An interlayer multiple wave prediction method and device, electronic equipment and medium

Abstract

The application discloses an interlayer multiple wave prediction method and device, electronic equipment and medium. The method can comprise: converting seismic data to a cylindrical coordinate system, performing Fourier transform on the cylindrical coordinate system data to transform from a spatial domain to a frequency domain; performing Bessel transform on the frequency domain data in a radial direction, performing constant background velocity migration on the wave number frequency domain data to obtain input data; calculating a vertical wave number according to a horizontal wave number of a polar radius, and further predicting an interlayer multiple wave; performing progressive Bessel inverse transform and Fourier inverse transform on the interlayer multiple wave to return to a time-space domain, and obtaining data after interlayer multiple wave suppression through an adaptive subtraction method. The application can predict an accurate phase and an approximate amplitude of the interlayer multiple wave, and effectively suppresses the interlayer multiple wave.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of geophysical exploration, and more particularly, to an interbed multiple wave prediction method and device, electronic equipment and medium. BACKGROUND

[0002] In marine and land seismic exploration, due to the existence of strong reflection interfaces of seabed and underground, the interbed multiple waves are formed by multiple reflections of seismic waves between the seabed and the strong reflection interfaces. The interbed multiple waves and the primary reflected waves interfere with each other, which seriously reduces the resolution of seismic data, increases the difficulty of identifying effective waves, and affects the quality of seismic imaging and the authenticity and reliability of seismic interpretation. Therefore, attenuating or eliminating the interbed multiple waves is an important link in seismic data processing.

[0003] In order to eliminate the interference of interbed multiple waves and improve the resolution of data, two types of multiple wave suppression methods have been proposed in the field of geophysical prospecting: one type is a filtering method based on the characteristic difference between primary waves and multiple waves; and the other type is a prediction subtraction method based on wave theory. The filtering method includes prediction deconvolution, f-k filtering, Radon transform, and beam filtering. When the assumed conditions are well met, the filtering method can effectively attenuate or eliminate multiple waves, and has small calculation amount, is easy to implement, and is high in efficiency. However, the filtering method needs more underground assumed information, and when the characteristic difference between primary waves and multiple waves is small or non-existent, it is difficult to obtain ideal results, and even the primary waves can be seriously damaged. The prediction subtraction method avoids the limitations of the filtering method, and does not need prior information, which is the main development trend of multiple wave suppression methods. It mainly includes feedback iteration method and inverse scattering series method. For interbed multiple wave suppression, the feedback iteration method needs certain artificial intervention to predict interbed multiple waves by specifying the multiple wave generation horizon one by one, while the inverse scattering series method is completely data-driven and does not need artificial intervention, and can predict all interbed multiple waves by itself, which is the most advanced interbed multiple wave suppression method at present. However, this method has high requirements for data and large calculation amount, and faces many problems in practical application.

[0004] At present, it is necessary to develop an interbed multiple wave prediction method based on progressive Bessel transform.

[0005] The information disclosed in the background section of the present application is only intended to deepen the understanding of the general background of the present application, and should not be regarded as acknowledging or implying in any form that the information constitutes prior art known to those skilled in the art. SUMMARY

[0006] This paper proposes a method, device, electronic device, and medium for predicting interlayer multiples. Using a one-dimensional approximation of the subsurface medium, the algorithm optimizes the medium's symmetry and employs a progressive Bessel transform, improving computational efficiency while maintaining multiple prediction accuracy. The improved algorithm maintains the advantages of the original method, is fully data-driven, and does not require a known subsurface velocity model.

[0007] In a first aspect, an embodiment of the present disclosure provides an interlayer multiple wave prediction method, comprising:

[0008] Convert the seismic data into a cylindrical coordinate system and perform Fourier transform on the cylindrical coordinate system data from the spatial domain to the frequency domain;

[0009] Perform Bessel transform on the frequency domain data in the radial direction and perform constant background velocity offset on the wavenumber frequency domain data to obtain input data;

[0010] The vertical wave number is calculated based on the horizontal wave number of the polar diameter, and then the interlayer multiple waves are predicted;

[0011] The interlayer multiple waves are subjected to progressive inverse Bessel transform and inverse Fourier transform to return to the time-space domain, and the data after the interlayer multiple waves are suppressed are obtained by adaptive subtraction method.

[0012] As a specific implementation of the embodiment of the present disclosure, the seismic data is converted to a cylindrical coordinate system using the following formula:

[0013]

[0014] in, and are the horizontal positions of the source and the receiver, r h is the horizontal distance between the detector and the source.

