Strong reflection removal method and device, electronic equipment and storage medium

Abstract

The invention discloses a strong reflection removal method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring seismic data; constructing a transformation basis based on a predetermined Ricker wavelet generating function; wherein the seismic data is sparse in the transformation base domain; determining a sparse transformation coefficient according to the transformation basis and the seismic data; determining a strong reflection seismic waveform by using the sparse transformation coefficient and the transformation base, and removing the strong reflection seismic waveform based on a predetermined target specification; wherein the target specification is dynamically changed. According to the technical scheme, the strong reflection is removed through dynamic specifications, the removal effect can be better improved, and the strong reflection elimination precision is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of strong reflection removal, and in particular to a strong reflection removal method, device, electronic equipment and storage medium. Background Art

[0002] As the exploration progresses, the exploration target gradually shifts to finding hidden oil and gas reservoirs, especially thin reservoir oil and gas reservoirs. However, low-velocity coal seams will form low-impedance bodies, which appear as strong reflection phase axes on seismic profiles. The seismic signal of the target layer becomes a weak signal, making it very difficult to identify the target layer. Therefore, it is particularly important to remove strong reflections and restore weak signals.

[0003] Many scholars have conducted in-depth research in this area, such as the layer and wavelet constrained matching pursuit strong reflection elimination method, which effectively eliminates the strong reflection of coal seams in three-dimensional data volumes. The problem of selecting control parameters for the atomic library dictionary, and the layer-constrained matching pursuit strong reflection elimination method, have been used to effectively separate the strong reflection layer of oil shale.

[0004] The overall idea of ​​the seismic strong reflection elimination method is the same. First, the strong reflection position is separated through the sparse theory of compressed sensing, then different wavelets are used for matching, and then they are eliminated according to certain criteria. The removal effect is not good or the removal is not clean when there are multiple strong reflections. Summary of the invention

[0005] The present invention provides a strong reflection removal method, device, electronic device and storage medium, which can better improve the removal effect and enhance the precision of strong reflection elimination by removing strong reflections in a dynamic and standardized manner.

[0006] According to one aspect of the present invention, a method for removing strong reflection is provided, the method comprising:

[0007] Acquisition of seismic data;

[0008] Constructing a transformation basis based on a predetermined Ricker wavelet mother function; wherein the seismic data is sparse in the transformation basis domain;

[0009] Determining sparse transformation coefficients according to the transformation basis and the seismic data;

[0010] The sparse transformation coefficient and the transformation basis are used to determine a strong reflection seismic waveform, and the strong reflection seismic waveform is removed based on a predetermined target specification; wherein the target specification is dynamically changing.

[0011] According to another aspect of the present invention, there is provided a strong reflection removal device, the device comprising:

[0012] A seismic data acquisition module, used for acquiring seismic data;

[0013] A transformation basis construction module, used for constructing a transformation basis based on a predetermined Ricker wavelet mother function; wherein the seismic data is sparse in the transformation basis domain;

[0014] A sparse transformation coefficient determination module, used for determining sparse transformation coefficients according to the transformation basis and the seismic data;

[0015] A strong reflection removal module is used to determine a strong reflection seismic waveform using the sparse transformation coefficient and the transformation basis, and remove the strong reflection seismic waveform based on a predetermined target specification; wherein the target specification is dynamically changing.

[0016] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0017] at least one processor; and

[0018] a memory communicatively connected to the at least one processor; wherein,

[0019] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute a strong reflection removal method described in any embodiment of the present invention.

[0020] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement a strong reflection removal method described in any embodiment of the present invention when executed.

[0021] The technical solution of the embodiment of the present invention obtains seismic data, constructs a transformation basis based on a predetermined Ricker wavelet mother function, and then determines a sparse transformation coefficient based on the transformation basis and the seismic data, determines a strong reflection seismic waveform using the sparse transformation coefficient and the transformation basis, and removes the strong reflection seismic waveform based on a predetermined target specification. This technical solution can better improve the removal effect and the precision of strong reflection elimination by removing strong reflections through dynamic specifications.

