Method, device and electronic equipment for determining seismic reflection coefficient

Abstract

The present invention discloses a method, device and electronic device for determining a seismic reflection coefficient. The method comprises: constructing a constraint relationship between a seismic wavelet and an L1 norm based on a convolution model of a target detection area; obtaining three-dimensional formation dip data of a target layer in a target monitoring area; and determining the seismic reflection coefficient of the target layer based on the three-dimensional formation dip data and the constraint relationship. The technical solution of an embodiment of the present invention constructs a constraint relationship between a seismic wavelet and an L1 norm based on a convolution model of a target detection area, obtains three-dimensional formation dip data of a target layer in a target monitoring area, and determines the seismic reflection coefficient of the target layer based on the three-dimensional formation dip data and the constraint relationship, thereby more accurately determining the seismic reflection coefficient, ensuring the fidelity of the seismic data, and making the high-resolution results more refined, providing a high-quality data foundation for reservoir prediction and seismic inversion.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas exploration, and in particular to a method, device and electronic equipment for determining a seismic reflection coefficient. Background Art

[0002] With the continuous deepening of exploration research, the demand for seismic data for deep weak signals, thin reservoir prediction, small fault identification and fine stratigraphic division is increasing. Obtaining high-resolution and high-fidelity seismic data has become the key to solving such problems.

[0003] Therefore, there is an urgent need for a seismic reflection coefficient determination method that can ensure the fidelity of seismic data, make high-resolution results more refined, and provide a high-quality data basis for reservoir prediction and seismic inversion. Summary of the Invention

[0004] The present invention provides a method, device and electronic equipment for determining a seismic reflection coefficient, so as to solve the problem that it is difficult to calculate an accurate result of the seismic reflection coefficient calculation result due to a low high-frequency signal-to-noise ratio.

[0005] According to one aspect of the present invention, a method for determining a seismic reflection coefficient is provided, the method comprising:

[0006] According to the convolution model of the target detection area, the constraint relationship between the seismic wavelet and the L1 norm is constructed;

[0007] Obtain three-dimensional formation dip data of the target layer in the target monitoring area;

[0008] The seismic reflection coefficient of the target layer is determined based on the three-dimensional formation dip data and constraint relationships.

[0009] According to another aspect of the present invention, there is provided a device for determining a seismic reflection coefficient, the device comprising:

[0010] A constraint relationship determination module is used to construct a constraint relationship between the seismic wavelet and the L1 norm based on the convolution model of the target detection area;

[0011] A dip data determination module is used to obtain three-dimensional dip data of a target layer in a target monitoring area;

[0012] The reflection coefficient determination module is used to determine the seismic reflection coefficient of the target layer based on the three-dimensional formation dip angle data and the constraint relationship.

[0013] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0014] at least one processor; and

[0015] a memory communicatively connected to at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by at least one processor. The computer program is executed by the at least one processor so that the at least one processor can perform the method for determining the seismic reflection coefficient of any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, which stores computer instructions for enabling a processor to implement the method for determining a seismic reflection coefficient according to any embodiment of the present invention when the computer instructions are executed.

[0018] The technical solution of the embodiment of the present invention constructs a constraint relationship between the seismic wavelet and the L1 norm based on the convolution model of the target detection area, obtains the three-dimensional stratigraphic dip data of the target layer in the target monitoring area, and determines the seismic reflection coefficient of the target layer based on the three-dimensional stratigraphic dip data and the constraint relationship, thereby more accurately determining the seismic reflection coefficient and ensuring the fidelity of the seismic data. The high-resolution results are more refined, providing a high-quality data foundation for reservoir prediction and seismic inversion.

[0019] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] 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.

[0021] Figure 1 is a flow chart of a method for determining a seismic reflection coefficient according to the first embodiment of the present invention;

[0022] Figure 2 is a schematic diagram of a wave impedance sequence of a target layer to which an embodiment of the present invention is applicable;

[0023] Figure 3 is a schematic diagram of the seismic reflection coefficient of the target layer to which the embodiment of the present invention is applicable;

[0024] Figure 4 is a schematic diagram of a seismic record of a target layer to which an embodiment of the present invention is applicable;

[0025] Figure 5Schematic diagram of the seismic reflection coefficient of the target layer under the L1 norm constraint to which the embodiment of the present invention is applicable;

[0026] Figure 6 is a flow chart of another method for determining a seismic reflection coefficient provided according to the second embodiment of the present invention;

[0027] Figure 7 A schematic diagram of a second-order difference operator applicable to an embodiment of the present invention;

[0028] Figure 8 2 is a schematic structural diagram of a device for determining a seismic reflection coefficient according to a third embodiment of the present invention;

[0029] Figure 9 It is a structural diagram of an electronic device for implementing the method for determining the seismic reflection coefficient according to an embodiment of the present invention. DETAILED DESCRIPTION

[0030] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions 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 embodiments described 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 making creative efforts should fall within the scope of protection of the present invention.

