Variable coefficient strong reflection separation method, device, electronic equipment and medium

Abstract

The present application discloses a variable coefficient strong reflection separation method, device, electronic device and medium. The method may include: determining the size of the bin, calculating the average optimal wavelet within the bin; specifying the upper and lower drift time windows respectively, extracting the root mean square amplitude attribute along the layer; performing normalization processing on the root mean square amplitude attribute, calculating the separation coefficient of each seismic trace; calculating the separated seismic data according to the original seismic signal, the separation coefficient of each seismic trace and the average optimal wavelet. The present invention adopts different separation coefficients on different seismic traces based on the relatively stable characteristics of strong background reflections in a small local range and the energy difference of the root mean square amplitude along the layer in the lateral direction, effectively detecting the energy anomaly hidden in the seismic phase axis, achieving the purpose of highlighting the hidden reservoir, and laying a data foundation for high-quality reservoir evaluation and fluid identification.

Description

Technical Field

[0001] The present invention relates to the field of geophysical exploration, and more specifically, to a variable coefficient strong reflection separation method, device, electronic equipment and medium. Background Art

[0002] With the continuous deepening of exploration and development, thin reservoirs and concealed reservoirs have become the focus of oil and gas exploration and development at home and abroad. Due to the influence of the geological sedimentary environment of the actual work area, there are usually different degrees of seismic reflection wave interference in the seismic data, which masks the effective wave reflection energy of the reservoir and brings huge challenges to reservoir prediction and fluid identification. For example, the development of multi-level unconformities and the deposition of coal-bearing strata make the strong energy reflection generated by the special geological environment overlap with the effective reflection of the reservoir, which is mainly manifested as strong interference of reflection energy on the seismic profile, reducing the accuracy of reservoir prediction.

[0003] To address this problem, traditional technologies usually use resolution enhancement or frequency division technology to improve the recognition of thin or concealed reservoirs, and have achieved good results. In recent years, a technology for identifying and separating strong reflections has been developed. Wavelet decomposition technology is used to effectively identify strong energy reflections caused by unconformities and coal-bearing strata, and some reflection energy is removed according to a specified ratio to highlight the reflection energy of the reservoir, thereby improving the accuracy of reservoir prediction. The separation coefficient used in this technology is a certain value, and this value is used to remove strong reflections in the entire three-dimensional work area; from a horizontal perspective, the relative relationship between the energy before and after the separation of strong reflections has not changed, and the abnormal energy hidden in the strong reflection energy has not been highlighted, which is not conducive to the identification of concealed reservoirs. At the same time, this technology adopts a single-channel processing method, which does not take into account the relative stability of the strong reflection strata in the local space, and cannot well identify the abnormal energy hidden in the strong background.

[0004] Therefore, it is necessary to develop a variable coefficient strong reflection separation method, device, electronic equipment and medium to remove strong background interference while highlighting the abnormal energy in the seismic profile and improve the recognition ability of hidden reservoirs.

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

[0006] The present invention proposes a variable coefficient strong reflection separation method, device, electronic equipment and medium, which adopts different separation coefficients on different seismic traces according to the relatively stable characteristics of strong background reflection in a local small range and the energy difference of the root mean square amplitude along the layer in the lateral direction, effectively detects energy anomalies hidden in the seismic phase axis, achieves the purpose of highlighting hidden reservoirs, and lays a data foundation for high-quality reservoir evaluation and fluid identification.

[0007] In a first aspect, an embodiment of the present disclosure provides a variable coefficient strong reflection separation method, comprising:

[0008] Determine the bin size and calculate the average optimal wavelet within the bin;

[0009] Specify the upper and lower drift time windows respectively, and extract the RMS amplitude attributes along the layers;

[0010] Normalize the root mean square amplitude attribute and calculate the separation coefficient of each seismic trace;

[0011] The separated seismic data are calculated based on the original seismic signal, the separation coefficient of each seismic trace and the average optimal wavelet.

[0012] Preferably, determining the bin size and calculating the average optimal wavelet in the bin includes:

[0013] For each seismic trace, extract the local optimal wavelet based on the seismic interpretation horizon;

[0014] The bin size is determined, and the average optimal wavelet in the bin is calculated based on the local optimal wavelet of the seismic traces contained in the bin.

