Fracture parameter prediction method and related devices

Abstract

The present disclosure provides a fracture parameter prediction method and related equipment, and relates to the technical field of fracture parameter prediction in oil and gas exploration and development. The method comprises: acquiring seismic data; performing variational mode decomposition on the seismic data to obtain connotation mode component (IMF) data of different scales; and based on the IMF data, using an alternating multiplier method to gradually perform fracture weakness inversion to obtain target fracture parameters. According to the embodiment of the present disclosure, the seismic data can be adaptively divided into different scales, and the noise in the effective wave frequency band range can also be suppressed to a certain extent. Finally, multi-scale fracture weakness inversion is realized in the time domain, and stable fracture prediction is realized.

Description

Technical Field

[0001] This disclosure relates to the field of fracture parameter prediction technology in oil and gas exploration and development, and in particular to a fracture parameter prediction method and related equipment. Background Technology

[0002] Fractured reservoirs are currently a key target for oil and gas exploration and development, as the presence of fractures facilitates the migration and storage of oil and gas. Therefore, stable fracture prediction can effectively guide the development of fractured reservoirs, improve the exploration results of fractured oil and gas reservoirs, and increase oilfield production.

[0003] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0004] This disclosure provides a method and related equipment for predicting crack parameters, which at least to some extent overcomes the problem of instability in related technologies that directly use the full-frequency components of seismic data to predict crack parameters.

[0005] Other features and advantages of this disclosure will become apparent from the following detailed description, or may be learned in part from practice of this disclosure.

[0006] According to one aspect of this disclosure, a method for predicting crack parameters is provided, comprising:

[0007] Acquire earthquake data;

[0008] Variational mode decomposition was performed on the seismic data to obtain intrinsic mode component (IMF) data at different scales;

[0009] Based on IMF data, the target fracture parameters are obtained by stepwise fracture weakness inversion using the alternating multiplier method.

[0010] In one embodiment of this disclosure, based on IMF data, the target fracture parameters are obtained by progressively performing fracture weakness inversion using the alternating multiplier method, including:

[0011] Construct the objective functional;

[0012] Using the alternating multiplier method and based on IMF data, the target functional is solved to obtain the target crack parameters.

[0013] In one embodiment of this disclosure, the target functional is solved using the alternating multiplier method based on IMF data to obtain the target crack parameters, including:

[0014] The first inversion used the low-frequency components obtained by filtering well logging curves as the initial constraint model;

[0015] The prediction results obtained from each inversion are used as the constraint model for the next inversion, and the inversion is carried out step by step until the inversion of all scales is completed to obtain the target crack parameters.

[0016] According to another aspect of this disclosure, a crack parameter prediction device is provided, comprising:

[0017] The data acquisition module is used to acquire earthquake data;

[0018] The data decomposition module is used to perform variational mode decomposition on seismic data to obtain intrinsic mode component (IMF) data at different scales.

[0019] The prediction module is used to perform crack weakness inversion step by step based on IMF data and the alternating multiplier method to obtain the target crack parameters.

[0020] According to another aspect of this disclosure, an electronic device is provided, comprising: a memory for storing instructions; and a processor for calling the instructions stored in the memory to implement the above-described crack parameter prediction method.

[0021] According to another aspect of this disclosure, a computer-readable storage medium is provided that stores computer instructions thereon, which, when executed by a processor, implement the crack parameter prediction method described above.

[0022] According to another aspect of this disclosure, a computer program product is provided, which stores instructions that, when executed by a computer, cause the computer to perform the crack parameter prediction method described above.

[0023] According to another aspect of this disclosure, a chip is provided, including at least one processor and an interface;

[0024] An interface is used to provide program instructions or data to at least one processor;

[0025] At least one processor is used to execute program instructions to implement the crack parameter prediction method described above.

[0026] The crack parameter prediction method and related equipment provided in this disclosure perform variational mode decomposition on seismic data, which can adaptively divide the seismic data into scales and also has a certain suppression effect on noise within the effective wave frequency band. Based on the IMF data obtained by decomposition, the crack weakness inversion is performed step by step using the alternating multiplier method to obtain the target crack parameters and achieve stable crack prediction.

