Diffraction Wave Imaging Method, Electronic Device and Medium

Abstract

The present application discloses a diffraction wave imaging method, an electronic device and a medium. The method includes: Step 1: For the data of one receiving line in a single-shot record, calculate the imaging range of this receiving line, and for each imaging point within the imaging range, perform the following steps: Step 101: Extract the amplitudes on each recording trace on this receiving line along the diffraction hyperbola for the imaging point; Step 102: Calculate the weight coefficient for suppressing the strong energy band corresponding to the imaging point; Step 103: Perform Kirchhoff time migration processing according to the weight coefficient; Step 2: Process the data of all receiving lines in all single-shot records in sequence, and accumulate the migration results falling on the same imaging point to obtain the final diffraction wave imaging result. The present invention locates the center position of the strong energy band through energy scanning, determines the range of the strong energy band based on the principle of the Fresnel zone, and suppresses the reflection wave imaging by superimposing the remaining energy outside the strong energy band, highlighting the diffraction wave imaging.

Description

Technical Field

[0001] The present invention relates to the field of geophysical exploration, and more specifically, to a diffraction wave imaging method, an electronic device, and a medium. Background Art

[0002] In exploration areas with carbonate fracture-vuggy reservoirs as the main target, it is necessary to finely depict geological target bodies such as underground faults, fractures, and holes. The seismic responses of these small-scale inhomogeneous bodies are mainly manifested as diffraction waves. Since the energy of diffraction waves is one to two orders of magnitude lower than that of reflected waves, small-scale anomalies formed by diffraction waves in conventional imaging results are easily masked by reflectors with strong energy. In order to avoid the interference of strong reflection energy and improve the recognition ability of small-scale anomalies, it is necessary to separate the reflected waves and diffraction waves and only image the diffraction waves. The diffraction wave imaging technology based on anti-stationary phase filtering realizes the separation of diffraction waves and reflected waves using an anti-stationary phase filter in the Kirchhoff migration framework. This technology requires first completing a conventional migration imaging calculation, then estimating the reflection dip angle of each imaging point underground, and finally constructing an anti-stationary phase filter using the dip angle information and performing another migration calculation. The calculation process of this technology is relatively cumbersome. When the underground structure is complex or the signal-to-noise ratio of the data is low and the underground reflection dip angle cannot be accurately calculated, the imaging accuracy of the diffraction wave is poor.

[0003] Therefore, it is necessary to develop a diffraction wave imaging method, an electronic device, and a medium.

[0004] The information disclosed in the background art section of the present invention is only intended to deepen the understanding of the general background art of the present invention, and should not be regarded as an admission or any form of suggestion that this information constitutes the prior art known to those skilled in the art. Summary of the Invention

[0005] The present invention provides a diffraction wave imaging method, an electronic device, and a medium, which can locate the center position of the strong energy band through energy scanning, determine the range of the strong energy band according to the principle of the Fresnel zone, and suppress the reflected wave imaging by stacking the remaining energy outside the strong energy band, highlighting the diffraction wave imaging.

[0006] In a first aspect, an embodiment of the present disclosure provides a diffraction wave imaging method, including:

[0007] Step 1: For the data of one receiving line of a single-shot record, calculate the imaging range of the receiving line according to the migration aperture, and perform the following steps for each imaging point within the imaging range:

[0008] Step 101: Extract the amplitudes of each recording trace on the receiving line along the diffraction hyperbola for the imaging point;

[0009] Step 102: Calculate the weight coefficient of the strong energy band corresponding to the imaging point;

[0010] Step 103: Perform Kirchhoff time migration processing according to the weight coefficient;

[0011] Step 2: Process all the receiver line data of all single-shot records in sequence, accumulate the migration results falling on the same imaging point, and obtain the final diffraction wave imaging result.

[0012] Preferably, determining the weight coefficient of the strong energy band corresponding to the receiver line includes:

[0013] Determine the central position and range of the strong energy band;

[0014] According to the range of the strong energy band, determine the weight coefficient for suppressing the strong energy band.

[0015] Preferably, determining the central position of the strong energy band includes:

[0016] Define a sliding window, perform sliding summation on the amplitude extracted from the diffraction hyperbola to obtain the sum of amplitude values W(j);

[0017] Calculate the number of amplitude values with the same polarity K(j) in each sliding window;

[0018] Determine the window with the largest absolute value of W(j) and K(j) greater than a preset value, and use the central sample point of this window as the central position of the strong energy band.

