A three-dimensional seismic signal extraction method, device, terminal and storage medium

Abstract

The present application provides a three-dimensional seismic signal extraction method, device, terminal and storage medium. The method includes: obtaining three-dimensional seismic data to be predicted, wherein the three-dimensional seismic data is a third-order tensor; performing singular value decomposition on the three-dimensional seismic data based on the tensor singular value decomposition algorithm to obtain the singular values ​​of the three-dimensional seismic data; shrinking the singular values ​​based on the Semi-Soft GoDec algorithm to obtain the target singular values; adjusting the target singular values ​​according to the Frobenius norm and the preset expected value, and determining the three-dimensional effective seismic data based on the adjusted target singular values. The present application can determine the three-dimensional effective seismic data by decomposing, shrinking and adjusting the three-dimensional seismic data, effectively avoiding the serious interference of noise on the three-dimensional seismic data, and improving the recognition effectiveness of the seismic signal.

Description

Technical Field

[0001] The present application relates to the field of seismic exploration technology, and in particular to a three-dimensional seismic signal extraction method, device, terminal and storage medium. Background Art

[0002] Seismic exploration is a geophysical exploration method that uses the elasticity and density differences of underlying media to observe and analyze the earth's response to artificially excited seismic waves to infer the properties and morphology of subsurface rock formations. It uses artificially excited elastic waves to locate mineral deposits and obtain engineering geological information. When collecting seismic data in the field, the receiving geophones are often subject to external interference, and the received data is often mixed with noise, seriously affecting the inversion and interpretation of seismic data. Field-collected seismic data is often filled with a large amount of noise, which seriously reduces the quality of the seismic data. Improving the signal-to-noise ratio of seismic data is a primary task in seismic signal processing.

[0003] At present, the main methods for suppressing random noise in seismic data for different noise types include: median filtering, fx domain predictive filtering, independent component analysis, wavelet transform, empirical mode decomposition, etc. However, in complex environments, since the collected seismic data contains a large amount of strong interference noise, the above methods cannot recognize the collected seismic data, and the recognition speed is relatively slow. Therefore, how to quickly suppress strong interference noise in the seismic exploration process is a technical problem that needs to be solved. Summary of the Invention

[0004] The present application provides a three-dimensional seismic signal extraction method, device, terminal and storage medium to solve the problem of poor recognition effectiveness of seismic signals during seismic exploration.

[0005] In a first aspect, the present application provides a three-dimensional seismic signal extraction method, comprising:

[0006] Acquiring three-dimensional seismic data to be predicted, wherein the three-dimensional seismic data is a third-order tensor;

[0007] Performing singular value decomposition on the three-dimensional seismic data based on a tensor singular value decomposition algorithm to obtain singular values ​​of the three-dimensional seismic data;

[0008] Based on the Semi-Soft GoDec algorithm, the singular value is shrunk to obtain the target singular value;

[0009] The target singular value is adjusted according to the Frobenius norm and the preset expected value, and three-dimensional effective seismic data is determined based on the adjusted target singular value.

[0010] In a second aspect, the present application provides a three-dimensional seismic signal extraction device, comprising:

[0011] An acquisition device, configured to acquire three-dimensional seismic data to be predicted, wherein the three-dimensional seismic data is a third-order tensor;

[0012] a decomposition module, configured to perform singular value decomposition on the three-dimensional seismic data based on a tensor singular value decomposition algorithm to obtain singular values ​​of the three-dimensional seismic data;

[0013] A shrinkage module, configured to shrink the singular value based on a Semi-Soft GoDec algorithm to obtain a target singular value;

[0014] The determination module is used to adjust the target singular value according to the Frobenius norm and the preset expected value, and determine the three-dimensional effective seismic data according to the target singular value after the adjustment.

[0015] In a third aspect, the present application provides a terminal comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the three-dimensional seismic signal extraction method described in the first aspect or any possible implementation of the first aspect are implemented.

[0016] In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the three-dimensional seismic signal extraction method described in the first aspect or any possible implementation of the first aspect.

