A wave number tuning based least squares migration imaging method and device

Abstract

The present application proposes a least squares migration imaging method and device based on wave number tuning, which belongs to the field of seismic wave imaging technology. The method comprises: obtaining a reverse time migration image and a point spread function volume of seismic data; dividing the reverse time migration image and the point spread function volume into blocks uniformly, and calculating the least squares imaging result l in any block of data using the reverse time migration image and the point spread function volume. i ; Using the pre-built wavenumber domain tuning operator g, the least squares imaging result l i Filter and obtain the wavenumber domain scattered wave imaging result s i ; According to the wave number domain scattered wave imaging results s i , and obtain the spatial domain wavenumber tuning least squares imaging result p′ i ; The spatial domain wavenumber-tuned least squares imaging result p′ for all block data i The stitching is performed to obtain the complete wavenumber-tuned least squares migration imaging result p′. This application improves the low-wavenumber component of least squares imaging, suppresses the low-frequency leakage problem caused by backscattering, and improves the least squares imaging effect.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of seismic wave imaging, and particularly relates to a least square migration imaging method and device based on wave number tuning. BACKGROUND

[0002] The classical least square migration imaging estimates the underground reflectivity model by matching seismic observation data iteratively, however, the method is highly dependent on the accuracy of the velocity model and seismic wavelet, and simultaneously adopts the data matching mechanism based on waveform residual, thereby leading to great challenges in the actual seismic data application. The imaging domain least square migration method calculates the point spread function to approximate the inverse operator of Hessian, and obtains the least square migration result without iteration, thereby relieving the requirement of high-precision velocity and wavelet, and showing good application effect in the actual seismic data processing.

[0003] When the global distribution point spread function is calculated by using the wave equation, all scattering points simultaneously excite the scattered wave field, and therefore, the interference between adjacent point spread functions cannot be avoided in the wave field propagation process. In addition, due to the influence of backscattering noise in the imaging process, the low wave number component in the imaging result has energy leakage. How to improve the wave number distribution characteristics of the migration imaging and then obtain high-quality least square imaging result is a technical problem to be solved. SUMMARY

[0004] Based on the above technical problem, the application provides a least square migration imaging method and device based on wave number tuning.

[0005] In a first aspect, the application provides a least square migration imaging method based on wave number tuning, comprising:

[0006] obtaining the reverse time migration image p and the point spread function body k of seismic data;

[0007] uniformly blocking the reverse time migration image p to obtain a plurality of first block data, and uniformly blocking the point spread function body k to obtain a plurality of second block data;

[0008] calculating the least square imaging result l i by using the reverse time migration image in the first block data and the point spread function body in the second block data;

[0009] filtering the least square imaging result l i by using the pre-constructed wave number domain tuning operator g to obtain the wave number domain scattered wave imaging result s i ;

[0010] obtaining the spatial domain wave number tuning least square imaging result p' according to the wave number domain scattered wave imaging result s i .i ;

[0011] The spatial domain wavenumber-tuned least squares imaging result p′ for all block data i The complete wavenumber-tuned least squares migration imaging result p′ is obtained by stitching.

[0012] The least square imaging result l is obtained by calculating the reverse time migration image in the first block of data and the point spread function in the second block of data. i ,include:

[0013] Use Fourier transform to transform the reverse time migration image p i and the point spread function k i Switch to the frequency wave number domain, and perform the reverse time migration of the frequency wave number domain as p i and the point spread function k i Perform spatial deconvolution to obtain the least squares imaging result l i , where p i is the reverse time migration image of the first block of data, k i is the point spread function of the second block of data of the i-th block;

[0014] According to the wavenumber domain scattered wave imaging results s i The spatial domain wavenumber tuning least squares imaging result p′ is obtained i , including: based on the wave number domain scattered wave imaging results s i Perform spatial inverse Fourier transform to obtain the spatial domain wavenumber tuned least squares imaging result p′ i .

[0015] The pre-constructed wavenumber domain tuning operator g is calculated as follows:

[0016]

[0017] Where g is the pre-built wavenumber domain tuning operator, r is the component factor, r0 is the set decay wavenumber starting point, and δ is the set regularization factor.

