Converted wave multiple prediction method and related apparatus

Abstract

The application discloses a converted wave multiple wave prediction method and related devices, and the method comprises the following steps: establishing multiple wave prediction models corresponding to various types of converted wave multiple wave propagation paths according to the various types of converted wave multiple wave propagation paths; transforming P-wave seismic time domain data and converted wave seismic time domain data to obtain P-wave seismic frequency domain data and converted wave seismic frequency domain data; predicting converted wave multiple wave frequency domain data according to the P-wave seismic frequency domain data, the converted wave seismic frequency domain data and the multiple wave prediction models corresponding to the various types of converted wave multiple wave propagation paths; and inversely transforming the converted wave multiple wave frequency domain data to obtain converted wave multiple wave time domain data. According to the P-wave seismic frequency domain data, the converted wave seismic frequency domain data and the multiple wave prediction models corresponding to the various types of converted wave multiple wave propagation paths, the converted wave multiple wave of the complex multiple propagation paths is efficiently and accurately predicted.

Description

Technical Field

[0001] This invention relates to the field of marine petroleum geophysical exploration, specifically to a method and apparatus for predicting converted wave multiples, a computing device, and a computer storage medium. Background Technology

[0002] In seismic exploration using reflection waves, seismic waves are reflected more than once at underground interfaces or sea surfaces and are ultimately received by geophones on the ground or sea surface. These multiple reflections, known as multiple waves, propagate back and forth between strong reflecting interfaces, resulting in more complex propagation paths and amplitude variations. The development of multiple waves can significantly reduce the accuracy of seismic data processing, thereby misleading interpreters and leading to incorrect interpretations.

[0003] Multiple prediction and processing has always been a key focus and challenge in seismic data processing. For converted waves, the multiple propagation paths are even more complex (there are two main types of multiple propagation paths for converted waves: one consists of downward P-wave-upward P-wave-downward P-wave-upward S-wave, and the other consists of downward P-wave-upward S-wave-downward P-wave-upward S-wave). Furthermore, due to the significant interference from multiples, multiple prediction is even more difficult, severely impacting the imaging quality of converted wave seismic data.

[0004] Existing methods for suppressing multiple waves often assume that all scattering sources undergo only one reflection at the subsurface interface, which is often ineffective in suppressing complex multiple propagation paths. Furthermore, the large computational demands limit the application of these methods to real-world data. Therefore, there is an urgent need to efficiently and accurately predict converted wave multiples and remove them from seismic data to avoid misunderstandings. Summary of the Invention

[0005] In view of the above problems, the present invention is proposed to provide a method and apparatus, computing device and computer storage medium for predicting converted wave multiples that can efficiently and accurately predict converted wave multiples.

[0006] According to one aspect of the present invention, a method for predicting multiples of converted waves is provided, comprising:

[0007] Based on the multiple propagation paths of various types of converted waves, multiple prediction models corresponding to the multiple propagation paths of various types of converted waves are established respectively.

[0008] Transform the time-domain data of P-wave earthquakes and the time-domain data of converted-wave earthquakes to obtain the frequency-domain data of P-wave earthquakes and the frequency-domain data of converted-wave earthquakes.

[0009] Based on the P-wave seismic frequency domain data, converted wave seismic frequency domain data, and multiple prediction models corresponding to various types of converted wave multiple propagation paths, the converted wave multiple frequency domain data is predicted.

[0010] The converted wave multiple frequency domain data is inversely transformed to obtain the converted wave multiple time domain data.

[0011] In an optional approach, the step of establishing multiple prediction models corresponding to various types of converted wave multiple propagation paths further includes:

[0012] A first multiple wave prediction model is established based on the pp-ps propagation path, wherein the pp-ps propagation path consists of a downlink longitudinal wave, an uplink longitudinal wave, a downlink longitudinal wave, and an uplink transverse wave.

[0013] Based on two different types of PS-PS propagation paths, a second multiple prediction model and a third multiple prediction model are established respectively. The PS-PS propagation path consists of a downlink longitudinal wave, an uplink transverse wave, a downlink longitudinal wave, and an uplink transverse wave.

