Seismic spectrum decomposition method, device and equipment based on multipole matching pursuit

Abstract

The invention provides a seismic spectrum decomposition method, device and equipment based on multi-pole matching pursuit, and the method comprises the steps: inputting single-channel seismic data, and taking the single-channel seismic data as an initial residual signal; carrying out complex analysis and calculation on the instantaneous envelope, and determining the delay time of the matched wavelet; constructing a variable-phase wavelet library according to the delay time of the matched wavelets; after multiple odd-even decomposition, performing convolution on each component and the variable-phase wavelet library to obtain a multi-pole wavelet library; the optimal wavelet is matched, a corresponding sub-signal is solved, and the sub-signal is subtracted from the current residual error signal to serve as a new residual error signal; repeating the steps until iteration is finished; the matched wavelets and the amplitude coefficients are correspondingly multiplied, and single-interface reflection seismic signals are obtained and serve as a sub-signal set; fourier transform is carried out on the sub-signals, and the sub-signals correspond to delay time corresponding to an interface, so that an ideal time-frequency spectrum decomposition result is obtained. According to the invention, the redundancy of the over-complete wavelet library is reduced, the efficiency of variable-phase matching pursuit is improved, and high-efficiency and high-time-frequency-resolution seismic spectrum decomposition is realized.

Description

Technical Field

[0001] This application relates to the field of geophysical exploration, specifically to a method, apparatus, and equipment for seismic spectral decomposition based on multipole matching tracing. Background Technology

[0002] The seismic spectral decomposition method based on matching pursuit (MP) is an adaptive time-frequency analysis method. Its main process is as follows: first, an overcomplete wavelet library is constructed; then, sparse decomposition of the signal is achieved based on matching pursuit; finally, the time-frequency distribution of each sub-component signal after decomposition is obtained using WVD, and the components are superimposed and summed.

[0003] Seismic spectral decomposition based on matching pursuit (RP) offers high time-frequency resolution and avoids the influence of cross terms, thus finding widespread application in seismic data interpretation and inversion. Commonly used variable-phase Ricker wavelet matching pursuit algorithms typically exhaustively enumerate the dominant frequency and phase of the seismic wavelet, as well as the reflection coefficients and locations of the reflecting interfaces, to construct an overcomplete wavelet library. This results in extremely high redundancy and low matching efficiency. In 2015, Zhang Fanchang et al. used an adaptive filter constructed from the instantaneous amplitude, frequency, and phase characteristics of seismic data to dynamically generate the wavelet library. However, this method relies on linear assumptions about the seismic signal and noise, failing to guarantee globally optimal matching. In 2019, Zeng Chunyan et al. implemented a deep learning-based dynamic wavelet library matching pursuit method. However, this method faces data bias and overfitting problems, and extensive training with labeled data may lead to decreased efficiency. Therefore, there is an urgent need to propose a seismic spectral decomposition method that achieves high efficiency and high time-frequency resolution. Summary of the Invention

[0004] In view of this, this application proposes a seismic spectral decomposition method, apparatus, and device based on multipole matched pursuit, aiming to solve the problem of low matching efficiency caused by high redundancy in ultracomplete wavelet libraries. It achieves high-efficiency, high-time-frequency resolution seismic spectral decomposition.

[0005] In a first aspect, embodiments of this application provide a seismic spectral decomposition method based on multipole matching pursuit, which mainly includes the following steps:

[0006] Step S1: Input single-channel seismic data and use the single-channel seismic data as the initial residual signal;

[0007] Step S2: Perform complex analysis on the current residual signal to obtain the Hilbert transform of the current residual signal; use the Hilbert transform of the current residual signal to calculate the instantaneous amplitude envelope of the complex analysis signal, and take the time corresponding to the maximum value of the instantaneous amplitude envelope as the delay time of the matched wavelet;

[0008] Step S3: Construct a zero-phase Recker wavelet based on the delay time of the matched wavelet, and scan the dominant frequency and phase of the zero-phase Recker wavelet to construct a variable-phase wavelet library;

[0009] Step S4: Based on prior knowledge of the reflective interface, perform n-fold parity decomposition on the multi-layer reflective interface to obtain 2 n Each pole component is made to have the same reflection coefficient value; the variable phase sub-wavelet library is convolved with each pole component to obtain a multipole sub-wavelet library.