[0022] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 is a flow chart of a strong reflection removal method provided according to Embodiment 1 of the present invention;

[0025] Figure 2 is the original seismic profile provided in Example 1 of the present application;

[0026] Figure 3 It is the 0 specification removal of strong reflection seismic profile provided in the first embodiment of the present application;

[0027] Figure 4 It is the well reflection coefficient and synthetic record provided in Example 1 of the present application;

[0028] Figure 5 It is the original earthquake record provided in Example 1 of the present application;

[0029] Figure 6 The g specification provided in Example 1 of the present application removes strong reflection seismic records;

[0030] Figure 7 is a schematic structural diagram of a strong reflection removal device provided according to Embodiment 2 of the present invention;

[0031] Figure 8 It is a structural schematic diagram of an electronic device for implementing a strong reflection removal method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0032] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0033] It should be noted that the term "target" and the like in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0034] Embodiment 1

[0035] Figure 1 This is a flowchart of a strong reflection removal method provided according to the first embodiment of the present invention. This embodiment is applicable to the case of removing strong reflection waves in seismic data. The method can be executed by a strong reflection removal device. The strong reflection removal device can be implemented in the form of hardware and / or software. The strong reflection removal device can be configured in an electronic device, for example, the device can be a background server or other device with communication and computing capabilities. Figure 1 As shown, the method includes:

[0036] S110, obtaining seismic data.

[0037] In this embodiment, seismic data may be acquired through field acquisition.

[0038] In this scheme, the seismic data signal of the target layer becomes a weak signal because it is shown as a strong reflection event axis on the seismic profile, which makes it very difficult to identify the target layer. In order to restore the signal of the seismic data, it is necessary to reduce the strong reflection shielding. Specifically, the strong reflection waveform must be separated first, and then reduced according to certain criteria. The compressed sensing theory can be used to create a sliding wavelet dictionary based on the waveform characteristics of the strong seismic reflection, and decompose the strong seismic reflection waveform into a sparse representation of the transformation basis. During waveform reconstruction, a strong reflection shielding function is created to suppress large transformation basis components to reduce strong reflections while maintaining the lateral continuity of the seismic event axis.

[0039] Furthermore, the core of compressed sensing is the sparse representation of signals. The finite-length discrete-time signal x is regarded as a real number R N Column vector of space, R N Any signal in can be represented by an N×1 Wiki basis vector Linear representation. Combining into an N×N matrix Ψ, we have:

[0040] x = Ψθ;

[0041] Among them, Ψ is called the transformation basis, θ is the N×1 dimensional coefficient vector, x is the representation of the signal in the time domain, and θ is the representation of the signal in the Ψ domain.

[0042] When the number of nonzeros in θ is much smaller than N, the signal is sparse in the Ψ domain.

[0043] The decomposition of strong reflection signals using compressed sensing theory is divided into three parts. The first is the sparse representation of the signal, that is, the representation of the signal x on Ψ is sparse; the second is to construct a reasonable basis matrix Ψ according to the actual problem; the third is to find the sparse coefficient θ.

[0044] S120, constructing a transformation basis based on a predetermined Ricker wavelet mother function; wherein the seismic data is sparse in the transformation basis domain.

[0045] In this embodiment, the Ricker wavelet is a type of seismic wavelet. A seismic wave excited by a seismic source, propagated underground and received by people on the ground or in a well is usually a short pulse vibration, which is called a vibration wavelet.

[0046] In this scheme, the signal of seismic data can be expressed as the convolution of reflection coefficient and wavelet. Ricker wavelet is the most commonly used wavelet in seismic interpretation, and Ricker wavelet can be used as the mother function. Since the location of strong reflection and the shape of strong reflection waveform are not known in advance, a dynamic dictionary based on Ricker wavelet mother function can be established as the transformation basis according to the characteristics of strong reflection waveform.

[0047] S130. Determine sparse transformation coefficients according to the transformation basis and the seismic data.

[0048] In this scheme, the transformation basis and seismic data can be combined to determine the optimal sparse transformation coefficients.

[0049] Optionally, determining a sparse transformation coefficient according to the transformation basis and the seismic data includes:

[0050] constructing an objective function based on the transformation basis, seismic data and sparse transformation coefficients;

[0051] According to the predetermined constraint conditions, the objective function is dynamically iterated to calculate the sparse transformation coefficients.

[0052] In this embodiment, the constraint condition may be constructed based on the target specification, wherein the target specification may refer to the 0 specification, 1 specification, etc. in compressed sensing, and the target specification may be represented by the g specification, which may change dynamically.

[0053] Optionally, constructing an objective function according to the transformation basis, seismic data and sparse transformation coefficients includes:

[0054] The objective function is constructed using the following formula;

[0055]

[0056] Among them, θ represents the sparse transformation coefficient, ψ represents the transformation basis, and x represents the seismic data.

[0057] Optionally, construct constraints in the following way;

[0058] st||θ| g ≤δ;

[0059]

[0060] Among them, θ represents the sparse transformation coefficient, g represents the target specification, and δ is the threshold.