[0015] As a specific implementation of the embodiment of the present disclosure, Bessel transform is performed on the frequency domain data in the radial direction using the following formula:

[0016]

[0017] Among them, b1(k rh ,ω) is the wavenumber frequency domain data, k rh is the horizontal distance r between the detector and the source h is the Fourier conjugate variable of , i.e. the horizontal wave number, ω is the circular frequency, and J0 is the Jacobian determinant.

[0018] As a specific implementation of the embodiment of the present disclosure, the vertical wave number is:

[0019]

[0020] where k rh is the Fourier conjugate variable of r h , i.e., the horizontal wavenumber, and ω is the circular frequency.

[0021] As a specific implementation manner of the embodiment of the present disclosure, the interbed multiple is predicted by the following formula:

[0022]

[0023] where k rh is the Fourier conjugate variable of r h , i.e., the horizontal wavenumber, q is the vertical wavenumber, ω is the circular frequency, i is the imaginary unit, c0 is the water speed in the background medium, ε s and ε g are the depths of the source and the receiver, z j (j = 1, 2, 3) are the pseudo-depths of constant background velocity imaging, b1 is the plane wave field of the common midpoint seismic data D in the pseudo-depth domain, and b3 is the first-order interbed multiple data predicted in the common midpoint domain.

[0024] As a specific implementation manner of the embodiment of the present disclosure, the progressive Bessel inverse transform is as follows:

[0025]

[0026] where r h is the horizontal distance between the receiver and the source, k rh is the Fourier conjugate variable of r h , i.e., the horizontal wavenumber, is the vertical wavenumber.

[0027] In a second aspect, the embodiment of the present disclosure further provides an interbed multiple prediction device, comprising:

[0028] A conversion module converts seismic data to a cylindrical coordinate system, and performs Fourier transform on the data in the cylindrical coordinate system to convert from a spatial domain to a frequency domain;

[0029] A Bessel transform module performs Bessel transform on the data in the frequency domain in a radial direction, performs constant background velocity migration on the wavenumber frequency domain data, and obtains input data;

[0030] A prediction module calculates the vertical wavenumber according to the horizontal wavenumber of the polar radius, and further predicts the interbed multiple;

[0031] An inverse transform module performs progressive Bessel inverse transform and Fourier inverse transform on the interbed multiple to the time-space domain, and obtains data after interbed multiple suppression by adaptive subtraction.

[0032] As a specific implementation manner of the embodiment of the present disclosure, the seismic data is converted to the cylindrical coordinate system by the following formula:

[0033]

[0034] wherein, and are the positions of the source and the receiver in the horizontal direction respectively, r h is the distance between the receiver and the source in the horizontal direction.

[0035] As a specific implementation manner of the embodiment of the present disclosure, the Bessel transform is performed on the frequency domain data in the radial direction by the following formula:

[0036]

[0037] wherein, b1(k rh ,ω) is the wave number frequency domain data, k rh is the Fourier conjugate variable of the distance r h between the receiver and the source in the horizontal direction, i.e., the horizontal wave number, ω is the circular frequency, and J0 is the Jacobian determinant.

[0038] As a specific implementation manner of the embodiment of the present disclosure, the vertical wave number is:

[0039]

[0040] wherein, k rh is the Fourier conjugate variable of the distance r h between the receiver and the source in the horizontal direction, i.e., the horizontal wave number, ω is the circular frequency, and c0 is the water speed in the background medium.

[0041] As a specific implementation manner of the embodiment of the present disclosure, the interbed multiple is predicted by the following formula:

[0042]

[0043] wherein, k rh is the Fourier conjugate variable of the distance r h between the receiver and the source in the horizontal direction, i.e., the horizontal wave number, q is the vertical wave number, ω is the circular frequency, i is the imaginary unit, c0 is the water speed in the background medium, ε s and ε g are the depths of the source and the receiver, and z j(j = 1, 2, 3) is the pseudo-depth of constant background velocity imaging, b1 is the plane wave field of common center point seismic data D in the pseudo-depth domain, and b3 is the first-order interbed multiple wave data predicted in the common center point domain.

[0044] As a specific implementation manner of the embodiment of the present disclosure, the progressive Bessel inverse transform is:

[0045]

[0046] wherein, r h is the distance between the receiver and the source in the horizontal direction, k rh is the Fourier conjugate variable of the distance r h between the receiver and the source in the horizontal direction, that is, the horizontal wave number, is the vertical wave number.