[0031] It should be noted that the terms "first", "second", etc. in the description 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 numbers 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 variations thereof 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 clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0032] Example 1

[0033] Figure 1 A flowchart of a method for determining a seismic reflection coefficient is provided for the first embodiment of the present invention. This embodiment is applicable to obtaining a relatively accurate seismic reflection coefficient through three-dimensional stratum dip data. The method can be executed by a device for determining a seismic reflection coefficient. The device for determining a seismic reflection coefficient can be implemented in the form of hardware and / or software. The device for determining a seismic reflection coefficient can be configured in an electronic device with data processing capabilities. Figure 1 As shown, the method includes:

[0034] S110. Construct a constraint relationship between the seismic wavelet and the L1 norm based on the convolution model of the target detection area.

[0035] The target detection area can be an area where the seismic reflection coefficient needs to be determined. A convolution model can be a model for producing synthetic seismic records. A seismic wavelet can be a signal with a definite start time, limited energy, and a certain duration, and is the basic unit of a seismic record. The L1 norm can be the sum of the absolute values ​​of each element in a vector.

[0036] According to the construction principle of the convolution model, a convolution model of seismic records, seismic reflection coefficients and seismic wavelets can be constructed, and the convolution model can be constrained by using the L1 norm, thereby establishing a constraint relationship between the seismic wavelet and the L1 norm.

[0037] In an optional solution, a constraint relationship between the seismic wavelet and the L1 norm is constructed based on the convolution model of the target detection area, which may include steps A1-A2:

[0038] Step A1: construct a seismic forward model of seismic records, seismic reflection coefficients and seismic wavelets based on the convolution model, and determine an inversion target expression based on the seismic forward model.

[0039] Step A2: Determine a sparse constraint expression based on the inversion target expression and the L1 norm as a constraint relationship between the seismic wavelet and the L1 norm.

[0040] Seismic records can be observational records of an earthquake from its inception to its conclusion, including amplitude, frequency, waveform, and time. The seismic reflection coefficient, which is the ratio of the reflected wave amplitude to the incident wave amplitude, is an important indicator of the reflective capacity of underground media. Seismic forward models can be models that determine the corresponding seismic response based on known mathematical and physical models or geological models. Sparse constraint expressions can be expressions that express the constraint relationship between seismic wavelets and the L1 norm.

[0041] Figure 2 A schematic diagram of a wave impedance sequence of a target layer applicable to an embodiment of the present invention. Figure 3 A schematic diagram of the seismic reflection coefficient of the target layer applicable to the embodiment of the present invention. Figure 4 A schematic diagram of a seismic record of a target layer to which an embodiment of the present invention is applicable. Figure 5 A schematic diagram of the seismic reflection coefficient of the target layer under the L1 norm constraint applicable to the embodiment of the present invention. The wave impedance sequence can be a sequence used to describe the ability of a medium to propagate seismic waves.

[0042] See also Figure 2 、 3 , 4, and 5. Since the wave impedance sequence is the product of the P-wave velocity and the propagation medium density, the P-wave velocity of the seismic wavelet and the propagation medium density in the target layer can be used to determine the wave impedance sequence of the target layer. The wave impedance sequence is then used to determine the seismic reflection coefficient, ultimately generating a seismic forward model. This seismic forward model is then convolved with the inverse wavelet to obtain the inversion target expression. The inversion target expression is regularized using the L1 norm to obtain a sparse constraint expression, which is then used as the constraint relationship between the seismic wavelet and the L1 norm.

[0043] Optionally, the expression of the earthquake forward model is:

[0044] s=w*r

[0045] Where s represents the time domain seismic signal, w represents the seismic wavelet, r represents the seismic reflection coefficient, and * represents the convolution operator.

[0046] Optionally, the inversion objective expression is:

[0047]

[0048] Where W represents the Toeplitz matrix form of the seismic wavelet; r represents the seismic reflection coefficient; and s represents the time domain seismic signal.

