Seismic spectrum inversion frequency expansion method, device and equipment based on semi-norm sparse constraint and medium
By introducing semi-normal regular terms in seismic spectral inversion, the problem of insufficient sparsity constraints in traditional methods is solved, and higher seismic resolution and more accurate thin layer recognition and reservoir prediction are achieved.
Patent Information
- Application Number
- CN202311664310.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2023-12-06
- Publication Date
- 2025-06-06
AI Technical Summary
Traditional seismic spectrum inversion frequency expansion methods are difficult to effectively improve seismic resolution when there is insufficient sparsity constraints, especially in the identification of special geological bodies such as thin layers and reservoir prediction.
In the spectral inversion process, the regular term of half-normal (i.e. L1/2 norm) is introduced to enhance the sparse constraints of the reflection coefficient, and the seismic resolution is improved through frequency extension processing.
Through semi-normal sparse constraints, the accuracy and reliability of the inversion results are improved, the seismic resolution is effectively improved, the thin layer weak signal is enhanced, and the recognition and prediction accuracy of thin reservoirs is improved.
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Figure CN120103466A_ABST
Abstract
Description
Technical Field
[0001] The invention belongs to the field of oil and gas exploration reservoir prediction, and specifically is a seismic spectrum inversion and extension method based on semi-norm sparse constraints. Background Art
[0002] With the continuous deepening of oil and gas exploration and development, low-porosity, low-permeability, complex thin interbedded reservoirs have gradually become the focus of unconventional oil and gas exploration, and the resolution of conventional seismic data is difficult to meet the requirements of fine identification and description of special geological bodies such as thin reservoirs. Therefore, how to expand the frequency of seismic data to improve seismic resolution has become increasingly important for the fine identification and prediction of thin reservoirs.
[0003] Seismic spectrum inversion is a data inversion method based on seismic signal spectrum decomposition technology. By establishing the objective function between the actual reflection coefficient sequence spectrum and the calculated spectrum, the magnitude and position of the reflection coefficient are inverted. Seismic spectrum inversion is an effective seismic data inversion method that can identify thin oil and gas reservoirs with a thickness less than the tuning thickness and improve the resolution of seismic data.
[0004] The objective function of traditional seismic spectrum inversion extension methods is mostly to construct regularization term constraints based on L2 and L1 norms, and the sparsity constraints on reflection coefficients are insufficient, resulting in distortion of seismic weak signals for special geological bodies such as thin layers after actual processing, which is not conducive to subsequent thin layer identification and reservoir prediction. Therefore, how to enhance the sparsity constraints on reflection coefficients in the seismic spectrum inversion process based on band-limited seismic data and further extend the spectrum to improve seismic resolution has become an urgent problem to be solved. Summary of the invention
[0005] In view of the problems existing in the existing technology, the semi-norm (i.e., L1 / 2 norm) regularization term of the reflection coefficient is added in the spectrum inversion process, and a seismic spectrum inversion extension method based on the semi-norm sparse constraint is proposed. This method is mainly used to effectively improve the seismic resolution, enhance the weak signal of thin layers, and improve the subsequent thin reservoir identification and prediction accuracy while ensuring the sparsity of the reflection coefficient, providing support for the actual oil and gas prediction work.
[0006] To achieve the above object, the present invention provides a seismic spectrum inversion extension method based on semi-norm sparse constraints, comprising:
[0007] Estimation of seismic wavelets from raw seismic data;
[0008] Construct wavelet diagonal matrix and perception matrix;
[0009] Establish the objective function to perform spectral inversion on the reflection coefficient;
[0010] Implement seismic frequency extension processing on the inversion results.
[0011] Furthermore, the wavelet of the original seismic record is estimated using the complex spectrum method, including:
[0012] Select the area with stable and slow-changing events in the original seismic data and set the time window range;
[0013] Perform Fourier transform on the seismic traces of the area to obtain its spectrum, then perform logarithmic transform and inverse Fourier transform on the spectrum to obtain the complex spectrum result;
[0014] The complex spectrum results are subjected to low-pass filtering, Fourier transform and inverse logarithmic transform to obtain the estimated seismic wavelet of the original seismic data.