[0014] In one alternative approach, the first multiple wave prediction model is:

[0015]

[0016] The second multiple wave prediction model is:

[0017]

[0018] The third multiple wave prediction model is as follows:

[0019]

[0020] Where ω is the angular frequency, t s For the depth delay of the seismic source, t k The depth delay of the detector point, where i is an imaginary number. For a complex function, x s ,y s ,x r ,y r These represent the x-coordinate between the shots, the y-coordinate between the shots, the x-coordinate of the receiver, and the y-coordinate of the receiver. k ,y k The x and y coordinates are to be summed, where P is the frequency domain data of the P-wave seismic data and R is the frequency domain data of the converted wave seismic data.

[0021] In one alternative approach, transforming the P-wave seismic time-domain data and the converted-wave seismic time-domain data to obtain the P-wave seismic frequency-domain data and the converted-wave seismic frequency-domain data further includes:

[0022] Performing Fourier transforms on the P-wave seismic time-domain data and the converted-wave seismic time-domain data yields the P-wave seismic frequency-domain data P and the converted-wave seismic frequency-domain data R.

[0023]

[0024]

[0025] Where ω is the angular frequency, N is the number of frequency domain samples, t is time, and j is the imaginary number. For complex functions, d(t) represents P-wave seismic time-domain data, and s(t) represents converted-wave seismic time-domain data.

[0026] In an optional approach, predicting the converted wave multiple frequency domain data based on the P-wave seismic frequency domain data, the converted wave seismic frequency domain data, and multiple prediction models corresponding to various types of converted wave multiple propagation paths further includes:

[0027] The P-wave seismic frequency domain data and the converted wave seismic frequency domain data are respectively input into the first multiple prediction model, the second multiple prediction model and the third multiple prediction model to obtain the prediction results of the first multiple prediction model, the second multiple prediction model and the third multiple prediction model;

[0028] The prediction results of the first multiple prediction model, the second multiple prediction model, and the third multiple prediction model are summed to obtain the total converted multiple frequency domain data M.

[0029] M(x s ,y s ,x r ,y r ,ω)=M1(x s ,y s ,x r ,y r ,ω)+M2(x s ,y s ,x r ,y r ,ω)+M3(x s ,y s ,x r ,y r ,ω).

[0030] In one alternative approach, the step of inversely transforming the converted wave multiple frequency domain data to obtain the converted wave multiple time domain data further includes:

[0031] The converted wave multiple frequency domain data are subjected to inverse Fourier transform to obtain the converted wave multiple time domain data.

[0032] In one alternative approach, the specific formula for performing an inverse Fourier transform on the converted wave multiple frequency domain data to obtain the converted wave multiple time domain data is as follows:

[0033]

[0034] Where m(t) represents the predicted converted wave multiple time-domain data at time t, ω is the angular frequency, N is the number of frequency domain samples, t is time, and j is an imaginary number. M(ω) is a complex function, and M(ω) is the total converted wave multiple frequency domain data.

[0035] According to another aspect of the present invention, a converted wave multiple prediction device is provided, comprising:

[0036] The model building module is used to build multiple prediction models corresponding to various types of converted wave multiple propagation paths, based on the multiple propagation paths of various types of converted waves.

[0037] The transformation module is used to transform the P-wave seismic time-domain data and the converted wave seismic time-domain data to obtain the P-wave seismic frequency-domain data and the converted wave seismic frequency-domain data.

[0038] The prediction module is used to predict the frequency domain data of converted wave multiples based on the P-wave seismic frequency domain data, the converted wave seismic frequency domain data, and the multiple prediction models corresponding to various types of converted wave multiple propagation paths.

[0039] The inverse transform module is used to perform an inverse transform on the converted wave multiple frequency domain data to obtain the converted wave multiple time domain data.

[0040] 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 via the communication bus;

[0041] The memory is used to store at least one executable instruction, which causes the processor to perform the operation corresponding to the above-described converted wave multiple prediction method.

[0042] 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 above-described converted wave multiple prediction method.