[0047] In a third aspect, the embodiment of the present disclosure further provides an electronic device, which comprises:

[0048] a memory, which stores executable instructions;

[0049] a processor, which runs the executable instructions in the memory to implement the interbed multiple wave prediction method.

[0050] In a fourth aspect, the embodiment of the present disclosure further provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the interbed multiple wave prediction method.

[0051] The method and device of the present disclosure have other characteristics and advantages, which will be apparent or will be described in detail in the accompanying drawings and subsequent specific embodiments incorporated herein, which are collectively used to explain the specific principles of the present disclosure. BRIEF DESCRIPTION OF DRAWINGS

[0052] The above and other objects, features and advantages of the present disclosure will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings, in which like reference characters refer to like parts throughout the several views, and wherein:

[0053] Figure 1 A flow chart showing the steps of the interbed multiple wave prediction method according to one embodiment of the present disclosure is shown.

[0054] Figure 2a 、 Figure 2b 、 Figure 2cFigures respectively show a schematic diagram of simulated data, interbed multiples predicted based on conventional Bessel transform data prediction, interbed multiples predicted based on progressive Bessel transform data prediction according to an embodiment of the present application.

[0055] Figure 3a 、 Figure 3b Figures respectively show a schematic diagram of simulated data, interbed multiples predicted based on conventional Bessel transform data prediction, interbed multiples predicted based on progressive Bessel transform data prediction according to an embodiment of the present application.

[0056] Figure 4 Figures respectively show a schematic diagram of simulated data, interbed multiples predicted based on conventional Bessel transform data prediction, interbed multiples predicted based on progressive Bessel transform data prediction according to an embodiment of the present application.

[0057] BRIEF DESCRIPTION OF DRAWINGS

[0058] 201, conversion module; 202, Bessel transform module; 203, prediction module; 204, inverse transform module. DETAILED DESCRIPTION

[0059] The preferred embodiments of the present application will be described in more detail below. Although the preferred embodiments of the present application are described below, it is understood that the present application can be implemented in various forms and should not be limited by the embodiments set forth herein.

[0060] To make the scheme and effects of the embodiments of the present application more clear, six specific application examples are given below. Those skilled in the art should understand that the examples are only for the purpose of understanding the present application, and any specific details are not intended to limit the present application in any way.

[0061] Example 1

[0062] Figure 1 Figures respectively show a schematic diagram of simulated data, interbed multiples predicted based on conventional Bessel transform data prediction, interbed multiples predicted based on progressive Bessel transform data prediction according to an embodiment of the present application.

[0063] As Figure 1 shown, the interbed multiple prediction method comprises: step 101, converting seismic data to a cylindrical coordinate system, and performing Fourier transform on the cylindrical coordinate system data to transform from a spatial domain to a frequency domain; step 102, performing Bessel transform on the frequency domain data in a radial direction, and performing constant background velocity migration on the wave number frequency domain data to obtain input data; step 103, calculating a vertical wave number according to a horizontal wave number of a polar radius, and further predicting interbed multiples; step 104, performing progressive Bessel inverse transform and Fourier inverse transform on the interbed multiples to return to a time spatial domain, and obtaining data after interbed multiple suppression by an adaptive subtraction method.

[0064] In one example, the seismic data is converted to a cylindrical coordinate system by the following formula:

[0065] In one example, the seismic data is converted to a cylindrical coordinate system by the following formula:

[0066] where, and are the positions of the source and receiver in the horizontal direction, r h is the distance between the receiver and the source in the horizontal direction.

[0067] In one example, the frequency domain data is radially transformed by a Bessel transform using the following equation:

[0068]

[0069] where, b1(k rh ,ω) is the wave number frequency domain data, k rh is the Fourier conjugate variable of the distance r h between the receiver and the source in the horizontal direction, i.e., the horizontal wave number, and ω is the circular frequency, and J0is the Jacobian determinant.

[0070] In one example, the vertical wave number is:

[0071]

[0072] where, k rh is the Fourier conjugate variable of the distance r h between the receiver and the source in the horizontal direction, i.e., the horizontal wave number, and ω is the circular frequency, and c0is the water velocity in the background medium.

[0073] In one example, the interbed multiples are predicted using the following equation:

[0074]

[0075] where, k rh is the Fourier conjugate variable of the distance r h between the receiver and the source in the horizontal direction, i.e., the horizontal wave number, q is the vertical wave number, ω is the circular frequency, i is the imaginary unit, c0is the water velocity in the background medium, ε s and ε g are the depths of the source and receiver, z j (j = 1, 2, 3) are the pseudo-depths of constant background velocity imaging, b1is the plane wave field of the common midpoint seismic data D in the pseudo-depth domain, and b3is the first order interbed multiple data predicted in the common midpoint domain.