[0027] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0028] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0029] Obviously, the accompanying drawings described below are merely some embodiments of this disclosure. Those skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0030] Figure 1 This diagram illustrates a flowchart of a crack parameter prediction method according to an embodiment of the present disclosure.

[0031] Figure 2 This diagram illustrates a component waveform obtained from variational mode decomposition in an embodiment of the present disclosure.

[0032] Figure 3 This diagram illustrates the component spectrum obtained by variational mode decomposition in an embodiment of the present disclosure.

[0033] Figure 4 The diagram shows a waveform comparison (left) and correlation coefficient (right) of a noisy seismic record and a denoised reconstructed seismic record compared with a noiseless record in an embodiment of this disclosure.

[0034] Figure 5 This diagram illustrates the actual value of crack weakness and the inversion result in an embodiment of this disclosure.

[0035] Figure 6 This diagram illustrates a flowchart of another crack parameter prediction method in an embodiment of this disclosure.

[0036] Figure 7 This diagram illustrates a crack parameter prediction device according to an embodiment of the present disclosure.

[0037] Figure 8 A structural block diagram of an electronic device according to an embodiment of the present disclosure is shown. Detailed Implementation

[0038] The exemplary implementation will now be described more fully with reference to the accompanying drawings.

[0039] It should be noted that the example implementation can be implemented in many forms and should not be construed as being limited to the examples set forth herein.

[0040] The fracture parameter prediction method and related equipment provided in this disclosure can be applied to the field of oil and gas exploration and development. Fractured reservoirs are currently one of the key targets in oil and gas exploration and development, as the presence of fractures is beneficial to the migration and storage of oil and gas. High-angle fractures cause seismic responses to exhibit azimuth anisotropy, thus, fractures can be predicted based on this.

[0041] The inventors discovered that crack prediction methods in related technologies often directly utilize the full-frequency components of seismic data, which lacks stability to some extent. Examples include elastic parameter ellipse fitting and pre-stack AVAz (Amplitude Versus Azimuth) inversion of wide-azimuth seismic data.

[0042] Furthermore, conventional time-domain fracture weakness inversion, which comprehensively utilizes the full-frequency components of seismic data, can lead to insufficient stability and accuracy in prediction results when the seismic data is noisy. Frequency-domain multi-scale fracture prediction first transforms the seismic data to the frequency domain using Fourier transform and selects a specific frequency band. This method can only remove noise outside the effective signal frequency band and requires manual definition of the frequency band, resulting in insufficient adaptability.

[0043] To address the aforementioned issues, this disclosure provides a crack parameter prediction method that can adaptively divide seismic data into scales and also suppress noise within the effective wave frequency band. Ultimately, it achieves multi-scale crack weakness inversion in the time domain, enabling stable crack prediction.

[0044] It should also be noted that the crack parameter prediction method of this disclosure embodiment can be applied to electronic devices. The execution subject of the crack parameter prediction method can be at least one of user terminals such as mobile phones, tablets, and wearable devices that can be configured to execute the crack parameter prediction method provided in this disclosure embodiment. Alternatively, the execution subject of the method can also be the client itself that can execute the method.

[0045] The following detailed description of this exemplary implementation method is provided in conjunction with the accompanying drawings and embodiments.

[0046] Figure 1 A flowchart of a crack parameter prediction method according to an embodiment of this disclosure is shown, as follows: Figure 1 As shown, the crack parameter prediction method provided in this embodiment includes steps S110-S130.

[0047] In S110, acquire earthquake data.

[0048] The seismic data obtained here can be seismic data with different incident angles and azimuth angles.

[0049] In one embodiment, the crack parameter prediction method provided in this disclosure can be applied to crack prediction for HTI media.

[0050] In S120, the seismic data is subjected to variational mode decomposition to obtain intrinsic mode component (IMF) data at different scales.

[0051] Variational Mode Decomposition (VMD) searches for the optimal variational model through an iterative process to determine the center frequency and bandwidth of each mode component, thereby realizing the frequency domain of the signal and the adaptive part of each mode component.

[0052] IMF (Intrinsic Mode Function) refers to the signal components obtained after the original signal is decomposed by EMD, which is the signal components obtained after the seismic data is decomposed by EMD.

[0053] In this disclosure, variational mode decomposition is performed on seismic data with different incident angles and azimuth angles in step S120 to obtain n IMFs with decreasing dominant frequencies. There are various ways to perform variational mode decomposition on seismic data, and no limitation is made here.