[0019] Preferably, perform sliding summation on the amplitude extracted from the diffraction hyperbola through formula (1):

[0020]

[0021] where W(j) is the sum of N + 1 amplitude values within the sliding window, A(i) is the amplitude value extracted from the diffraction hyperbola, i is the amplitude sample point serial number, j is the serial number of the central sample point of the sliding window, j = N / 2 + 1, N / 2 + 2, …, NR - N / 2, and NR is the total number of all amplitude values extracted from the diffraction hyperbola.

[0022] Preferably, calculate the number of sample points with the same amplitude polarity in the sliding window through formula (2):

[0023]

[0024] where K(j) is the number of sample points with the same amplitude polarity in the sliding window, A(i) is the amplitude value extracted from the diffraction hyperbola, i is the amplitude sample point serial number, j is the serial number of the central sample point of the sliding window, j = N / 2 + 1, N / 2 + 2, …, NR - N / 2, and NR is the total number of all amplitude values extracted from the diffraction hyperbola.

[0025] Preferably, the range of the strong energy band is:

[0026] |τ - τ d | ≤ T / 4 (3)

[0027] where τ d is the travel time corresponding to the central position of the strong energy band, τ d = t s + t rmax , r max is the geophone position corresponding to the central position of the strong energy band, τ is the travel time corresponding to all amplitudes extracted from the diffraction hyperbola, τ = t s + t r , t s is the travel time from the shot point to the imaging point, t r is the travel time from the geophone to the imaging point, t rmax is the travel time from the geophone r max to the imaging point, T is the period of the seismic wavelet where the sampling point u(r max, τ d ) is located in the trace recorded at the geophone r max.

[0028] Preferably, the weight coefficient is:

[0029]

[0030] where e(m, r) is the weight coefficient, and U(r max) is the spatial neighborhood centered on the geophone position r max.

[0031] Preferably, Kirchhoff time migration imaging based on the weight coefficient is:

[0032]

[0033] where V(m) is the migration result of the imaging point m, e(m, r) is the weight coefficient, m is the imaging point, r is the geophone, ω(m, r) is the weight coefficient used to compensate for the energy loss caused by spherical spreading, and u(r, t) is the seismic wavefield amplitude.

[0034] As a specific implementation manner of the embodiments of the present disclosure,

[0035] In a second aspect, the embodiments of the present disclosure further provide an electronic device, which includes:

[0036] A memory storing executable instructions;

[0037] A processor that runs the executable instructions in the memory to implement the diffraction wave imaging method described above.

[0038] In a third aspect, an embodiment of the present disclosure further provides a computer-readable storage medium storing a computer program, which when executed by a processor implements the diffraction wave imaging method described above.

[0039] The method and apparatus of the present invention have other characteristics and advantages, which will be apparent from the accompanying drawings incorporated herein and the subsequent detailed description, or will be described in detail in the accompanying drawings incorporated herein and the subsequent detailed description. These drawings and the detailed description together are used to explain the specific principles of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] By describing the exemplary embodiments of the present invention in more detail in conjunction with the accompanying drawings, the above and other objects, features, and advantages of the present invention will become more apparent. In the exemplary embodiments of the present invention, the same reference numerals generally represent the same components.

[0041] Figure 1 A schematic diagram showing the amplitude values of diffraction curve extraction when the imaging point is a point on the reflection interface according to an embodiment of the present invention.

[0042] Figure 2 A schematic diagram showing the amplitude values of diffraction curve extraction when the imaging point is a diffractor according to an embodiment of the present invention.

[0043] Figure 3 A flowchart showing the steps of the diffraction wave imaging method according to an embodiment of the present invention.

[0044] Figure 4 A schematic diagram showing the SIGBEE2A model according to an embodiment of the present invention.

[0045] Figure 5 Shows according to Figure 4 A schematic diagram of a conventional Kirchhoff migration imaging profile.

[0046] Figure 6 Shows according to Figure 4 A schematic diagram of the imaging profile processed by the present method.

[0047] Figure 7 A schematic diagram of a conventional Kirchhoff migration imaging profile according to an embodiment of the present invention.

[0048] Figure 8 A schematic diagram of the imaging profile processed by the present method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0049] The preferred embodiments of the present invention will be described in more detail below. Although the preferred embodiments of the present invention are described below, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein.