[0010] Step S5: Extract a wavelet that best matches the current residual signal from the multipole wavelet library, and calculate the amplitude coefficient corresponding to the best-matching wavelet; multiply the amplitude coefficient by the best-matching wavelet to obtain the decomposed sub-signal; subtract the decomposed sub-signal from the current residual signal, and use the resulting residual as the new residual signal;

[0011] Step S6: Repeat steps S2 to S5 until the number of iterations reaches a preset value or the remaining residual energy is less than a certain threshold.

[0012] Step S7: After the iteration is completed, the wavelets that best match the residual signals and their corresponding amplitude coefficients are obtained. The wavelets that best match and their corresponding amplitude coefficients are multiplied together to obtain the single-interface reflection seismic signal, which is used as the sub-signal set.

[0013] Step S8: Perform a Fourier transform on each sub-signal in the sub-signal set, and map the obtained Fourier transform spectrum to the corresponding delay time of the interface to obtain the ideal time-spectrum decomposition result.

[0014] In one possible implementation, the expression for performing complex analysis on the current residual signal is:

[0015] S(t)=s(t)+is H (t)

[0016] Where S(t) represents the complex analysis signal, s(t) represents the residual signal, and s H (t) represents the Hilbert transform of the residual signal, and i represents the imaginary unit.

[0017] In one possible implementation, the formula for calculating the instantaneous amplitude envelope of the complex analysis signal is:

[0018]

[0019] Where A(t) represents the instantaneous amplitude envelope, s(t) represents the residual signal, and s H (t) represents the Hilbert transform of the residual signal.

[0020] In one possible implementation, the zero-phase Ricker wavelet is calculated as follows:

[0021] w(t)={1-2[πf d (t-τ)] 2}exp{-[πf d (t-τ)] 2}

[0022] Where w(t) represents the zero-phase Ricker wavelet, f d τ represents the dominant frequency of the Rayleigh wavelet, t represents time, and τ represents the delay time of the matched wavelet.

[0023] In one possible implementation, the formula for calculating the variable-phase wavelet library is:

[0024] w γ (t)=w(t-τ,f d cos(φ)-w H (t-τ,f d sin(φ)

[0025] Among them, w γ (t) represents the phase-shifted wavelet coulomb, φ represents the phase, and w(t-τ,f) represents the phase. d () indicates a delay time of τ and a main frequency of f. d The zero-phase Reichsson wavelet, w H (t-τ,f d ) represents w(t-τ,f d Hilbert transform of ).

[0026] In one possible implementation, the matching condition for extracting a wavelet from the multipole wavelet library that best matches the current residual signal is:

[0027]

[0028] Where match index represents the directory of the best-matching wavelet in the variable-phase wavelet library; argmax represents the maximum optimization function; This represents the signal residual from the previous iteration of matching; Let θ represent the multipole components in the quadratic parity decomposition, and let θ represent the odd component of the odd component, the even component of the odd component, the odd component of the even component, and the even component of the even component, respectively; oo θ oe θ eo θ ee Representing the corresponding components and The angle between vectors in a multidimensional space.

[0029] In one possible implementation, the formula for calculating the Fourier transform of each sub-signal in the sub-signal set is as follows:

[0030]

[0031] Among them, S k (f) represents the Fourier transform spectrum, s k (t) represents the sub-signal, t represents time, f represents frequency, and k represents the number of iterations;

[0032] The formula for calculating the time-spectrum decomposition result is as follows:

[0033]

[0034] Among them, IDFT s (t,f) represents the time spectrum of the residual signal s(t), K represents the total number of iterations, and δ(t-τ) k ) represents a delay time of τ k The unit impulse function.