[0049] The Toeplitz matrix may refer to a matrix in which the elements on each diagonal line from the upper left to the lower right are the same.

[0050] Optionally, the expression of the earthquake forward model is:

[0051]

[0052] Where W represents the Toeplitz matrix form of the seismic wavelet; r represents the seismic reflection coefficient; s represents the time domain seismic signal; and λ represents the L1 norm.

[0053] S120: Acquire three-dimensional formation dip data of the target layer in the target monitoring area.

[0054] The three-dimensional formation dip data may be the formation dip of the target layer at different times, wherein the three dimensions include x, y and t of the formation dip, wherein x and y represent the position of the formation dip, and t represents the time of the formation dip.

[0055] Through the formation dip scanning technology, the target layer in the target detection area is scanned to obtain the three-dimensional formation dip data of the target layer at different times and positions.

[0056] S130: Determine the seismic reflection coefficient of the target layer based on the three-dimensional formation dip data and the constraint relationship.

[0057] After obtaining the three-dimensional formation dip data, the three-dimensional formation dip data can be used to determine the weight coefficient of the influence of different three-dimensional formation dip data on the seismic reflection coefficient, thereby determining the seismic reflection coefficient of the target layer.

[0058] According to the technical solution of the embodiment of the present invention, by constructing a constraint relationship between the seismic wavelet and the L1 norm based on the convolution model of the target detection area, the three-dimensional stratigraphic dip data of the target layer in the target monitoring area is obtained, and the seismic reflection coefficient of the target layer is determined based on the three-dimensional stratigraphic dip data and the constraint relationship, thereby more accurately determining the seismic reflection coefficient and ensuring the fidelity of the seismic data. The high-resolution results are more refined, providing a high-quality data foundation for reservoir prediction and seismic inversion.

[0059] Example 2

[0060] Figure 6 This is a flow chart of another method for determining seismic reflection coefficients provided in the second embodiment of the present invention. This embodiment further optimizes the process of determining the seismic reflection coefficient of the target layer based on the three-dimensional stratum dip data and the constraint relationship in the above embodiment. This embodiment can be combined with various optional solutions in one or more of the above embodiments. Figure 6 As shown, a method for determining a seismic reflection coefficient in this embodiment may include the following steps:

[0061] S210: Construct a constraint relationship between the seismic wavelet and the L1 norm according to the convolution model of the target detection area.

[0062] S220: Acquire three-dimensional formation dip data of the target layer in the target monitoring area.

[0063] S230: Determine a dip weight coefficient based on the three-dimensional formation dip data and the formation dip projection angle of the target layer.

[0064] S240 , processing the sparse constraint expression according to the dip weight coefficient and the second-order difference operator corresponding to the three-dimensional formation dip data to obtain the dip constraint expression.

[0065] S250. Determine the seismic reflection coefficient according to the dip constraint expression.

[0066] The formation dip projection angle may be a projection of the three-dimensional formation dip on a two-dimensional plane. The dip weight coefficient may be the magnitude of the effect of the three-dimensional formation dip data on the seismic reflection coefficient. The dip constraint expression may be an expression obtained by processing the sparse constraint expression using a second-order difference operator.

[0067] Since the 3D formation dip angle data contains three dimensions, namely x, y and t, it is necessary to project the 3D formation dip angle data on the xy, tx and ty planes respectively to obtain the formation dip projection angle θ of the target layer. xy ,θ tx and θ ty Then the inclination weight coefficient is determined as (cosθ xy , cosθ tx , cosθ ty ). Figure 7 Schematic diagram of a second-order difference operator applicable to an embodiment of the present invention. Figure 7 Since the constraint matrix contains nine operators, the dip constraint expression can be constructed using the second-order difference operator and the dip weight coefficient. Since the dip constraint expression involves a large matrix operation process, in order to improve the calculation speed and accuracy of the dip constraint expression, the split Bregman algorithm can be used to solve it and obtain the seismic reflection coefficient.

[0068] Optionally, the inclination constraint expression is:

[0069]

[0070] Where W represents the Toeplitz matrix form of the seismic wavelet; r represents the seismic reflection coefficient; s represents the time domain seismic signal; λ represents the L1 norm; ρ represents the dip space constraint weight coefficient; cosθ k represents the inclination weight coefficient of k; l k represents the second-order difference operator of k.