[0043] According to the scheme provided by this invention, multiple prediction models corresponding to various types of converted wave multiple propagation paths are established. The time-domain data of P-wave seismic waves and converted wave seismic waves are transformed to obtain frequency-domain data of P-wave seismic waves and converted wave seismic waves. Based on the frequency-domain data of P-wave seismic waves, converted wave seismic waves, and the multiple prediction models corresponding to various types of converted wave multiple propagation paths, the frequency-domain data of converted wave multiples is predicted. The frequency-domain data of converted wave multiples is then inversely transformed to obtain the time-domain data of converted wave multiples. This invention establishes multiple prediction models based on the frequency-domain data of P-wave seismic waves, the frequency-domain data of converted wave seismic waves, and various types of converted wave multiple propagation paths, achieving efficient and accurate prediction of converted wave multiples with complex propagation paths.

[0044] 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

[0045] 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:

[0046] Figure 1 A flowchart illustrating the converted wave multiple prediction method according to an embodiment of the present invention is shown;

[0047] Figure 2 This diagram illustrates the multiple propagation path of a first type of converted wave seismic data according to an embodiment of the present invention.

[0048] Figure 3 This diagram illustrates the multiple propagation path of the second type of converted wave seismic data according to an embodiment of the present invention.

[0049] Figure 4 This diagram illustrates the multiple propagation path of the third type of converted wave seismic data according to an embodiment of the present invention.

[0050] Figure 5 This diagram illustrates the prediction effect of multiple waves from a forward model shot set according to an embodiment of the present invention.

[0051] Figure 6 This diagram illustrates the actual data shot set multiple wave prediction effect of an embodiment of the present invention.

[0052] Figure 7A schematic diagram of the structure of the converted wave multiple prediction device according to an embodiment of the present invention is shown;

[0053] Figure 8 A schematic diagram of the structure of a computing device according to an embodiment of the present invention is shown. Detailed Implementation

[0054] 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.

[0055] Before implementing the embodiments of the present invention, the technical terms used below are uniformly explained as follows:

[0056] P-wave: P stands for Primary or Pressure, and it is a longitudinal wave in which the direction of particle vibration is parallel to the direction of wave propagation.

[0057] S-wave: S stands for secondary or shear, and it is a transverse wave in which the direction of particle vibration is perpendicular to the direction of wave propagation.

[0058] Converted waves, also known as converted reflected waves or C-waves, generate reflected transverse waves, reflected longitudinal waves, transmitted transverse waves, and transmitted longitudinal waves simultaneously when either longitudinal or transverse waves are incident obliquely on an elastic interface. For example, when a longitudinal wave (P-wave) is incident on a reflective layer interface at a non-zero angle of incidence, the following types of waves can be formed: reflected upward P-wave (PP), transmitted downward P-wave, converted upward S-wave (PS), and converted transmitted S-wave.

[0059] PP wave: also known as reflected longitudinal wave.

[0060] PS wave: also known as reflected transverse wave, PS wave is a type of S wave.

[0061] Seismic multiples are seismic waves that are reflected more than once at underground interfaces or sea surfaces and are ultimately received by geophones on the ground or sea surface. Based on the location of the descending reflection, multiples can be divided into free-surface multiples and inter-layer multiples. Compared to single-reflection waves, multiples have longer propagation paths underground, covering a wider area and illuminating shadowed areas that single-reflection waves cannot reach. The reflection angle of multiples is generally smaller than that of single-reflection waves, providing excellent information about the fine structure of the Earth's interior.

[0062] Figure 1A flowchart illustrating the converted wave multiple prediction method according to an embodiment of the present invention is shown. This method predicts converted wave multiples of various propagation paths by performing convolution and inverse transform processing on P-wave and converted wave seismic frequency domain data. Specifically, as... Figure 1 As shown, it includes the following steps:

[0063] Step S101: Based on the multiple propagation paths of various types of converted waves, establish multiple prediction models corresponding to the multiple propagation paths of various types of converted waves.

[0064] The multiple propagation paths of converted waves mainly include two types: one consists of a descending longitudinal wave - an ascending longitudinal wave - a descending longitudinal wave - an ascending transverse wave, and the other consists of a descending longitudinal wave - an ascending transverse wave - a descending longitudinal wave - an ascending transverse wave. In this embodiment, based on the characteristics of the multiple propagation paths of converted waves described above, multiple prediction models corresponding to various types of converted wave multiple propagation paths are established.