[0076] In one example, the progressive Bessel inverse transform is:

[0077]

[0078] where, r hr is the horizontal distance between the receiver and the source rh r is the horizontal distance between the receiver and the source h k is the Fourier conjugate variable of r, i.e., the horizontal wavenumber, is the vertical wavenumber.

[0079] Specifically, the three-dimensional algorithm assumes an experiment of a point source (a three-dimensional source) and a three-dimensional subsurface medium, i.e., the point source on the ground excites seismic waves, the wavefield propagates to the three-dimensional subsurface medium and reflects back to the ground, and the three-dimensional multi-line receiver records the wavefield seismic data. In actual seismic exploration, sometimes the subsurface medium is approximated as a horizontally layered structure, so the subsurface medium can be assumed to be a one-dimensional medium, and according to the symmetry of the data in the one-dimensional medium, the three-dimensional seismic data are only related to the offset and time, i.e., Meanwhile, according to the spatial symmetry in the cylindrical coordinate system, the data can be transformed from the Cartesian rectangular coordinate system to the cylindrical coordinate system, i.e., wherein and are the positions of the source and the receiver in the horizontal direction, respectively, and r h is the horizontal distance between the receiver and the source. In the cylindrical coordinate system, the three-dimensional inverse scattering series interbedded multiple wave prediction algorithm can be degenerated into the inverse scattering series interbedded multiple wave prediction algorithm of a three-dimensional point source and a one-dimensional medium as follows:

[0080]

[0081] wherein k rh is the Fourier conjugate variable of r h , i.e., the horizontal wavenumber, is the vertical wavenumber, ω is the circular frequency, i is the imaginary unit, c0 is the water velocity in the background medium; ε s and ε g are the depths of the source and the receiver; z j (j = 1, 2, 3) are the pseudo-depths of the constant background velocity imaging. b1 is the plane wavefield of the common midpoint seismic data D in the pseudo-depth domain, and b3 is the first-order interbedded multiple wave data predicted in the common midpoint domain. In order to obtain the input data, the Fourier transform is firstly performed on the seismic data, and then the Bessel transform is performed:

[0082]

[0083] wherein k rh is the Fourier conjugate variable of r h , and J0 is the Jacobian determinant. Finally, the constant background velocity migration is performed to obtain b1(k rh , z). In order to transform to the data domain, the conventional method is to firstly perform the Bessel inverse transform to obtain the spatial frequency domain seismic data:

[0084]

[0085] Further Fourier inverse transform of frequency obtains the seismic data after suppressing multiples in space-time domain.

[0086]

[0087] Wherein, r h is the horizontal distance between the receiver and the source, k rh is the Fourier conjugate variable of the horizontal distance r h between the receiver and the source, that is, the horizontal wave number, is the vertical wave number.

[0088] When the approximate one-dimensional underground medium is simplified, the three-dimensional inverse scattering series interlayer multiple prediction algorithm can be simplified, the multiple prediction is carried out for each common midpoint gather, the integral number is reduced, and the calculation efficiency is improved. Meanwhile, in the data forward and inverse transformation, the progressive Bessel transformation is used to replace the original transformation, the calculation efficiency is improved under the premise of ensuring the accuracy. The simplified algorithm retains the advantages of the original algorithm, only needs to input the seismic data, is completely data-driven, and does not need to know the underground speed model.

[0089] Firstly, the seismic data is converted into common midpoint gathers, then Fourier transformed into the frequency wave number domain, and substituted into to predict the interlayer multiple. Finally, the processing of the model data is verified, it is proved that the method is completely data-driven, does not need to know the underground structure and speed model, can effectively predict the accurate phase and approximate amplitude of the interlayer multiple, and provides a reliable multiple model for the suppression of the interlayer multiple.

[0090] The seismic data is converted into the cylindrical coordinate system, the cylindrical coordinate system data is Fourier transformed from the space domain to the frequency domain; the is used to perform the Bessel transformation on the radial direction of the frequency domain data; the wave number frequency domain data is offset again under the constant background speed; the vertical wave number is calculated according to the horizontal wave number of the polar radius; the is used to predict the interlayer multiple; the predicted interlayer multiple is subjected to the progressive Bessel inverse transformation and the Fourier inverse transformation to return to the time space domain; and the data after suppressing the interlayer multiple is obtained through the adaptive subtraction method.

