Multiple wave prediction method and device, electronic equipment and storage medium

Abstract

The application provides a multiple wave prediction method and device, electronic equipment and storage medium. The method comprises the following steps: converting the obtained first seismic data into second seismic data in a cylindrical coordinate system; converting the second seismic data from a time domain to a frequency domain to obtain third seismic data in the frequency domain; transforming the third seismic data into a wave number frequency domain to obtain fourth seismic data in the wave number frequency domain; performing constant background medium velocity bias on the fourth seismic data to a pseudo-depth domain to obtain fifth seismic data in the pseudo-depth domain; calculating a vertical wave number according to a horizontal wave number of a polar radius; and inputting the vertical wave number and the fifth seismic data into a multiple wave prediction model to determine multiple wave data, which can improve the prediction accuracy of the multiple wave and thus can attenuate or eliminate the multiple wave.

Description

Technical Field

[0001] This application relates to the field of geophysical exploration technology, and in particular to a method, apparatus, electronic device and storage medium for predicting multiple waves. Background Technology

[0002] In marine and terrestrial seismic exploration, the presence of strong reflection interfaces between the seabed and subsurface causes seismic waves to reflect multiple times between the seabed and these interfaces, forming interlayer multiples. These interlayer multiples superimpose and interfere with the primary reflected waves, severely reducing the resolution of seismic data, increasing the difficulty of identifying effective waves, and affecting the quality of seismic imaging and the accuracy and reliability of seismic interpretation. Therefore, attenuating or eliminating interlayer multiples is a crucial step in seismic data processing. However, current techniques for predicting multiples suffer from low accuracy, high computational difficulty, and low efficiency. Summary of the Invention

[0003] In view of the problems in the above-mentioned related technologies, this application provides a method, apparatus, electronic device and storage medium for predicting multiple waves.

[0004] The acquired first seismic data is converted into second seismic data in cylindrical coordinates;

[0005] The second seismic data is converted from the time domain to the frequency domain to obtain the third seismic data in the frequency domain;

[0006] The third seismic data is transformed to the wavenumber frequency domain to obtain the fourth seismic data in the wavenumber frequency domain;

[0007] The fourth seismic data is migrated to the pseudo-depth domain using constant background medium velocity to obtain the fifth seismic data in the pseudo-depth domain.

[0008] Calculate the vertical wavenumber based on the horizontal wavenumber of the polar radius;

[0009] The vertical wave number and the fifth earthquake data are input into the multiple wave prediction model to determine the multiple wave data.

[0010] In some embodiments, transforming the third seismic data to the wavenumber frequency domain to obtain fourth seismic data in the wavenumber frequency domain includes:

[0011] The Hankel transform is applied to the third seismic data to obtain the fourth seismic data in the wavenumber frequency domain, wherein the Hankel transform formula is:

[0012]

[0013] Among them, b1(k rh ,ω) represents the fourth earthquake data, k rh For r hThe Fourier conjugate variable, J0 is the Jacobian determinant, and ω is the circumferential frequency; b1(r h ,ω) represents the third earthquake data, r h This is the horizontal distance between the detector and the seismic source.

[0014] In some embodiments, converting the second seismic data from the time domain to the frequency domain to obtain the third seismic data in the frequency domain includes:

[0015] The second seismic data is subjected to a Fourier transform to convert it from the time domain to the frequency domain, resulting in the third seismic data in the frequency domain.

[0016] In some embodiments, calculating the vertical wavenumber based on the horizontal wavenumber according to the polar radius includes:

[0017] The vertical wavenumber is calculated based on the horizontal wavenumber of the polar radius using the following formula:

[0018]

[0019] Where q is the vertical wave number.

[0020] In some embodiments, the multiple wave prediction model is:

[0021]

[0022] Where b3 represents the predicted first-order inter-wave multiple data, i is the imaginary unit, c0 is the water velocity in the background medium; b1 is the fifth earthquake data, z j (j=1,2,3) represents the pseudo-depth of the constant background velocity imaging. ε Used to constrain z1>z2 and z3>z2 to hold.

[0023] In some embodiments, the method further includes:

[0024] Predict the multiple wave data;

[0025] The predicted earthquake data is subjected to inverse Hankel transform to obtain earthquake data in the frequency domain;

[0026] The inverse Fourier transform of the frequency domain seismic data is used to obtain the time domain seismic data.

[0027] Based on the time-domain seismic data and the first seismic data, an adaptive subtraction method is used to obtain the seismic data after suppressing multiple waves.