[0065] Figure 2 A typical pp-ps propagation path is shown, which consists of a downlink P-wave, an uplink P-wave, a downlink P-wave, and an uplink S-wave. This embodiment establishes a first multiple prediction model based on this type of pp-ps propagation path.

[0066] Figure 3 and Figure 4 Two different types of PS-PS propagation paths are shown, each consisting of a downlink longitudinal wave, an uplink transverse wave, a downlink longitudinal wave, and an uplink transverse wave. In this embodiment, a second multiple prediction model and a third multiple prediction model are established based on the two different types of PS-PS propagation paths.

[0067] In this embodiment, based on the type of converted wave multiple propagation path, the multiple prediction of converted wave ps is performed in the frequency domain using the convolution method. Specifically, by performing convolution processing on P-wave seismic data, converted wave seismic data (shot gather data), and converted wave shot gather data itself, the prediction of converted wave multiples for two reflection paths, pp-ps and ps-ps, is achieved.

[0068] In one alternative approach, the first multiple prediction model is:

[0069]

[0070] The second multiple wave prediction model is:

[0071]

[0072] The third multiple wave prediction model is:

[0073]

[0074] Where ω is the angular frequency, t s For the depth delay of the gun seismic source, t k The depth delay of the detector point, where i is an imaginary number. For a complex function, x s ,y s ,x r ,y r These represent the x-coordinate between the shots, the y-coordinate between the shots, the x-coordinate of the receiver, and the y-coordinate of the receiver. k ,y k The x and y coordinates are to be summed, where P is the frequency domain data of the P-wave seismic data and R is the frequency domain data of the converted wave seismic data.

[0075] Step S102: Transform the P-wave seismic time-domain data and the converted wave seismic time-domain data to obtain the P-wave seismic frequency-domain data and the converted wave seismic frequency-domain data.

[0076] Specifically, Fourier transform is performed on the P-wave seismic time-domain data and the converted-wave seismic time-domain data to obtain the P-wave seismic frequency-domain data P and the converted-wave seismic frequency-domain data R.

[0077] In one alternative approach, the specific formula for performing Fourier transforms on the P-wave seismic time-domain data and the converted-wave seismic time-domain data to obtain the P-wave seismic frequency-domain data P and the converted-wave seismic frequency-domain data R is as follows:

[0078]

[0079]

[0080] Where ω is the angular frequency, N is the number of frequency domain samples, t is time, and j is the imaginary number. For complex functions, d(t) represents P-wave seismic time-domain data, and s(t) represents converted-wave seismic time-domain data.

[0081] Step S103: Based on the P-wave seismic frequency domain data, the converted wave seismic frequency domain data, and the multiple prediction models corresponding to the multiple propagation paths of various types of converted waves, predict the frequency domain data of converted wave multiples.

[0082] In one alternative approach, P-wave seismic frequency domain data and converted wave seismic frequency domain data are input into the first multiple prediction model, the second multiple prediction model, and the third multiple prediction model, respectively, to obtain the prediction results of the first multiple prediction model, the second multiple prediction model, and the third multiple prediction model.

[0083] The prediction results of the first, second, and third multiple prediction models are summed to obtain the total converted multiple frequency domain data M.

[0084] M(x s ,y s ,x r ,y r ,ω)=M1(x s ,y s ,x r ,y r ,ω)+M2(x s ,y s ,x r ,y r ,ω)+M3(x s ,y s ,x r ,y r ,ω).

[0085] Step S104: Perform an inverse transform on the frequency domain data of the converted wave multiple to obtain the time domain data of the converted wave multiple.

[0086] Specifically, the frequency domain data of the converted wave multiple is subjected to inverse Fourier transform to obtain the time domain data of the converted wave multiple.

[0087] In one alternative approach, the inverse Fourier transform is performed on the frequency domain data of the converted wave multiple to obtain the time domain data of the converted wave multiple. The specific formula is as follows:

[0088]

[0089] Where m(t) represents the predicted converted wave multiples at time t, i.e., the multiples of the converted wave in the seismic data; ω is the angular frequency; N is the number of frequency domain samples; t is time; and j is an imaginary number. M(ω) is a complex function, and M(ω) is the total converted wave multiple frequency domain data.