[0091] Example 2

[0092] The application also provides an interlayer multiple prediction device, comprising:

[0093] A transform module transforms the seismic data into cylindrical coordinates, and performs Fourier transform on the cylindrical coordinate data from spatial domain to frequency domain;

[0094] A Bessel transform module performs Bessel transform on the frequency domain data in radial direction, and performs constant background velocity migration on the wave number frequency domain data to obtain input data;

[0095] A prediction module calculates vertical wave number according to horizontal wave number of the polar radius, and further predicts interbed multiple wave;

[0096] An inverse transform module performs progressive Bessel inverse transform and Fourier inverse transform on the interbed multiple wave back to time spatial domain, and obtains data after interbed multiple wave suppression through adaptive subtraction method.

[0097] In one example, the seismic data is transformed into cylindrical coordinates by the following formula:

[0098]

[0099] wherein, and are positions of the seismic source and the receiver in the horizontal direction respectively, r h is the distance between the receiver and the seismic source in the horizontal direction.

[0100] In one example, the Bessel transform is performed on the frequency domain data in radial direction by the following formula:

[0101]

[0102] wherein, b1(k rh ,ω) is the wave number frequency domain data, k rh is the Fourier conjugate variable of the distance r h between the receiver and the seismic source in the horizontal direction, i.e. horizontal wave number, ω is the circular frequency, and J0 is the Jacobian determinant.

[0103] In one example, the vertical wave number is:

[0104]

[0105] wherein, k rh is the Fourier conjugate variable of the distance r h between the receiver and the seismic source in the horizontal direction, i.e. horizontal wave number, ω is the circular frequency, and c0 is the water speed in the background medium.

[0106] In one example, the interbed multiple wave is predicted by the following formula:

[0107]

[0108] wherein, krh r is the distance between the receiver and the source h is the Fourier conjugate variable of r, i.e., the horizontal wavenumber, q is the vertical wavenumber, ω is the circular frequency, i is the imaginary unit, c0is the water velocity in the background medium, ε s and ε g are the depths of the source and the receiver, z j are the pseudo-depths of constant background velocity imaging, b1is the plane wave field of the common midpoint seismic data D in the pseudo-depth domain, and b3is the predicted first-order interbed multiple wave data in the common midpoint domain.

[0109] In one example, the progressive Bessel inverse transform is:

[0110]

[0111] where r h is the distance between the receiver and the source, k rh is the Fourier conjugate variable of r h , i.e., the horizontal wavenumber, is the vertical wavenumber.

[0112] Specifically, the experiment assumed by the three-dimensional algorithm is a point source (a three-dimensional source) and a three-dimensional underground medium, i.e., the point source excites seismic waves on the ground, the wave field propagates to the three-dimensional medium underground and is reflected back to the ground, and the seismic data of the wave field are recorded by a three-dimensional multi-line receiver. In actual seismic exploration, sometimes the underground medium is approximately a horizontal layered structure, so the underground medium can be assumed to be a one-dimensional medium. According to the symmetry of the data in the one-dimensional medium, the three-dimensional seismic data are only related to the offset and time, i.e., Meanwhile, according to the spatial symmetry in the cylindrical coordinate system, the data can be transformed from the Cartesian rectangular coordinate system to the cylindrical coordinate system, i.e., where and are the positions of the source and the receiver in the horizontal direction, respectively, and r h is the distance between the receiver and the source. In the cylindrical coordinate system, the three-dimensional inverse scattering series interbed multiple wave prediction algorithm can be degenerated into the inverse scattering series interbed multiple wave prediction algorithm for a three-dimensional point source in a one-dimensional medium as follows:

[0113]

[0114] where k rh is the Fourier conjugate variable of r h , i.e., the horizontal wavenumber, is the vertical wavenumber, ω is the circular frequency, i is the imaginary unit, and c0is the water velocity in the background medium.s and ε g is the depth of the source and receiver; z j are the pseudo-depths of constant background velocity imaging. b1 is the plane wave field of common midpoint seismic data D in the pseudo-depth domain, and b3 is the predicted first-order interbed multiple wave data in the common midpoint domain. In order to obtain the input data, the Fourier transform is first performed on the seismic data, and then the Bessel transform is performed:

[0115]

[0116] where k rh is the Fourier conjugate variable of r h , and J0 is the Jacobian determinant. Finally, constant background velocity migration is performed to obtain b1(k rh , z). In order to transform into the data domain, the conventional method is to first perform the Bessel inverse transform to obtain the spatial domain frequency domain seismic data:

[0117]

[0118] Further Fourier inverse transform of the frequency is performed to obtain the seismic data after multiple wave suppression in the space-time domain. However, the Bessel inverse transform has a large amount of calculation, and the progressive Bessel inverse transform is used instead to improve the calculation efficiency:

[0119]

[0120] where r h is the distance between the receiver and the source in the horizontal direction, k rh is the Fourier conjugate variable of r h in the horizontal direction, that is, the horizontal wave number, is the vertical wave number.