[0071] According to the technical solution of an embodiment of the present invention, the dip weight coefficient is determined based on the three-dimensional formation dip data and the formation dip projection angle of the target layer, and the sparse constraint expression is processed according to the dip weight coefficient and the second-order difference operator corresponding to the three-dimensional formation dip data to obtain the dip constraint expression. According to the dip constraint expression, the seismic reflection coefficient is determined, so that the application process of the three-dimensional formation dip data is clearer, and the second-order difference operator is used to maintain the layered information of the three-dimensional formation dip data.

[0072] Example 3

[0073] Figure 8 The present invention provides a structural block diagram of a device for determining seismic reflection coefficients. This embodiment is applicable to situations where a relatively accurate seismic reflection coefficient is obtained through three-dimensional stratum dip data. The device for determining seismic reflection coefficients can be implemented in the form of hardware and / or software, and can be configured in an electronic device with data processing capabilities. Figure 8As shown, the apparatus for determining seismic reflection coefficients of this embodiment may include: a constraint relationship determination module 310, a dip data determination module 320, and a reflection coefficient determination module 330. Among them:

[0074] The constraint relationship determination module 310 is used to construct the constraint relationship between the seismic wavelet and the L1 norm according to the convolution model of the target detection area;

[0075] The dip data determination module 320 is used to obtain three-dimensional formation dip data of the target layer in the target monitoring area;

[0076] The reflection coefficient determination module 330 is used to determine the seismic reflection coefficient of the target layer based on the three-dimensional formation dip data and the constraint relationship.

[0077] Based on the above embodiment, optionally, the constraint relationship determination module 310 includes:

[0078] An inversion expression determination unit is used to construct a seismic forward model of seismic records, seismic reflection coefficients and seismic wavelets according to the convolution model, and to determine an inversion target expression based on the seismic forward model;

[0079] The constraint relationship acquisition unit is used to determine a sparse constraint expression as a constraint relationship between the seismic wavelet and the L1 norm according to the inversion target expression and the L1 norm.

[0080] Based on the above embodiment, optionally, the expression of the earthquake forward model is:

[0081] s=w*r

[0082] Where s represents the time domain seismic signal, w represents the seismic wavelet, r represents the seismic reflection coefficient, and * represents the convolution operator.

[0083] Based on the above embodiment, optionally, the inversion target expression is:

[0084]

[0085] Where W represents the Toeplitz matrix form of the seismic wavelet; r represents the seismic reflection coefficient; and s represents the time domain seismic signal.

[0086] Based on the above embodiment, optionally, the constraint relationship expression is:

[0087]

[0088] Where W represents the Toeplitz matrix form of the seismic wavelet; r represents the seismic reflection coefficient; s represents the time domain seismic signal; and λ represents the L1 norm.

[0089] Based on the above embodiment, optionally, the reflection coefficient determination module 330 includes:

[0090] A weight coefficient determination unit, configured to determine a dip weight coefficient based on three-dimensional formation dip data and a formation dip projection angle of a target layer;

[0091] The dip constraint determination unit is used to process the sparse constraint expression according to the dip weight coefficient and the second-order difference operator corresponding to the three-dimensional formation dip data to obtain the dip constraint expression;

[0092] The reflection coefficient acquisition unit is used to determine the seismic reflection coefficient according to the dip constraint expression.

[0093] Based on the above embodiment, optionally, the inclination angle constraint expression is:

[0094]

[0095] Where W represents the Toeplitz matrix form of the seismic wavelet; r represents the seismic reflection coefficient; s represents the time domain seismic signal; λ represents the L1 norm; ρ represents the dip space constraint weight coefficient; cosθ k represents the inclination weight coefficient of k; l k represents the second-order difference operator of k.

[0096] The device for determining a seismic reflection coefficient provided in an embodiment of the present invention can execute the method for determining a seismic reflection coefficient provided in any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method.

[0097] Example 4

[0098] Figure 9 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 claimed herein.

[0099] like Figure 9As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11. The memory stores a computer program that can be executed by the at least one processor. 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 into the random access memory (RAM) 13. Various programs and data required for the operation of the electronic device 10 can also be stored in the RAM 13. The processor 11, ROM 12, and RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0100] Multiple 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 magnetic 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 via a computer network such as the Internet and / or various telecommunication networks.

[0101] The processor 11 can be any general-purpose and / or specialized processing component 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 specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the method for determining the seismic reflection coefficient.