[0018] The calculation formula of the component factor is as follows:

[0019]

[0020] Wherein, kx is the wave number component in the x-axis direction in the spatial rectangular coordinate system, ky is the wave number component in the y-axis direction in the spatial rectangular coordinate system, and kz is the wave number component in the z-axis direction in the spatial rectangular coordinate system, and the wave number components are calculated from seismic data.

[0021] The wavenumber domain scattered wave imaging result s i , the calculation formula is as follows:

[0022] s i =l i ·g

[0023] wherein g is a pre-constructed wave number domain tuning operator, l i is the least square imaging result.

[0024] In a second aspect, the present application provides a wave number tuning based least square migration device, comprising:

[0025] a data acquisition module, configured to acquire a reverse time migration image p and a point spread function body k of seismic data;

[0026] a data blocking module, configured to uniformly block the reverse time migration image p to obtain a plurality of first block data, and uniformly block the point spread function body k to obtain a plurality of second block data;

[0027] a first result imaging module, configured to calculate a least square imaging result l i using the reverse time migration image in the first block data and the point spread function body in the second block data;

[0028] a data filtering module, configured to filter the least square imaging result l i using a pre-constructed wave number domain tuning operator g to obtain a wave number domain scattered wave imaging result s i ;

[0029] a second result imaging module, configured to obtain a spatial domain wave number tuning least square migration result p i ′ according to the wave number domain scattered wave imaging result s i ;

[0030] a result splicing module, configured to splice the spatial domain wave number tuning least square migration results p i ′ of all the block data to obtain a complete wave number tuning least square migration result p′.

[0031] The first result imaging module comprises a frequency conversion unit and a result imaging unit.

[0032] The frequency conversion unit is configured to convert the reverse time migration image p i and the point spread function k i to a frequency wave number domain using Fourier transform;

[0033] The result imaging unit is configured to perform spatial deconvolution on the reverse time migration image p i and the point spread function k i in the frequency wave number domain to obtain the least square imaging result l i , wherein p i is the reverse time migration image of the i-th data in the first block data, ki a point spread function body of the i-th data in the second block of data;

[0034] The second result imaging module is configured to obtain a wave number domain scattering wave imaging result s i performing spatial inverse Fourier transform to obtain a spatial domain wave number tuning least square imaging result p i ′.

[0035] In the data filtering module, the pre-constructed wave number domain tuning operator g is calculated according to the following formula:

[0036]

[0037] wherein g is a pre-constructed wave number domain tuning operator, r is a component factor, r0 is a set attenuation wave number starting point, and δ is a set regularization factor.

[0038] In a third aspect, the present application provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer program is executed by the processor to perform the wave number tuning based least square migration imaging method.

[0039] In a fourth aspect, the present application provides a computer readable storage medium, which stores executable instructions, and the instructions, when executed, cause a processor to perform the wave number tuning based least square migration imaging method.

[0040] Beneficial effects:

[0041] The present application provides a wave number tuning based least square migration imaging method and device, which optimizes the migration imaging result by using a wave number domain spectrum tuning operator in a block of data under a least square inversion framework, improves the low wave number component of least square imaging, suppresses the low frequency leakage problem caused by backscattering, and improves the least square imaging effect. BRIEF DESCRIPTION OF DRAWINGS

[0042] Figure 1 A wave number tuning based least square migration imaging method flowchart of an embodiment of the present application;

[0043] Figure 2 A wave number tuning based least square migration imaging method flowchart of an example of the present application;

[0044] Figure 3 A three-dimensional actual data reverse time migration two-dimensional slice of an embodiment of the present application;

[0045] Figure 4 A three-dimensional point spread function local two-dimensional slice of an embodiment of the present application;

[0046] Figure 5A local slice of the three-dimensional wave number tuning operator of the embodiment of the application;

[0047] Figure 6 A two-dimensional slice of the three-dimensional actual data wave number tuning least square imaging of the embodiment of the application;

[0048] Figure 7 An apparatus principle block diagram of the wave number tuning based least square migration imaging of the embodiment of the application. DETAILED DESCRIPTION

[0049] The present disclosure will be further described below in conjunction with the embodiments shown in the accompanying drawings.