[0017] The present application provides a three-dimensional seismic signal extraction method, device, terminal and storage medium, which obtains three-dimensional seismic data to be predicted; performs singular value decomposition on the three-dimensional seismic data based on a tensor singular value decomposition algorithm to obtain the singular values ​​of the three-dimensional seismic data; shrinks the singular values ​​based on a Semi-Soft GoDec algorithm to obtain target singular values; adjusts the target singular values ​​according to a Frobenius norm and a preset expected value, and determines three-dimensional effective seismic data based on the adjusted target singular values. The present application can decompose the three-dimensional seismic data according to a tensor singular value decomposition algorithm, shrink the singular values ​​in the three-dimensional seismic data according to a Semi-Soft GoDec algorithm, and then determine the three-dimensional effective seismic data according to the Frobenius norm, thereby effectively avoiding the serious interference of noise on the three-dimensional seismic data and improving the recognition effectiveness of the three-dimensional seismic data. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0019] Figure 1 This is a flowchart of the implementation of the three-dimensional seismic signal extraction method provided in the embodiment of the present application;

[0020] Figure 2 Schematic diagram of the decomposition of the tensor singular value decomposition algorithm provided in the embodiment of the present application;

[0021] Figure 3 is a flowchart of a three-dimensional seismic signal extraction method provided in an embodiment of the present application;

[0022] Figure 4 This is a standard three-dimensional seismic data model diagram provided in the embodiment of the present application;

[0023] Figure 5 This is a noisy three-dimensional seismic data model diagram provided in an embodiment of the present application;

[0024] Figure 6 This is a noise-reduced three-dimensional seismic data model diagram provided by an embodiment of the present application;

[0025] Figure 7 Schematic diagram of the structure of the three-dimensional seismic signal extraction device provided in an embodiment of the present application;

[0026] Figure 8 It is a schematic diagram of a terminal provided in an embodiment of the present application. DETAILED DESCRIPTION

[0027] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0028] In order to make the purpose, technical solutions and advantages of this application clearer, specific embodiments will be described below with reference to the accompanying drawings.

[0029] Figure 1 The implementation flow chart of the 3D seismic signal extraction method provided in the embodiment of the present application is detailed as follows:

[0030] In S101 , three-dimensional seismic data to be predicted is obtained, wherein the three-dimensional seismic data is a third-order tensor.

[0031] Since the signal-to-noise ratio of 3D seismic data obtained from actual environments is usually low, the acquired 3D seismic data is represented as a The third-order tensor of Figure 2 , the obtained 3D seismic data is Figure 2 The noisy observation tensor in .

[0032] In the embodiment of the present application, the three-dimensional seismic data to be predicted is the three-dimensional seismic data within a preset period, which can be recorded as .

[0033] In S102 , singular value decomposition is performed on the three-dimensional seismic data based on a tensor singular value decomposition algorithm to obtain singular values ​​of the three-dimensional seismic data.

[0034] The tensor singular value decomposition (T-SVD) algorithm is a matrix factorization technique that transforms the matrix Divided into , where reference is made to Figure 3 , and Respectively Figure 2 in and The matrix, express The matrix, specifically:

[0035] Step 1: Input ;

[0036] Step 2: Calculation For each frontal slice, the calculation is as follows:

[0037] for do

[0038] ;

[0039] end for

[0040] for do

[0041] ;

[0042] ;

[0043] ;

[0044] end for

[0045] Step 3: ; ; .

[0046] In a possible implementation, after S101, the method may further include:

[0047] Performing Fourier transform on the predicted three-dimensional seismic data to obtain three-dimensional seismic data in the frequency domain;

[0048] Accordingly, based on the tensor singular value decomposition algorithm, the 3D seismic data is subjected to singular value decomposition, and the obtained singular values ​​of the 3D seismic data include:

[0049] Based on the tensor singular value decomposition algorithm, the three-dimensional seismic data in the frequency domain is subjected to singular value decomposition to obtain the singular values ​​of the three-dimensional seismic data in the frequency domain.

[0050] Since the three-dimensional seismic data to be detected obtained in the embodiment of the present application is a third-order tensor within a preset period and is a three-dimensional data body in the time domain, and the data when performing tensor singular value decomposition needs to be data in the frequency domain, therefore, the present application needs to first perform Fourier transform on the three-dimensional seismic data, transform the time domain data into frequency domain data, and then perform singular value decomposition.

[0051] In a possible implementation, S102 may include:

[0052] Performing singular value decomposition calculation on the three-dimensional seismic data in the frequency domain by using the first formula to obtain the singular values ​​of the three-dimensional seismic data;

[0053] The first formula is:

[0054]

[0055] in, is the tensor singular value decomposition formula, is the amplitude spectrum of 3D seismic data in the frequency domain, is the first component, is a singular value, is the second component.