[0121] When the near one-dimensional subsurface medium is approximated, the three-dimensional inverse scattering series interbed multiple wave prediction algorithm can be simplified, the multiple wave prediction is performed on each common midpoint gather, the number of integrals is reduced, and the calculation efficiency is improved. At the same time, the progressive Bessel transform is used instead of the original transform in the data forward and inverse transform, the calculation efficiency is improved on the premise of ensuring the accuracy. The simplified algorithm retains the advantages of the original algorithm, only needs to input the seismic data, is completely data-driven, and does not need to know the subsurface velocity model.

[0122] First, the seismic data is converted to a common midpoint gather, and then Fourier transformed to the frequency wave number domain, substituted into The interbed multiple is predicted. Finally, the method is verified by processing model data, and it is proved that the method is completely data-driven, and the underground structure and velocity model are not needed, and the accurate phase and approximate amplitude of the interbed multiple can be effectively predicted, and a reliable multiple model is provided for suppressing the interbed multiple.

[0123] The seismic data is converted to the cylindrical coordinate system, Fourier transform is performed on the cylindrical coordinate system data from the spatial domain to the frequency domain, and the Bessel transform is performed on the frequency domain data in the radial direction, constant background velocity migration is performed on the wave number frequency domain data, the vertical wave number is calculated according to the horizontal wave number of the polar radius, and the interbed multiple is predicted, the predicted interbed multiple is subjected to progressive Bessel inverse transform and Fourier inverse transform to return to the time-space domain, and the data after the interbed multiple is suppressed is obtained by using the adaptive subtraction method.

[0124] Example 3

[0125] Figure 2a 、 Figure 2b 、 Figure 2c The schematic diagrams of the simulation data, the interbed multiple predicted based on the conventional Bessel transform data, and the interbed multiple predicted based on the progressive Bessel transform data according to one embodiment of the present application are respectively shown.

[0126] Figure 3a 、 Figure 3b The comparative schematic diagrams of the simulation data and the interbed multiples predicted based on the conventional and progressive Bessel transform data in near traces and far traces according to one embodiment of the present application are respectively shown.

[0127] According to the one-dimensional underground medium assumption of the method, the simulation data is generated by a layered model, the model has two reflection interfaces at 100 m and 150 m respectively, the source and the receiver are on the surface 0 m, the source adopts a Ricker wavelet with a main frequency of 25 Hz, there are 301 traces, the trace interval is 5 m, the sampling interval is 0.001 s, and the recording time is 0.5 s. Figure 2a The simulation data has three same-phase axes, which are the first primary wave, the second primary wave, and the first-order interbed multiple. Figure 2b The interbed multiple predicted based on the conventional Bessel transform data is shown in FIG. 2, Figure 2c The interbed multiple predicted based on the progressive Bessel transform data is shown in FIG. 3. It can be seen that both of them can predict the accurate phase and the very close amplitude of the multiple, and provide a prepared multiple model for eliminating the interbed multiple, and the data based on the progressive Bessel transform has a reduced calculation amount and improved calculation efficiency. In order to further compare the waveforms and amplitudes, the multiple parts of the near traces and the far traces in the data are compared, Figures 2a-2c Figure 3a and Figure 3b ​The comparison of the interbed multiples predicted by the simulation data, the conventional and the progressive Bessel transform data in near trace and far trace, wherein the red line represents the simulation data, the green line represents the interbed multiples predicted based on the conventional Bessel transform data, and the blue dotted line represents the interbed multiples predicted based on the progressive Bessel transform data. It can be seen that the interbed multiples predicted by the two in near trace and far trace are completely accurate in travel time, and the amplitudes of the predicted interbed multiples are close to the original data. In the test process, only the simulation data needs to be input, and the known velocity model is not required. The test data results verify the accuracy of the interbed multiple prediction based on the progressive Bessel transform data in the three-dimensional point source and one-dimensional underground medium. The processing results of the model data verify that the method can predict the accurate phase and approximate amplitude of the interbed multiples, and effectively suppress the interbed multiples.