[0050] The classical least square migration imaging iteratively estimates the underground reflectivity model by matching the seismic observation data, however, the method is highly dependent on the accuracy of the velocity model and the seismic wavelet, and the low wave number component obtained by the classical least square imaging is prone to suppress the backscattering, which leads to the low frequency leakage problem. The present application proposes a wave number tuning based least square migration imaging method and device, which optimizes the migration imaging result in the wave number domain spectrum tuning operator within the block data under the least square inversion framework, and further improves the low wave number component of the least square imaging, solves the low frequency leakage problem caused by suppressing the backscattering, and improves the least square imaging effect.

[0051] Embodiment one,

[0052] The present embodiment proposes a wave number tuning based least square migration imaging method, as shown in Figure 1 , which comprises:

[0053] Step S1: obtaining the reverse time migration image p and the point spread function body k of the seismic data;

[0054] In the present embodiment, the seismic data is first obtained, and then the reverse time migration image p is calculated based on the obtained seismic data, which belongs to the prior art, and the present application will not be described again. In some specific implementation modes, the point spread function body k is obtained through a series of calculations based on the obtained seismic data, for example, the point spread function calculation method provided in the patent application publication document with the application number 202111205359.9 and the name of the point spread function calculation method, device, electronic equipment and medium of shot data, wherein the shot data is a kind of seismic data.

[0055] Step S2: uniformly block the reverse time migration image p to obtain a plurality of first block data, and uniformly block the point spread function body k to obtain a plurality of second block data;

[0056] In this embodiment, both the reverse time migration image p and the point spread function body k need to be evenly divided into blocks, and both need to be evenly divided into blocks using the same block division method, so that the sizes of the obtained first block data and the second block data are consistent.

[0057] Step S3: Calculate the least squares imaging result l using the reverse time migration image in the first block of data and the point spread function in the second block of data i ;,include:

[0058] Use Fourier transform to transform the reverse time migration image p i and the point spread function k i Switch to the frequency wave number domain, and perform the reverse time migration of the frequency wave number domain as p i and the point spread function k i Perform spatial deconvolution to obtain the least squares imaging result l i , where p i is the reverse time migration image of the first block of data, k i is the point spread function of the second block of data of the i-th block;

[0059] According to the wavenumber domain scattered wave imaging results s i The spatial domain wavenumber tuning least squares imaging result p′ is obtained i , including: based on the wave number domain scattered wave imaging results s i Perform spatial inverse Fourier transform to obtain the spatial domain wavenumber tuned least squares imaging result p′ i .

[0060] Step S4: Use the pre-built wavenumber domain tuning operator g to perform the least squares imaging result l i Filter and obtain the wavenumber domain scattered wave imaging result s i ;

[0061] The pre-constructed wavenumber domain tuning operator g is calculated as follows:

[0062]

[0063] Where g is the pre-built wavenumber domain tuning operator, r is the component factor, r0 is the set decay wavenumber starting point, and δ is the set regularization factor.

[0064] The calculation formula of the component factor is as follows:

[0065]

[0066] Wherein, kx is the wave number component in the x-axis direction in the spatial rectangular coordinate system, ky is the wave number component in the y-axis direction in the spatial rectangular coordinate system, and kz is the wave number component in the z-axis direction in the spatial rectangular coordinate system, and the wave number components are calculated from seismic data.

[0067] The wavenumber domain scattered wave imaging results s i , the calculation formula is as follows:

[0068] s i =l i ·g

[0069] Where g is the pre-built wavenumber domain tuning operator, l i is the least squares imaging result.

[0070] In the prior art, seismic data is generally filtered first after acquisition, which may result in the loss of some important seismic information after filtering. However, the present application performs block processing after calculating the reverse time migration image p and the point spread function body k, and uses a pre-built wavenumber domain tuning operator for filtering in any block. Finally, the least squares imaging results are spliced ​​together to obtain a complete wavenumber-tuned least squares migration imaging result, which improves the low-wavenumber component of the least squares imaging, solves the low-frequency leakage problem caused by suppressed backscattering, and improves the least squares imaging effect.

[0071] Step S5: Based on the wave number domain scattered wave imaging result s i , and obtain the spatial domain wavenumber tuning least squares imaging result p′ i ;

[0072] Step S6: The spatial domain wavenumber tuning least square imaging result p′ of all block data i The complete wavenumber-tuned least squares migration imaging result p′ is obtained by stitching.