[0028] This application provides a multiple wave prediction device, including:

[0029] The first conversion module is used to convert the acquired first seismic data into second seismic data in cylindrical coordinate system;

[0030] The second conversion module is used to convert the second seismic data from the time domain to the frequency domain to obtain the third seismic data in the frequency domain.

[0031] The transformation module is used to transform the third seismic data to the wavenumber frequency domain to obtain the fourth seismic data in the wavenumber frequency domain.

[0032] The offset module is used to offset the fourth seismic data to the pseudo-depth domain using a constant background medium velocity, thereby obtaining the fifth seismic data in the pseudo-depth domain.

[0033] The calculation module is used to calculate the vertical wavenumber based on the horizontal wavenumber of the polar radius;

[0034] The determination module is used to input the vertical wave number and the fifth earthquake data into the multiple wave prediction model to determine the multiple wave data.

[0035] In some embodiments, transforming the third seismic data to the wavenumber frequency domain to obtain fourth seismic data in the wavenumber frequency domain includes:

[0036] The Hankel transform is applied to the third seismic data to obtain the fourth seismic data in the wavenumber frequency domain, wherein the Hankel transform formula is:

[0037]

[0038] Among them, b1(k rh ,ω) represents the fourth earthquake data, k rh For r h The Fourier conjugate variable, J0 is the Jacobian determinant, and ω is the circumferential frequency; b1(r h ,ω) represents the third earthquake data, r h This is the horizontal distance between the detector and the seismic source.

[0039] This application provides an electronic device, including a memory and a processor. The memory stores a computer program, which, when executed by the processor, performs any of the above-described multiple wave prediction methods.

[0040] This application provides a storage medium storing a computer program that can be executed by one or more processors and can be used to implement any of the above-described multiple wave prediction methods.

[0041] This application provides a method, apparatus, electronic device, and storage medium for predicting multiples. The method involves converting acquired first seismic data into second seismic data in cylindrical coordinates; converting the second seismic data from the time domain to the frequency domain to obtain third seismic data in the frequency domain; transforming the third seismic data into the wavenumber frequency domain to obtain fourth seismic data in the wavenumber frequency domain; performing constant background medium velocity migration on the fourth seismic data to obtain fifth seismic data in the pseudo-depth domain; calculating the vertical wavenumber based on the horizontal wavenumber of the polar radius; and inputting the vertical wavenumber and the fifth seismic data into a multiple prediction model to determine the multiple data. This improves the prediction accuracy of multiples and allows for the attenuation or elimination of multiples. Attached Figure Description

[0042] The present application will be described in more detail below based on embodiments and with reference to the accompanying drawings.

[0043] Figure 1 A schematic diagram illustrating the implementation process of a multiple wave prediction method provided in this application embodiment;

[0044] Figure 2 This is a schematic diagram illustrating the implementation process of multiple wave prediction and suppression provided in the embodiments of this application;

[0045] Figure 3 A schematic diagram of simulated data provided in an embodiment of this application;

[0046] Figure 4 A schematic diagram of inter-layer multiples predicted by the multiple prediction method provided in this application embodiment;

[0047] Figure 5 A schematic diagram of inter-layer multiples predicted by a two-dimensional linear source provided in an embodiment of this application;

[0048] Figure 6 A schematic diagram comparing inter-layer multiples predicted by the simulated data, this method, and the two-dimensional linear source algorithm in the near-track.

[0049] Figure 7 A schematic diagram comparing the inter-layer multiples predicted by the simulated data, our method, and the two-dimensional linear source algorithm at the far end;

[0050] Figure 8 This is a schematic diagram of the composition structure of the electronic device provided in the embodiments of this application.

[0051] In the accompanying drawings, the same parts are referred to by the same reference numerals, and the drawings are not drawn to scale. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0053] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0054] If the application documents contain similar descriptions such as "first, second, third", the following explanation shall be added: In the following description, the terms "first, second, third" are used only to distinguish similar objects and do not represent a specific order of objects. It is understood that "first, second, third" may be interchanged in a specific order or sequence where permitted, so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0055] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0056] Before introducing the embodiments of this application, a brief overview of the problems in the related technologies will be provided:

[0057] To eliminate interference from interlayer multiples and improve data resolution, geophysical exploration has proposed two types of multiple suppression methods: one is a filtering method based on the characteristic differences between primary and multiple waves; the other is a predictive subtraction method based on wave theory. Filtering methods include predictive deconvolution, FK filtering, Radon transform, and cluster filtering. When the assumptions are well met, filtering methods can effectively attenuate or eliminate multiples, and are computationally inexpensive, easy to implement, and efficient. However, filtering methods require a significant amount of subsurface assumption information, and when the characteristic differences between primary and multiple waves are small or nonexistent, it is difficult to achieve ideal results, and may even severely damage the primary wave. Predictive subtraction avoids the limitations of filtering methods, requires no prior information, and represents the main development trend in multiple suppression methods. The main methods include the feedback iteration method and the inverse scattering series method. For inter-layer multiple suppression, the feedback iteration method requires some manual intervention, predicting inter-layer multiples by specifying the multiple generation layers layer by layer. In contrast, the inverse scattering series method is completely data-driven, requiring no manual intervention; it predicts all inter-layer multiples in a single operation, making it the most advanced inter-layer multiple suppression method currently available. However, this method has high data requirements and a large computational load, facing numerous challenges in practical applications.

[0058] Example 1:

[0059] To address the problems existing in related technologies, this application provides a multiple wave prediction method. This method is applied to an electronic device, such as a mobile terminal or computer. The functionality of the multiple wave prediction method provided in this application can be implemented by the processor of the electronic device calling program code, which can be stored in a computer storage medium.

[0060] This application provides a method for predicting multiple waves. Figure 1 This is a schematic diagram illustrating the implementation process of a multiple wave prediction method provided in an embodiment of this application, as shown below. Figure 1 As shown, it includes:

[0061] Step S1: Convert the acquired first seismic data into second seismic data in cylindrical coordinate system.

[0062] In this embodiment of the application, the first earthquake data can be obtained through a network or an input device.

[0063] In this embodiment, the cylindrical coordinate system uses planar polar coordinates and Z-direction distance to define the spatial coordinates of an object.

[0064] In this embodiment, seismic data can be acquired and preprocessed to obtain first seismic data. The preprocessing involves converting the seismic data to a Cartesian coordinate system.

[0065] In this embodiment of the application, the first seismic data can be transformed from the Cartesian coordinate system to the cylindrical coordinate system, that is... in and These represent the horizontal positions of the seismic source and the detector, respectively. h This is the horizontal distance between the detector and the seismic source.

[0066] Step S2: Convert the second seismic data from the time domain to the frequency domain to obtain the third seismic data in the frequency domain.

[0067] In this embodiment of the application, the second seismic data can be subjected to Fourier transform to convert the second seismic data from the time domain to the frequency domain, thereby obtaining the third seismic data in the frequency domain.

[0068] Step S3: Transform the third seismic data into the wavenumber frequency domain to obtain the fourth seismic data in the wavenumber frequency domain.

[0069] In this embodiment of the application, the third seismic data can be subjected to Hankel transform to obtain the fourth seismic data in the wavenumber frequency domain, wherein the Hankel transform formula is:

[0070]

[0071] Among them, b1(k rh ,ω) represents the fourth earthquake data, k rh For r h Fourier conjugate variable, k rh Let J0 be the horizontal wavenumber, J0 be the Jacobian determinant, and ω be the circumferential frequency; b1(r h ,ω) represents the third earthquake data, r h This is the horizontal distance between the detector and the seismic source.

[0072] Step S4: The fourth seismic data is migrated to the pseudo-depth domain using a constant background medium velocity to obtain the fifth seismic data in the pseudo-depth domain.

[0073] Step S5: Calculate the vertical wavenumber based on the horizontal wavenumber of the polar radius.

[0074] In this embodiment of the application, the vertical wavenumber can be calculated based on the horizontal wavenumber of the polar radius using a formula, wherein the formula is:

[0075]

[0076] Where q is the vertical wave number.

[0077] Step S6: Input the vertical wave number and the fifth earthquake data into the multiple wave prediction model to determine the multiple wave data.

[0078] In this embodiment of the application, the multiple wave prediction model is:

[0079]

[0080] Where b3 represents the predicted first-order inter-wave multiple data, i is the imaginary unit, c0 is the water velocity in the background medium; b1 is the fifth earthquake data, z j (j=1,2,3) represents the pseudo-depth of the constant background velocity imaging. ε Used to constrain z1>z2 and z3>z2 to hold.

[0081] This application provides a multiple prediction method, which involves converting acquired first seismic data into second seismic data in cylindrical coordinates; converting the second seismic data from the time domain to the frequency domain to obtain third seismic data in the frequency domain; transforming the third seismic data into the wavenumber frequency domain to obtain fourth seismic data in the wavenumber frequency domain; performing constant background medium velocity migration on the fourth seismic data to obtain fifth seismic data in the pseudo-depth domain; calculating the vertical wavenumber based on the horizontal wavenumber of the polar radius; and inputting the vertical wavenumber and the fifth seismic data into a multiple prediction model to determine the multiple data. This method can improve the prediction accuracy of multiples, thereby enabling the attenuation or elimination of multiples.