[0056] The tensor singular value decomposition algorithm is used to perform singular value decomposition calculation on the amplitude spectrum of the three-dimensional seismic data in the frequency domain after Fourier transformation of the predicted three-dimensional seismic data to obtain the singular value .

[0057] In S103 , the singular values ​​are shrunk based on the Semi-Soft GoDec algorithm to obtain target singular values.

[0058] Among them, the advantage of the Semi-Soft GoDec algorithm is its fast decomposition speed. The algorithm is based on the optimization problem to transform the matrix Decomposed into three components, the calculation formula of the optimization problem is formula (1):

[0059] (1)

[0060] The matrix The calculation formula decomposed into three components is formula (2):

[0061] (2)

[0062] in, is the amplitude spectrum of the noisy signal after MSST decomposition, and its dimension is , is the low-rank component of the amplitude spectrum, and its dimension is , is the sparse component of the amplitude spectrum, and its dimension is , and are low-rank components The maximum rank and sparse component of The maximum number of non-zero elements in , is the approximation error, and its dimension is , is a regularization parameter, which serves to balance the sparse conditions and low-rank constraints, where The smaller the value, the more thorough the noise suppression. The larger the value, the easier it is to damage the effective signal.

[0063] In formula (1), the cost function includes the Frobenius norm and the L1 norm. The role of the Frobenius norm is to minimize the approximation error. , the role of L1 norm is to increase the sparsity constraint.

[0064] In the iterative Semi-Soft GoDec algorithm, The low-rank component of the iteration Calculated by formula (3):

[0065] (3)

[0066] in, , whose dimensions are ; , whose dimensions are ; and are two random matrices.

[0067] No. The sparse component of the iteration Calculated by formula (4), its physical meaning is The soft threshold operation of the incidental leave is:

[0068] (4)

[0069] In the embodiment of the present application, the Semi-Soft GoDec algorithm is used to shrink the singular values.

[0070] In a possible implementation, base S103 may include:

[0071] Calculate the singular value using the second formula to obtain the target singular value;

[0072] The second formula is:

[0073]

[0074] in, is the target singular value, is the Semi-Soft GoDec algorithm formula, is a singular value.

[0075] According to the three-dimensional seismic data obtained after the singular value decomposition of S102, the singular values ​​in the decomposed three-dimensional seismic data are Perform the Semi-Soft GoDec algorithm to solve and obtain the target singular value after contraction .

[0076] In a possible implementation, after S103, the method may further include:

[0077] The target singular value is subjected to inverse Fourier transform to obtain the target singular value after decomposition and contraction.

[0078] Perform an inverse Fourier transform on the three-dimensional seismic data in the frequency domain after steps S102 and S103 to obtain decomposed and shrunk three-dimensional seismic data, wherein the decomposed and shrunk three-dimensional seismic data includes target singular values, namely: ; ; .

[0079] In S104, the target singular value is adjusted according to the Frobenius norm and the preset expected value, and the three-dimensional effective seismic data is determined based on the adjusted target singular value.

[0080] Among them, the Frobenius norm is a matrix norm, referred to as F-norm, recorded as The Frobenius norm can be used to approximate a single data matrix using a low-rank matrix.

[0081] Specifically, for the matrix ,matrix The Frobenius norm of the matrix is ​​defined as The sum of the squares of the absolute values ​​of each element, formula (5) is:

[0082] (5)

[0083] In one possible implementation, accordingly, adjusting the target singular value according to the Frobenius norm and the preset expected value may include:

[0084] Based on the Frobenius norm, calculate the first norm value of the difference between the sum of the target singular values ​​after decomposition and contraction in the current calculation cycle and the first target singular value after decomposition and contraction, and simultaneously calculate the second norm value of the first target singular value after decomposition and contraction, and calculate the ratio of the first norm value to the second norm value, where the first target singular value is any one of the target singular values ​​after decomposition and contraction in the current calculation cycle;

[0085] Determine whether the ratio is greater than a preset expected value;

[0086] If the ratio is greater than a preset expected value, the decomposed and shrunk three-dimensional seismic data is returned to the tensor-based singular value decomposition algorithm, and singular value decomposition is performed on the three-dimensional seismic data to obtain the singular value step of the three-dimensional seismic data. The step is continued until the ratio is no greater than the preset expected value; the decomposed and shrunk three-dimensional seismic data includes the target singular value after decomposition and shrinkage.