[0061] In this scheme, the vector l g The standard expression is:

[0062]

[0063] When x = 0, it represents the number of non-zero elements contained in θ. The zero norm is the most direct measure of sparsity. When the amount of seismic data near the strong reflection is not large, the zero norm can be used to measure the sparsity of the seismic data. However, when the target area has complex strong reflections, it is inappropriate to use the zero norm. It can be constrained by a variable g norm, that is, by a target norm. If the signal x is sparse in the Ψ domain, the objective function of the optimization problem is:

[0064]

[0065] Where θ represents the sparse transformation coefficient, ψ represents the transformation basis, x represents the seismic data, and δ is the threshold. The number of iterations is controlled by the threshold δ. The transformation basis selected in each iteration is most correlated with the strong seismic reflection component.

[0066] S140. Determine a strong reflection seismic waveform using the sparse transformation coefficient and the transformation basis, and remove the strong reflection seismic waveform based on a predetermined target specification; wherein the target specification changes dynamically.

[0067] In this scheme, the sparse transformation coefficients and the transformation basis can be combined to determine the strong reflection seismic waveform

[0068] Optionally, using the sparse transformation coefficient and the transformation basis to determine a strong reflection seismic waveform includes:

[0069] The sparse transformation coefficients and the transformation basis are multiplied to determine a strong reflection seismic waveform.

[0070] Specifically, after the sparse transformation coefficients are obtained, the matrix product of the transformation basis and the sparse transformation coefficients is the seismic waveform around the strong reflection.

[0071] By removing strong reflections through dynamic specifications, the removal effect can be better improved and the accuracy of strong reflection elimination can be increased.

[0072] The technical solution of the embodiment of the present invention obtains seismic data, constructs a transformation basis based on a predetermined Ricker wavelet mother function, and then determines a sparse transformation coefficient based on the transformation basis and the seismic data, determines a strong reflection seismic waveform using the sparse transformation coefficient and the transformation basis, and removes the strong reflection seismic waveform based on a predetermined target specification. By executing this technical solution, strong reflections can be removed through dynamic specifications, which can better improve the removal effect and increase the precision of strong reflection elimination.

[0073] In this program, Figure 2 is the original seismic profile provided in Example 1 of the present application, Figure 3 The 0 standard removal of strong reflection seismic profile provided in Example 1 of the present application, there is a set of stable coal seams in a certain work area of ​​the Songliao Basin, which forms a strong reflection interface with the underlying strata, such as Figure 2 As shown in the figure, T2 is a strong reflection interface, which covers the weak reflection information of the underlying strata. The conventional 0 standard is used to remove the T2 reflection. The removal result is shown in Figure 3 As shown, it can be clearly seen that the strong reflection is removed very cleanly and the weak reflection is also enhanced.

[0074] Figure 4 The well reflection coefficient and synthetic record provided in Example 1 of this application are Figure 5 is the original earthquake record provided in Example 1 of the present application, Figure 6 The g specification provided in Example 1 of the present application removes the strong reflection seismic record. In the Longdong area of ​​Qingyang, Gansu, the dense sandstone is overlying, the carbonate rock is underlying, and the interlayer with the middle bauxite is formed. The upper interface forms a strong wave peak, and the lower interface forms a strong wave trough, such as Figure 4 As shown, it is difficult to separate two different interfaces at the same time using the 0 specification. Figure 5 ) It can be seen that due to the strong reflection of the upper and lower interfaces, the response of the middle reservoir is unstable. The g specification is used to process the section, and the results are as follows Figure 6 As shown, it can be seen that the strong peak of the upper interface and the strong trough of the lower interface are weakened at the same time, and the reflection characteristics of the middle reservoir are more stable, which are just at the conversion point of the peak and trough.

[0075] Embodiment 2

[0076] Figure 7 Schematic diagram of the structure of a strong reflection removal device provided according to the second embodiment of the present invention. Figure 7 As shown, the device comprises:

[0077] A seismic data acquisition module 710 is used to acquire seismic data;

[0078] A transform basis construction module 720 is used to construct a transform basis based on a predetermined Ricker wavelet mother function; wherein the seismic data is sparse in the transform basis domain;

[0079] A sparse transformation coefficient determination module 730, configured to determine a sparse transformation coefficient based on the transformation basis and the seismic data;

[0080] The strong reflection removal module 740 is used to determine the strong reflection seismic waveform using the sparse transformation coefficient and the transformation basis, and remove the strong reflection seismic waveform based on a predetermined target specification; wherein the target specification is dynamically changing.