[0090] The solution provided by the above embodiments of the present invention can efficiently and accurately predict converted wave multiples. Among them, Figure 5 This is a diagram showing the prediction results of multiple waves from the shot gather data in the forward model. Figure 5 (a) is the forward P-wave shot gather data. Figure 5 (b) shows the forward-modeled P-wave and converted-wave shot gather data, clearly revealing the multiple waves beneath the effective reflector layer. From... Figure 5 (c) clearly shows that multiple waves were predicted very well. Figure 6 The image shows the prediction effect of multiple waves from actual shot gather data. Figure 6 (a) and Figure 6(b) shows the original shot gather data for P-waves and converted waves. It is clear that many multiple waves exist beneath the strong reflecting layer. Figure 6 (c) clearly shows that multiple waves were predicted very well.

[0091] The solution provided in the above embodiments of the present invention transforms P-wave seismic time-domain data and converted-wave seismic time-domain data into frequency-domain seismic data through Fourier transform, and converts converted-wave multiple frequency-domain prediction data into converted-wave multiple time-domain prediction data through inverse Fourier transform. Since Fourier transform reduces complex convolution operations to simple multiplication operations, and the discrete form of Fourier transform can be rapidly computed by digital computers, the computational load is relatively small, thus enabling its widespread application in practical data. Furthermore, based on P-wave seismic frequency-domain data, converted-wave seismic frequency-domain data, and various types of converted-wave multiple propagation paths, a multiple prediction model is established, achieving efficient and accurate prediction of converted-wave multiples with complex propagation paths.

[0092] Figure 7 A schematic diagram of the converted wave multiple prediction device according to an embodiment of the present invention is shown. The converted wave multiple prediction device includes: a model building module 710, a transformation module 720, a prediction module 730, and an inverse transformation module 740.

[0093] The model building module 710 is used to establish multiple prediction models corresponding to various types of converted wave multiple propagation paths based on the multiple propagation paths of various types of converted waves.

[0094] The transformation module 720 is used to transform the P-wave seismic time-domain data and the converted wave seismic time-domain data to obtain the P-wave seismic frequency-domain data and the converted wave seismic frequency-domain data.

[0095] The prediction module 730 is used to predict the converted wave multiple frequency domain data based on the P-wave seismic frequency domain data, the converted wave seismic frequency domain data, and the multiple prediction models corresponding to various types of converted wave multiple propagation paths.

[0096] The inverse transformation module 740 is used to perform an inverse transformation on the converted wave multiple frequency domain data to obtain the converted wave multiple time domain data.

[0097] In an alternative embodiment, the model building module 710 is further configured to:

[0098] A first multiple wave prediction model is established based on the pp-ps propagation path, wherein the pp-ps propagation path consists of a downlink longitudinal wave, an uplink longitudinal wave, a downlink longitudinal wave, and an uplink transverse wave.

[0099] Based on two different types of PS-PS propagation paths, a second multiple prediction model and a third multiple prediction model are established respectively. The PS-PS propagation path consists of a downlink longitudinal wave, an uplink transverse wave, a downlink longitudinal wave, and an uplink transverse wave.

[0100] In one alternative approach, the first multiple wave prediction model is:

[0101]

[0102] The second multiple wave prediction model is:

[0103]

[0104] The third multiple wave prediction model is as follows:

[0105]

[0106] Where ω is the angular frequency, t s For the depth delay of the gun seismic source, t k The depth delay of the detector point, where i is an imaginary number. For a complex function, x s ,y s ,x r ,y r These represent the x-coordinate between the shots, the y-coordinate between the shots, the x-coordinate of the receiver, and the y-coordinate of the receiver. k ,y k The x and y coordinates are to be summed, where P is the frequency domain data of the P-wave seismic data and R is the frequency domain data of the converted wave seismic data.

[0107] In an alternative embodiment, the transformation module 720 is further configured to:

[0108] Performing Fourier transforms on the P-wave seismic time-domain data and the converted-wave seismic time-domain data yields the P-wave seismic frequency-domain data P and the converted-wave seismic frequency-domain data R.

[0109]

[0110]

[0111] Where ω is the angular frequency, N is the number of frequency domain samples, t is time, and j is the imaginary number. For complex functions, d(t) represents P-wave seismic time-domain data, and s(t) represents converted-wave seismic time-domain data.

