Seismic reflection coefficient determination method and device and electronic equipment

Abstract

The invention discloses a seismic reflection coefficient determination method and apparatus, and an electronic device. The method comprises the following steps: constructing a constraint relationship between seismic wavelets and L1 norms according to a convolution model of a target detection area; acquiring three-dimensional stratigraphic dip angle data of a target layer in the target monitoring area; according to the three-dimensional stratigraphic dip angle data and the constraint relation, the seismic reflection coefficient of the target stratum is determined. According to the technical scheme provided by the embodiment of the invention, the constraint relationship between the seismic wavelet and the L1 norm is constructed according to the convolution model of the target detection area, the three-dimensional stratigraphic dip angle data of the target stratum in the target monitoring area is acquired, and the seismic reflection coefficient of the target stratum is determined according to the three-dimensional stratigraphic dip angle data and the constraint relationship. Therefore, the seismic reflection coefficient is accurately determined, the fidelity of seismic data is ensured, the high-resolution result is finer, and a high-quality data basis is provided 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, a device and an electronic device 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 getting higher and higher. 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 method for determining seismic reflection coefficients 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] Acquire 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 according to the convolution model of the target detection area;

[0011] A dip data determination module, used to obtain three-dimensional formation 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 according to the three-dimensional formation dip angle data and 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 at least one processor so that the at least one processor can execute 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, wherein the computer-readable storage medium 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 formation 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 formation 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 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

[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 provided according to Embodiment 1 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 5is a schematic diagram of the seismic reflection coefficient under the L1 norm constraint of the target layer 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 is a structural schematic diagram of a device for determining a seismic reflection coefficient provided according to Embodiment 3 of the present invention;

[0029] Fig. 9 It is a schematic diagram of the structure of an electronic device for implementing the method for determining the seismic reflection coefficient of an embodiment of the present invention. DETAILED DESCRIPTION

[0030] 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.

[0031] It should be noted that the terms "first", "second", etc. 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 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 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.

[0032] Embodiment 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 can be applied to obtaining a relatively accurate seismic reflection coefficient through three-dimensional formation 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 according to the convolution model of the target detection area.

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

[0036] According to the construction principle of the convolution model, the 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, the constraint relationship between the seismic wavelet and the L1 norm is constructed according to 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 according to the convolution model, and determine an inversion target expression based on the seismic forward model.

[0039] Step A2: 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.

[0040] Seismic records can be observation records of the entire process of an earthquake from its inception to its termination, including amplitude, frequency, waveform, and time. The seismic reflection coefficient can be the ratio of the amplitude of the reflected wave to the amplitude of the incident wave, and is an important indicator for describing the reflection capacity of underground media. A seismic forward model can be a model of the process of obtaining the corresponding seismic response based on a known mathematical and physical model or geological model. A sparse constraint expression can be an expression used to represent the constraint relationship between a seismic wavelet 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 to which the embodiment of the present invention is applicable. 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 under the L1 norm constraint of the target layer applicable to the embodiment of the present invention. The wave impedance sequence can be a sequence used to describe the ability of the medium to propagate seismic waves.

[0042] See also Figure 2 , 3 , 4 and 5, since the wave impedance sequence is the product of the longitudinal wave velocity and the propagation medium density, the wave impedance sequence of the target layer can be determined by using the longitudinal wave velocity of the seismic wavelet and the propagation medium density in the target layer. The wave impedance sequence is used to determine the seismic reflection coefficient, and finally a seismic forward model is generated. The seismic forward model is convolved with the anti-wavelet to obtain the inversion target expression. The inversion target expression is regularized and constrained by the L1 norm to obtain a sparse constraint expression, and the sparse constraint expression is used as the constraint relationship between the seismic wavelet and the L1 norm.

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

[0044] s=w*r

[0045] Among them, 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 seismic signal in the time domain.

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

[0050] Optionally, the expression of the seismic 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; λ represents the L1 norm.