[0082] Example 2:

[0083] Based on the foregoing embodiments, this application further provides a method for predicting multiple waves, including:

[0084] Step S11: Convert the acquired first seismic data to second seismic data in cylindrical coordinate system.

[0085] In this embodiment of the application, the first earthquake data can be obtained through a network or an input device.

[0086] In this embodiment, the cylindrical coordinate system uses planar polar coordinates and Z-direction distance to define the spatial coordinates of an object.

[0087] In this embodiment, seismic data can be acquired and preprocessed to obtain first seismic data. The preprocessing involves converting the seismic data to a Cartesian coordinate system.

[0088] In this embodiment of the application, the first seismic data can be transformed from the Cartesian coordinate system to the cylindrical coordinate system, that is... in and These represent the horizontal positions of the seismic source and the detector, respectively. h This is the horizontal distance between the detector and the seismic source.

[0089] Step S12: Convert the second seismic data from the time domain to the frequency domain to obtain the third seismic data in the frequency domain.

[0090] In this embodiment of the application, the second seismic data can be subjected to Fourier transform to convert the second seismic data from the time domain to the frequency domain, thereby obtaining the third seismic data in the frequency domain.

[0091] Step S13: Transform the third seismic data into the wavenumber frequency domain to obtain the fourth seismic data in the wavenumber frequency domain.

[0092] In this embodiment of the application, the third seismic data can be subjected to Hankel transform to obtain the fourth seismic data in the wavenumber frequency domain, wherein the Hankel transform formula is:

[0093]

[0094] Among them, b1(k rh ,ω) represents the fourth earthquake data, k rh For r h Fourier conjugate variable, k rh Let J0 be the horizontal wavenumber, J0 be the Jacobian determinant, and ω be the circumferential frequency; b1(r h ,ω) represents the third earthquake data, r h This is the horizontal distance between the detector and the seismic source.

[0095] Step S14: The fourth seismic data is migrated to the pseudo-depth domain using constant background medium velocity to obtain the fifth seismic data in the pseudo-depth domain.

[0096] Step S15: Calculate the vertical wavenumber based on the horizontal wavenumber of the polar radius.

[0097] In this embodiment of the application, the vertical wavenumber can be calculated based on the horizontal wavenumber of the polar radius using a formula, wherein the formula is:

[0098]

[0099] Where q is the vertical wave number.

[0100] Step S6: Input the vertical wave number and the fifth earthquake data into the multiple wave prediction model to determine the multiple wave data.

[0101] In this embodiment of the application, the multiple wave prediction model is:

[0102]

[0103] Where b3 represents the predicted first-order inter-wave multiple data, i is the imaginary unit, c0 is the water velocity in the background medium; b1 is the fifth earthquake data, zj (j=1,2,3) represents the pseudo-depth of the constant background velocity imaging. ε Used to constrain z1>z2 and z3>z2 to hold.

[0104] Step S16: Predict the multiple wave data.

[0105] In this application embodiment, multiples can be suppressed using three types of methods. The first type utilizes the difference in normal time difference between multiples and primary waves. Many multiple suppression methods are based on this difference, such as the common center point superposition method, two-dimensional filtering method, and various transformations. The second type utilizes the repeatability and statistical characteristics of multiples. This type of method utilizes the statistical characteristic of the periodic recurrence of multiples, employing digital prediction operators to eliminate multiples, such as predictive deconvolution. The third type is the multiple model subtraction method. This type of multiple suppression method involves first obtaining an accurate multiple model, and then subtracting the obtained multiples from the data affected by multiple interference, thereby achieving the purpose of eliminating multiples. Examples include wave equation extrapolation, model fitting, free interface multiple attenuation, and subtraction methods.

[0106] Step S17: Perform Hankel inverse transform on the predicted seismic data to obtain seismic data in the frequency domain.

[0107] Step S18: Perform an inverse Fourier transform on the frequency domain seismic data to obtain time domain seismic data.

[0108] Step S19: Based on the time-domain seismic data and the first seismic data, an adaptive subtraction method is used to obtain the seismic data after suppressing multiple waves.

[0109] In this embodiment of the application, the adaptive subtraction method can be used to subtract multiple waves, thereby achieving the purpose of multiple wave suppression.