[0112] In an alternative embodiment, the prediction module 730 is further configured to:

[0113] The P-wave seismic frequency domain data and the converted wave seismic frequency domain data are respectively input into the first multiple prediction model, the second multiple prediction model and the third multiple prediction model to obtain the prediction results of the first multiple prediction model, the second multiple prediction model and the third multiple prediction model;

[0114] The prediction results of the first multiple prediction model, the second multiple prediction model, and the third multiple prediction model are summed to obtain the total converted multiple frequency domain data M.

[0115] M(x s ,y s ,x r ,y r ,ω)=M1(x s ,y s ,x r ,y r ,ω)+M2(x s ,y s ,x r ,y r ,ω)+M3(x s ,y s ,x r ,y r ,ω).

[0116] In an alternative embodiment, the inverse transform module 740 is further configured to:

[0117] The converted wave multiple frequency domain data are subjected to inverse Fourier transform to obtain the converted wave multiple time domain data.

[0118] In an alternative embodiment, the inverse transform module 740 is further configured to:

[0119] The specific formula for performing an inverse Fourier transform on the frequency domain data of the converted wave multiple to obtain the time domain data of the converted wave multiple is as follows:

[0120]

[0121] Where m(t) represents the predicted converted wave multiple time-domain data at time t, ω is the angular frequency, N is the number of frequency domain samples, t is time, and j is an imaginary number. M(ω) is a complex function, and M(ω) is the total converted wave multiple frequency domain data.

[0122] The solution provided in the above embodiments of the present invention transforms P-wave seismic time-domain data and converted-wave seismic time-domain data into frequency-domain seismic data through Fourier transform, and converts converted-wave multiple frequency-domain prediction data into converted-wave multiple time-domain prediction data through inverse Fourier transform. Since Fourier transform reduces complex convolution operations to simple multiplication operations, and the discrete form of Fourier transform can be rapidly computed by digital computers, the computational load is relatively small, thus enabling its widespread application in practical data. Furthermore, based on P-wave seismic frequency-domain data, converted-wave seismic frequency-domain data, and various types of converted-wave multiple propagation paths, a multiple prediction model is established, achieving efficient and accurate prediction of converted-wave multiples with complex propagation paths.

[0123] Figure 8 The diagram shows a structural schematic of an embodiment of the computing device of the present invention. The specific embodiments of the present invention do not limit the specific implementation of the computing device.

[0124] like Figure 8 As shown, the computing device may include: a processor 802, a communications interface 804, a memory 806, and a communications bus 808.

[0125] The processor 802, communication interface 804, and memory 806 communicate with each other via communication bus 808. Communication interface 804 is used to communicate with other network elements such as clients or other servers. The processor 802 executes program 810, specifically performing the relevant steps in the above-described embodiment of the converted wave multiple prediction method.

[0126] Specifically, program 810 may include program code that includes computer operation instructions.

[0127] Processor 802 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.

[0128] Memory 806 is used to store program 810. Memory 806 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0129] This invention provides a non-volatile computer storage medium storing at least one executable instruction that can execute the converted wave multiple prediction method in any of the above method embodiments.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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 multiples of converted waves, characterized in that, include: A first multiple prediction model is established based on the pp-ps propagation path, wherein the pp-ps propagation path consists of a downlink P-wave, an uplink P-wave, a downlink P-wave, and an uplink S-wave; a second multiple prediction model and a third multiple prediction model are established based on two different types of ps-ps propagation paths, wherein the ps-ps propagation path consists of a downlink P-wave, an uplink S-wave, a downlink P-wave, and an uplink S-wave. Transform the time-domain data of P-wave earthquakes and the time-domain data of converted-wave earthquakes to obtain the frequency-domain data of P-wave earthquakes and the frequency-domain data of converted-wave earthquakes. Based on the P-wave seismic frequency domain data, converted wave seismic frequency domain data, first multiple prediction model, second multiple prediction model and third multiple prediction model, predict the converted wave multiple frequency domain data. The converted wave multiple frequency domain data is inversely transformed to obtain the converted wave multiple time domain data; The first multiple wave prediction model is as follows: The second multiple wave prediction model is: The third multiple wave prediction model is as follows: in, Angular frequency, For the depth delay of the seismic source, For the depth delay of the detector point, It is an imaginary number. , , It is a complex function. These represent the x-coordinate between the shots, the y-coordinate between the shots, the x-coordinate of the receiver point, and the y-coordinate of the receiver point, respectively. To sum the x and y coordinates, For P-wave seismic frequency domain data, This is converted wave seismic frequency domain data.