[0073] The least squares migration imaging method based on wavenumber tuning proposed in this embodiment first obtains the reverse time migration image p and point spread function body k of the seismic data; divides the reverse time migration image p and point spread function body k into blocks uniformly, and calculates the least squares imaging result l in any block data. i ; Secondly, the pre-built wavenumber domain tuning operator g is used to adjust the least squares imaging result l i Filter and obtain the wavenumber domain scattered wave imaging result s i ; According to the wave number domain scattered wave imaging results s i , and obtain the spatial domain wavenumber tuning least squares imaging result p′ i Finally, the spatial domain wavenumber-tuned least squares imaging result p′ of all block data is i The complete wavenumber-tuned least squares migration imaging result p′ is obtained by stitching. This improves the low-wavenumber component of least squares imaging, solves the low-frequency leakage problem caused by suppressed backscattering, and enhances the least squares imaging effect.

[0074] Example 2

[0075] This embodiment is to further illustrate the specific application process of the present application, based on example one to provide a specific example, as Figure 2 shown:

[0076] Step S101: obtaining the reverse-time migration image p and the point spread function body k of seismic data;

[0077] In the example, first, the seismic data is obtained, and then based on the obtained seismic data, the reverse-time migration image p is calculated, which belongs to the prior art, and the present application will not be repeated. In some specific implementation modes, based on the obtained seismic data, the point spread function body k is obtained through a series of calculations, for example, the point spread function calculation method provided in the patent application publication document with the name of Point Spread Function Calculation Method, Device, Electronic Equipment and Medium for Shot Data of Application No. 202111205359.9, wherein the shot data is a kind of seismic data.

[0078] As Figure 3 , Figure 4 shown, Figure 4 is the two-dimensional slice of the three-dimensional actual data reverse-time migration of the embodiment of the present application; Figure 4 is the local two-dimensional slice of the three-dimensional point spread function of the embodiment of the present application; it can be seen that there is strong amplitude backscattering energy in the reverse-time migration image, and in addition to the central energy of the block, interference noise also appears in other areas. In this case, it is difficult to solve with existing technology.

[0079] Step S102: uniformly block the reverse-time migration image p to obtain a plurality of first block data, and uniformly block the point spread function body k to obtain a plurality of second block data;

[0080] In this example, both the reverse-time migration image p and the point spread function body k need to be uniformly blocked, and the same blocking method needs to be used for uniform blocking, and the size of the first block data and the second block data is consistent.

[0081] Step S103: using Fourier transform to convert the reverse-time migration image p i and the point spread function k i to the frequency wavenumber domain, and performing spatial deconvolution on the reverse-time migration image p i and the point spread function k i in the frequency wavenumber domain to obtain the least squares imaging result l i , wherein p i is the reverse-time migration image of the i-th first block data, k i is the point spread function body of the i-th second block data;

[0082] Step S104: Using the pre-built wavenumber domain tuning operator g, the least squares imaging result l i Filter and obtain the wavenumber domain scattered wave imaging result s i ;

[0083] The pre-constructed wavenumber domain tuning operator g is calculated as follows:

[0084]

[0085] Where g is the pre-built wavenumber domain tuning operator, r is the component factor, r0 is the set decay wavenumber starting point, and δ is the set regularization factor. The local slice of the three-dimensional pre-built wavenumber tuning operator is as follows: Figure 5 shown.

[0086] The calculation formula of the component factor is as follows:

[0087]

[0088] Wherein, kx is the wave number component in the x-axis direction in the spatial rectangular coordinate system, ky is the wave number component in the y-axis direction in the spatial rectangular coordinate system, and kz is the wave number component in the z-axis direction in the spatial rectangular coordinate system, and the wave number components are calculated from seismic data.

[0089] The wavenumber domain scattered wave imaging result s i , the calculation formula is as follows:

[0090] s i =l i ·g

[0091] Where g is the pre-built wavenumber domain tuning operator, l i is the least squares imaging result.

[0092] Step S105: Based on the wave number domain scattered wave imaging result s i Perform spatial inverse Fourier transform to obtain the spatial domain wavenumber tuned least squares imaging result p′ i .