[0081] Optionally, the sparse transform coefficient determination module 730 includes:

[0082] An objective function construction submodule, used to construct an objective function according to the transformation basis, seismic data and sparse transformation coefficients;

[0083] The sparse transform coefficient calculation submodule is used to dynamically iterate the objective function according to predetermined constraints to calculate the sparse transform coefficients.

[0084] Optional, objective function construction submodule, specifically used for:

[0085] The objective function is constructed using the following formula;

[0086]

[0087] Among them, θ represents the sparse transformation coefficient, ψ represents the transformation basis, and x represents the seismic data.

[0088] Optionally, the sparse transform coefficient calculation submodule is specifically used for:

[0089] The constraints are constructed in the following way;

[0090] st||θ|| g ≤δ;

[0091]

[0092] Among them, θ represents the sparse transformation coefficient, g represents the target specification, and δ is the threshold.

[0093] Optionally, the strong reflection removal module 740 is specifically used for:

[0094] The sparse transformation coefficients and the transformation basis are multiplied to determine a strong reflection seismic waveform.

[0095] A strong reflection removal device provided in an embodiment of the present invention can execute a strong reflection removal method provided in any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method.

[0096] Embodiment 3

[0097] Figure 8 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0098] like Figure 8 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0099] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0100] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The processor 11 performs the various methods and processes described above, such as a strong reflection removal method.

[0101] In some embodiments, a strong reflection removal method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the strong reflection removal method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform a strong reflection removal method in any other appropriate manner (e.g., by means of firmware).

[0102] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0103] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0104] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0105] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0106] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0107] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0108] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0109] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for removing strong reflections, characterized in that: include: Acquisition of seismic data; Constructing a transformation basis based on a predetermined Ricker wavelet mother function; wherein the seismic data is sparse in the transformation basis domain; Determining sparse transformation coefficients according to the transformation basis and the seismic data; The sparse transformation coefficient and the transformation basis are used to determine a strong reflection seismic waveform, and the strong reflection seismic waveform is removed based on a predetermined target specification; wherein the target specification is dynamically changing.

2. The method according to claim 1, characterized in that Determining sparse transformation coefficients according to the transformation basis and the seismic data includes: constructing an objective function based on the transformation basis, seismic data and sparse transformation coefficients; According to the predetermined constraint conditions, the objective function is dynamically iterated to calculate the sparse transformation coefficients.

3. The method according to claim 2, characterized in that According to the transformation basis, seismic data and sparse transformation coefficients, an objective function is constructed, including: The objective function is constructed using the following formula; Among them, θ represents the sparse transformation coefficient, ψ represents the transformation basis, and x represents the seismic data.

4. The method according to claim 2, characterized in that: include: The constraints are constructed in the following way; st||θ|| g ≤δ; Among them, θ represents the sparse transformation coefficient, g represents the target specification, and δ is the threshold.

5. The method according to claim 1, characterized in that Determining a strong reflection seismic waveform by using the sparse transformation coefficient and the transformation basis includes: The sparse transformation coefficients and the transformation basis are multiplied to determine a strong reflection seismic waveform.

6. A strong reflection removal device, characterized in that: include: A seismic data acquisition module, used for acquiring seismic data; A transformation basis construction module, used for constructing a transformation basis based on a predetermined Ricker wavelet mother function; wherein the seismic data is sparse in the transformation basis domain; A sparse transformation coefficient determination module, used for determining sparse transformation coefficients according to the transformation basis and the seismic data; A strong reflection removal module is used to determine a strong reflection seismic waveform using the sparse transformation coefficient and the transformation basis, and remove the strong reflection seismic waveform based on a predetermined target specification; wherein the target specification is dynamically changing.

7. The device according to claim 6, characterized in that The sparse transformation coefficient determination module comprises: An objective function construction submodule, used to construct an objective function according to the transformation basis, seismic data and sparse transformation coefficients; The sparse transform coefficient calculation submodule is used to dynamically iterate the objective function according to predetermined constraints to calculate the sparse transform coefficients.

8. The device according to claim 7, characterized in that The objective function constructs a submodule, which is specifically used for: The objective function is constructed using the following formula; Among them, θ represents the sparse transformation coefficient, ψ represents the transformation basis, and x represents the seismic data.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory in communication with the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute a strong reflection removal method according to any one of claims 1 to 5.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement a strong reflection removal method according to any one of claims 1 to 5 when executed.

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