2. The converted wave multiple prediction method according to claim 1, characterized in that, The step of transforming the P-wave seismic time-domain data and the converted-wave seismic time-domain data to obtain the P-wave seismic frequency-domain data and the converted-wave seismic frequency-domain data further includes: Performing Fourier transforms on the P-wave seismic time-domain data and the converted-wave seismic time-domain data yields the P-wave seismic frequency-domain data P and the converted-wave seismic frequency-domain data R. in, Angular frequency, This represents the number of samples in the frequency domain. For time, It is an imaginary number. It is a complex function. For P-wave seismic time-domain data, This is converted wave seismic time-domain data.

3. The converted wave multiple prediction method according to claim 1, characterized in that, The step of predicting the converted wave multiple frequency domain data based on the P-wave seismic frequency domain data, the converted wave seismic frequency domain data, the first multiple prediction model, the second multiple prediction model, and the third multiple prediction model further includes: The P-wave seismic frequency domain data and the converted wave seismic frequency domain data are respectively input into the first multiple prediction model, the second multiple prediction model and the third multiple prediction model to obtain the prediction results of the first multiple prediction model, the second multiple prediction model and the third multiple prediction model; The prediction results of the first multiple prediction model, the second multiple prediction model, and the third multiple prediction model are summed to obtain the total converted multiple frequency domain data. , 。 4. The converted wave multiple prediction method according to claim 2, characterized in that, The step of performing an inverse transform on the converted wave multiple frequency domain data to obtain the converted wave multiple time domain data further includes: The converted wave multiple frequency domain data are subjected to inverse Fourier transform to obtain the converted wave multiple time domain data.

5. The converted wave multiple prediction method according to claim 4, characterized in that, The specific formula for performing an inverse Fourier transform on the frequency domain data of the converted wave multiple to obtain the time domain data of the converted wave multiple is as follows: in, For prediction in Time-domain data of the converted wave multiple at time points. Angular frequency, This represents the number of samples in the frequency domain. For time, It is an imaginary number. It is a complex function. This is the total converted wave multiple frequency domain data.

6. A converted wave multiple prediction device, characterized in that, include: The model building module is used to establish a first multiple prediction model based on the PP-PS propagation path, wherein the PP-PS propagation path consists of a downlink P-wave, an uplink P-wave, a downlink P-wave, and an uplink S-wave; and to establish a second multiple prediction model and a third multiple prediction model based on two different types of PS-PS propagation paths, wherein the PS-PS propagation path consists of a downlink P-wave, an uplink S-wave, a downlink P-wave, and an uplink S-wave; the first multiple prediction model is: The second multiple wave prediction model is: The third multiple wave prediction model is as follows: in, Angular frequency, For the depth delay of the seismic source, For the depth delay of the detector point, It is an imaginary number. , , It is a complex function. These represent the x-coordinate between the shots, the y-coordinate between the shots, the x-coordinate of the receiver point, and the y-coordinate of the receiver point, respectively. To sum the x and y coordinates, For P-wave seismic frequency domain data, Converted wave seismic frequency domain data; The transformation module is used to transform the P-wave seismic time-domain data and the converted wave seismic time-domain data to obtain the P-wave seismic frequency-domain data and the converted wave seismic frequency-domain data. The prediction module is used to predict the converted wave multiple frequency domain data based on the P-wave seismic frequency domain data, the converted wave seismic frequency domain data, the first multiple prediction model, the second multiple prediction model, and the third multiple prediction model. The inverse transform module is used to perform an inverse transform on the converted wave multiple frequency domain data to obtain the converted wave multiple time domain data.

7. A computing device, comprising: 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 converted wave multiple prediction method as described in any one of claims 1-5.

8. A computer storage medium storing at least one executable instruction that causes a processor to perform an operation corresponding to the converted wave multiple prediction method as described in any one of claims 1-5.

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