[0015] Furthermore, the estimated seismic wavelet of the original seismic data is:
[0016]
[0017] Where, s(t) is seismic data, PS(t) is the complex spectrum, w(t) is the estimated seismic wavelet, FT() and IFT() are forward and inverse Fourier transforms, respectively, ln() is the logarithmic function, exp() is the exponential function, and LF[] is the low-pass filter.
[0018] Further, constructing the wavelet diagonal matrix includes constructing a frequency domain diagonal wavelet measurement matrix based on the estimated seismic wavelets:
[0019]
[0020] Where W is the measurement matrix of diagonal wavelet measurement, diag() is the diagonal function, FT() is the Fourier transform, and w(t) is the estimated seismic wavelet;
[0021] It also includes constructing a Fourier-based sparse matrix (Formula 3) and a corresponding perception matrix (Formula 4) according to the time range and frequency domain range of the seismic data:
[0022]
[0023] G=WF (4)
[0024] Where F is the Fourier basis sparse matrix, i is the imaginary unit, the time range of seismic data is [t1, tN], the frequency domain range is [f1, fN], and G is the perception matrix.
[0025] Furthermore, establishing the objective function to perform spectral inversion on the reflection coefficient includes:
[0026] Perform Fourier transform on single-channel seismic data to convert it into frequency domain data, construct a spectral inversion objective function based on the half-norm, and give the regularization factor and error accuracy;
[0027] The reflection coefficient is sparsely inverted using a fast semi-threshold shrinkage iterative algorithm.
[0028] Furthermore, the spectral inversion objective function is:
[0029]
[0030] Where G is the sensing matrix, r is the reflection coefficient, S is the frequency domain single-channel seismic data, and L is the regularization factor, which is set to 0.1.
[0031] Furthermore, all seismic data are processed in a single loop to obtain the sparse inversion results of all reflection coefficients, which are then convolved with the broadband seismic wavelet (Formula 6) to obtain the final extended spectrum seismic data:
[0032] S * (t)=conv(r,w * )=∫r(τ)w * (t-τ)dτ (6)
[0033] Where S* is the extended frequency seismic data, r is the inversion reflection coefficient, and w* is the broadband seismic wavelet.
[0034] According to another aspect of the present invention, a seismic spectrum inversion and extension device based on semi-norm sparse constraints is provided, comprising:
[0035] An estimation module estimates seismic wavelets from raw seismic data;
[0036] Matrix module, constructs wavelet diagonal matrix and perception matrix;
[0037] Inversion module, establishes the objective function to perform spectral inversion on the reflection coefficient;
[0038] The frequency extension module implements seismic frequency extension processing on the inversion results.
[0039] According to another aspect of the present invention, there is provided an electronic device, the electronic device comprising:
[0040] A memory storing executable instructions;
[0041] A processor runs the executable instructions in the memory to implement the seismic spectrum inversion and frequency extension method based on semi-norm sparse constraints.
[0042] According to another aspect of the present invention, a non-transitory computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the seismic spectrum inversion and extension method based on semi-norm sparse constraints is implemented.
[0043] Compared with the existing technology, the present invention has the following advantages:
[0044] The present invention performs spectrum extension processing based on half-norm seismic spectrum inversion for thin layers, etc. On the basis of traditional spectrum inversion spectrum extension, the half-norm (i.e., L1 / 2 norm) regularization term is used to perform sparse constraints on the reflection coefficient to address the problem of insufficient sparsity constraints on the inversion results, thereby enhancing the accuracy and reliability of the inversion results. At the same time, the spectrum extension processing can effectively improve the seismic resolution, enhance the weak signals of thin layers, and improve the subsequent thin reservoir identification and prediction accuracy, providing support for efficient exploration and development of oil and gas reservoirs. BRIEF DESCRIPTION OF THE DRAWINGS
[0045] The above and other objects, features and advantages of the present invention will become more apparent through a more detailed description of exemplary embodiments of the present invention in conjunction with the accompanying drawings, wherein like reference numerals generally represent like components throughout the exemplary embodiments of the present invention.
[0046] Figure 1 The figure is a flow chart of the seismic spectrum inversion and frequency extension method based on semi-norm sparse constraint according to the present invention.
[0047] Figure 2 The flowchart of the method for seismic spectrum inversion and frequency extension based on half-norm sparse constraint according to an embodiment of the present invention is shown in FIG.
[0048] Figure 3 It is a geological model diagram containing thin layers and sand bodies according to an embodiment of the present invention.