[0015] Preferably, the local optimal wavelet is:

[0016]

[0017] Among them, M(t) is the local optimal wavelet, m(t) is the time-frequency atom, a is the wavelet amplitude, u is the time delay, s is the scale parameter, f is the main frequency, and φ is the phase parameter.

[0018] Preferably, the separation coefficient is calculated by formula (2):

[0019] λ i =1-V RMS-Nom [i] (2)

[0020] Among them, V RMS-Norm is the normalized RMS amplitude property, λ i is the separation coefficient of the ith channel, and i is the corresponding channel number.

[0021] Preferably, calculating the separated seismic data according to the original seismic signal, the separation coefficient of each seismic trace and the average optimal wavelet comprises:

[0022] The seismic data after the separation of the seismic trace is calculated according to the original seismic signal of the seismic trace, the separation coefficient and the average optimal wavelet of the bin corresponding to the seismic trace.

[0023] Preferably, the seismic data after the seismic trace separation is calculated by formula (3):

[0024]

[0025] Among them, S new,i (t) is the seismic data after the ith track is separated, S i (t) is the original seismic signal of the ith track, λ i is the separation coefficient of the ith channel, is the average optimal wavelet of the jth bin corresponding to the ith channel.

[0026] Preferably, it also includes:

[0027] For each seismic trace in the seismic data volume, the seismic data after separation of the seismic trace is calculated, thereby obtaining the separated seismic data of the seismic data volume.

[0028] As a specific implementation method of the embodiment of the present disclosure,

[0029] In a second aspect, the embodiment of the present disclosure further provides a variable coefficient strong reflection separation device, comprising:

[0030] The average optimal wavelet calculation module determines the bin size and calculates the average optimal wavelet within the bin;

[0031] The extraction module specifies the upper and lower drift time windows respectively and extracts the RMS amplitude attributes along the layers;

[0032] The separation coefficient calculation module performs normalization processing on the root mean square amplitude attribute and calculates the separation coefficient of each seismic trace;

[0033] The separation module calculates the separated seismic data based on the original seismic signal, the separation coefficient of each seismic trace and the average optimal wavelet.

[0034] Preferably, determining the bin size and calculating the average optimal wavelet in the bin includes:

[0035] For each seismic trace, extract the local optimal wavelet based on the seismic interpretation horizon;

[0036] The bin size is determined, and the average optimal wavelet in the bin is calculated based on the local optimal wavelet of the seismic traces contained in the bin.

[0037] Preferably, the local optimal wavelet is:

[0038]

[0039] Among them, M(t) is the local optimal wavelet, m(t) is the time-frequency atom, a is the wavelet amplitude, u is the time delay, s is the scale parameter, f is the main frequency, and φ is the phase parameter.

[0040] Preferably, the separation coefficient is calculated by formula (2):

[0041] λ i =1-V RMS-Nom [i] (2)

[0042] Among them, V RMS-Norm is the normalized RMS amplitude property, λ i is the separation coefficient of the ith channel, and i is the corresponding channel number.

[0043] Preferably, calculating the separated seismic data according to the original seismic signal, the separation coefficient of each seismic trace and the average optimal wavelet comprises:

[0044] The seismic data after the separation of the seismic trace is calculated according to the original seismic signal of the seismic trace, the separation coefficient and the average optimal wavelet of the bin corresponding to the seismic trace.

[0045] Preferably, the seismic data after the seismic trace separation is calculated by formula (3):

[0046]

[0047] Among them, S new,i (t) is the seismic data after the ith track is separated, S i (t) is the original seismic signal of the ith track, λ i is the separation coefficient of the ith channel, is the average optimal wavelet of the jth bin corresponding to the ith channel.

[0048] Preferably, it also includes:

[0049] For each seismic trace in the seismic data volume, the seismic data after separation of the seismic trace is calculated, thereby obtaining the separated seismic data of the seismic data volume.

[0050] In a third aspect, an embodiment of the present disclosure further provides an electronic device, the electronic device comprising:

[0051] A memory storing executable instructions;

[0052] A processor runs the executable instructions in the memory to implement the variable coefficient strong reflection separation method.

[0053] In a fourth aspect, an embodiment of the present disclosure further provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the variable coefficient strong reflection separation method is implemented.

[0054] The methods and apparatus of the present invention have other features and advantages that will be apparent from, or will be described in detail in, the accompanying drawings and subsequent detailed descriptions incorporated herein, which together serve to explain the specific principles of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The above and other objects, features and advantages of the present invention will become more apparent through a more detailed description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, wherein like reference numerals generally represent like components throughout the exemplary embodiments of the present invention.