[0035] Secondly, embodiments of this application provide a seismic spectral decomposition device based on multipole matched pursuit, which mainly includes the following modules:

[0036] The data input module is used to input single-channel seismic data and use the single-channel seismic data as the initial residual signal;

[0037] The matching wavelet delay time acquisition module is used to perform complex analysis on the current residual signal to obtain a complex analysis signal; calculate the instantaneous amplitude envelope of the complex analysis signal, and take the time corresponding to the maximum value of the instantaneous amplitude envelope as the delay time of the matching wavelet;

[0038] A variable-phase wavelet library construction module is used to construct a zero-phase Lake wavelet based on the delay time of the matched wavelet, and to scan the dominant frequency and phase of the zero-phase Lake wavelet to construct a variable-phase wavelet library.

[0039] The multipole wavelet library construction module is used to perform n-fold parity decomposition on multi-layered reflection interfaces based on prior knowledge of the reflection interfaces, to obtain 2 n Each pole component is made to have the same reflection coefficient value; the variable phase sub-wavelet library is convolved with each pole component to obtain a multipole sub-wavelet library.

[0040] The new residual signal construction module is used to extract a wavelet that best matches the current residual signal from the multipole wavelet library, and to obtain the amplitude coefficient corresponding to the best-matching wavelet; multiply the amplitude coefficient by the best-matching wavelet to obtain the decomposed sub-signal; subtract the decomposed sub-signal from the current residual signal, and use the resulting residual as the new residual signal;

[0041] The iterative loop module is used to repeat the working process of the matching wavelet delay time acquisition module, the phase-changing wavelet library construction module, the multipole wavelet library construction module, and the new residual signal construction module until the number of iterations reaches a preset value or the remaining residual energy is less than a certain threshold.

[0042] The sub-signal set construction module is used to obtain the best-matching wavelet of several residual signals and its corresponding amplitude coefficient after the iteration. The best-matching wavelet and its corresponding amplitude coefficient are multiplied to obtain the single-interface reflection seismic signal, which is used as the sub-signal set.

[0043] The time-spectrum decomposition result construction module is used to perform Fourier transform on each sub-signal in the sub-signal set and map the obtained Fourier transform spectrum to the corresponding delay time of the interface to obtain the ideal time-spectrum decomposition result.

[0044] Thirdly, embodiments of this application provide an electronic device, including:

[0045] processor;

[0046] Memory;

[0047] And a computer program, wherein the computer program is stored in the memory, the computer program including instructions that, when executed by the processor, cause the electronic device to perform the method of any one of claims 1 to 7.

[0048] Fourthly, embodiments of this application provide a computer-readable storage medium comprising a stored program, wherein, when the program is executed, it controls the device containing the computer-readable storage medium to perform the method described in any one of claims 1 to 7.

[0049] This invention significantly reduces the redundancy of the overcomplete wavelet library through multipole decomposition. By slightly increasing the complexity of the optimal matching solution, it greatly reduces the number of matching iterations, thereby improving the efficiency of variable phase matching pursuit. Furthermore, the seismic spectral decomposition method based on multipole matching pursuit can provide time-frequency resolution time-spectral information. Attached Figure Description

[0050] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 A flowchart illustrating the seismic spectral decomposition method based on multipole matching pursuit provided in this application embodiment;

[0052] Figure 2 A schematic diagram of the multipole matching conditions for the seismic spectral decomposition method based on multipole matching tracing provided in the embodiments of this application;

[0053] Figure 3 A test result of the synthetic record matching efficiency of the seismic spectral decomposition method based on multipole matching pursuit provided in the embodiments of this application;