[0087] Specifically, according to the target singular value after decomposition and contraction obtained in S103 , get the sum of the target singular values ​​after decomposition and contraction within the period ,Right now Based on the Frobenius norm, calculate The first target singular value after decomposition and contraction The first norm value of the difference, that is, the first norm value is , calculate the first target singular value after decomposition and contraction The second norm value of , according to the first norm value and the second norm value The ratio of the expected value to the preset value Compare and determine whether the ratio is greater than the preset expected value:

[0088] like , the 3D seismic data after shrinkage decomposition is returned to step S102 and continued until Stop parameter adjustment.

[0089] In one possible implementation, determining three-dimensional effective seismic data based on the target singular value after parameter adjustment may include:

[0090] The decomposed and shrunk three-dimensional seismic data corresponding to the target singular value whose ratio is not greater than the preset expected value is determined as the three-dimensional effective seismic data.

[0091] like , the decomposed and shrunk 3D seismic data corresponding to the target singular value is determined as the 3D effective seismic data. Specifically, according to the decomposed and shrunk 3D seismic data obtained in S102, that is: ; ; ,according to get ; ; ,according to Determine the three-dimensional effective seismic data, that is, .

[0092] The above three-dimensional seismic signal extraction method is described below through an embodiment.

[0093] Reference Figure 4 , obtain the 3D seismic data containing noise to be predicted within a preset period ; For this 3D seismic data Perform Fourier transform to obtain three-dimensional seismic data in the frequency domain ; Based on the tensor singular value decomposition algorithm, the three-dimensional seismic data in the frequency domain Perform singular value decomposition to obtain the singular values ​​of the 3D seismic data, that is, Based on the Semi-Soft GoDec algorithm, the singular values ​​of the 3D seismic data are shrunk to obtain the target singular values, namely ; According to the decomposed and shrunk 3D seismic data, the inverse Fourier transform is performed to obtain the decomposed and shrunk 3D seismic data. The decomposed and shrunk 3D seismic data includes the matrix , target singular value after decomposition and contraction and matrix ,Right now

[0094] ; ; ; Based on the Frobenius norm, calculate the sum of the target singular values ​​after decomposition and contraction within the period The first target singular value after decomposition and contraction The first norm value of the difference, that is, the first norm value is , calculate the first target singular value after decomposition and contraction The second norm value of , according to the first norm value and the second norm value The ratio of the expected value to the preset value Compare and determine whether the ratio is greater than the preset expected value:

[0095] like , the 3D seismic data after shrinkage decomposition is returned to step S102 and continued until , stop adjusting parameters;

[0096] like , then the decomposed and shrunk 3D seismic data corresponding to the target singular value after decomposition and shrinkage is determined as the 3D effective seismic data, that is, the 3D seismic data after decomposition and shrinkage includes the matrix , target singular value after decomposition and contraction and matrix , get the matrix after decomposition and contraction within the period The sum of the target singular values ​​and the matrix The sum of ; ; ,according to Three-dimensional effective seismic data is determined, wherein the three-dimensional effective seismic data is the three-dimensional seismic data after noise reduction.

[0097] For details, please refer to Figure 4 、 Figure 5 and Figure 6 Comparison of three 3D seismic data models, Figure 4 represents the standard 3D seismic data model, Figure 5 represents a 3D seismic data model containing noisy data, Figure 6 It represents the 3D seismic data model after noise reduction processed by the present application. By comparison, it can be seen that the 3D seismic data model after noise reduction processed by the present application achieves an obvious noise reduction effect.

[0098] The present application provides a three-dimensional seismic signal extraction method, which obtains three-dimensional seismic data to be predicted; performs singular value decomposition on the three-dimensional seismic data based on a tensor singular value decomposition algorithm to obtain singular values ​​of the three-dimensional seismic data; shrinks the singular values ​​based on a Semi-Soft GoDec algorithm to obtain target singular values; adjusts the target singular values ​​according to a Frobenius norm and a preset expected value, and determines three-dimensional effective seismic data based on the adjusted target singular values. The present application can decompose the three-dimensional seismic data according to a tensor singular value decomposition algorithm, shrink the singular values ​​in the three-dimensional seismic data according to a Semi-Soft GoDec algorithm, and then determine the three-dimensional effective seismic data according to the Frobenius norm, thereby effectively avoiding serious interference of noise on the three-dimensional seismic data and improving the recognition effectiveness of the three-dimensional seismic data.