[0056] Figure 1 A flow chart showing the steps of a strong reflection separation method with a variable coefficient according to an embodiment of the present invention.

[0057] Figure 2 A block diagram of a variable coefficient strong reflection separation device according to an embodiment of the present invention is shown.

[0058] Description of reference numerals:

[0059] 201, average optimal wavelet calculation module; 202, extraction module; 203, separation coefficient calculation module; 204, separation module. DETAILED DESCRIPTION

[0060] 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 to the embodiments set forth herein.

[0061] The present invention provides a variable coefficient strong reflection separation method, comprising:

[0062] Determine the bin size and calculate the average optimal wavelet within the bin. In one example, determine the bin size and calculate the average optimal wavelet within the bin, including: for each seismic trace, extract the local optimal wavelet based on the seismic interpretation layer; determine the bin size, and calculate the average optimal wavelet within the bin based on the local optimal wavelet of the seismic trace contained in the bin.

[0063] In one example, the local optimal wavelet is:

[0064]

[0065] Among them, M(t) is the local optimal wavelet, m(t) is the time-frequency atom, a is the wavelet amplitude, u is the time delay, s is the scale parameter, f is the main frequency, and φ is the phase parameter.

[0066] Specifically, for each seismic trace, based on the seismic interpretation horizon Hor, the maximum matching projection principle is used to search for the local optimal wavelet as formula (1), where the local optimal wavelet M(t) satisfies the maximum matching projection principle shown in formula (4):

[0067]

[0068] Where D = {m(t)} is the time-frequency atomic dictionary, "s(t), m(t)" represents the inner product of functions s(t) and m(t), s(t) is the input seismic signal, ||m(t)|| represents the length of the sub-wave vector, and argmax represents the search for m(t) in the time-frequency atomic dictionary such that Reach the maximum.

[0069] Given the bin sizes M and N in the line and track directions respectively, the local optimal wavelets of each track in the specified bin are averaged to obtain the average optimal wavelet in a small range.

[0070] Specify the upper and lower drift time windows respectively, and extract the RMS amplitude attributes along the layers.

[0071] Specifically, specify the upper and lower drift time windows uWin and dWin respectively, and extract the root mean square amplitude attribute V along the layer RMS , where the upper and lower time window sizes should be set according to the relationship between the reservoir and the strong reflection.

[0072] The root mean square amplitude attribute is normalized and the separation coefficient of each seismic trace is calculated. In one example, the separation coefficient is calculated by formula (2):

[0073] λ i =1-V RMS-Nom [i] (2)

[0074] Among them, V RMS-Norm is the normalized RMS amplitude property, λ i is the separation coefficient of the ith channel, and i is the corresponding channel number.

[0075] Specifically, for V RMS Perform normalization to obtain the normalized root mean square amplitude attribute V RMS-Norm, then the separation coefficient of each channel can be expressed as formula (2).

[0076] The separated seismic data are calculated based on the original seismic signal, the separation coefficient of each seismic trace and the average optimal wavelet. In one example, the separated seismic data are calculated based on the original seismic signal, the separation coefficient of each seismic trace and the average optimal wavelet, including: calculating the separated seismic data of the seismic trace based on the original seismic signal of the seismic trace, the separation coefficient and the average optimal wavelet of the face element corresponding to the seismic trace.

[0077] In one example, the seismic data after the seismic trace separation is calculated by formula (3):

[0078]

[0079] Among them, S new,i (t) is the seismic data after the ith track is separated, S i (t) is the original seismic signal of the ith track, λ i is the separation coefficient of the ith channel, is the average optimal wavelet of the jth bin corresponding to the ith channel.

[0080] In one example, the method further includes: for each seismic trace in the seismic data volume, calculating the seismic data after the seismic trace is separated, thereby obtaining the separated seismic data of the seismic data volume.

[0081] Specifically, according to the original seismic signal of the seismic trace, the separation coefficient and the average optimal sub-wavelet of the face element corresponding to the seismic trace, the seismic data after the seismic trace is separated is calculated by formula (3), and for each seismic trace in the seismic data body, the seismic data after the seismic trace is separated is calculated, thereby obtaining the seismic data after the seismic data body is separated.