[0093] Step S106: The spatial domain wavenumber tuning least square imaging result p′ of all block data is obtained. i The complete wavenumber-tuned least squares migration imaging result p′ is obtained by stitching.

[0094] like Figure 6 The figure shows the complete wavenumber-tuned least squares migration imaging results calculated using the method proposed in this example. It is obvious that after processing with the wavenumber-tuned least squares imaging method, the imaging results are significantly improved, the backscattered energy is significantly suppressed, and the imaging effect of the shallow weak energy reflection axis is significantly improved.

[0095] The least squares migration imaging method based on wavenumber tuning proposed in this embodiment uses the actual seismic data to calculate the reverse time migration image p and the point spread function body k; the reverse time migration image p and the point spread function body k are evenly divided into blocks, and in any block data, the reverse time migration image p is converted into i and the point spread function k i Switch to the frequency wave number domain, and perform the reverse time migration of the frequency wave number domain as p i and the point spread function k i Perform spatial deconvolution to obtain the least squares imaging result l i , then, the pre-built wavenumber domain tuning operator g is used to calculate the least squares imaging result l i Filter and obtain the wavenumber domain scattered wave imaging result s i ; According to the wave number domain scattered wave imaging results s i Perform spatial inverse Fourier transform to obtain the spatial domain wavenumber tuned least squares imaging result p′ i Finally, the spatial domain wavenumber-tuned least squares imaging result p′ of all block data is i The complete wavenumber-tuned least squares migration imaging result p′ is obtained by stitching. The simulation results of this embodiment show that the backscattered energy is significantly suppressed, which improves the imaging effect of the shallow weak energy reflection axis.

[0096] Example 3:

[0097] This embodiment proposes a least squares migration imaging device based on wave number tuning, such as Figure 7 Shown, including:

[0098] A data acquisition module, used to obtain the reverse time migration image p and point spread function body k of seismic data;

[0099] A data block module is used to uniformly block the reverse time migration image p to obtain a plurality of first blocks of data, and to uniformly block the point spread function body k to obtain a plurality of second blocks of data;

[0100] The first result imaging module is used to calculate the least square imaging result l using the reverse time migration image in the first block of data and the point spread function in the second block of data. i ;

[0101] The data filtering module is used to use the pre-built wavenumber domain tuning operator g to filter the least squares imaging result l i Filter and obtain the wavenumber domain scattered wave imaging result s i ;

[0102] The second result imaging module is used to image the scattered wave according to the wave number domain s i, and obtain the spatial domain wavenumber tuning least squares imaging result p′ i ;

[0103] The result stitching module is used to tune the least squares imaging results p′ in the spatial domain of all block data. i The complete wavenumber-tuned least squares migration imaging result p′ is obtained by stitching.

[0104] The first result imaging module includes: a frequency conversion unit and a result imaging unit;

[0105] The frequency conversion unit is used to convert the reverse time offset image p i and the point spread function k i Switch to the frequency-wavenumber domain;

[0106] The result imaging unit is used to image the reverse time migration of the frequency wave number domain p i and the point spread function k i Perform spatial deconvolution to obtain the least squares imaging result l i , where p i is the reverse time migration image of the i-th data in the first block of data, k i is the point spread function of the i-th data in the second block of data;

[0107] The second result imaging module is used to image the scattered wave according to the wave number domain. i Perform spatial inverse Fourier transform to obtain the spatial domain wavenumber tuned least squares imaging result p′ i .

[0108] The pre-constructed wavenumber domain tuning operator g is calculated as follows:

[0109]

[0110] Where g is the pre-built wavenumber domain tuning operator, r is the component factor, r0 is the set decay wavenumber starting point, and δ is the set regularization factor. The local slice of the three-dimensional pre-built wavenumber tuning operator is as follows: Figure 5 shown.

[0111] The calculation formula of the component factor is as follows:

[0112]

[0113] Wherein, kx is the wave number component in the x-axis direction in the spatial rectangular coordinate system, ky is the wave number component in the y-axis direction in the spatial rectangular coordinate system, and kz is the wave number component in the z-axis direction in the spatial rectangular coordinate system, and the wave number components are calculated from seismic data.

[0114] The wavenumber domain scattered wave imaging result si , the calculation formula is as follows:

[0115] s i =l i ·g

[0116] Where g is the pre-built wavenumber domain tuning operator, l i is the least squares imaging result.