[0049] Figure 4 is an original reflection coefficient model profile according to an embodiment of the present invention.
[0050] Figure 5 4 is a forward modeling seismic data section according to an embodiment of the present invention.
[0051] Figure 6 Spectral inversion result of the reflection coefficient according to an embodiment of the present invention.
[0052] Figure 7 is the residual between the inversion result according to the embodiment of the present invention and the original reflection coefficient.
[0053] Figure 8 This is the final frequency extension result of the seismic data according to the embodiment of the present invention. DETAILED DESCRIPTION
[0054] The preferred embodiments of the present invention will be described in more detail below. Although the preferred embodiments of the present invention are described below, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to make the present invention more thorough and complete, and to fully convey the scope of the present invention to those skilled in the art.
[0055] The present invention proposes a seismic spectrum inversion extension method based on half-norm sparse constraints, which belongs to the field of oil and gas exploration reservoir prediction. The method first estimates the seismic wavelet of the original seismic data, thereby constructing a wavelet diagonal matrix and a sensing matrix, and then establishes an objective function to perform spectral inversion on the reflection coefficient, and finally implements seismic extension processing on the inversion result to obtain high-resolution seismic data. In view of the problem that the sparsity constraints of traditional spectral inversion results are insufficient, the present invention uses a half-norm (i.e., L1 / 2 norm) regularization term to perform sparse constraints on the reflection coefficient, thereby enhancing the accuracy and reliability of the inversion results. At the same time, the extension processing can effectively improve the seismic resolution, enhance the weak signal of thin layers, and improve the subsequent thin reservoir identification and prediction accuracy.
[0056] Embodiment 1
[0057] like Figure 1 As shown, this embodiment provides a seismic spectrum inversion extension method based on semi-norm sparse constraints, including:
[0058] Estimation of seismic wavelets from raw seismic data;
[0059] Construct wavelet diagonal matrix and perception matrix;
[0060] Establish the objective function to perform spectral inversion on the reflection coefficient;
[0061] Implement seismic frequency extension processing on the inversion results.
[0062] Furthermore, the wavelet of the original seismic record is estimated using the complex spectrum method, including:
[0063] Select the area with stable and slow-changing events in the original seismic data and set the time window range;
[0064] Perform Fourier transform on the seismic traces of the area to obtain its spectrum, then perform logarithmic transform and inverse Fourier transform on the spectrum to obtain the complex spectrum result;
[0065] The complex spectrum results are subjected to low-pass filtering, Fourier transform and inverse logarithmic transform to obtain the estimated seismic wavelet of the original seismic data.
[0066] Furthermore, the estimated seismic wavelet of the original seismic data is:
[0067]
[0068] Where, s(t) is seismic data, PS(t) is the complex spectrum, w(t) is the estimated seismic wavelet, FT() and IFT() are forward and inverse Fourier transforms, respectively, ln() is the logarithmic function, exp() is the exponential function, and LF[] is the low-pass filter.
[0069] Further, constructing the wavelet diagonal matrix includes constructing a frequency domain diagonal wavelet measurement matrix based on the estimated seismic wavelets:
[0070]
[0071] Where W is the measurement matrix of diagonal wavelet measurement, diag() is the diagonal function, FT() is the Fourier transform, and w(t) is the estimated seismic wavelet;
[0072] It also includes constructing a Fourier-based sparse matrix (Formula 3) and a corresponding perception matrix (Formula 4) according to the time range and frequency domain range of the seismic data:
[0073]
[0074] G=WF (4)
[0075] Where F is the Fourier basis sparse matrix, i is the imaginary unit, the time range of seismic data is [t1, tN], the frequency domain range is [f1, fN], and G is the perception matrix.
[0076] Furthermore, establishing the objective function to perform spectral inversion on the reflection coefficient includes:
[0077] Perform Fourier transform on single-channel seismic data to convert it into frequency domain data, construct a spectral inversion objective function based on the half-norm, and give the regularization factor and error accuracy;
[0078] The reflection coefficient is sparsely inverted using a fast semi-threshold shrinkage iterative algorithm.
[0079] Furthermore, the spectral inversion objective function is:
[0080]
[0081] Where G is the sensing matrix, r is the reflection coefficient, S is the frequency domain single-channel seismic data, and L is the regularization factor, which is set to 0.1.