[0050] When Kirchhoff migration is used to image a point, a diffraction curve (abbreviated as diffraction hyperbola) is established using the migration velocity, and then the amplitudes are extracted along the diffraction curve for weighted superposition imaging. Equation (6) is the basic theoretical formula for Kirchhoff time migration imaging in the shot domain:

[0051]

[0052] where V(m) is the migration result of the imaging point m, r represents the position of the geophone, ω(m,r) represents the weight coefficient used to compensate for the energy loss caused by spherical spreading, u(r,t) represents the recorded trace at the geophone r, and t s +t r is the travel time of the seismic wave propagation, t s represents the travel time from the shot point to the imaging point, and t r represents the travel time from the geophone to the imaging point. The δ symbol represents the Dirac δ function.

[0053] If the underground imaging point is a reflection point, there will be a strong energy band in the amplitudes extracted from the diffraction hyperbola. "Reflection wave imaging" mainly comes from the contribution of this "strong energy band", rather than all the amplitude values extracted from the diffraction hyperbola. However, if the point is a diffraction point, the amplitude energy extracted from the diffraction hyperbola is relatively uniform. The energy of diffraction wave imaging comes from all the amplitude values extracted from the diffraction hyperbola.

[0054] When performing Kirchhoff migration, if the strong energy band in all the amplitudes extracted from the diffraction hyperbola is located and suppressed, then the reflection imaging can be well suppressed and the diffraction imaging can be highlighted. This is because the contribution of reflection wave imaging mainly comes from the contribution of the strong energy band, and the excision of the strong energy band will greatly suppress the reflection wave imaging. The contribution of diffraction wave imaging comes from all the amplitudes extracted from the diffraction hyperbola, and the energy is relatively uniform, and the excision of the strong energy band has little impact on it.

[0055] The present invention provides a diffraction wave imaging method, including:

[0056] Step 1: For the data of one receiving line of a single-shot record, calculate the imaging range of this receiving line according to the migration aperture, and perform the following steps for each imaging point within the imaging range:

[0057] Step 101: Extract the amplitudes on each recorded trace on this receiving line along the diffraction hyperbola for the imaging point;

[0058] Step 102: Calculate the weight coefficients for suppressing the strong energy band corresponding to the imaging point;

[0059] Step 103: Perform Kirchhoff time migration processing according to the weight coefficients;

[0060] Step 2: Process all the receiver line data of all single-shot records in sequence, accumulate the migration results falling on the same imaging point, and obtain the final diffraction wave imaging result.

[0061] In one example, determining the weight coefficients for suppressing the strong energy band corresponding to the receiver line includes:

[0062] Determine the center position and range of the strong energy band;

[0063] According to the range of the strong energy band, determine the weight coefficients for suppressing the strong energy band.

[0064] In one example, determining the center position of the strong energy band includes:

[0065] Define a sliding window, perform sliding summation on the amplitudes extracted from the diffraction hyperbola, and obtain the sum W(j) of the amplitude values;

[0066] Calculate the number K(j) of amplitude values with the same polarity in each sliding window;

[0067] Determine the window where the absolute value of W(j) is the largest and K(j) is greater than a preset value, and use the central sample point of this window as the center position of the strong energy band.

[0068] In one example, perform sliding summation on the amplitudes extracted from the diffraction hyperbola through formula (1):

[0069]

[0070] Among them, W(j) is the sum of N + 1 amplitude values within the sliding window, A(i) is the amplitude value extracted from the diffraction hyperbola, i is the amplitude sample point serial number, j is the serial number of the central sample point of the sliding window, j = N / 2 + 1, N / 2 + 2, …, NR - N / 2, and NR is the total number of all amplitude values extracted from the diffraction hyperbola.

[0071] In one example, calculate the number of sample points with the same amplitude polarity in the sliding window through formula (2):

[0072]

[0073] Among them, K(j) is the number of samples with the same amplitude polarity in the sliding window, A(i) is the amplitude value extracted from the diffraction hyperbola, i is the amplitude sample number, j is the number of the central sample of the sliding window, j = N / 2 + 1, N / 2 + 2, …, NR - N / 2, and NR is the total number of all amplitude values extracted from the diffraction hyperbola.