[0128] Example 4

[0129] Figure 4 A block diagram of an interbed multiple prediction device according to an embodiment of the present application is shown.

[0130] As shown in Figure 4 , the interbed multiple prediction device comprises:

[0131] The conversion module 201 converts the seismic data to the cylindrical coordinate system, and performs Fourier transform on the cylindrical coordinate system data from the spatial domain to the frequency domain.

[0132] The Bessel transform module 202 performs Bessel transform on the frequency domain data in the radial direction, performs constant background velocity migration on the wave number frequency domain data, and obtains the input data.

[0133] The prediction module 203 calculates the vertical wave number according to the horizontal wave number of the polar radius, and further predicts the interbed multiples.

[0134] The inverse transform module 204 performs progressive Bessel inverse transform and Fourier inverse transform on the interbed multiples back to the time spatial domain, and obtains the data after the interbed multiple suppression by the adaptive subtraction method.

[0135] As a specific implementation manner of the embodiments of the present disclosure, the seismic data is converted to the cylindrical coordinate system by the following formula:

[0136]

[0137] wherein, and are the positions of the source and the receiver in the horizontal direction, respectively, r h is the distance between the receiver and the source in the horizontal direction.

[0138] As a specific implementation manner of the embodiment of the present disclosure, the frequency domain data is subjected to the Bessel transform in the radial direction by the following formula:

[0139]

[0140] wherein b1(k rh ,ω) is the wave number frequency domain data, k rh is the Fourier conjugate variable of the distance r h between the receiver and the source in the horizontal direction, i.e., the horizontal wave number, ω is the circular frequency, and J0 is the Jacobian determinant.

[0141] As a specific implementation manner of the embodiment of the present disclosure, the vertical wave number is:

[0142]

[0143] wherein k rh is the Fourier conjugate variable of the distance r h between the receiver and the source in the horizontal direction, i.e., the horizontal wave number, ω is the circular frequency, and c0 is the water speed in the background medium.

[0144] As a specific implementation manner of the embodiment of the present disclosure, the interlayer multiple wave is predicted by the following formula:

[0145]

[0146] wherein k rh is the Fourier conjugate variable of the distance r h between the receiver and the source in the horizontal direction, i.e., the horizontal wave number, q is the vertical wave number, ω is the circular frequency, i is the imaginary unit, c0 is the water speed in the background medium, ε s and ε g are the depths of the source and the receiver, z j (j=1, 2, 3) is the pseudo-depth of the constant background velocity imaging, b1 is the plane wave field of the common midpoint seismic data D in the pseudo-depth domain, and b3 is the first-order interlayer multiple wave data predicted in the common midpoint domain.

[0147] As a specific implementation manner of the embodiment of the present disclosure, the progressive Bessel inverse transform is:

[0148]

[0149] wherein r h is the distance between the receiver and the source in the horizontal direction, k rh is the Fourier conjugate variable of the distance r h between the receiver and the source in the horizontal direction, i.e., the horizontal wave number, is the vertical wave number.

[0150] Example 5

[0151] The electronic device includes a memory storing executable instructions, and a processor running the executable instructions in the memory to implement the interlayer multiple wave prediction method.

[0152] The electronic device according to an embodiment of the disclosure includes a memory and a processor.

[0153] The memory is configured to store non-transitory computer-readable instructions. Specifically, the memory can include one or more computer program products that can include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM), cache, and / or the like. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, and / or the like.

[0154] The processor can be a central processing unit (CPU) or other form of processing unit having data processing and / or instruction execution capabilities, and can control other components in the electronic device to perform desired functions. In an embodiment of the disclosure, the processor is configured to run the computer-readable instructions stored in the memory.

[0155] Those skilled in the art will understand that, in order to solve the technical problem of how to obtain a good user experience effect, the embodiment can also include well-known structures such as a communication bus, an interface, and the like, which should also be included in the protection scope of the disclosure.

[0156] Detailed descriptions of the embodiment can refer to the corresponding descriptions in the foregoing embodiments, which will not be repeated here.

[0157] Example 6

[0158] The embodiment of the disclosure provides a computer-readable storage medium storing a computer program, which is executed by a processor to implement the interlayer multiple wave prediction method.

[0159] The computer-readable storage medium according to an embodiment of the disclosure stores non-transitory computer-readable instructions. When the non-transitory computer-readable instructions are run by a processor, all or part of the steps of the method according to the embodiments of the disclosure are performed.