[0082] Furthermore, all seismic data are processed in a single loop to obtain the sparse inversion results of all reflection coefficients, which are then convolved with the broadband seismic wavelet (Formula 6) to obtain the final extended spectrum seismic data:
[0083] S * (t)=conv(r,w * )=∫r(τ)w * (t-τ)dτ (6)
[0084] Where S* is the extended frequency seismic data, r is the inversion reflection coefficient, and w* is the broadband seismic wavelet.
[0085] Embodiment 2
[0086] The method provided in this embodiment mainly includes four parts: seismic wavelet estimation, perception matrix and objective function construction, reflection coefficient spectrum inversion and seismic frequency extension processing. First, the seismic wavelet estimation is estimated using the original seismic data, and then the wavelet diagonal matrix and the corresponding perception matrix are constructed according to the estimated wavelet; then, the spectrum inversion objective function with semi-norm sparse constraints is established in the frequency domain to invert the reflection coefficient; finally, broadband wavelets are used to implement frequency extension processing to improve seismic resolution. In the application of seismic data, this method has achieved good experimental results.
[0087] Figure 2 FIG. 1 is a flow chart of a method for inverting and extending a seismic spectrum based on a semi-norm sparse constraint according to an embodiment of the present invention. Figure 2 As shown, the specific steps of this method are as follows:
[0088] In the first step, the wavelet of the original seismic record is estimated using the complex spectrum method.
[0089] First, select the area with stable and slowly changing event axes in the original seismic data and set the time window range; then perform Fourier transform on the seismic traces in this area to obtain its spectrum, then perform logarithmic transform and inverse Fourier transform on the spectrum to obtain the "rematch spectrum" result, and design a low-pass filter to perform low-pass filtering and Fourier and inverse logarithmic transform on the "rematch spectrum" result, and finally obtain the estimated sub-wave (as shown in Formula 1) of the original seismic record.
[0090]
[0091] Where, s(t) is seismic data, PS(t) is the complex spectrum, w(t) is the estimated seismic wavelet, FT() and IFT() are forward and inverse Fourier transforms, respectively, ln() is the logarithmic function, exp() is the exponential function, and LF[] is the low-pass filter.
[0092] The second step is to construct the frequency domain diagonal wavelet measurement matrix, Fourier basis sparse matrix and perception matrix.
[0093] According to the seismic wavelet estimated in the first step, the frequency domain diagonal wavelet measurement matrix is constructed (such as formula 2).
[0094]
[0095] Where W is the diagonal wavelet measurement matrix, diag() is the diagonal function, FT() is the Fourier transform, and w(t) is the estimated seismic wavelet.
[0096] Then, according to the time range and frequency domain range of the seismic data, a Fourier basis sparse matrix (such as
[0097] Equation 3) and the corresponding perception matrix (such as Equation 4)
[0098]
[0099] G=WF (4)
[0100] Where F is the Fourier basis sparse matrix, i is the imaginary unit, the time range of seismic data is [t1, tN], the frequency domain range is [f1, fN], and G is the perception matrix.
[0101] The third step is to construct the inversion objective function and perform spectral inversion.
[0102] The single-channel seismic data is converted into frequency domain data by Fourier transform, and the spectral inversion objective function based on the half-norm is constructed (such as formula 5), and the regularization factor and error accuracy are given; then the fast semi-threshold shrinkage iterative algorithm can be used to perform sparse inversion on the reflection coefficient.
[0103]
[0104] Where G is the sensing matrix, r is the reflection coefficient, S is the frequency domain single-channel seismic data, and L is the regularization factor, which can generally be set to 0.1.
[0105] The fourth step is to perform frequency extension processing on the seismic data.
[0106] After that, all seismic data are processed in a single-channel loop to obtain all reflection coefficient sparse inversion results, and at the same time, they are convolved with broadband seismic wavelets (such as morlet wavelets with higher main frequencies) (such as formula 6) to obtain the final extended frequency seismic data.
[0107] S * (t)=conv(r,w * )=∫r(τ)w * (t-τ)dτ (6)
[0108] Where S* is the extended frequency seismic data, r is the inversion reflection coefficient, and w* is the broadband seismic wavelet.