[0082] The present invention also provides a variable coefficient strong reflection separation device, comprising:

[0083] The average optimal wavelet calculation module determines the bin size and calculates the average optimal wavelet within the bin. In one example, determining the bin size and calculating the average optimal wavelet within the bin includes: for each seismic trace, extracting the local optimal wavelet based on the seismic interpretation layer; determining the bin size, and calculating the average optimal wavelet within the bin based on the local optimal wavelet of the seismic trace contained in the bin.

[0084] In one example, the local optimal wavelet is:

[0085]

[0086] Among them, M(t) is the local optimal wavelet, m(t) is the time-frequency atom, a is the wavelet amplitude, u is the time delay, s is the scale parameter, f is the main frequency, and φ is the phase parameter.

[0087] Specifically, for each seismic trace, based on the seismic interpretation horizon Hor, the maximum matching projection principle is used to search for the local optimal wavelet as formula (1), where the local optimal wavelet M(t) satisfies the maximum matching projection principle shown in formula (4).

[0088] Given the bin sizes M and N in the line and track directions respectively, the local optimal wavelets of each track in the specified bin are averaged to obtain the average optimal wavelet in a small range.

[0089] The extraction module specifies the upper and lower drift time windows respectively and extracts the RMS amplitude attributes along the layers.

[0090] Specifically, specify the upper and lower drift time windows uWin and dWin respectively, and extract the root mean square amplitude attribute V along the layer RMS , where the upper and lower time window sizes should be set according to the relationship between the reservoir and the strong reflection.

[0091] The separation coefficient calculation module performs normalization processing on the root mean square amplitude attribute and calculates the separation coefficient of each seismic trace. In an example, the separation coefficient is calculated by formula (2):

[0092] λ i =1-V RMS-Nom [i] (2)

[0093] Among them, V RMS-Norm is the normalized RMS amplitude property, λ i is the separation coefficient of the ith channel, and i is the corresponding channel number.

[0094] Specifically, for V RMS Perform normalization to obtain the normalized root mean square amplitude attribute V RMS-Norm , then the separation coefficient of each channel can be expressed as formula (2).

[0095] The separation module calculates the separated seismic data according to the original seismic signal, the separation coefficient of each seismic trace and the average optimal wavelet. In one example, the separation module calculates the separated seismic data according to the original seismic signal, the separation coefficient of each seismic trace and the average optimal wavelet, including: calculating the separated seismic data of the seismic trace according to the original seismic signal of the seismic trace, the separation coefficient and the average optimal wavelet of the bin corresponding to the seismic trace.

[0096] In one example, the seismic data after the seismic trace separation is calculated by formula (3):

[0097]

[0098] Among them, S new,i (t) is the seismic data after the ith track is separated, S i (t) is the original seismic signal of the ith track, λ i is the separation coefficient of the ith channel, is the average optimal wavelet of the jth bin corresponding to the ith channel.

[0099] In one example, the method further includes: for each seismic trace in the seismic data volume, calculating the seismic data after the seismic trace is separated, thereby obtaining the separated seismic data of the seismic data volume.

[0100] Specifically, according to the original seismic signal of the seismic trace, the separation coefficient and the average optimal sub-wavelet of the face element corresponding to the seismic trace, the seismic data after the seismic trace is separated is calculated by formula (3), and for each seismic trace in the seismic data body, the seismic data after the seismic trace is separated is calculated, thereby obtaining the seismic data after the seismic data body is separated.

[0101] The present invention also provides an electronic device, which includes: a memory storing executable instructions; and a processor, which runs the executable instructions in the memory to implement the above-mentioned variable coefficient strong reflection separation method.

[0102] The present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the variable coefficient strong reflection separation method is implemented.

[0103] To facilitate understanding of the solutions and effects of the embodiments of the present invention, four specific application examples are given below. Those skilled in the art should understand that the examples are only for facilitating understanding of the present invention, and any specific details thereof are not intended to limit the present invention in any way.

[0104] Example 1

[0105] Figure 1 A flow chart showing the steps of a strong reflection separation method with a variable coefficient according to an embodiment of the present invention.

[0106] like Figure 1 As shown, the variable coefficient strong reflection separation method includes: step 101, determining the size of the surface element, and calculating the average optimal sub-wave within the surface element; step 102, specifying the upper and lower drift time windows respectively, and extracting the root mean square amplitude attribute along the layer; step 103, normalizing the root mean square amplitude attribute, and calculating the separation coefficient of each seismic trace; step 104, calculating the separated seismic data according to the original seismic signal, the separation coefficient of each seismic trace and the average optimal sub-wave.