[0110] This application provides a multiple prediction method, which involves converting acquired first seismic data into second seismic data in cylindrical coordinates; converting the second seismic data from the time domain to the frequency domain to obtain third seismic data in the frequency domain; transforming the third seismic data into the wavenumber frequency domain to obtain fourth seismic data in the wavenumber frequency domain; performing constant background medium velocity migration on the fourth seismic data to obtain fifth seismic data in the pseudo-depth domain; calculating the vertical wavenumber based on the horizontal wavenumber of the polar radius; and inputting the vertical wavenumber and the fifth seismic data into a multiple prediction model to determine the multiple data. This method can improve the prediction accuracy of multiples, thereby enabling the attenuation or elimination of multiples.

[0111] Example 3:

[0112] Based on the foregoing embodiments, this application further provides a method for predicting interlayer multiples. This method, based on a three-dimensional backscattering series interlayer multiple prediction method, optimizes the algorithm through medium symmetry under the condition of a one-dimensional approximation of the subsurface medium, improving computational efficiency while maintaining the accuracy of multiple prediction. The improved algorithm retains the advantages of the original method, is entirely data-driven, and requires no known subsurface velocity model. Processing results of the model data show that this method can predict the accurate phase and approximate amplitude of interlayer multiples, effectively suppressing them.

[0113] The three-dimensional inverse scattering series interlayer multiple prediction algorithm is as follows:

[0114]

[0115] Where ω is the circumferential frequency, i is the imaginary unit, and π is the constant of pi; κ s and κ g κ1 and κ2 are the horizontal wavenumbers in the x and y directions, respectively, of the seismic source and the detector. λ∈(g,1,2,s) is the vertical wavenumber, c0 is the background medium velocity; ε s and ε g The depth of the seismic source and detector; z j (j=1,2,3) represents the pseudo-depth for constant background velocity imaging. ε is introduced to ensure the strict adherence of the "low-high-low" constraint (z1>z2 and z3>z2). In practical data processing, its value is related to the wavelet length. Input data b1(κ) g ,κ s ,z) is

[0116] b1(κ g ,κ s ,ω)=-2iq s D(κ g ,κ s ,ω) (2)

[0117] The result of constant background velocity migration imaging, i.e., the plane wavefield of seismic data D in the pseudo-depth domain. b3 is the predicted first-order inter-wave multiple data.

[0118] The aforementioned three-dimensional algorithm assumes an experiment using a point source (three-dimensional source) and a three-dimensional subsurface medium. Specifically, the point source generates seismic waves at the surface, the wavefield propagates to the subsurface three-dimensional medium, reflects back to the surface, and is received and recorded by a three-dimensional multi-line geophone. In actual seismic exploration, the subsurface medium sometimes approximates a horizontally layered structure. In such cases, we can assume the subsurface medium is one-dimensional. Based on the symmetry of one-dimensional media, the three-dimensional seismic data depends only on the offset and time. Furthermore, based on the spatial symmetry of the cylindrical coordinate system, data can be transformed from the Cartesian coordinate system to the cylindrical coordinate system, that is... in and These represent the horizontal positions of the seismic source and the detector, respectively. h Let be the horizontal distance between the detector and the seismic source. In cylindrical coordinates, the algorithm for predicting the inverse scattering order and interlayer multiples in a one-dimensional medium with a three-dimensional point source is derived as follows:

[0119]

[0120] Where, k rh The horizontal distance r between the detector and the seismic source h The Fourier conjugate variable, i.e., the horizontal wavenumber, Let ω be the vertical wavenumber, i be the circular frequency, and c0 be the water velocity in the background medium; b1 be the plane wavefield of the cylindrical coordinate seismic data D in the pseudo-depth domain; and b3 be the predicted first-order interstage multiple wave data. To obtain the input data, the seismic data are first subjected to a Fourier transform, and then a Hankel transform.

[0121]

[0122] Where k rh For r h The Fourier conjugate variable is J0, which is the Jacobian determinant. Finally, a constant background velocity migration is performed to obtain b1(k). rh After multiple wave suppressions, the data undergoes corresponding inverse transformations. First, the Hankel inverse transform is used to obtain the spatial frequency domain seismic data.

[0123]

[0124] Further inverse Fourier transform of the frequency yields seismic data after multiple wave suppression in the spatiotemporal domain.

[0125] Based on the above derivation and analysis, in the case of an approximately one-dimensional subsurface medium, the three-dimensional inverse scattering series interlayer multiple prediction algorithm is simplified to formula (3), reducing the number of integrals. Furthermore, the polar coordinate direction can be taken as the x-direction, allowing for multiple prediction of each survey line, significantly improving computational efficiency. The simplified algorithm retains the advantages of the original algorithm, requiring only seismic data input, and is entirely data-driven, without the need for a known subsurface velocity model.