[0109] Embodiment 3
[0110] Reference Figure 3-8 As shown, this embodiment carries out research and application work on the problem of spectral inversion extension of thin layers, uses the semi-norm regularization term to perform sparse constraints on the reflection coefficient, and proposes a seismic spectral inversion extension method based on the semi-norm sparse constraints, which can effectively improve the reliability and resolution of the inversion results.
[0111] First, a two-dimensional geological model containing thin interbeds and thin sand bodies is established, as shown in the attached figure. Figure 3 , 4The model has 201 channels, each with 251 sampling points, and the time sampling interval is 2 milliseconds. The velocity range of the surrounding rock is 2100-4300m / s, the velocity of the upper thin sand body is 2500m / s, and the velocity of the lower thin sand body is 3000m / s. Afterwards, the convolution forward modeling is performed using the 25Hz main frequency Ricker wavelet to obtain the forward seismic data. The seismic waveforms in the thin layers and thin sand bodies are superimposed, the resolution is insufficient, and the thin layer information is difficult to distinguish, as shown in the attached figure. Figure 5 shown.
[0112] Then, the seismic data is subjected to semi-norm sparse constrained seismic spectrum inversion. The spectrum inversion results of the reflection coefficient are shown in the attached figure. Figure 6 As shown in the figure, the residual error between the original reflection coefficient and the inverted reflection coefficient is calculated, as shown in the attached figure. Figure 7 As shown; it can be seen from the figure that the present invention can accurately realize the sparse inversion of the reflection coefficient, the inversion result is relatively accurate, and has good consistency with the original model, and errors only occur at the thin layer pinch-out.
[0113] Finally, the reflection coefficient inversion result is convolved with the 50 Hz main frequency Ricker wavelet to perform spectrum processing to obtain high-resolution seismic data, as shown in the attached figure. Figure 8 Compared with the original seismic data (such as Figure 5 As shown in the figure, while maintaining the overall structure, the seismic resolution of the extended frequency seismic data is greatly improved, and the seismic waveforms at the thin layer interface, thin sand body and underlying bottom layer are effectively separated, which specifically improves the recognition and prediction accuracy of thin sand body and thin layer.
[0114] Embodiment 4
[0115] This embodiment provides a seismic spectrum inversion and frequency extension device based on semi-norm sparse constraints, comprising:
[0116] An estimation module estimates seismic wavelets from raw seismic data;
[0117] Matrix module, constructs wavelet diagonal matrix and perception matrix;
[0118] Inversion module, establishes the objective function to perform spectral inversion on the reflection coefficient;
[0119] The frequency extension module implements seismic frequency extension processing on the inversion results.
[0120] Embodiment 5
[0121] This embodiment provides an electronic device, the electronic device comprising:
[0122] A memory storing executable instructions;
[0123] A processor, wherein the processor runs the executable instructions in the memory to implement the seismic spectrum inversion extension method based on semi-norm sparse constraints, the method comprising:
[0124] Estimation of seismic wavelets from raw seismic data;
[0125] Construct wavelet diagonal matrix and perception matrix;
[0126] Establish the objective function to perform spectral inversion on the reflection coefficient;
[0127] Implement seismic frequency extension processing on the inversion results.
[0128] Embodiment 6
[0129] This embodiment provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the seismic spectrum inversion extension method based on semi-norm sparse constraints is implemented. The method includes:
[0130] Estimation of seismic wavelets from raw seismic data;
[0131] Construct wavelet diagonal matrix and perception matrix;
[0132] Establish the objective function to perform spectral inversion on the reflection coefficient;
[0133] Implement seismic frequency extension processing on the inversion results.
[0134] The above-mentioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or mobile hard disk), media with built-in rewritable non-volatile memory (e.g., memory card) and media with built-in ROM (e.g., ROM box).
[0135] In summary, the present invention performs frequency extension processing based on half-norm seismic spectrum inversion for thin layers, and on the basis of traditional spectral inversion frequency extension, the half-norm (i.e., L1 / 2 norm) regularization term is used to perform sparse constraints on the reflection coefficient to address the problem of insufficient sparsity constraints on the inversion results, thereby enhancing the accuracy and reliability of the inversion results. At the same time, the frequency extension processing can effectively improve the seismic resolution, enhance the weak signals of thin layers, and improve the subsequent thin reservoir identification and prediction accuracy, providing support for efficient exploration and development of oil and gas reservoirs.