[0074] In one example, the range of the strong energy band is:

[0075] |τ - τ d | ≤ T / 4 (3)

[0076] Among them, τ d is the travel time corresponding to the central position of the strong energy band, τ d = t s + t rmax , r max is the geophone position corresponding to the central position of the strong energy band, τ is the travel time corresponding to all amplitudes extracted from the diffraction hyperbola, τ = t s + t r , t s is the travel time from the shot point to the imaging point, t r is the travel time from the geophone point to the imaging point, t rmax is the travel time from the geophone point r max to the imaging point, T is the period of the seismic wavelet where the sampling point u(r max, τ d ) is located in the trace recorded at the geophone point r max.

[0077] In one example, the weight coefficient is:

[0078]

[0079] Among them, e(m, r) is the weight coefficient, and U(r max) is the spatial neighborhood centered on the geophone position r max.

[0080] In one example, Kirchhoff time migration imaging based on the weight coefficient is:

[0081]

[0082] Among them, V(m) is the migration result of the imaging point m, e(m, r) is the weight coefficient, m is the imaging point, r is the geophone point, ω(m, r) is the weight coefficient used to compensate for the energy loss caused by spherical spreading, and u(r, t) is the amplitude of the seismic wave field.

[0083] Specifically, on the preprocessed single-shot seismic record, process the data of one receiving line of the single-shot record, perform Kirchhoff migration imaging on a certain imaging point underground, and extract the amplitudes on each recording trace of this receiving line along the diffraction hyperbola. The specific implementation can refer to the basic formula of Kirchhoff time migration in the conventional shot domain, that is, formula (6). The difference is that here only the amplitudes on each recording trace in this receiving line are extracted along the diffraction hyperbola and then weighted processing for energy compensation is performed, without performing stacking calculation.

[0084] Figure 1 FIG. shows a schematic diagram of the amplitude values extracted from the diffraction curve when the imaging point according to an embodiment of the present invention is a point on the reflection interface.

[0085] Determine the distribution range of the strong energy band in the extracted amplitude data. If the underground imaging point is a point on the reflection interface, there will be a strong energy band in the extracted amplitude data. As Figure 1 shown, the energy of the reflection wave imaging mainly comes from this strong energy band, rather than all the amplitude values extracted from the diffraction hyperbola.

[0086] Figure 2 FIG. shows a schematic diagram of the amplitude values extracted from the diffraction curve when the imaging point according to an embodiment of the present invention is a diffractor.

[0087] If the underground imaging point is a diffractor similar to a small-scale karst cave, the energy of the extracted amplitude data is relatively uniform. As Figure 2 shown, the energy of the diffraction wave imaging comes from all the amplitude values extracted from the diffraction hyperbola.

[0088] Through the above analysis, it can be inferred that when performing Kirchhoff migration, if the strong energy band in the extracted data is determined and suppressed, the reflection imaging can be well suppressed and the diffraction imaging can be highlighted. This is because the contribution of the reflection wave imaging mainly comes from the contribution of the strong energy band, and the excision of the strong energy band will greatly suppress the reflection wave imaging. The contribution of the diffraction wave imaging comes from all the amplitudes extracted from the diffraction hyperbola, and the energy is relatively uniform, and the excision of the strong energy band has little impact on it. Therefore, it is necessary to determine the distribution range of the strong energy band. Since the strong energy band has two typical characteristics, one is strong energy and the other is the same amplitude polarity. Accordingly, the specific steps to determine the strong energy band are as follows:

[0089] Locate the center position of the strong energy band. Define a sliding window with a width of N, perform sliding summation on the amplitude data extracted along the diffraction hyperbola through formula (1), and calculate the number of amplitude values with the same polarity in each sliding window through formula (2).

[0090] Determine the window with the largest absolute value of W(j) and K(j) greater than a preset value. Take the central sample point of this window as the central position of the strong energy band, and then determine the position r max of the detection point corresponding to the central position of the strong energy band, and further obtain the travel time τ corresponding to this sample point d = t s + t rmax . In the above actual calculation, the parameter N is obtained through trial calculation.

[0091] Determine the range of the strong energy band. The generation of the strong energy band is mainly related to the Fresnel zone in the data domain. In the extracted amplitude data, within the spatial neighborhood centered on the position r max of the detection point, a set of amplitude values that satisfy formula (3) is the range of the strong energy band.

[0092] Diffraction wave separation imaging. Remove the determined strong energy band, and stack the remaining energy, which can suppress the reflection wave imaging and highlight the diffraction wave imaging. For this purpose, define the weight coefficient for realizing the excision of the strong energy band as formula (4), and the basic theoretical formula for diffraction wave separation imaging is formula (5).