[0117] The least squares migration imaging device based on wavenumber tuning proposed in this embodiment uses a data acquisition module to obtain the reverse time migration image p and point spread function body k of seismic data, uses a data blocking module to evenly block the reverse time migration image p and point spread function body k to obtain multiple block data, and uses the reverse time migration image in the first block data and the point spread function body in the second block data to calculate the least squares imaging result l i , the data filtering module is used to filter the least square imaging result l through the pre-built wavenumber domain tuning operator g i Filter and obtain the wavenumber domain scattered wave imaging result s i , using the second result imaging module according to the wave number domain scattered wave imaging result s i , and obtain the spatial domain wavenumber tuning least squares imaging result p′ i Finally, the result splicing module is used to perform the spatial domain wavenumber tuning least square imaging results p′ of all block data. i The stitching is performed to obtain a complete wavenumber-tuned least squares migration imaging result p'. This embodiment improves the low-wavenumber component of least squares imaging, solves the low-frequency leakage problem caused by suppressed backscattering, and improves the least squares imaging effect.

[0118] Example 4:

[0119] This embodiment provides an electronic device, including: a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the least squares migration imaging method based on wavenumber tuning is performed.

[0120] The electronic device can be a mobile phone, computer, or tablet computer, and includes a memory and a processor. The memory stores a computer program that, when executed by the processor, implements the least-squares migration imaging method based on wavenumber tuning as described in the embodiments. It is understood that the electronic device may also include an input / output (I / O) interface and a communication component.

[0121] The processor is configured to execute all or part of the steps of the wave number tuning based least square migration imaging method described in the above embodiments. The memory is configured to store various types of data, which may, for example, include instructions of any application program or method in the electronic device, and application related data.

[0122] The processor can be an Application Specific Integrated Cricuit (ASIC), a Digital Signal Processor (DSP), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a controller, a microcontroller, a microprocessor, or other electronic elements, and is configured to execute the wave number tuning based least square migration imaging method described in the above embodiments.

[0123] Embodiment five,

[0124] The embodiment provides a computer readable storage medium storing executable instructions, which, when executed, cause a processor to execute the wave number tuning based least square migration imaging method.

[0125] If implemented in the form of a software function unit and sold or used as an independent product, it can be stored in a computer readable storage medium.

[0126] Based on such understanding, the technical solutions of the present application can be embodied in the form of a software product, and the computer software product is stored in a storage medium, and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the wave number tuning based least square migration imaging method described in the embodiments of the present application.

[0127] The aforementioned storage medium includes a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., an SD (Secure Digital Memory Card) or a DX (an abbreviation of Memory Data Register, MDR) memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, a server, an APP (an abbreviation of Application) application market, etc., which can store a program check code, and stores a computer program thereon, the computer program being executed by a processor to implement each step of the wave number tuning-based least square migration method described above.

[0128] Each of the embodiments in the present disclosure is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other. Each of the embodiments focuses on the difference from other embodiments.

[0129] The scope of protection of the present disclosure is not limited to the above-described embodiments, and it is obvious that those skilled in the art can make various modifications and changes to the present disclosure without departing from the scope and spirit of the present disclosure. If these modifications and changes belong to the scope of the claims of the present disclosure and equivalent technologies thereof, the intention of the present disclosure also includes these modifications and changes.

Claims

1. A least squares migration imaging method based on wavenumber tuning, characterized in that: include: Obtain the reverse time migration image p and point spread function k of seismic data; The reverse time migration image p is uniformly divided into blocks to obtain a plurality of first blocks of data, and the point spread function body k is uniformly divided into blocks to obtain a plurality of second blocks of data; The least squares imaging result l is calculated using the reverse time migration image in the first block of data and the point spread function in the second block of data. i ; The pre-built wavenumber domain tuning operator g is used to calculate the least squares imaging result l i Filter and obtain the wavenumber domain scattered wave imaging result s i ; According to the wavenumber domain scattered wave imaging results s i , and obtain the spatial domain wavenumber tuning least squares imaging result p′ i ; The spatial domain wavenumber-tuned least squares imaging result p′ for all block data i The complete wavenumber-tuned least squares migration imaging result p′ is obtained by stitching.