[0099] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0100] The following are device embodiments of the present application. For details not fully described therein, please refer to the corresponding method embodiments described above.

[0101] Figure 7 The following is a schematic diagram of the structure of the three-dimensional seismic signal extraction device provided in an embodiment of the present application. For ease of explanation, only the parts related to the embodiment of the present application are shown, which are detailed as follows:

[0102] like Figure 7 As shown, the three-dimensional seismic signal extraction device 7 includes:

[0103] An acquisition device 71 is used to acquire three-dimensional seismic data to be predicted, wherein the three-dimensional seismic data is a third-order tensor;

[0104] A decomposition module 72 is configured to perform singular value decomposition on the three-dimensional seismic data based on a tensor singular value decomposition algorithm to obtain singular values ​​of the three-dimensional seismic data;

[0105] A contraction module 73 is used to contract the singular values ​​based on a Semi-Soft GoDec algorithm to obtain target singular values;

[0106] The determination module 74 is used to adjust the target singular value according to the Frobenius norm and the preset expected value, and determine the three-dimensional effective seismic data according to the adjusted target singular value.

[0107] The present application provides a three-dimensional seismic signal extraction device, which obtains three-dimensional seismic data to be predicted; performs singular value decomposition on the three-dimensional seismic data based on a tensor singular value decomposition algorithm to obtain singular values ​​of the three-dimensional seismic data; shrinks the singular values ​​based on a Semi-Soft GoDec algorithm to obtain target singular values; adjusts the target singular values ​​according to a Frobenius norm and a preset expected value, and determines three-dimensional effective seismic data based on the adjusted target singular values. The present application can decompose the three-dimensional seismic data according to a tensor singular value decomposition algorithm, shrink the singular values ​​in the three-dimensional seismic data according to a Semi-Soft GoDec algorithm, and then determine the three-dimensional effective seismic data according to the Frobenius norm, thereby effectively avoiding serious interference of noise on the three-dimensional seismic data and improving the recognition effectiveness of the three-dimensional seismic data.

[0108] In a possible implementation, after obtaining the device, the device may further include a transformation module, where the transformation module is configured to:

[0109] Performing Fourier transform on the predicted three-dimensional seismic data to obtain three-dimensional seismic data in the frequency domain;

[0110] Accordingly, the decomposition module can be used to:

[0111] Based on the tensor singular value decomposition algorithm, the three-dimensional seismic data in the frequency domain is subjected to singular value decomposition to obtain the singular values ​​of the three-dimensional seismic data in the frequency domain.

[0112] In a possible implementation, the decomposition module may be specifically used to:

[0113] Performing singular value decomposition calculation on the three-dimensional seismic data in the frequency domain by using the first formula to obtain the singular values ​​of the three-dimensional seismic data;

[0114] The first formula is:

[0115]

[0116] in, is the tensor singular value decomposition formula, is the amplitude spectrum of 3D seismic data in the frequency domain, is the first component, are the singular values ​​under singular value decomposition, is the second component.

[0117] In a possible implementation, the shrink module may be specifically configured to:

[0118] Calculate the singular value using the second formula to obtain the target singular value;

[0119] The second formula is:

[0120]

[0121] in, is the target singular value, is the Semi-Soft GoDec algorithm formula, is a singular value.

[0122] In a possible implementation, after the shrinking module, the apparatus may further include an inverse transformation module, where the inverse transformation module is configured to:

[0123] Perform inverse Fourier transform on the target singular value to obtain the target singular value after decomposition and contraction;

[0124] Accordingly, the determination module can be used to:

[0125] Based on the Frobenius norm, calculate the first norm value of the difference between the sum of the target singular values ​​after decomposition and contraction in the current calculation cycle and the first target singular value after decomposition and contraction, and simultaneously calculate the second norm value of the first target singular value after decomposition and contraction, and calculate the ratio of the first norm value to the second norm value, where the first target singular value is any one of the target singular values ​​after decomposition and contraction in the current calculation cycle;

[0126] Determine whether the ratio is greater than a preset expected value;

[0127] If the ratio is greater than a preset expected value, the decomposed and shrunk three-dimensional seismic data is returned to the tensor-based singular value decomposition algorithm, and singular value decomposition is performed on the three-dimensional seismic data to obtain the singular value step of the three-dimensional seismic data. The step is continued until the ratio is no greater than the preset expected value; the decomposed and shrunk three-dimensional seismic data includes the target singular value after decomposition and shrinkage.