[0126] First, the actual data is preprocessed, converted to cylindrical coordinates, Fourier transformed to the frequency domain, then Hankel transformed to the frequency-wavenumber domain, and offset imaging to the pseudo-depth domain. Finally, the data is substituted into formula (3) to predict interlayer multiples. The results are then verified through model data processing, demonstrating that this method is entirely data-driven, requires no prior knowledge of the subsurface velocity model, and can effectively predict the accurate phase and approximate amplitude of interlayer multiples, providing a reliable multiple model for suppressing interlayer multiples.

[0127] After the actual data is preprocessed, Figure 2 This is a schematic diagram illustrating the implementation process of multiple wave prediction and suppression provided in the embodiments of this application, as shown below. Figure 2 As shown, the specific implementation steps for predicting and suppressing interlayer multiples of one-dimensional medium backscattering order from a three-dimensional point source are as follows:

[0128] Step 1: Convert the preprocessed seismic data to cylindrical coordinates;

[0129] Step 2: Perform a Fourier transform on the cylindrical coordinate data to transform it from the time domain to the frequency domain;

[0130] Step 3: Use formula (4) to perform Hankel transform on the polar radius of the frequency domain data to the wavenumber frequency domain;

[0131] Step 4: Perform a constant background medium velocity offset on the wavenumber frequency domain data and transfer it to the pseudo-depth domain;

[0132] Step 5: Calculate the vertical wavenumber based on the horizontal wavenumber of the polar radius;

[0133] Step 6: Predict interlayer multiples using formula (3);

[0134] Step 7: Perform Hankel inverse transform and Fourier inverse transform on the predicted interlayer multiples to return to the time-space domain;

[0135] Step 8: Obtain the data after interlayer multiple wave suppression using the adaptive subtraction method.

[0136] The method is validated using simulation data. The results show that this method is purely data-driven, requires no known subsurface velocity model, and can accurately predict the phase and approximate amplitude of interlayer multiples, effectively suppressing them.

[0137] Based on the one-dimensional subsurface medium assumption of this method, the simulation data is generated by a layered model with two reflection interfaces at 100m and 150m respectively. The source and detector are at 0m on the surface. The source uses a Ricker wavelet with a dominant frequency of 25Hz, with a total of 301 channels, a channel spacing of 5m, a sampling interval of 0.001s, and a recording time of 0.5s. Figure 3 A schematic diagram of simulated data provided in an embodiment of this application, such as... Figure 3 As shown, the simulation data has three in-phase axes: the first primary wave, the second primary wave, and the first-level interphase multiple wave. Figure 4 This application provides a schematic diagram of inter-layer multiples predicted by the multiple prediction method in an embodiment of the present application. Figure 5 This is a schematic diagram illustrating inter-layer multiple waves predicted by a two-dimensional linear source, provided as an embodiment of this application. Figure 4 and Figure 5 It can be seen that the two-dimensional linear source algorithm can predict the accurate phase of multiples, but the amplitudes differ greatly. In contrast, our method can predict both the accurate phase and very close amplitude of multiples, effectively eliminating inter-layer multiples. To further compare the waveforms and amplitudes, from... Figures 3 to 5 The data from the shortest and longest routes were selected for comparison. Figure 6 and Figure 7 The diagrams show a comparison of inter-layer multiples predicted by simulated data, our proposed method, and the two-dimensional linear source algorithm in the near and far paths. 1 represents simulated data, 2 represents inter-layer multiples predicted by our proposed method, and 3 represents inter-layer multiples predicted by the two-dimensional linear source algorithm. It can be seen that the two-dimensional linear source algorithm accurately predicts the travel times of the multiples, with amplitudes differing by 3-4 orders of magnitude. Our proposed method provides completely accurate travel times for inter-layer multiples in both the near and far paths, and the predicted amplitudes are close to the original data. During testing, only simulated data needs to be input; a known subsurface velocity model is not required. The test results demonstrate the accuracy of our proposed method in predicting inter-layer multiples in a one-dimensional subsurface medium with a three-dimensional point source.

[0138] Example 4:

[0139] Based on the foregoing embodiments, this application provides a multiple wave prediction device. The various modules and units included in the device can be implemented by a processor in a computer device; of course, they can also be implemented by specific logic circuits. In the implementation process, the processor can be a central processing unit (CPU), a microprocessor unit (MPU), a digital signal processor (DSP), or a field programmable gate array (FPGA), etc.