[0054] Figure 4 A test result of the time-frequency resolution capability of the synthetic record of the seismic spectral decomposition method based on multipole matched pursuit provided in the embodiments of this application;

[0055] Figure 5 A test result of the time-frequency resolution capability of the seismic spectral decomposition method based on multipole matched pursuit provided in the embodiments of this application;

[0056] Figure 6 A structural block diagram of a seismic spectral decomposition device based on multipole matching pursuit provided in an embodiment of this application;

[0057] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0058] To better understand the technical solution of this application, the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0059] It should be understood that the described embodiments are merely some, not all, of the embodiments in this application. All other embodiments obtained by those skilled in the art based on the embodiments in this application without inventive effort are within the scope of protection of this application.

[0060] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of this application. The singular forms “a,” “the,” and “the” used in the embodiments of this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0061] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0062] See Figure 1 This is a flowchart illustrating the seismic spectral decomposition method based on multipole matching pursuit provided in an embodiment of this application. Figure 1 As shown, it mainly includes the following steps.

[0063] Step S1: Input single-channel seismic data and use the single-channel seismic data as the initial residual signal;

[0064] Step S2: Perform complex analysis on the current residual signal to obtain the Hilbert transform of the current residual signal; use the Hilbert transform of the current residual signal to calculate the instantaneous amplitude envelope of the complex analysis signal, and take the time corresponding to the maximum value of the instantaneous amplitude envelope as the delay time of the matched wavelet;

[0065] The expression for performing complex analysis on the current residual signal is:

[0066] S(t)=s(t)+is H (t)

[0067] Where S(t) represents the complex analysis signal, s(t) represents the residual signal, and s H (t) represents the Hilbert transform of the residual signal, and i represents the imaginary unit.

[0068] The formula for calculating the instantaneous amplitude envelope of the complex analysis signal is:

[0069]

[0070] Where A(t) represents the instantaneous amplitude envelope, s(t) represents the residual signal, and s H (t) represents the Hilbert transform of the residual signal.

[0071] Step S3: Construct a zero-phase Recker wavelet based on the delay time of the matched wavelet, and scan the dominant frequency and phase of the zero-phase Recker wavelet to construct a variable-phase wavelet library;

[0072] The formula for calculating the zero-phase Ricker wavelet is as follows:

[0073] w(t)={1-2[τf d (t-τ)] 2}exp{-[πf d (t-τ)]2}

[0074] Where w(t) represents the zero-phase Ricker wavelet, f d τ represents the dominant frequency of the Rayleigh wavelet, t represents time, and τ represents the delay time of the matched wavelet.

[0075] The formula for calculating the variable-phase wavelet library is as follows:

[0076] w γ (t)=w(t-τ,f d cos(φ)-w H (t-τ,f d sin(φ)

[0077] Among them, w γ (t) represents the phase-shifted wavelet coulomb, φ represents the phase, and w(t-τ,f) represents the phase. d () indicates a delay time of τ and a main frequency of f. d The zero-phase Reichsson wavelet, w H (t-τ,f d ) represents w(t-τ,f d Hilbert transform of ).

[0078] Step S4: Based on prior knowledge of the reflective interface, perform n-fold parity decomposition on the multi-layer reflective interface to obtain 2 n Each pole component is made to have the same reflection coefficient value; the variable phase sub-wavelet library is convolved with each pole component to obtain a multipole sub-wavelet library.

