodiments of the present 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 inter-layer multiples, characterized in that the method... The method comprises the following steps: splitting the seismic data to obtain upper data and lower data after splitting; performing first transformation processing on the upper data and the lower data respectively to obtain corresponding frequency domain upper data and frequency domain lower data; performing multiple product processing on the frequency domain lower data and the conjugate complex of the frequency domain upper data to obtain a product data body; performing second transformation processing on the product data body to obtain predicted interbed multiple wave data.

2. The method of claim 1, wherein, The splitting of the seismic data to obtain upper data and lower data after splitting further comprises: splitting the stacked data body of the seismic data according to the layer position of the interbed multiple wave to obtain upper data and lower data after splitting.

3. The method of claim 1, wherein, The first transformation processing on the upper data and the lower data respectively to obtain corresponding frequency domain upper data and frequency domain lower data further comprises: performing Fourier transform processing on the upper data and the lower data respectively to obtain corresponding frequency domain upper data and frequency domain lower data.

4. The method of claim 3, wherein, The Fourier transform processing on the upper data and the lower data respectively to obtain corresponding frequency domain upper data and frequency domain lower data specifically comprises: wherein D1 is the upper data, F1(f) is the frequency domain upper data, D2 is the lower data, F2(f) is the frequency domain lower data, f is the frequency, N is the frequency domain sampling number, the frequency domain upper data and the frequency domain lower data are complex numbers, and j represents the complex imaginary part.

5. The method of claim 1, wherein, The multiple product processing on the frequency domain lower data and the conjugate complex of the frequency domain upper data to obtain a product data body further comprises: performing complex conjugate processing on the frequency domain upper data to obtain the conjugate complex of the frequency domain upper data; performing product processing on the frequency domain lower data and the frequency domain upper data to obtain a frequency domain data body; performing product processing on the frequency domain lower data and the negative value of the frequency domain data body to obtain a product data body.

6. The method of claim 1, wherein, The second transformation processing on the product data body to obtain predicted interbed multiple wave data further comprises: performing inverse Fourier transform processing on the product data body to obtain predicted interbed multiple wave data.

7. The method of claim 6, wherein, The inverse Fourier transform processing on the product data body to obtain predicted interbed multiple wave data specifically comprises: where D m is the predicted interbed multiple data, F m (f) is the product data volume, f is frequency, N is the number of frequency domain samples, and j represents the complex imaginary part.

8. An inter-layer multiple wave prediction device, characterized in that, The device comprises: a splitting module adapted to split the seismic data to obtain upper data and lower data after splitting; a first transformation module adapted to perform first transformation processing on the upper data and the lower data respectively to obtain corresponding frequency domain upper data and frequency domain lower data; a product processing module adapted to perform multiple product processing on the frequency domain lower data and the conjugate complex of the frequency domain upper data to obtain a product data body; a second transformation module adapted to perform second transformation processing on the product data body to obtain predicted interbed multiple wave data.

9. A computing device comprising: The processor, the memory, the communication interface and the communication bus complete communication among each other through the communication bus. The memory is configured to store at least one executable instruction, and the executable instruction is configured to enable the processor to perform operations corresponding to the interbed multiple prediction method according to any one of claims 1-7.

10. A computer storage medium, the storage medium storing at least one executable instruction, and the executable instruction is configured to enable the processor to perform operations corresponding to the interbed multiple prediction method according to any one of claims 1-7.

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