[0140] This application provides a multiple wave prediction device, which includes:

[0141] The first conversion module is used to convert the acquired first seismic data into second seismic data in cylindrical coordinate system;

[0142] The second conversion module is used to convert the second seismic data from the time domain to the frequency domain to obtain the third seismic data in the frequency domain.

[0143] The transformation module is used to transform the third seismic data to the wavenumber frequency domain to obtain the fourth seismic data in the wavenumber frequency domain.

[0144] The offset module is used to offset the fourth seismic data to the pseudo-depth domain using a constant background medium velocity, thereby obtaining the fifth seismic data in the pseudo-depth domain.

[0145] The calculation module is used to calculate the vertical wavenumber based on the horizontal wavenumber of the polar radius;

[0146] The determination module is used to combine the vertical wave number and the fifth seismic number.

[0147] In some embodiments, transforming the third seismic data to the wavenumber frequency domain to obtain fourth seismic data in the wavenumber frequency domain includes:

[0148] The Hankel transform is applied to the third seismic data to obtain the fourth seismic data in the wavenumber frequency domain, wherein the Hankel transform formula is:

[0149]

[0150] Among them, b1(k rh ,ω) represents the fourth earthquake data, k rh For r h The Fourier conjugate variable, J0 is the Jacobian determinant, and ω is the circumferential frequency; b1(r h ,ω) represents the third earthquake data, r h This is the horizontal distance between the detector and the seismic source.

[0151] In some embodiments, converting the second seismic data from the time domain to the frequency domain to obtain the third seismic data in the frequency domain includes:

[0152] The second seismic data is subjected to a Fourier transform to convert it from the time domain to the frequency domain, resulting in the third seismic data in the frequency domain.

[0153] In some embodiments, calculating the vertical wavenumber based on the horizontal wavenumber according to the polar radius includes:

[0154] The vertical wavenumber is calculated based on the horizontal wavenumber of the polar radius using the following formula:

[0155]

[0156] Where q is the vertical wave number.

[0157] In some embodiments, the multiple wave prediction model is:

[0158]

[0159] Where b3 represents the predicted first-order inter-wave multiple data, i is the imaginary unit, c0 is the water velocity in the background medium; b1 is the fifth earthquake data, z j (j=1,2,3) represents the pseudo-depth of the constant background velocity imaging. ε Used to constrain z1>z2 and z3>z2 to hold.

[0160] In some embodiments, the multiple wave prediction device is further configured to:

[0161] Predict the multiple wave data;

[0162] The predicted earthquake data is subjected to inverse Hankel transform to obtain earthquake data in the frequency domain;

[0163] The inverse Fourier transform of the frequency domain seismic data is used to obtain the time domain seismic data.

[0164] Based on the time-domain seismic data and the first seismic data, an adaptive subtraction method is used to obtain the seismic data after suppressing multiple waves.

[0165] It should be noted that, in the embodiments of this application, if the above-described multiple wave prediction method is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiments of this application, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), magnetic disks, or optical disks. Thus, the embodiments of this application are not limited to any specific hardware and software combination.

[0166] Accordingly, this application provides a storage medium storing a computer program thereon, characterized in that the computer program, when executed by a processor, implements the steps in the multiple wave prediction method provided in the above embodiments.

[0167] This application provides an electronic device; Figure 8 This is a schematic diagram of the composition structure of the electronic device provided in the embodiments of this application, such as... Figure 8As shown, the electronic device 300 includes: a processor 301, at least one communication bus 302, a user interface 303, at least one external communication interface 304, and a memory 305. The communication bus 302 is configured to enable communication between these components. The user interface 303 may include a display screen, and the external communication interface 304 may include standard wired and wireless interfaces. The processor 301 is configured to execute a program of a multiple wave prediction method stored in the memory to implement the steps in the multiple wave prediction method provided in the above embodiment.

[0168] It should be noted that the descriptions of the above storage medium and electronic device embodiments are similar to the descriptions of the above method embodiments, and have similar beneficial effects. For technical details not disclosed in the storage medium and device embodiments of this application, please refer to the descriptions of the method embodiments of this application for understanding.

[0169] It should be understood that the phrase "one embodiment" or "an embodiment" throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of this application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. It should be understood that in the various embodiments of this application, the sequence numbers of the above-described processes do not imply a sequential order of execution; the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application. The sequence numbers of the above-described embodiments are merely descriptive and do not represent the superiority or inferiority of the embodiments.

[0170] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, object, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, object, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, object, or apparatus that includes that element.