[0054] In one embodiment, variational mode decomposition can be performed based on the following formula:

[0055]

[0056] Where S represents the earthquake record, θ represents the incident angle of the seismic wave, φ represents the azimuth angle, and imf m Let m represent the m-th IMF, and residual represent the residual.

[0057] like Figure 2 The image shows the IMF and residual waveforms obtained from the noisy seismic record after variational mode decomposition. Figure 3 Based on its spectrum, it can be seen that variational mode decomposition can decompose noisy seismic records into multi-scale data with different main frequencies, and obtain residuals with indistinct main frequencies, i.e., random noise.

[0058] By retaining only the IMF and discarding the residuals, a reconstructed seismic record can be obtained:

[0059]

[0060] Where C represents the reconstructed seismic data obtained after denoising and discarding residuals.

[0061] Figure 4 The waveforms and correlation coefficients of the noisy seismic records, the reconstructed seismic records after denoising, and the noiseless records are shown. It can be seen that after denoising based on variational mode decomposition, the noise is well suppressed, and the correlation coefficient between the reconstructed seismic records and the noiseless records is significantly improved.

[0062] In S130, based on IMF data, the crack weakness inversion is performed step by step using the alternating multiplier method to obtain the target crack parameters.

[0063] In some embodiments, S130 may include the following steps:

[0064] Construct the objective functional;

[0065] Using the Alternating Direction Method of Multipliers (ADMM) based on IMF data, the target functional is solved to obtain the target crack parameters.

[0066] The method involves using the alternating multiplier method to solve the target functional based on IMF data to obtain the target fracture parameters. This can include using the low-frequency components obtained by filtering the well logging curves in the first inversion as the initial constraint model. The prediction results obtained from each inversion are then used as the constraint model for the next inversion, and the inversion is carried out step by step until all scales of inversion are completed to obtain the target fracture parameters.

[0067] The crack parameter prediction method provided in this embodiment performs variational mode decomposition on seismic data, which can adaptively divide the seismic data into scales and has a certain suppression effect on noise within the effective wave frequency band. Based on the IMF data obtained by decomposition, the crack weakness inversion is performed step by step using the alternating multiplier method to obtain the target crack parameters and achieve stable crack prediction.

[0068] The implementation process of S130 above will be explained in detail below with a specific example.

[0069] First, construct the objective functional.

[0070] The forward model of HTI media can be expressed as:

[0071]

[0072] Where s is the seismic record matrix at each azimuth and incident angle, G is the forward modeling matrix, composed of the wavelet matrix W at each azimuth, the coefficient matrix c of the HTI reflection approximation equation, and the difference matrix D, and m=[ln(V p ) ln(V s ln(ρ) δ N δ T ] T These are the inversion parameters.

[0073] The above formula (3) can be simplified to the following equation:

[0074] S=Gm (4)

[0075] Based on the sparsity assumption, the reflectivity of the inversion parameters is constrained by the L1 norm, which is equivalent to assuming that the strata are sparsely and massively distributed. The inversion objective functional can be expressed as:

[0076]

[0077] in, I5 is a 5×5 identity matrix, λ>0, μ>0 are regularization parameters, and m mod This is the initial model.

[0078] The objective functional, i.e., the above formula (5), is solved using the Alternating Direction Method of Multipliers (ADMM). First, the objective functional is written in ADMM form:

[0079]

[0080] The update rules for each parameter are as follows:

[0081]

[0082] Where u and v are dual variables, and H is the soft valve operator:

[0083]

[0084] After variational mode decomposition, the sum of the (n-i+1)th to nth IMFs is used as the data for the i-th inversion:

[0085]

[0086] S i This represents the data used for the i-th inversion.

[0087] The first inversion uses the low-frequency components obtained by filtering the well logging curves as the initial constraint model. The prediction results obtained from each inversion are used as the constraint model for the next inversion. The inversion is carried out step by step until the inversion of all scales is completed, and finally stable and accurate fracture parameters are obtained.

[0088] Figure 5 The true value of crack weakness and the inversion results are shown, as follows: Figure 5 As shown, the crack parameter prediction method provided in this embodiment has higher adaptability and better noise resistance.