[0107] For each seismic trace, based on the seismic interpretation horizon Hor, the maximum matching projection principle is used to search for the local optimal wavelet as formula (1), where the local optimal wavelet M(t) satisfies the maximum matching projection principle shown in formula (4).

[0108] The value of the given line-direction bin M is 3, and the value of the given track-direction bin N is 3, that is, the specified range contains 9 tracks. The local optimal wavelets of each track in the specified bin are averaged to obtain the average optimal wavelet M(t) in a small range.

[0109] Specifically, the upward drift window is set to 0 milliseconds, the downward drift window is set to 30 milliseconds, and the root mean square amplitude attribute V along the layer is extracted. RMS , where the upper and lower time window sizes should be set according to the relationship between the reservoir and the strong reflection.

[0110] Specifically, for V RMS Perform normalization to obtain the normalized root mean square amplitude attribute V RMS-Norm , then the separation coefficient of each channel can be expressed as formula (2).

[0111] Specifically, according to the original seismic signal of the seismic trace, the separation coefficient and the average optimal sub-wavelet of the face element corresponding to the seismic trace, the seismic data after the seismic trace is separated is calculated by formula (3), and for each seismic trace in the seismic data body, the seismic data after the seismic trace is separated is calculated, thereby obtaining the seismic data after the seismic data body is separated.

[0112] Example 2

[0113] Figure 2 A block diagram of a variable coefficient strong reflection separation device according to an embodiment of the present invention is shown.

[0114] like Figure 2 As shown, the variable coefficient strong reflection separation device comprises:

[0115] The average optimal wavelet calculation module 201 determines the bin size and calculates the average optimal wavelet in the bin;

[0116] Extraction module 202, specifies upper and lower drift time windows respectively, and extracts root mean square amplitude attributes along the layer;

[0117] The separation coefficient calculation module 203 performs normalization processing on the root mean square amplitude attribute and calculates the separation coefficient of each seismic trace;

[0118] The separation module 204 calculates the separated seismic data according to the original seismic signal, the separation coefficient of each seismic trace and the average optimal wavelet.

[0119] As an optional solution, determining the bin size and calculating the average optimal wavelet within the bin includes:

[0120] For each seismic trace, extract the local optimal wavelet based on the seismic interpretation horizon;

[0121] The bin size is determined, and the average optimal wavelet in the bin is calculated based on the local optimal wavelet of the seismic traces contained in the bin.

[0122] As an alternative, the local optimal wavelet is:

[0123]

[0124] Among them, M(t) is the local optimal wavelet, m(t) is the time-frequency atom, a is the wavelet amplitude, u is the time delay, s is the scale parameter, f is the main frequency, and φ is the phase parameter.

[0125] As an alternative, the separation factor can be calculated using formula (2):

[0126] λ i =1-V RMS-Nom [i] (2)

[0127] Among them, V RMS-Norm is the normalized RMS amplitude property, λ i is the separation coefficient of the ith channel, and i is the corresponding channel number.

[0128] As an optional solution, based on the original seismic signal, the separation coefficient of each seismic trace and the average optimal wavelet, the separated seismic data are calculated including:

[0129] The seismic data after the separation of the seismic trace is calculated according to the original seismic signal of the seismic trace, the separation coefficient and the average optimal wavelet of the bin corresponding to the seismic trace.

[0130] As an optional solution, the seismic data after the seismic trace separation is calculated by formula (3):

[0131]

[0132] Among them, S new,i (t) is the seismic data after the ith track is separated, S i (t) is the original seismic signal of the ith track, λ i is the separation coefficient of the ith channel, is the average optimal wavelet of the jth bin corresponding to the ith channel.

[0133] As an option, it also includes:

[0134] For each seismic trace in the seismic data volume, the seismic data after separation of the seismic trace is calculated, thereby obtaining the separated seismic data of the seismic data volume.

[0135] Example 3

[0136] The present disclosure provides an electronic device, which includes: a memory storing executable instructions; a processor running the executable instructions in the memory to implement the above-mentioned variable coefficient strong reflection separation method.

[0137] An electronic device according to an embodiment of the present disclosure includes a memory and a processor.