[0171] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0172] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0173] In addition, each functional unit in the various embodiments of this application can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0174] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as mobile storage devices, read-only memory (ROM), magnetic disks, or optical disks.

[0175] Alternatively, if the integrated units described above are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a controller to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROMs, magnetic disks, or optical disks.

[0176] The above description is merely an embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for predicting multiple waves, characterized in that, include: The acquired first seismic data is converted into second seismic data in cylindrical coordinates; The second seismic data is converted from the time domain to the frequency domain to obtain the third seismic data in the frequency domain; The third seismic data is transformed to the wavenumber frequency domain to obtain the fourth seismic data in the wavenumber frequency domain; The fourth seismic data is migrated to the pseudo-depth domain using constant background medium velocity to obtain the fifth seismic data in the pseudo-depth domain. Calculate the vertical wavenumber based on the horizontal wavenumber of the polar radius; The vertical wave number and the fifth earthquake data are input into the multiple wave prediction model to determine the multiple wave data; The step of transforming the third seismic data to the wavenumber frequency domain to obtain the fourth seismic data in the wavenumber frequency domain includes: The Hankel transform is applied to the third seismic data to obtain the fourth seismic data in the wavenumber frequency domain, wherein the Hankel transform formula is: ； in, This is the fourth earthquake data. for Fourier conjugate variable, For Jacobi determinant, It is the circumferential frequency; This is the third earthquake data. This is the horizontal distance between the detector and the seismic source; The multiple wave prediction model is as follows: in, For the predicted first-order inter-wave multiple data, The imaginary unit, The water velocity in the background medium; The pseudo-depth for imaging with constant background velocity. , , For the fifth earthquake data at different depths The value, For integration variables, Used for constraints and Established; This represents the vertical wave number.

2. The method according to claim 1, characterized in that, The step of converting the second seismic data from the time domain to the frequency domain to obtain the third seismic data in the frequency domain includes: The second seismic data is subjected to a Fourier transform to convert it from the time domain to the frequency domain, resulting in the third seismic data in the frequency domain.

3. The method according to claim 1, characterized in that, The calculation of the vertical wavenumber based on the horizontal wavenumber of the polar radius includes: The vertical wavenumber is calculated based on the horizontal wavenumber of the polar radius using the following formula: in, This represents the vertical wave number.

4. The method according to claim 1, characterized in that, The method further includes: Predict the multiple wave data; The predicted earthquake data is subjected to inverse Hankel transform to obtain earthquake data in the frequency domain. The inverse Fourier transform of the frequency domain seismic data is used to obtain the time domain seismic data. Based on the time-domain seismic data and the first seismic data, an adaptive subtraction method is used to obtain the seismic data after suppressing multiple waves.

5. A multiple wave prediction device, characterized in that, include: The first conversion module is used to convert the acquired first seismic data into second seismic data in cylindrical coordinate system; The second conversion module is used to convert the second seismic data from the time domain to the frequency domain to obtain the third seismic data in the frequency domain. The transformation module is used to transform the third seismic data to the wavenumber frequency domain to obtain the fourth seismic data in the wavenumber frequency domain. The offset module is used to offset the fourth seismic data to the pseudo-depth domain using a constant background medium velocity, thereby obtaining the fifth seismic data in the pseudo-depth domain. The calculation module is used to calculate the vertical wavenumber based on the horizontal wavenumber of the polar radius; The determination module is used to input the vertical wave number and the fifth earthquake data into the multiple wave prediction model to determine the multiple wave data; The step of transforming the third seismic data to the wavenumber frequency domain to obtain the fourth seismic data in the wavenumber frequency domain includes: The Hankel transform is applied to the third seismic data to obtain the fourth seismic data in the wavenumber frequency domain, wherein the Hankel transform formula is: ； in, This is the fourth earthquake data. for Fourier conjugate variable, For Jacobi determinant, It is the circumferential frequency; This is the third earthquake data. This is the horizontal distance between the detector and the seismic source; The multiple wave prediction model is as follows: in, For the predicted first-order inter-wave multiple data, The imaginary unit, The water velocity in the background medium; The pseudo-depth for imaging with constant background velocity. , , For the fifth earthquake data at different depths The value, For integration variables, Used for constraints and Established, This represents the vertical wave number.

6. An electronic device, characterized in that, include: A memory and a processor, wherein the memory stores a computer program that, when executed by the processor, performs the multiple wave prediction method as described in any one of claims 1 to 4.

7. A storage medium, characterized in that, The computer program stored in the storage medium can be executed by one or more processors and can be used to implement the multiple wave prediction method as described in any one of claims 1 to 4.

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