[0089] Figure 6 A flowchart of a crack parameter prediction method according to an embodiment of this disclosure is shown, as follows: Figure 6 As shown, the crack parameter prediction method provided in this embodiment includes steps S601-S609.

[0090] In S601, the seismic data is subjected to Variational Mode Decomposition (VMD) to obtain the intrinsic mode component (IMF) data at different scales in S602, which is the data Si used for the i-th inversion.

[0091] The first inversion used the low-frequency components obtained from well logging curve filtering as the initial constraint model, that is... Figure 6 S603-S604 in the example.

[0092] Then, the prediction result obtained from each inversion is used as the constraint model for the next inversion, and the inversion is carried out step by step until all scales are inverted, finally obtaining stable and accurate crack parameters, that is... Figure 6 Steps S605-S609 in the process.

[0093] The embodiments disclosed herein utilize variational mode decomposition to decompose seismic data into data at different scales, and gradually invert to obtain stable fracture parameters, which has higher adaptability and better noise resistance.

[0094] Furthermore, although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result.

[0095] In some embodiments, certain steps may be omitted, multiple steps may be combined into one step for execution, and / or one step may be broken down into multiple steps for execution.

[0096] Based on the same inventive concept, this disclosure also provides a crack parameter prediction device, as described in the following embodiments. Since the principle by which this device solves the problem is similar to that of the method embodiments described above, the implementation of this device embodiment can refer to the implementation of the method embodiments described above, and repeated details will not be repeated.

[0097] Figure 7 This diagram illustrates a crack parameter prediction device according to an embodiment of the present disclosure, such as... Figure 7 As shown, the crack parameter prediction device 700 includes:

[0098] Data acquisition module 702 is used to acquire earthquake data;

[0099] The data decomposition module 704 is used to perform variational mode decomposition on the seismic data to obtain intrinsic mode component (IMF) data at different scales;

[0100] The prediction module 706 is used to perform crack weakness inversion step by step based on the IMF data using the alternating multiplier method to obtain the target crack parameters.

[0101] In some embodiments, the data acquisition module 702 can be used to acquire seismic data with different incident angles and azimuth angles.

[0102] In some embodiments, the data decomposition module 704 can be used to perform variational mode decomposition on seismic data with different incident angles and azimuth angles to obtain n IMFs with decreasing dominant frequencies.

[0103] In some embodiments, the data decomposition module 704 can perform variational mode decomposition based on formula (1) above.

[0104] In some embodiments, the prediction module 706 may include:

[0105] Objective functional building unit, used to construct objective functionals;

[0106] The prediction unit is used to solve the target functional based on IMF data using the alternating multiplier method to obtain the target crack parameters.

[0107] In some embodiments, the prediction unit may implement the following:

[0108] The first inversion used the low-frequency components obtained by filtering well logging curves as the initial constraint model;

[0109] The prediction results obtained from each inversion are used as the constraint model for the next inversion, and the inversion is carried out step by step until the inversion of all scales is completed to obtain the target crack parameters.

[0110] The concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to define the order of functions performed by these devices, modules or units or their interdependencies.

[0111] Regarding the crack parameter prediction device in the above embodiments, the specific way in which each module performs its operation has been described in detail in the embodiments related to the crack parameter prediction method, and will not be elaborated here.

[0112] In summary, in the crack parameter prediction device provided in this application embodiment, variational mode decomposition of seismic data can adaptively divide the seismic data into scales and also has a certain suppression effect on noise within the effective wave frequency band. Based on the IMF data obtained by decomposition, the crack weakness inversion is performed step by step using the alternating multiplier method to obtain the target crack parameters and achieve stable crack prediction.

[0113] It should be noted that although several modules or units of the device used for action execution are mentioned in the detailed description above, this division is not mandatory.

[0114] In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0115] Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0116] The following reference Figure 8 This describes the electronic device provided in the embodiments of this disclosure. Figure 8 The electronic device 800 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments disclosed herein.

[0117] Figure 8 This diagram illustrates the architecture of an electronic device 800 according to an embodiment of the present invention. Figure 8 As shown, the electronic device 800 includes, but is not limited to, at least one processor 810 and at least one memory 820.

[0118] Memory 820 is used to store instructions.

[0119] In some embodiments, memory 820 may include a readable medium in the form of volatile memory cells, such as random access memory (RAM) 8201 and / or cache memory 8202, and may further include read-only memory (ROM) 8203.