[0128] In a possible implementation, the determination module may also be used to:

[0129] The decomposed and shrunk three-dimensional seismic data corresponding to the target singular value whose ratio is not greater than the preset expected value is determined as the three-dimensional effective seismic data.

[0130] Figure 8 Schematic diagram of the terminal provided in the embodiment of the present application. Figure 8 As shown, the terminal 8 of this embodiment includes: a processor 80, a memory 81, and a computer program 82 stored in the memory 81 and executable on the processor 80. When the processor 80 executes the computer program 82, the steps in each of the above-mentioned three-dimensional seismic signal extraction method embodiments are implemented, for example Figure 1 Alternatively, when the processor 80 executes the computer program 82, the functions of the modules / units in the above-mentioned device embodiments are realized, for example, Figure 7 The functions of modules 71 to 74 are shown.

[0131] For example, the computer program 82 may be divided into one or more modules / units, which are stored in the memory 81 and executed by the processor 80 to complete the present application. The one or more modules / units may be a series of computer program instruction segments that can complete specific functions, and the instruction segments are used to describe the execution process of the computer program 82 in the terminal 8. For example, the computer program 82 may be divided into Figure 7 Modules 71 to 74 are shown.

[0132] The terminal 8 can be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The terminal 8 can include, but is not limited to, a processor 80 and a memory 81. Those skilled in the art will understand that Figure 8 It is only an example of terminal 8 and does not constitute a limitation on terminal 8. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the terminal may also include input and output devices, network access devices, buses, etc.

[0133] The processor 80 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0134] The memory 81 can be an internal storage unit of the terminal 8, such as a hard drive or memory of the terminal 8. Alternatively, the memory 81 can be an external storage device of the terminal 8, such as a plug-in hard drive, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. Furthermore, the memory 81 can include both an internal storage unit of the terminal 8 and an external storage device. The memory 81 is used to store the computer program and other programs and data required by the terminal. The memory 81 can also be used to temporarily store data that has been output or is about to be output.

[0135] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0136] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0137] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0138] In the embodiments provided in this application, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are merely illustrative. For example, the division of the modules or units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0139] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0140] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0141] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application can implement all or part of the processes in the above-mentioned embodiment method by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of each of the above-mentioned three-dimensional seismic signal extraction method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practices in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practices, computer-readable media does not include electrical carrier signals and telecommunication signals.

[0142] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A three-dimensional seismic signal extraction method, characterized in that: include: Acquiring three-dimensional seismic data to be predicted, wherein the three-dimensional seismic data is a third-order tensor; Performing singular value decomposition on the three-dimensional seismic data based on a tensor singular value decomposition algorithm to obtain singular values ​​of the three-dimensional seismic data; Based on the Semi-Soft GoDec algorithm, the singular value is shrunk to obtain the target singular value; Adjusting the target singular value according to the Frobenius norm and the preset expected value, and determining three-dimensional effective seismic data according to the adjusted target singular value; After shrinking the singular value based on the Semi-Soft GoDec algorithm to obtain the target singular value, the method further includes: Performing an inverse Fourier transform on the target singular value to obtain a decomposed and shrunk target singular value; Accordingly, adjusting the target singular value according to the Frobenius norm and the preset expected value includes: Calculating, based on the Frobenius norm, a first norm value of a difference between a sum of the target singular values ​​after decomposition and contraction in a current calculation cycle and a first target singular value after decomposition and contraction, and simultaneously calculating a second norm value of the first target singular value after decomposition and contraction, and calculating a ratio of the first norm value to the second norm value, where the first target singular value is any one of the target singular values ​​after decomposition and contraction in the current calculation cycle; Determining whether the ratio is greater than the preset expected value; If the ratio is greater than the preset expected value, the decomposed and shrunk three-dimensional seismic data is returned to the tensor-based singular value decomposition algorithm, and singular value decomposition is performed on the three-dimensional seismic data to obtain the singular values ​​of the three-dimensional seismic data. The step is continued until the ratio is no greater than the preset expected value; the decomposed and shrunk three-dimensional seismic data includes the target singular value after decomposition and shrinkage.