2. The least squares migration imaging method based on wavenumber tuning according to claim 1, characterized in that: The least square imaging result l is obtained by calculating the reverse time migration image in the first block of data and the point spread function in the second block of data. i ,include: Use Fourier transform to transform the reverse time migration image p i and the point spread function k i Switch to the frequency wave number domain, and perform the reverse time migration of the frequency wave number domain as p i and the point spread function k i Perform spatial deconvolution to obtain the least squares imaging result l i , where p i is the reverse time migration image of the first block of data, k i is the point spread function of the second block of data of the i-th block; According to the wavenumber domain scattered wave imaging results s i The spatial domain wavenumber tuning least squares imaging result p′ is obtained i , including: based on the wave number domain scattered wave imaging results s i Perform spatial inverse Fourier transform to obtain the spatial domain wavenumber tuned least squares imaging result p′ i .

3. The least squares migration imaging method based on wavenumber tuning according to claim 1, characterized in that: The pre-constructed wavenumber domain tuning operator g is calculated as follows: Where g is the pre-built wavenumber domain tuning operator, r is the component factor, r0 is the set decay wavenumber starting point, and δ is the set regularization factor.

4. The least squares migration imaging method based on wavenumber tuning according to claim 3, characterized in that: The calculation formula of the component factor is as follows: Wherein, kx is the wave number component in the x-axis direction in the spatial rectangular coordinate system, ky is the wave number component in the y-axis direction in the spatial rectangular coordinate system, and kz is the wave number component in the z-axis direction in the spatial rectangular coordinate system, and the wave number components are calculated from seismic data.

5. The least squares migration imaging method based on wavenumber tuning according to claim 1, characterized in that: The wavenumber domain scattered wave imaging results s i , the calculation formula is as follows: s i =l i ·g Where g is the pre-built wavenumber domain tuning operator, l i is the least squares imaging result.

6. A least squares migration imaging device based on wavenumber tuning, characterized in that: include: A data acquisition module, used to obtain the reverse time migration image p and point spread function body k of seismic data; A data block module is used to uniformly block the reverse time migration image p to obtain a plurality of first blocks of data, and to uniformly block the point spread function body k to obtain a plurality of second blocks of data; The first result imaging module is used to calculate the least square imaging result l using the reverse time migration image in the first block of data and the point spread function in the second block of data. i ; The data filtering module is used to use the pre-built wavenumber domain tuning operator g to filter the least squares imaging result l i Filter and obtain the wavenumber domain scattered wave imaging result s i ; The second result imaging module is used to image the scattered wave according to the wave number domain s i , and obtain the spatial domain wavenumber tuning least squares imaging result p′ i ; The result stitching module is used to tune the least squares imaging results p′ in the spatial domain of all block data. i The complete wavenumber-tuned least squares migration imaging result p′ is obtained by stitching.

7. The least squares migration imaging device based on wavenumber tuning according to claim 6, characterized in that: The first result imaging module includes: a frequency conversion unit and a result imaging unit; The frequency conversion unit is used to convert the reverse time offset image p i and the point spread function k i Switch to the frequency-wavenumber domain; The result imaging unit is used to image the reverse time migration of the frequency wave number domain p i and the point spread function k i Perform spatial deconvolution to obtain the least squares imaging result l i , where p i is the reverse time migration image of the i-th data in the first block of data, k i is the point spread function of the i-th data in the second block of data; The second result imaging module is used to image the scattered wave according to the wave number domain. i Perform spatial inverse Fourier transform to obtain the spatial domain wavenumber tuned least squares imaging result p′ i .

8. The least squares migration imaging device based on wavenumber tuning according to claim 6, characterized in that: In the data filtering module, the pre-constructed wavenumber domain tuning operator g is calculated as follows: Where g is the pre-built wavenumber domain tuning operator, r is the component factor, r0 is the set decay wavenumber starting point, and δ is the set regularization factor.

9. An electronic device, characterized in that: include: A memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the least squares migration imaging method based on wavenumber tuning according to any one of claims 1 to 5 is executed.

10. A computer-readable storage medium, characterized in that The device stores executable instructions, which, when executed, enable a processor to perform the least squares migration imaging method based on wavenumber tuning according to any one of claims 1 to 5.

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