[0120] In some embodiments, the memory 820 may also include a program / utility 8204 having a set (at least one) program module 8205, such program module 8205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0121] In some embodiments, memory 820 may store an operating system. This operating system may be a real-time operating system (RTX), such as Linux, UNIX, Windows, or OS X.

[0122] In some embodiments, the memory 820 may also store data.

[0123] As an example, processor 810 can read data stored in memory 820, which may be stored at the same memory address as the instruction, or the data may be stored at a different memory address than the instruction.

[0124] Processor 810 is configured to invoke instructions stored in memory 820 to implement the steps described in the "Exemplary Methods" section above, according to various exemplary embodiments of this disclosure. For example, processor 810 may execute the following steps of the above method embodiments:

[0125] Acquire earthquake data;

[0126] The seismic data were subjected to variational mode decomposition to obtain intrinsic mode component (IMF) data at different scales;

[0127] Based on the IMF data, the crack weakness inversion is performed step by step using the alternating multiplier method to obtain the target crack parameters.

[0128] It should be noted that the processor 810 described above can be a general-purpose processor or a special-purpose processor. The processor 810 may include one or more processing cores, and the processor 810 executes various functional applications and data processing by running instructions.

[0129] In some embodiments, processor 810 may include a central processing unit (CPU) and / or a baseband processor.

[0130] In some embodiments, the processor 810 may determine an instruction based on the priority identifier and / or function category information carried in each control instruction.

[0131] In this disclosure, the processor 810 and the memory 820 can be configured separately or integrated together.

[0132] As an example, the processor 810 and memory 820 can be integrated on a single board or a system-on-a-chip (SOC).

[0133] like Figure 8 As shown, the electronic device 800 is presented in the form of a general-purpose computing device. The electronic device 800 may also include a bus 830.

[0134] Bus 830 can represent one or more of several types of bus structures, including a memory bus or memory controller, peripheral bus, graphics acceleration port, processor, or a local bus using any of the various bus structures.

[0135] Electronic device 800 can also communicate with one or more external devices 840 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 800, and / or with any device that enables electronic device 800 to communicate with one or more other computing devices (e.g., router, modem, etc.). Such communication can be performed through input / output (I / O) interface 850.

[0136] Furthermore, the electronic device 800 can also communicate with one or more networks (such as local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via the network adapter 860.

[0137] like Figure 8 As shown, the network adapter 860 communicates with other modules of the electronic device 800 via the bus 830.

[0138] It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 800, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0139] It is understood that the structures illustrated in the embodiments of this disclosure do not constitute a specific limitation on the electronic device 800. In other embodiments of this disclosure, the electronic device 800 may include... Figure 8 This may involve more or fewer components, or combining certain components, or splitting certain components, or different component arrangements. Figure 8 The components shown can be implemented in hardware, software, or a combination of both.

[0140] This disclosure also provides a computer-readable storage medium storing computer instructions thereon, which, when executed by a processor, implement the crack parameter prediction method described in the above method embodiments.

[0141] In this embodiment of the disclosure, the computer-readable storage medium is a computer instruction that can be sent, propagated, or transmitted for use by or in conjunction with an instruction execution system, apparatus, or device.

[0142] As an example, a computer-readable storage medium is a non-volatile storage medium.

[0143] In some embodiments, more specific examples of computer-readable storage media in this disclosure may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, USB flash drives, portable hard drives, or any suitable combination of the foregoing.

[0144] In this embodiment of the disclosure, the computer-readable storage medium may include data signals propagated in baseband or as part of a carrier wave, wherein computer instructions (readable program code) are carried.

[0145] The transmitted data signal can take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof.

[0146] In some examples, computational instructions contained on a computer-readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0147] This disclosure also provides a computer program product that stores instructions that, when executed by a computer, cause the computer to implement the crack parameter prediction method described in the above method embodiments.

[0148] The aforementioned instructions can be program code. In practice, the program code can be written using any combination of one or more programming languages.

[0149] Programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages.

[0150] The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0151] In cases involving remote computing devices, the remote computing devices can be connected to user computing devices via any type of network, including local area networks (LANs) or wide area networks (WANs), or they can be connected to external computing devices (e.g., via the Internet using an Internet service provider).