[0102] In some embodiments, the method for determining the seismic reflection coefficient can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the method for determining the seismic reflection coefficient described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the method for determining the seismic reflection coefficient in any other appropriate manner (e.g., by means of firmware).

[0103] Various embodiments of the systems and techniques described 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), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes 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.

[0104] 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, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0105] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0106] To provide interaction with a user, the systems and techniques described herein can 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 pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0107] The systems and techniques described herein can be implemented in a computing system that includes back-end 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 front-end components (e.g., a user computer with a graphical user interface or 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 back-end components, middleware components, or front-end components. The components of the system can 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.

[0108] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0109] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0110] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A method for determining a seismic reflection coefficient, characterized in that: include: According to the convolution model of the target detection area, the constraint relationship between the seismic wavelet and the L1 norm is constructed; Acquire three-dimensional formation dip data of the target layer in the target detection area; determining a seismic reflection coefficient of the target layer according to the three-dimensional formation dip data and the constraint relationship; Wherein, determining the seismic reflection coefficient of the target layer according to the three-dimensional formation dip data and the constraint relationship includes: Determining a dip weight coefficient according to the three-dimensional formation dip data and a formation dip projection angle of the target layer; Processing the sparse constraint expression according to the dip weight coefficient and a second-order difference operator corresponding to the three-dimensional formation dip data to obtain a dip constraint expression; determining the seismic reflection coefficient according to the dip angle constraint expression; Wherein, the inclination angle constraint expression is: Where, Toeplitz matrix form representing seismic wavelet; represents the seismic reflection coefficient; represents the time domain seismic signal; represents the coefficient of L1 norm; represents the weight coefficient of the inclination space constraint; represents the inclination weight coefficient of k; represents the second-order difference operator of k; x, y and t represent the three dimensions of the values ​​of the three-dimensional formation dip data.

2. The method according to claim 1, characterized in that The method of constructing a constraint relationship between the seismic wavelet and the L1 norm based on the convolution model of the target detection area includes: Constructing a seismic forward model of seismic records, seismic reflection coefficients, and seismic wavelets based on the convolution model, and determining an inversion target expression based on the seismic forward model; According to the inversion target expression and the L1 norm, a sparse constraint expression is determined as a constraint relationship between the seismic wavelet and the L1 norm.

3. The method according to claim 2, characterized in that The expression of the earthquake forward model is: in, represents the time domain seismic signal, represents the seismic wavelet, represents the seismic reflection coefficient, Represents the convolution operator.

4. The method according to claim 2, characterized in that The inversion objective expression is: Where, Toeplitz matrix form representing seismic wavelet; represents the seismic reflection coefficient; Represents the time domain seismic signal.

5. The method according to claim 2, characterized in that The expression of the constraint relationship is: Where, Toeplitz matrix form representing seismic wavelet; represents the seismic reflection coefficient; represents the time domain seismic signal; Denotes the coefficient of the L1 norm.

6. A device for determining seismic reflection coefficient, characterized in that: include: A constraint relationship determination module is used to construct a constraint relationship between the seismic wavelet and the L1 norm based on the convolution model of the target detection area; A dip data determination module, configured to obtain three-dimensional dip data of a target layer within the target detection area; a reflection coefficient determination module, configured to determine the seismic reflection coefficient of the target layer based on the three-dimensional formation dip data and the constraint relationship; Wherein, the reflection coefficient determination module includes: Determining a dip weight coefficient according to the three-dimensional formation dip data and a formation dip projection angle of the target layer; Processing the sparse constraint expression according to the dip weight coefficient and a second-order difference operator corresponding to the three-dimensional formation dip data to obtain a dip constraint expression; determining the seismic reflection coefficient according to the dip angle constraint expression; Wherein, the inclination angle constraint expression is: Where, Toeplitz matrix form representing seismic wavelet; represents the seismic reflection coefficient; represents the time domain seismic signal; represents the coefficient of L1 norm; represents the weight coefficient of the inclination space constraint; represents the inclination weight coefficient of k; represents the second-order difference operator of k; x, y and t represent the three dimensions of the values ​​of the three-dimensional formation dip data.

7. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor. The computer program is executed by the at least one processor to enable the at least one processor to perform the method for determining a seismic reflection coefficient according to any one of claims 1 to 5.

8. 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 the method for determining a seismic reflection coefficient according to any one of claims 1 to 5 when executed.

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