[0136] The embodiments of the present invention have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.
Claims
1. A method for seismic spectrum inversion extension based on semi-norm sparse constraints. It is characterized in that include: Estimation of seismic wavelets from raw seismic data; Construct wavelet diagonal matrix and perception matrix; Establish the objective function to perform spectral inversion on the reflection coefficient; Implement seismic frequency extension processing on the inversion results.
2. The seismic spectrum inversion extension method based on semi-norm sparse constraint according to claim 1, It is characterized in that The wavelet of the original seismic record is estimated using the complex spectrum method, including: Select the area with stable and slowly changing events in the original seismic data and set the time window range; Perform Fourier transform on the seismic traces of the area to obtain its spectrum, then perform logarithmic transform and inverse Fourier transform on the spectrum to obtain the complex spectrum result; The complex spectrum results are subjected to low-pass filtering, Fourier transform and inverse logarithmic transform to obtain the estimated seismic wavelet of the original seismic data.
3. The seismic spectrum inversion extension method based on semi-norm sparse constraint according to claim 2, It is characterized in that The estimated seismic wavelet of the original seismic data is: Where, s(t) is seismic data, PS(t) is the complex spectrum, w(t) is the estimated seismic wavelet, FT() and IFT() are forward and inverse Fourier transforms, respectively, ln() is the logarithmic function, exp() is the exponential function, and LF[] is the low-pass filter.
4. The seismic spectrum inversion extension method based on semi-norm sparse constraint according to claim 1, It is characterized in that Constructing the wavelet diagonal matrix involves constructing a frequency-domain diagonal wavelet measurement matrix based on the estimated seismic wavelets: Where W is the measurement matrix of diagonal wavelet measurement, diag() is the diagonal function, FT() is the Fourier transform, and w(t) is the estimated seismic wavelet; It also includes constructing a Fourier-based sparse matrix (Formula 3) and a corresponding sensing matrix (Formula 4) according to the time range and frequency domain range of the seismic data: G=WF (4) Where F is the Fourier basis sparse matrix, i is the imaginary unit, the time range of seismic data is [t1, tN], the frequency domain range is [f1, fN], and G is the perception matrix.
5. The seismic spectrum inversion extension method based on semi-norm sparse constraint according to claim 1, It is characterized in that Establishing the objective function to perform spectral inversion of the reflection coefficient includes: Perform Fourier transform on single-channel seismic data to convert it into frequency domain data, construct a spectral inversion objective function based on the half-norm, and give the regularization factor and error accuracy; The reflection coefficient is sparsely inverted using a fast semi-threshold shrinkage iterative algorithm.
6. The seismic spectrum inversion extension method based on semi-norm sparse constraint according to claim 5, It is characterized in that The spectral inversion objective function is: Where G is the sensing matrix, r is the reflection coefficient, S is the frequency domain single-channel seismic data, and L is the regularization factor, which is set to 0.
1.
7. The seismic spectrum inversion extension method based on semi-norm sparse constraint according to claim 1, It is characterized in that All seismic data are processed in a single loop to obtain the sparse inversion results of all reflection coefficients, which are then convolved with the broadband seismic wavelet (Formula 6) to obtain the final extended spectrum seismic data: S * (t)=conv(r,w * )=∫r(τ)w * (t-τ)dτ (6) Where S* is the extended frequency seismic data, r is the inversion reflection coefficient, and w* is the broadband seismic wavelet.
8. A seismic spectrum inversion and extension device based on semi-norm sparse constraints, It is characterized in that include: An estimation module estimates seismic wavelets from raw seismic data; Matrix module, constructs wavelet diagonal matrix and perception matrix; Inversion module, establishes the objective function to perform spectral inversion on the reflection coefficient; The frequency extension module implements seismic frequency extension processing on the inversion results.
9. An electronic device, It is characterized in that The electronic device comprises: A memory storing executable instructions; A processor, wherein the processor runs the executable instructions in the memory to implement the seismic spectrum inversion and extension method based on semi-norm sparse constraints as described in any one of claims 1 to 7.
10. A non-transitory computer-readable storage medium having a computer program stored thereon, It is characterized in that When the computer program is executed by a processor, the seismic spectrum inversion and extension method based on semi-norm sparse constraints as described in any one of claims 1 to 7 is implemented.