[0152] This disclosure also provides a chip, including at least one processor and an interface;

[0153] An interface is used to provide program instructions or data to at least one processor;

[0154] At least one processor is used to execute program instructions to implement the crack parameter prediction method described in the above method embodiments.

[0155] In some embodiments, the chip may further include a memory for storing program instructions and data, the memory being located within or outside the processor.

[0156] Those skilled in the art will understand that all or part of the steps of the above embodiments can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, which can be collectively referred to as "circuit", "module" or "system".

[0157] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein.

[0158] This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the appended claims.

Claims

1. A method for predicting crack parameters, characterized in that, include: Acquire earthquake data; The seismic data were subjected to variational mode decomposition to obtain intrinsic mode component (IMF) data at different scales; Based on the IMF data, the fracture weakness inversion is performed step by step using the alternating multiplier method to obtain the target fracture parameters. The step-by-step fracture weakness inversion based on the IMF data using the alternating multiplier method includes: Construct the following objective functional: ； in, , for The identity matrix, For regularization parameters, As the initial model, This is the seismic record matrix for various orientations and incident angles. It is an orthogonal matrix; The wavelet matrix, HTI reflection approximation equation coefficient matrix, and difference matrix are used for each orientation. composition; These are the inversion parameters; The objective functional is solved using the alternating multiplier method. First, the objective functional is written in ADMM form: ； ； The update rules for each parameter are as follows: ； ； ； in, and As dual variables, For soft valve operators, The penalty parameters of the ADMM algorithm: ； After variational mode decomposition, the sum of the (n-i+1)th to nth IMFs is used as the data for the i-th inversion: ； This represents the data used for the i-th inversion. Indicates the angle of incidence of seismic waves. Indicates azimuth. This represents the m-th IMF.

2. The method according to claim 1, characterized in that, The acquisition of earthquake data includes: Obtain seismic data with different incident angles and azimuth angles.

3. The method according to claim 2, characterized in that, The step of performing variational mode decomposition on the seismic data to obtain intrinsic mode component (IMF) data at different scales includes: Variational mode decomposition was performed on seismic data with different incident angles and azimuth angles to obtain n IMFs with decreasing dominant frequencies.

4. The method according to claim 3, characterized in that, Variational mode decomposition is performed based on the following formula: ； in, Represents earthquake records. Indicates the angle of incidence of seismic waves. Indicates azimuth. This represents the m-th IMF. Represents the residual.

5. A crack parameter prediction device, characterized in that, include: The data acquisition module is used to acquire earthquake data; The data decomposition module is used to perform variational mode decomposition on the seismic data to obtain intrinsic mode component (IMF) data at different scales; The prediction module is used to perform crack weakness inversion step by step based on the IMF data using the alternating multiplier method to obtain the target crack parameters; The prediction module, based on the IMF data, uses the alternating multiplier method to progressively invert fracture weakness, including: Construct the following objective functional: ； in, , for The identity matrix, For regularization parameters, As the initial model, This is the seismic record matrix for various orientations and incident angles. It is an orthogonal matrix; The wavelet matrix, HTI reflection approximation equation coefficient matrix, and difference matrix are used for each orientation. composition; These are the inversion parameters; The objective functional is solved using the alternating multiplier method. First, the objective functional is written in ADMM form: ； ； The update rules for each parameter are as follows: ； ； ； in, and As dual variables, For soft valve operators, The penalty parameters of the ADMM algorithm: ； After variational mode decomposition, the sum of the (n-i+1)th to nth IMFs is used as the data for the i-th inversion: ； This represents the data used for the i-th inversion. Indicates the angle of incidence of seismic waves. Indicates azimuth. This represents the m-th IMF.

6. An electronic device, characterized in that, include: Memory, used to store instructions; A processor is configured to invoke instructions stored in the memory to implement the crack parameter prediction method as described in any one of claims 1-4.

7. A computer-readable storage medium storing computer instructions thereon, characterized in that, When the computer instructions are executed by the processor, they implement the crack parameter prediction method according to any one of claims 1-4.

8. A chip, characterized in that, Includes at least one processor and interface; The interface is used to provide program instructions or data to the at least one processor; The at least one processor is used to execute the program instructions to implement the crack parameter prediction method as described in any one of claims 1-4.

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