[0138] The memory is used to store non-temporary computer-readable instructions. Specifically, the memory may include one or more computer program products, which may 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) and / or cache memory (cache), etc. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc.

[0139] 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 one embodiment of the present disclosure, the processor is used to run the computer-readable instructions stored in the memory.

[0140] Those skilled in the art should be able to understand that in order to solve the technical problem of how to obtain a good user experience, the present embodiment may also include well-known structures such as a communication bus and an interface, and these well-known structures should also be included in the protection scope of the present disclosure.

[0141] For detailed description of this embodiment, reference may be made to the corresponding descriptions in the aforementioned embodiments, which will not be repeated here.

[0142] Example 4

[0143] An embodiment of the present disclosure provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the variable coefficient strong reflection separation method is implemented.

[0144] According to the computer-readable storage medium of the embodiment of the present disclosure, non-transitory computer-readable instructions are stored thereon. When the non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the above-mentioned methods of each embodiment of the present disclosure are executed.

[0145] The above-mentioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or mobile hard disk), media with built-in rewritable non-volatile memory (e.g., memory card) and media with built-in ROM (e.g., ROM box).

[0146] 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 given examples.

[0147] The embodiments of the present invention have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A variable coefficient strong reflection separation method, characterized in that: include: Determine the bin size and calculate the average optimal wavelet within the bin; Specify the upper and lower drift time windows respectively, and extract the RMS amplitude attributes along the layers; Normalize the root mean square amplitude attribute and calculate the separation coefficient of each seismic trace; Calculate the separated seismic data according to the original seismic signal, the separation coefficient of each seismic trace and the average optimal wavelet; The separation coefficient is calculated by formula (2): λ i =1-V RMS-Nom [i] (2) Among them, V RMS-Norm is the normalized RMS amplitude property, λ i is the separation coefficient of the ith channel, and i is the corresponding channel number.

2. The variable coefficient strong reflection separation method according to claim 1, wherein: Determine the bin size and calculate the average optimal wavelet within the bin including: For each seismic trace, extract the local optimal wavelet based on the seismic interpretation horizon; The bin size is determined, and the average optimal wavelet in the bin is calculated based on the local optimal wavelet of the seismic traces contained in the bin.

3. The variable coefficient strong reflection separation method according to claim 2, wherein: The local optimal wavelet is: Among them, M(t) is the local optimal wavelet, m(t) is the time-frequency atom, a is the wavelet amplitude, u is the time delay, σ is the scale parameter, f is the main frequency, φ is the phase parameter, and t is time.

4. The variable coefficient strong reflection separation method according to claim 1, wherein: According to the original seismic signal, the separation coefficient of each seismic trace and the average optimal wavelet, the separated seismic data are calculated including: The seismic data after the separation of the seismic trace is calculated according to the original seismic signal of the seismic trace, the separation coefficient and the average optimal wavelet of the bin corresponding to the seismic trace.

5. The variable coefficient strong reflection separation method according to claim 4, wherein: The seismic data after the seismic trace separation is calculated by formula (3): Among them, Snew,i(t) is the seismic data after the ith separation, S i (t) is the original seismic signal of the ith track, λ i is the separation coefficient of the ith channel, is the average optimal wavelet of the jth bin corresponding to the ith channel.

6. The variable coefficient strong reflection separation method according to claim 4, wherein: Also includes: For each seismic trace in the seismic data volume, the seismic data after separation of the seismic trace is calculated, thereby obtaining the separated seismic data of the seismic data volume.

7. A variable coefficient strong reflection separation device, characterized in that: include: The average optimal wavelet calculation module determines the bin size and calculates the average optimal wavelet within the bin; The extraction module specifies the upper and lower drift time windows respectively and extracts the RMS amplitude attributes along the layers; The separation coefficient calculation module performs normalization processing on the root mean square amplitude attribute and calculates the separation coefficient of each seismic trace; The separation module calculates the separated seismic data according to the original seismic signal, the separation coefficient of each seismic trace and the average optimal wavelet; The separation coefficient is calculated by formula (2): λ i =1-V RMS-Nom [i] (2) Among them, V RMS-Norm is the normalized RMS amplitude property, λ i is the separation coefficient of the ith channel, and i is the corresponding channel number.

8. 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 variable coefficient strong reflection separation method according to any one of claims 1 to 6.

9. 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 variable coefficient strong reflection separation method according to any one of claims 1 to 6 is implemented.

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