[0160] The above computer readable storage medium includes, but is not limited to, an optical storage medium (for example, a CD-ROM and a DVD), a magneto-optical storage medium (for example, an MO), a magnetic storage medium (for example, a magnetic tape or a magnetic hard disk), a medium having a built-in rewritable nonvolatile memory (for example, a memory card), and a medium having a built-in ROM (for example, a ROM cartridge).

[0161] Those skilled in the art will understand that the above description of the embodiments of the present application is given for the purpose of exemplifying the advantageous effects of the embodiments of the present application and is not intended to limit the embodiments of the present application to any of the examples given.

[0162] The above has described the embodiments of the present application, and the above description is exemplary and is not exhaustive and is not limited to the disclosed embodiments. Many modifications and changes are obvious to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A method for predicting interlayer multiple waves, characterized in that: include: Convert the seismic data into a cylindrical coordinate system and perform Fourier transform on the cylindrical coordinate system data from the spatial domain to the frequency domain; Perform Bessel transform on the frequency domain data in the radial direction and perform constant background velocity offset on the wavenumber frequency domain data to obtain input data; The vertical wave number is calculated based on the horizontal wave number of the polar radius, and then the interlayer multiple waves are predicted based on the vertical wave number and the input data; The interlayer multiple waves are subjected to progressive inverse Bessel transform and inverse Fourier transform to return to the time-space domain, and the data after the interlayer multiple waves are suppressed are obtained by adaptive subtraction method.

2. The interlayer multiple wave prediction method according to claim 1, wherein: The seismic data is converted to the cylindrical coordinate system by the following formula: in, and are the horizontal positions of the source and the receiver, is the horizontal distance between the detector and the source.

3. The interlayer multiple wave prediction method according to claim 1, wherein: The frequency domain data is Bessel transformed in the radial direction by the following formula: in, is the wavenumber frequency domain data, is the horizontal distance between the detector and the source The Fourier conjugate variable of , i.e. the horizontal wave number, is the circular frequency, is the Jacobian determinant.

4. The interlayer multiple wave prediction method according to claim 1, wherein: The vertical wave number is: in, is the horizontal distance between the detector and the source The Fourier conjugate variable of , i.e. the horizontal wave number, is the circular frequency, is the water velocity in the background medium.

5. The interlayer multiple wave prediction method according to claim 1, wherein: The interlayer multiples are predicted by the following equation: in, is the horizontal distance between the detector and the source The Fourier conjugate variable is the horizontal wave number, q is the vertical wave number, is the circular frequency, is the imaginary unit, is the pseudo depth of constant background velocity imaging, Common center point earthquake data In the plane wave field of pseudo-depth domain, First-order inter-order multiple wave data predicted for the common center point domain.

6. The interlayer multiple wave prediction method according to claim 1, wherein: The progressive inverse Bessel transform is: in, is the horizontal distance between the detector and the source, is the horizontal distance between the detector and the source The Fourier conjugate variable of , i.e. the horizontal wave number, is the vertical wave number, is the circular frequency, is the water velocity in the background medium.

7. An interlayer multiple wave prediction device, characterized in that: include: The conversion module converts the seismic data into a cylindrical coordinate system and performs Fourier transform on the cylindrical coordinate system data from the spatial domain to the frequency domain; Bessel transform module, which performs Bessel transform on frequency domain data in radial direction and performs constant background velocity offset on wave number frequency domain data to obtain input data; The prediction module calculates the vertical wave number based on the horizontal wave number of the polar radius, and then predicts the interlayer multiple waves based on the vertical wave number and the input data; The inverse transformation module performs progressive inverse Bessel transformation and inverse Fourier transformation on the interlayer multiple waves to return to the time and space domain, and obtains the data after the interlayer multiple waves are suppressed by an adaptive subtraction method.

8. The interlayer multiple wave prediction device according to claim 7, wherein: The progressive inverse Bessel transform is: in, is the horizontal distance between the detector and the source, is the horizontal distance between the detector and the source The Fourier conjugate variable of , i.e. the horizontal wave number, is the vertical wave number, is the circular frequency, is the water velocity in the background medium.

9. An electronic device, characterized in that: The electronic device comprises: a memory storing executable instructions; A processor, wherein the processor runs the executable instructions in the memory to implement the inter-layer multiple wave prediction method according to any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the interlayer multiple wave prediction method according to any one of claims 1 to 6 is implemented.

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