[0079] Step S5: Extract a wavelet that best matches the current residual signal from the multipole wavelet library, and calculate the amplitude coefficient corresponding to the best-matching wavelet; multiply the amplitude coefficient by the best-matching wavelet to obtain the decomposed sub-signal; subtract the decomposed sub-signal from the current residual signal, and use the resulting residual as the new residual signal;

[0080] See Figure 2 This is a schematic diagram of the multipole matching conditions for the seismic spectral decomposition method based on multipole matching pursuit provided in an embodiment of this application. Figure 2 As shown, the matching condition for extracting a wavelet that best matches the current residual signal from the multipole wavelet library is:

[0081]

[0082] Where match index represents the directory of the best-matching wavelet in the variable-phase wavelet library; argmax represents the maximum optimization function; This represents the signal residual from the previous iteration (the original signal was used in the first iteration); Let θ represent the multipole components in the quadratic parity decomposition, and let θ represent the odd component of the odd component, the even component of the odd component, the odd component of the even component, and the even component of the even component, respectively; oo θ oe θ eo θ ee Representing the corresponding components and The angle between vectors in a multidimensional space.

[0083] Step S6: Repeat steps S2 to S5 until the number of iterations reaches a preset value or the remaining residual energy is less than a certain threshold.

[0084] Step S7: After the iteration is completed, the wavelets that best match the residual signals and their corresponding amplitude coefficients are obtained. The wavelets that best match and their corresponding amplitude coefficients are multiplied together to obtain the single-interface reflection seismic signal, which is used as the sub-signal set.

[0085] Step S8: Perform a Fourier transform on each sub-signal in the sub-signal set, and map the obtained Fourier transform spectrum to the corresponding delay time of the interface to obtain the ideal time-spectrum decomposition result.

[0086] The formula for calculating the Fourier transform of each sub-signal in the sub-signal set is as follows:

[0087]

[0088] Among them, S k (f) represents the Fourier transform spectrum, s k (t) represents the sub-signal, t represents time, f represents frequency, and k represents the number of iterations.

[0089] The formula for calculating the time-spectrum decomposition result is as follows:

[0090]

[0091] Among them, IDFT s (t,f) represents the time spectrum of the residual signal s(t), K represents the total number of iterations, and δ(t-τ) k ) represents a delay time of τ k The unit impulse function.

[0092] The seismic spectral decomposition method based on multipole matched pursuit in this embodiment significantly reduces the redundancy of the overcomplete wavelet library through multipole decomposition. By slightly increasing the complexity of the optimal matching solution, the number of matching iterations is greatly reduced, thus improving the efficiency of variable phase matched pursuit. Simultaneously, the seismic spectral decomposition method based on multipole matched pursuit can provide time-frequency resolution time-spectral information.

[0093] To verify the matching pursuit efficiency of this method, the seismic spectral decomposition method based on multipole matching pursuit in this embodiment was applied to test noisy synthetic seismic records of thin interbedded layers. See [link to relevant documentation]. Figure 3 The image shows the test results of the synthetic record matching efficiency of the seismic spectral decomposition method based on multipole matching pursuit provided in the embodiments of this application. Figure 3 As shown in the figure, the black curve in the left figure represents the red reflection coefficient synthesized seismic record, and the thin-layer coherent tuning effect is obvious; the middle figure shows the residuals corresponding to 10 matching iterations, which shows that the method has fast residual convergence speed and high matching efficiency; the right figure compares the blue matching results with the red reflection coefficient, which shows that the method has high accuracy and can identify thin layers below the tuning thickness to a certain extent.

[0094] To verify the time-frequency resolution of this spectral decomposition method, the seismic spectral decomposition method based on multipole matched pursuit in this embodiment was applied to test different wavelet synthetic seismic records and well-side measured seismic data. (See [link to relevant documentation]). Figure 4 The image shows the test results of the time-frequency resolution capability of the synthetic record based on the multipole matched pursuit seismic spectral decomposition method provided in this application embodiment; see also Figure 5 The image shows the time-frequency resolution test results of the seismic spectral decomposition method based on multipole matching pursuit provided in this application embodiment, as shown in the figure. Figure 4 and Figure 5 As shown, the spectral decomposition results of the seismic spectral decomposition method based on multipole matching pursuit in this embodiment have high time-frequency resolution and can play an important role in formation identification and reservoir prediction.

[0095] Corresponding to the above embodiments, this application also provides a seismic spectral decomposition device based on multipole matching pursuit.