[0093] Calculate the imaging range of the data of this receiving line according to the migration aperture. For all imaging points within the imaging range, complete the calculation of the above steps. Thus, the migration processing of the data of one receiving line is completed. Then, process the data of all receiving lines of all single-shot records in sequence, and accumulate the migration results falling on the same imaging point to finally obtain the diffraction wave imaging result.

[0094] The present invention also provides an electronic device, which includes: a memory storing executable instructions; a processor that runs the executable instructions in the memory to implement the above diffraction wave imaging method.

[0095] The present invention also provides a computer-readable storage medium, which stores a computer program that implements the above diffraction wave imaging method when executed by a processor.

[0096] To facilitate the understanding of the solution and its effects of the embodiments of the present invention, the following gives three specific application examples. Those skilled in the art should understand that this example is only for facilitating the understanding of the present invention, and any specific details are not intended to limit the present invention in any way.

[0097] Example 1

[0098] Figure 3 Shows a flowchart of the steps of a diffraction wave imaging method according to an embodiment of the present invention.

[0099] As Figure 3As shown, the diffraction wave imaging method includes: Step 1, for the data of one receiving line in a single-shot record, calculate the imaging range of the receiving line according to the migration aperture, and for each imaging point within the imaging range, perform the following steps: Step 101, extract the amplitudes on each recording trace on the receiving line along the diffraction hyperbola for the imaging point; Step 102, calculate the weight coefficients for suppressing the strong energy band corresponding to the imaging point; Step 103, perform Kirchhoff time migration processing according to the weight coefficients; Step 2, sequentially process the data of all receiving lines in all single-shot records, and accumulate the migration results falling on the same imaging point to obtain the final diffraction wave imaging result.

[0100] Figure 4 The figure shows a schematic diagram of the SIGBEE2A model according to an embodiment of the present invention, and the arrow indicates the target diffractor for testing.

[0101] Figure 5 The figure shows according to Figure 4 a schematic diagram of a conventional Kirchhoff migration imaging profile.

[0102] Figure 6 The figure shows according to Figure 4 a schematic diagram of the imaging profile processed by the present method.

[0103] The present method is used to process partial shot records of the SIGBEE2A model as shown in Figure 4 , and the method is tested. The processing effect is as shown in Figure 5 and Figure 6 . It can be seen by comparison that Figure 6 the reflected waves in

[0104] Figure 7 are suppressed, and the diffractors that were previously masked by the reflected waves and could not be recognized become clear and prominent.

[0105] Figure 8 The figure shows a schematic diagram of a conventional Kirchhoff migration imaging profile according to an embodiment of the present invention.

[0106] The method is applied to trial calculations of actual data and compared with the conventional migration results. As shown in Figure 7 and 8 , it can be seen that in the processing result of the present invention, the reflected waves are well suppressed, and the "bead" - shaped reflections caused by underground karst caves are highlighted.

[0107] Example 2

[0108] The present disclosure provides an electronic device, which includes: a memory storing executable instructions; and a processor that runs the executable instructions in the memory to implement the above-mentioned diffraction wave imaging method.

[0109] The electronic device according to an embodiment of the present disclosure includes a memory and a processor.

[0110] The memory is used to store non-transitory computer-readable instructions. Specifically, the memory may include one or more computer program products, and the computer program products may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory, etc. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0111] The processor may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In an embodiment of the present disclosure, the processor is used to run the computer-readable instructions stored in the memory.

[0112] Those skilled in the art should understand that, in order to solve the technical problem of how to obtain good user experience effects, the present embodiment may also include well-known structures such as communication buses, interfaces, etc., and these well-known structures should also be included in the protection scope of the present disclosure.

[0113] For the detailed description of this embodiment, reference may be made to the corresponding descriptions in the foregoing embodiments, and details will not be repeated here.

[0114] Example 3

[0115] The embodiment of the present disclosure provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the diffraction wave imaging method is implemented.

[0116] The computer-readable storage medium according to an embodiment of the present 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 methods of the foregoing embodiments of the present disclosure are executed.

[0117] The above-mentioned computer-readable storage media include, but are not limited to: optical storage media (such as: CD-ROM and DVD), magneto-optical storage media (such as: MO), magnetic storage media (such as: magnetic tape or removable hard disk), media with built-in rewritable non-volatile memory (such as: memory card) and media with built-in ROM (such as: ROM cartridge).