2. The three-dimensional seismic signal extraction method according to claim 1, characterized in that: After acquiring the three-dimensional seismic data to be predicted, the method further includes: Performing Fourier transform on the three-dimensional seismic data to be predicted to obtain three-dimensional seismic data in the frequency domain; Accordingly, performing singular value decomposition on the three-dimensional seismic data based on a tensor singular value decomposition algorithm to obtain singular values ​​of the three-dimensional seismic data includes: Based on a tensor singular value decomposition algorithm, singular value decomposition is performed on the three-dimensional seismic data in the frequency domain to obtain singular values ​​of the three-dimensional seismic data in the frequency domain.

3. The three-dimensional seismic signal extraction method according to claim 2, characterized in that: The performing singular value decomposition on the three-dimensional seismic data based on a tensor singular value decomposition algorithm to obtain the singular values ​​of the three-dimensional seismic data includes: Performing singular value decomposition calculation on the three-dimensional seismic data in the frequency domain using a first formula to obtain singular values ​​of the three-dimensional seismic data; The first formula is: in, is the tensor singular value decomposition formula, is the amplitude spectrum of 3D seismic data in the frequency domain, is the first component, are the singular values, is the second component.

4. The three-dimensional seismic signal extraction method according to claim 1, wherein: The method of shrinking the singular value based on the Semi-SoftGoDec algorithm to obtain the target singular value includes: Calculating the singular value using a second formula to obtain the target singular value; The second formula is: in, is the target singular value, is the Semi-Soft GoDec algorithm formula, are the singular values.

5. The three-dimensional seismic signal extraction method according to claim 1, characterized in that: Determining three-dimensional effective seismic data based on the target singular value after parameter adjustment includes: The decomposed and shrunk three-dimensional seismic data corresponding to the target singular value whose ratio is not greater than the preset expected value is determined as the three-dimensional effective seismic data.

6. A three-dimensional seismic signal extraction device, characterized in that: include: An acquisition device, configured to acquire three-dimensional seismic data to be predicted, wherein the three-dimensional seismic data is a third-order tensor; a decomposition module, configured to perform singular value decomposition on the three-dimensional seismic data based on a tensor singular value decomposition algorithm to obtain singular values ​​of the three-dimensional seismic data; A shrinkage module, configured to shrink the singular value based on a Semi-Soft GoDec algorithm to obtain a target singular value; a determination module, configured to adjust the target singular value according to the Frobenius norm and a preset expected value, and determine three-dimensional effective seismic data according to the adjusted target singular value; After the shrinking module, the device further includes an inverse transformation module, and the inverse transformation module is used to: Performing an inverse Fourier transform on the target singular value to obtain a decomposed and shrunk target singular value; Accordingly, the determining module is used to: Calculating, based on the Frobenius norm, a first norm value of a difference between a sum of the target singular values ​​after decomposition and contraction in a current calculation cycle and a first target singular value after decomposition and contraction, and simultaneously calculating a second norm value of the first target singular value after decomposition and contraction, and calculating a ratio of the first norm value to the second norm value, where the first target singular value is any one of the target singular values ​​after decomposition and contraction in the current calculation cycle; Determining whether the ratio is greater than the preset expected value; If the ratio is greater than the preset expected value, the decomposed and shrunk three-dimensional seismic data is returned to the tensor-based singular value decomposition algorithm, and singular value decomposition is performed on the three-dimensional seismic data to obtain the singular values ​​of the three-dimensional seismic data. The step is continued until the ratio is no greater than the preset expected value; the decomposed and shrunk three-dimensional seismic data includes the target singular value after decomposition and shrinkage.

7. The three-dimensional seismic signal extraction device according to claim 6, characterized in that: The decomposition module is used to: Performing singular value decomposition calculation on the three-dimensional seismic data in the frequency domain using the first formula to obtain singular values ​​of the three-dimensional seismic data; The first formula is: in, is the tensor singular value decomposition formula, is the amplitude spectrum of 3D seismic data in the frequency domain, is the low-rank component under the singular value decomposition.

8. A terminal comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the three-dimensional seismic signal extraction method according to any one of claims 1 to 5 are implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the three-dimensional seismic signal extraction method according to any one of claims 1 to 5 are implemented.

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