[0096] See Figure 6 This is a structural block diagram of the seismic spectral decomposition device based on multipole matched tracing provided in an embodiment of this application. Figure 6 As shown, it mainly includes the following modules.

[0097] The data input module 601 is used to input single-channel seismic data and use the single-channel seismic data as the initial residual signal.

[0098] The matching wavelet delay time acquisition module 602 is used to perform complex analysis on the current residual signal to obtain a complex analysis signal; calculate the instantaneous amplitude envelope of the complex analysis signal, and take the time corresponding to the maximum value of the instantaneous amplitude envelope as the delay time of the matching wavelet;

[0099] The variable-phase wavelet library construction module 603 is used to construct a zero-phase Lake wavelet based on the delay time of the matched wavelet, and to scan the dominant frequency and phase of the zero-phase Lake wavelet to construct a variable-phase wavelet library.

[0100] The multipole wavelet library construction module 604 is used to perform n-fold parity decomposition on the multi-layered reflection interface based on prior knowledge of the reflection interface, to obtain 2 n Each pole component is made to have the same reflection coefficient value; the variable phase sub-wavelet library is convolved with each pole component to obtain a multipole sub-wavelet library.

[0101] The new residual signal construction module 605 is used to extract a wavelet that best matches the current residual signal from the multipole wavelet library, calculate the amplitude coefficient corresponding to the best-matching wavelet, multiply the amplitude coefficient by the best-matching wavelet to obtain the decomposed sub-signal, and subtract the decomposed sub-signal from the current residual signal to obtain the new residual signal.

[0102] The iterative loop module 606 is used to repeat the working process of the matching wavelet delay time acquisition module 602, the phase-changing wavelet library construction module 603, the multipole wavelet library construction module 604, and the new residual signal construction module 605 until the number of iterations reaches a preset value or the remaining residual energy is less than a certain threshold.

[0103] The sub-signal set construction module 607 is used to obtain the wavelet that best matches several residual signals and its corresponding amplitude coefficient after the iteration is completed. The best-matching wavelet and its corresponding amplitude coefficient are multiplied to obtain the single-interface reflection seismic signal, which is used as the sub-signal set.

[0104] The time-spectrum decomposition result construction module 608 is used to perform Fourier transform on each sub-signal in the sub-signal set and map the obtained Fourier transform spectrum to the corresponding delay time of the interface to obtain the ideal time-spectrum decomposition result.

[0105] It should be noted that the specific content involved in the embodiments of this application can be found in the description of the above method embodiments, and will not be repeated here for the sake of brevity.

[0106] Corresponding to the above embodiments, this application also provides an electronic device.

[0107] See Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 7 As shown, the electronic device 700 may include a processor 701, a memory 702, and a communication unit 703. These components communicate via one or more buses. Those skilled in the art will understand that the electronic device structure shown in the figures does not constitute a limitation on the embodiments of this application. It may be a bus topology or a star topology, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0108] The communication unit 703 is used to establish a communication channel, thereby enabling the electronic device to communicate with other devices.

[0109] The processor 701 serves as the control center of the electronic device, connecting various parts of the device via various interfaces and lines. It executes software programs and / or modules stored in the memory 702, and calls data stored in the memory to perform various functions and / or process data. The processor can be composed of integrated circuits (ICs), such as a single packaged IC or multiple packaged ICs with the same or different functions connected together. For example, the processor 701 may consist only of a central processing unit (CPU). In this embodiment, the CPU may have a single processing core or include multiple processing cores.

[0110] Memory 702 is used to store the execution instructions of processor 701. Memory 702 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0111] When the execution instructions in memory 702 are executed by processor 701, the electronic device 700 is able to perform some or all of the steps in the above method embodiments.