[0118] Those skilled in the art should understand that the purpose of the above description of the embodiments of the present invention is only to exemplarily illustrate the beneficial effects of the embodiments of the present invention, and is not intended to limit the embodiments of the present invention to any of the examples given.

[0119] The above has described the various embodiments of the present invention. The above description is exemplary and not exhaustive, and is also not limited to the disclosed embodiments. Many modifications and variations will be obvious to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A diffracted wave imaging method, characterized in that, Including: Step 1: For the data of one receiving line recorded by a single shot, calculate the imaging range of this receiving line according to the migration aperture, and perform the following steps for each imaging point within the imaging range: Step 101: Extract the amplitudes on each recording trace of this receiving line along the diffraction hyperbola for the imaging point. Step 102: Calculate the weight coefficient for suppressing the strong energy band corresponding to the imaging point. Step 103: Perform Kirchhoff time migration processing according to the weight coefficient. Step 2: Process the data of all receiving lines of all single-shot records in sequence, accumulate the migration results falling on the same imaging point, and obtain the final diffraction wave imaging result. Among them, determining the weight coefficient for suppressing the strong energy band corresponding to this receiving line includes: Determining the central position and range of the strong energy band. Determining the weight coefficient for suppressing the strong energy band according to the range of the strong energy band. Among them, determining the central position of the strong energy band includes: Defining a sliding window, performing sliding summation on the amplitudes extracted from the diffraction hyperbola, and obtaining the sum W(j) of the amplitude values. Calculating the number K(j) of amplitude values with the same polarity in each sliding window. Determining the window with the largest absolute value of W(j) and K(j) greater than a preset value, and taking the central sample point of this window as the central position of the strong energy band. Among them, the range of the strong energy band is: |τ - τ d | ≤ T / 4 (3) where τ d is the travel time corresponding to the center position of the strong energy band, τ d = t s + t rmax , rmax is the geophone position corresponding to the center position of the strong energy band, τ is the travel time corresponding to all amplitudes extracted from the diffraction hyperbola, τ = t s + t r , t s is the travel time from the shot point to the imaging point, t r is the travel time from the geophone to the imaging point, t rmax is the travel time from the geophone rmax to the imaging point, T is the period of the seismic wavelet where the sampling point u(rmax, τ d ) is located in the trace recorded by the geophone rmax; Among them, the weight coefficient is: Among them, e(m,r) is the weight coefficient, and U(rmax) is the spatial neighborhood centered on the geophone position rmax.

2. The diffracted wave imaging method according to claim 1, wherein, Perform sliding summation on the amplitudes extracted from the diffraction hyperbola through formula (1): Among them, W(j) is the sum of N + 1 amplitude values within the sliding window, A(i) is the amplitude value extracted from the diffraction hyperbola, i is the amplitude sample point serial number, j is the serial number of the central sample point of the sliding window, j = N / 2 + 1, N / 2 + 2, …, NR - N / 2, and NR is the total number of all amplitude values extracted from the diffraction hyperbola.

3. The diffracted wave imaging method according to claim 1, wherein, Calculate the number of sample points with the same amplitude polarity in the sliding window through formula (2): Among them, K(j) is the number of sample points with the same amplitude polarity in the sliding window, A(i) is the amplitude value extracted from the diffraction hyperbola, i is the amplitude sample point serial number, j is the serial number of the central sample point of the sliding window, j = N / 2 + 1, N / 2 + 2, …, NR - N / 2, and NR is the total number of all amplitude values extracted from the diffraction hyperbola.

4. The diffracted wave imaging method according to claim 1, wherein, Performing Kirchhoff time migration imaging according to the weight coefficient is: Among them, V(m) is the migration result of imaging point m, e(m,r) is the weight coefficient, m is the imaging point, r is the geophone, ω(m,r) is the weight coefficient for compensating the energy loss caused by spherical spreading, u(r,t) is the seismic wavefield amplitude, the δ symbol represents the Dirac δ function, and τ is the travel time corresponding to all amplitudes extracted from the diffraction hyperbola.

5. An electronic device, characterized in that, The electronic device includes: A memory storing executable instructions. A processor that runs the executable instructions in the memory to implement the diffraction wave imaging method according to any one of claims 1 - 4.

6. A computer-readable storage medium, characterized in that, This computer-readable storage medium stores a computer program, and when this computer program is executed by a processor, it implements the diffraction wave imaging method according to any one of claims 1 - 4.

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