[0053] S120, obtaining 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 locations.

[0056] S130. Determine the seismic reflection coefficient of the target layer according to 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 the constraint relationship between the seismic wavelet and the L1 norm based on the convolution model of the target detection area, the three-dimensional formation 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 formation 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] Embodiment 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 according to the three-dimensional formation dip angle 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, obtaining three-dimensional formation dip data of the target layer in the target monitoring area.

[0063] S230, determining a dip weight coefficient according to 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 angle 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 influence 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 through a second-order difference operator.

[0067] Since the value of the three-dimensional formation dip angle data contains three dimensions, namely x, y and t, it is necessary to project the three-dimensional formation dip angle data on the xy, tx and ty planes respectively to obtain the formation dip angle 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 relationship matrix contains 9 operators, the dip constraint expression can be constructed through 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 according to 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 layered information of the three-dimensional formation dip data is maintained by using the second-order difference operator.

[0072] Embodiment 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 the case where a relatively accurate seismic reflection coefficient is obtained through three-dimensional formation dip data. The device for determining seismic reflection coefficients can be implemented in the form of hardware and / or software, and the device for determining seismic reflection coefficients can be configured in an electronic device with data processing capabilities. Figure 8As shown, the device for determining seismic reflection coefficient 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 according to the three-dimensional formation dip angle 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 determine an inversion target expression according to 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 seismic forward model is:

[0081] s=w*r

[0082] Among them, 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 seismic signal in the time domain.

[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; 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, used to determine the dip weight coefficient according to the three-dimensional formation dip data and the formation dip projection angle of the 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 the seismic reflection coefficient provided in the embodiment of the present invention can execute the method for determining the seismic reflection coefficient provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0097] Embodiment 4

[0098] Fig. 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 required herein.

[0099] like Fig. 9As 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.

[0100] 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.

[0101] 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 special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a method for determining a seismic reflection coefficient.

[0102] In some embodiments, the method for determining the seismic reflection coefficient 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 method for determining the seismic reflection coefficient described above may be performed. Alternatively, in other embodiments, the processor 11 may 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 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.

[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, 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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 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 monitoring area; The seismic reflection coefficient of the target layer is determined according to the three-dimensional formation dip data and the constraint relationship.

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 according to the convolution model of the target detection area includes: According to the convolution model, a seismic forward model of seismic records, seismic reflection coefficients and seismic wavelets is constructed, and an inversion target expression is determined according to the seismic forward model; According to the inversion target expression and the L1 norm, the 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: s=w*r Among them, s represents the time domain seismic signal, w represents the seismic wavelet, r represents the seismic reflection coefficient, and * represents the convolution operator.

4. The method according to claim 2, characterized in that: The inversion target expression is: Where W represents the Toeplitz matrix form of the seismic wavelet; r represents the seismic reflection coefficient; and s represents the seismic signal in the time domain.

5. The method according to claim 2, characterized in that: The constraint relationship is expressed as: 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.

6. The method according to claim 2, characterized in that 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; The sparse constraint expression is processed according to the inclination weight coefficient and a second-order difference operator corresponding to the three-dimensional formation inclination data to obtain an inclination constraint expression; The seismic reflection coefficient is determined according to the dip angle constraint expression.

7. The method according to claim 6, characterized in that The inclination constraint expression is: 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.

8. A device for determining a 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 according to the convolution model of the target detection area; A dip data determination module, used to obtain three-dimensional formation dip data of a target layer in the target monitoring area; The reflection coefficient determination module is used to determine the seismic reflection coefficient of the target layer according to the three-dimensional formation dip angle data and the constraint relationship.

9. 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, and the computer program is executed by the at least one processor so that the at least one processor can perform the method for determining a seismic reflection coefficient according to any one of claims 1 to 7.

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 the method for determining a seismic reflection coefficient according to any one of claims 1 to 7 when executed.

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