[0112] Corresponding to the above embodiments, this application also provides a computer-readable storage medium, wherein the computer-readable storage medium may store a program, wherein when the program runs, it can control the device where the computer-readable storage medium is located to execute some or all of the steps in the above method embodiments. Specifically, the computer-readable storage medium may be a magnetic disk, an optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0113] Corresponding to the above embodiments, this application also provides a computer program product containing executable instructions that, when executed on a computer, cause the computer to perform some or all of the steps in the above method embodiments.

[0114] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent the existence of A alone, the simultaneous existence of A and B, or the existence of B alone. A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, and c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0115] Those skilled in the art will recognize that the units and algorithm steps described in the embodiments disclosed herein can be implemented using electronic hardware, computer software, or a combination of electronic hardware and software. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art 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.

[0116] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0117] In the several embodiments provided in this application, any function, if implemented as a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0118] The above description is merely a specific embodiment of this application. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the protection scope of this application. The protection scope of this application should be determined by the protection scope of the claims.

Claims

1. A seismic spectral decomposition method based on multipole matched pursuit, characterized in that, The main steps include: Step S1: Input single-channel seismic data and use the single-channel seismic data as the initial residual signal; Step S2: Perform complex analysis on the current residual signal to obtain the Hilbert transform of the current residual signal; use the Hilbert transform of the current residual signal to calculate the instantaneous amplitude envelope of the complex analysis signal, and take the time corresponding to the maximum value of the instantaneous amplitude envelope as the delay time of the matched wavelet; Step S3: Construct a zero-phase Lake wavelet based on the delay time of the matched wavelet, and scan the dominant frequency and phase of the zero-phase Lake wavelet to construct a variable-phase wavelet library; Step S4: Based on prior knowledge of the reflective interface, perform n-fold parity decomposition on the multi-layer reflective interface to obtain 2 n Each pole component is made to have the same reflection coefficient value; the variable phase sub-wavelet library is convolved with each pole component to obtain a multipole sub-wavelet library. Step S5: Extract a wavelet that best matches the current residual signal from the multipole wavelet library, and calculate the amplitude coefficient corresponding to the best-matching wavelet; multiply the amplitude coefficient by the best-matching wavelet to obtain the decomposed sub-signal; subtract the decomposed sub-signal from the current residual signal, and use the resulting residual as the new residual signal; Step S6: Repeat steps S2 to S5 until the number of iterations reaches a preset value or the remaining residual energy is less than a certain threshold. Step S7: After the iteration is completed, the wavelets that best match the residual signals and their corresponding amplitude coefficients are obtained. The wavelets that best match and their corresponding amplitude coefficients are multiplied together to obtain the single-interface reflection seismic signal, which is used as the sub-signal set. Step S8: Perform a Fourier transform on each sub-signal in the sub-signal set, and map the obtained Fourier transform spectrum to the corresponding delay time of the interface to obtain the ideal time-spectrum decomposition result.

2. The method according to claim 1, characterized in that, The expression for performing complex analysis on the current residual signal is: S(t)=s(t)+is H (t) Where S(t) represents the complex analysis signal, s(t) represents the residual signal, and s H (t) represents the Hilbert transform of the residual signal, and i represents the imaginary unit.

3. The method according to claim 2, characterized in that, The formula for calculating the instantaneous amplitude envelope of a complex analysis signal is: Where A(t) represents the instantaneous amplitude envelope, s(t) represents the residual signal, and s H (t) represents the Hilbert transform of the residual signal.

4. The method according to claim 1, characterized in that, The formula for calculating the zero-phase Ricker wavelet is as follows: w(t)={1-2[πf d (t-t)] 2 }exp{-[πf d (t-t)] 2 } Where w(t) represents the zero-phase Ricker wavelet, f d τ represents the dominant frequency of the Rayleigh wavelet, t represents time, and τ represents the delay time of the matched wavelet.

5. The method according to claim 4, characterized in that, The formula for calculating the variable-phase wavelet library is as follows: w γ (t)=w(t-τ,f d )cos(φ)-w H (t-τ,f d )sin(φ) Among them, w γ (t) represents the phase-shifted wavelet coulomb, φ represents the phase, and w(t-τ,f) represents the phase. d () indicates a delay time of τ and a main frequency of f. d The zero-phase Reichsson wavelet, w H (t-τ,f d ) represents w(t-τ,f d Hilbert transform of ).

6. The method according to claim 4, characterized in that, The matching condition for extracting a wavelet that best matches the current residual signal from the multipole wavelet library is as follows: Where match index represents the directory of the best-matching wavelet in the variable-phase wavelet library; argmax represents the maximum optimization function; This represents the signal residual from the previous iteration of matching; Let θ represent the multipole components in the quadratic parity decomposition, and let θ represent the odd component of the odd component, the even component of the odd component, the odd component of the even component, and the even component of the even component, respectively; oo θ oe θ eo θ ee Representing the corresponding components and The angle between vectors in a multidimensional space.

7. The method according to claim 1, characterized in that, The formula for calculating the Fourier transform of each sub-signal in the sub-signal set is as follows: Among them, S k (f) represents the Fourier transform spectrum, s k (t) represents the sub-signal, t represents time, f represents frequency, and k represents the number of iterations; The formula for calculating the time-spectrum decomposition result is as follows: Among them, IDFT s (t,f) represents the time spectrum of the residual signal s(t), K represents the total number of iterations, and δ(t-τ) k ) represents a delay time of τ k The unit impulse function.

8. A seismic spectral decomposition device based on multipole matched pursuit, characterized in that, It mainly includes the following modules: The data input module is used to input single-channel seismic data and use the single-channel seismic data as the initial residual signal; The matching wavelet delay time acquisition module is used to perform complex analysis on the current residual signal to obtain a complex analysis signal; calculate the instantaneous amplitude envelope of the complex analysis signal, and take the time corresponding to the maximum value of the instantaneous amplitude envelope as the delay time of the matching wavelet; A variable-phase wavelet library construction module is used to construct a zero-phase Lake wavelet based on the delay time of the matched wavelet, and to scan the dominant frequency and phase of the zero-phase Lake wavelet to construct a variable-phase wavelet library. The multipole wavelet library construction module is used to perform n-fold parity decomposition on multi-layered reflection interfaces based on prior knowledge of the reflection interfaces, to obtain 2 n Each pole component is made to have the same reflection coefficient value; the variable phase sub-wavelet library is convolved with each pole component to obtain a multipole sub-wavelet library. The new residual signal construction module is used to extract a wavelet that best matches the current residual signal from the multipole wavelet library, and to obtain the amplitude coefficient corresponding to the best-matching wavelet; multiply the amplitude coefficient by the best-matching wavelet to obtain the decomposed sub-signal; subtract the decomposed sub-signal from the current residual signal, and use the resulting residual as the new residual signal; The iterative loop module is used to repeat the working process of the matching wavelet delay time acquisition module, the phase-changing wavelet library construction module, the multipole wavelet library construction module, and the new residual signal construction module until the number of iterations reaches a preset value or the remaining residual energy is less than a certain threshold. The sub-signal set construction module is used to obtain the best-matching wavelet of several residual signals and its corresponding amplitude coefficient after the iteration. The best-matching wavelet and its corresponding amplitude coefficient are multiplied to obtain the single-interface reflection seismic signal, which is used as the sub-signal set. The time-spectrum decomposition result construction module is used to perform Fourier transform on each sub-signal in the sub-signal set and map the obtained Fourier transform spectrum to the corresponding delay time of the interface to obtain the ideal time-spectrum decomposition result.

9. An electronic device, characterized in that, include: processor; Memory; And a computer program, wherein the computer program is stored in the memory, the computer program including instructions that, when executed by the processor, cause the electronic device to perform the method of any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform the method according to any one of claims 1 to 7.

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